Abbasi, MH, Taki, M, Rajabi, A, Li, L & Zhang, J 2019, 'Coordinated operation of electric vehicle charging and wind power generation as a virtual power plant: A multi-stage risk constrained approach', Applied Energy, pp. 1294-1307.
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© 2019 Elsevier Ltd As the number of electric vehicles (EVs) is steadily increasing, their aggregation can offer significant storage to improve the electric system operation in many aspects. To this end, a comprehensive stochastic optimization framework is proposed in this paper for the joint operation of a fleet of EVs with a wind power producer (WPP) in a three-settlement pool-based market. An aggregator procures enough energy for the EVs based on their daily driving patterns, and schedules the stored energy to counterbalance WPP fluctuations. Different sources of uncertainty including the market prices and WPP generation are modeled through proper scenarios, and the risk is hedged by adding a risk measure to the formulation. To obtain more accurate results, the battery degradation costs are also included in the problem formulation. A detailed case study is presented based on the Iberian electricity market data as well as the technical information of three different types of EVs. The proposed approach is benchmarked against the disjoint operation of EVs and WPP. Numerical simulations demonstrate that the proposed strategy can effectively benefit EV owners and WPP by reducing the energy costs and increasing the profits.
Abdelkarim, A, Gaber, AFD, Youssef, AM & Pradhan, B 2019, 'Flood Hazard Assessment of the Urban Area of Tabuk City, Kingdom of Saudi Arabia by Integrating Spatial-Based Hydrologic and Hydrodynamic Modeling.', Sensors (Basel, Switzerland), vol. 19, no. 5.
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This study deals with the use of remote sensing (RS), geographic information systems (GISs), hydrologic modeling (water modeling system, WMS), and hydraulic modeling (Hydrologic Engineering Center River Analysis System, HEC-RAS) to evaluate the impact of flash flood hazards on the sustainable urban development of Tabuk City, Kingdom of Saudi Arabia (KSA). Determining the impact of flood hazards on the urban area and developing alternatives for protection and prevention measures were the main aims of this work. Tabuk City is exposed to frequent flash flooding due to its location along the outlets of five major wadis. These wadis frequently carry flash floods, seriously impacting the urban areas of the city. WMS and HEC-HMS models and RS data were used to determine the paths and morphological characteristics of the wadis, the hydrographic flow of different drainage basins, flow rates and volumes, and the expansion of agricultural and urban areas from 1998 to 2018. Finally, hydraulic modeling of the HEC-RAS program was applied to delineate the urban areas that could be inundated with floodwater. Ultimately, the most suitable remedial measures are proposed to protect the future sustainable urban development of Tabuk City from flood hazards. This approach is rarely used in the KSA. We propose a novel method that could help decision-makers and planners in determining inundated flood zones before planning future urban and agricultural development in the KSA.
Abidi, S, Piccardi, M, Tsang, WH & Williams, M-A 2019, 'Well-M³N: A Maximum-Margin Approach to Unsupervised Structured Prediction', IEEE Transactions on Emerging Topics in Computational Intelligence.
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Unsupervised structured prediction is of fundamental importance for the clustering and classification of unannotated structured data. To date, its most common approach still relies on the use of structural probabilistic models and the expectation-maximization (EM) algorithm. Conversely, structural maximum-margin approaches, despite their extensive success in supervised and semi-supervised classification, have not raised equivalent attention in the unsupervised case. For this reason, in this paper we propose a novel approach that extends the maximum-margin Markov networks (M3N) to an unsupervised training framework. The main contributions of our extension are new formulations for the feature map and loss function of M3N that decouple the labels from the measurements and support multiple ground-truth training. Experiments on two challenging segmentation datasets have achieved competitive accuracy and generalization compared to other unsupervised algorithms such as k-means, EM and unsupervised structural SVM, and comparable performance to a contemporary deep learning-based approach.
Ahmed, AA & Pradhan, B 2019, 'Vehicular traffic noise prediction and propagation modelling using neural networks and geospatial information system.', Environmental monitoring and assessment, vol. 191, no. 3, p. 190.
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This study proposes a neural network (NN) model to predict and simulate the propagation of vehicular traffic noise in a dense residential area at the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia. The proposed model comprises of two main simulation steps: that is, the prediction of vehicular traffic noise using NN and the simulation of the propagation of traffic noise emission using a mathematical model. First, the NN model was developed with the following selected noise predictors: the number of motorbikes, the sum of vehicles, car ratio, heavy vehicle ratio (e.g. truck, lorry and bus), highway density and a light detection and ranging (LiDAR)-derived digital surface model (DSM). Subsequently, NN and its hyperparameters were optimised by a systematic optimisation procedure based on a grid search approach. The noise propagation model was then developed in a geographic information system (GIS) using five variables, namely road geometry, barriers, distance, interaction of air particles and weather parameters. The noise measurement was conducted continuously at 15-min intervals and the data were analysed by taking the minimum, maximum and average values recorded during the day. The measurement was performed four times a day (i.e. morning, afternoon, evening, and midnight) over two days of the week (i.e. Sunday and Monday). An optimal radial basis function NN was used with 17 hidden layers. The learning rate and momentum values were 0.05 and 0.9, respectively. Finally, the accuracy of the proposed method achieved 78.4% with less than 4.02 dB (A) error in noise prediction. Overall, the proposed models were found to be promising tools for traffic noise assessment in dense urban areas.
Akgun, B, Krunz, M & Ozan Koyluoglu, O 2019, 'Vulnerabilities of massive MIMO systems to pilot contamination attacks', IEEE Transactions on Information Forensics and Security, vol. 14, no. 5, pp. 1251-1263.
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© 2018 IEEE. We consider a single-cell massive multiple-input multiple-output (MIMO) system in which a base station (BS) with a large number of antennas transmits simultaneously to several single-antenna users. The BS acquires the channel state information (CSI) for various receivers using uplink pilot transmissions. We demonstrate the vulnerability of the CSI estimation process to pilot-contamination (PC) attacks. In our attack model, the attacker aims at minimizing the sum rate of downlink transmissions by contaminating the uplink pilots. We first study these attacks for two downlink power allocation strategies under the assumption that the attacker knows the locations of the BS and its users. Later on, we relax this assumption and consider the case when such knowledge is probabilistic. The formulated problems are solved using stochastic optimization, Lagrangian minimization, and game-theoretic methods. A closed-form solution for a special case of the problem is obtained. Furthermore, we analyze the achievable individual secrecy rates under PC attacks and provide an upper bound on these rates. We also study this scenario without a priori knowledge of user locations at the attacker by introducing chance constraints. Our results indicate that such attacks can degrade the throughput of a massive MIMO system by more than 50%.
Akther, N, Lim, S, Tran, VH, Phuntsho, S, Yang, Y, Bae, TH, Ghaffour, N & Shon, HK 2019, 'The effect of Schiff base network on the separation performance of thin film nanocomposite forward osmosis membranes', Separation and Purification Technology, vol. 217, pp. 284-293.
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© 2019 Elsevier B.V. In this study, Schiff base network-1 (SNW-1) nanoparticles, which are covalent organic frameworks (COFs), were used as fillers in the polyamide (PA) active layer to elevate the performance of thin-film nanocomposite (TFN) forward osmosis (FO) membranes. The TFN membranes were prepared by interfacial polymerization (IP) of m-phenylenediamine (MPD) and trimesoyl chloride (TMC), and the SNW-1 nanoparticles were dispersed in the MPD aqueous solution at various concentrations. The secondary amine groups of SNW-1 nanoparticles reacted with the acyl chloride groups of TMC during the IP reaction to form strong covalent/amide bonds, which facilitated better interface integration of SNW-1 nanoparticles in the PA layer. Additionally, the incorporation of amine-rich SNW-1 nanoparticles into the TFN membranes improved their surface hydrophilicity, and the porous structure of SNW-1 nanoparticles offered additional channels for transport of water molecules. The TFN0.005 membrane with a SNW-1 nanoparticle loading of 0.005 wt% demonstrated a higher water flux than that of pristine TFC membrane in both AL-FS (12.0 vs. 9.3 L m −2 h −1 ) and AL-DS (25.2 vs. 19.4 L m −2 h −1 ) orientations when they were tested with deionized water and 0.5 M NaCl as feed and draw solution, respectively.
Alamdari, MM, Dang Khoa, NL, Wang, Y, Samali, B & Zhu, X 2019, 'A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge', Structural Health Monitoring, vol. 18, no. 1, pp. 35-48.
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© The Author(s) 2018. A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities.
Alilou, H, Rahmati, O, Singh, VP, Choubin, B, Pradhan, B, Keesstra, S, Ghiasi, SS & Sadeghi, SH 2019, 'Evaluation of watershed health using Fuzzy-ANP approach considering geo-environmental and topo-hydrological criteria.', Journal of environmental management, vol. 232, pp. 22-36.
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Assessment of watershed health and prioritization of sub-watersheds are needed to allocate natural resources and efficiently manage watersheds. Characterization of health and spatial prioritization of sub-watersheds in data scarce regions helps better comprehend real watershed conditions and design and implement management strategies. Previous studies on the assessment of health and prioritization of sub-watersheds in ungauged regions have not considered environmental factors and their inter-relationship. In this regard, fuzzy logic theory can be employed to improve the assessment of watershed health. The present study considered a combination of climate vulnerability (Climate Water Balance), relative erosion rate of surficial rocks, slope weighted K-factor, topographic indices, thirteen morphometric characteristics (linear, areal, and relief aspects), and potential non-point source pollution to assess watershed health, using a new framework which considers the complex linkage between human activities and natural resources. The new framework, focusing on watershed health score (WHS), was employed for the spatial prioritization of 31 sub-watersheds in the Khoy watershed, West Azerbaijan Province, Iran. In this framework, an analytical network process (ANP) and fuzzy theory were used to investigate the inter-relationships between the above mentioned geo-environmental factors and to classify and rank the health of each sub-watershed in four classes. Results demonstrated that only one sub-watershed (C15) fell into the class that was defined as 'a potentially critical zone'. This article provides a new framework and practical recommendations for watershed management agencies with a high level of assurance when there is a lack of reliable hydrometric gauge data.
Al-Muhsen, NFO, Huang, Y & Hong, G 2019, 'Effects of direct injection timing associated with spark timing on a small spark ignition engine equipped with ethanol dual-injection', Fuel, pp. 852-861.
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© 2018 Elsevier Ltd Dual injection of ethanol fuel (DualEI) has been in development. DualEI has the potential in increasing the compression ratio and thermal efficiency of spark ignition engines by taking the advantages of ethanol fuel properties and the direct injection. This paper reports an experimental investigation of the effect of direct injection (DI) timing associated with spark timing on the performance of a small DualEI engine. Experiments were conducted with fixed port injection timing and varied DI timing before (early) and after (late) the intake valve closed at 3500 RPM and two load conditions. Results show that the engine performance is enhanced by early DI timing, although the variation of IMEP and indicated thermal efficiency with DI timing is not significant either with early DI timing or in most of the tested conditions with late DI timing. Only in the medium load condition when the DI timing is retarded from 80 to 60 CAD bTDC, the IMEP and thermal efficiency significantly reduced by about 16% due to the increased initial combustion duration, resulting in reduced flame speed and increased combustion instability. The results also show different effects of early and late DI timing associated with the spark timing on engine emissions. With late DI timing, the engine emissions of CO and NO increase with the advance of late DI timing and spark timing. With early DI timing, the engine emissions increase with the advance of spark timing. However, the variation of engine emissions with early DI timing is more complicated than that late.
Al-soeidat, M, Lu, DDC & Zhu, J 2019, 'An Analog BJT-Tuned Maximum Power Point Tracking Technique for PV Systems', IEEE Transactions on Circuits and Systems II: Express Briefs.
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IEEE In this paper, an analog, BJT-tuned voltage reference maximum power point tracking (MPPT) method for photovoltaic (PV) modules is proposed. Conventional fixed voltage reference method is the simplest method for tracking, but it does not obtain good MPPT efficiency because the maximum power point (MPP) voltage changes at different insolation levels. In reality, an approximately linear slope is formed when connecting the MPPs measured from the highest insolation level to the lowest. Utilizing this characteristic, a bipolar junction transistor (BJT), which has a similar electrical property, is used to implement a variable voltage reference that improves the accuracy of the MPP voltage when the insolation changes. The proposed circuit is simple, easy to implement and it can track the maximum power point very quickly without the need for a digital controller or PID controller. Hence, the circuit’s cost and complexity are reduced. Experimental results are given to verify the feasibility of the proposed MPPT method.
Altulyan, M, Yao, L, Kanhere, SS, Wang, X & Huang, C 2019, 'A unified framework for data integrity protection in people-centric smart cities', Multimedia Tools and Applications.
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With the rapid increase in urbanisation, the concept of smart cities has attracted considerable attention. By leveraging emerging technologies such as the Internet of Things (IoT), artificial intelligence and cloud computing, smart cities have the potential to improve various indicators of residents’ quality of life. However, threats to data integrity may affect the delivery of such benefits, especially in the IoT environment where most devices are inherently dynamic and have limited resources. Prior work has focused on ensuring integrity of data in a piecemeal manner and covering only some parts of the smart city ecosystem. In this paper, we address integrity of data from an end-to-end perspective, i.e., from the data source to the data consumer. We propose a holistic framework for ensuring integrity of data in smart cities that covers the entire data lifecycle. Our framework is founded on three fundamental concepts, namely, secret sharing, fog computing and blockchain. We provide a detailed description of various components of the framework and also utilize smart healthcare as use case.
Al-Zubaydi, AYT & Hong, G 2019, 'Experimental study of a novel water-spraying configuration in indirect evaporative cooling', Applied Thermal Engineering, pp. 283-293.
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© 2019 Elsevier Ltd Indirect evaporative cooler (IEC) is a clean cooling device in development for air conditioning systems. It uses the water to partially replace the refrigerant and can be applied as a standalone cooling system or an energy recovery ventilation system. Driven by the concern about the sustainability, IEC has been required to reduce the power to operate and increase its capacity in order to reduce the energy consumption and increase the overall efficiency of the air condition system. The water distribution over the plate walls in the cooler is a key factor affecting the IEC performance and efficiency. This study investigates the effect of a novel suggested water spray configuration on the performance of a ventilation energy recovery IEC. Experiments were conducted to investigate three water spray modes: external spray, internal spray and mixed internal and external sprays. The results show that the mixed mode performs best and internal spraying mode performs better than the external spraying mode does in terms of the wet-bulb efficiency, cooling capacity and the COP of IEC. The mixed mode improves the performance further by increasing wettability.
Amirbagheri, K, Núñez-Carballosa, A, Guitart-Tarrés, L & Merigó, JM 2019, 'Research on green supply chain: a bibliometric analysis', Clean Technologies and Environmental Policy, vol. 21, no. 1, pp. 3-22.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Abstract: Recently, the emergent concept of green supply chain has received increasing attention. Although popular among scholars, many literature reviews have only examined GSC from a general point of view or focused on a specific issue related to GSC. This study presents a comprehensive analysis of the influence and productivity of research on GSC from 1995 to 2017 by reporting trends among authors, countries and institutions based on a bibliometric approach. To this end, the study analyzes around 1900 papers on GSC. This study uses the Web of Science Core Collection database to analyze the bibliometric data and the visualization of similarities viewer method to graphically map those data. The graphical analysis uses bibliographic coupling, co-citation, co-authorship and co-occurrence of keywords. Graphical abstract: [Figure not available: see fulltext.].
Amiri, M, Tofigh, F, Shariati, N, Lipman, J & Abolhasan, M 2019, 'Miniature tri-wideband Sierpinski–Minkowski fractals metamaterial perfect absorber', IET Microwaves, Antennas & Propagation.
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Amjadipour, M, MacLeod, J, Lipton-Duffin, J, Tadich, A, Boeckl, JJ, Iacopi, F & Motta, N 2019, 'Electron effective attenuation length in epitaxial graphene on SiC.', Nanotechnology, vol. 30, no. 2, p. 025704.
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The inelastic mean free path (IMFP) for carbon-based materials is notoriously challenging to model, and moving from bulk materials to 2D materials may exacerbate this problem, making the accurate measurements of IMFP in 2D carbon materials critical. The overlayer-film method is a common experimental method to estimate IMFP by measuring electron effective attenuation length (EAL). This estimation relies on an assumption that elastic scattering effects are negligible. We report here an experimental measurement of electron EAL in epitaxial graphene on SiC using photoelectron spectroscopy over an electron kinetic energy range of 50-1150 eV. We find a significant effect of the interface between the 2D carbon material and the substrate, indicating that the attenuation length in the so-called 'buffer layer' is smaller than for free-standing graphene. Our results also suggest that the existing models for estimating IMFPs may not adequately capture the physics of electron interactions in 2D materials.
Anis, Z, Wissem, G, Riheb, H, Biswajeet, P & Mohamed Essghaier, G 2019, 'Effects of clay properties in the landslides genesis in flysch massif: Case study of Aïn Draham, North Western Tunisia', Journal of African Earth Sciences, vol. 151, pp. 146-152.
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© 2018 Elsevier Ltd Heavy rainfall in Aïn Draham province in the North-Western of Tunisia lead to the formation of some landslides which could poses danger to lives and properties. The geological outcrops of the region mainly consist of Numidian flysch rocks. In this study, field based 15 undisturbed samples were taken, from 11 boreholes drilled in 4 landslide points, to understand the real behaviour of soils when landslides occur. For this purpose, the geotechnical characterization of all samples was carried out. The grain size distribution shows that the clay and silt fractions prevail. The clay fraction ranges between 4% and 64% with an average of 40.4%, the silt fraction ranging from 19% to 71% averaging 39.8% and the sand fraction was between 6% and 44% with an average of 19.8%. The Casagrande plasticity chart indicates that 33.3% of samples were in the high plasticity group (CH group) and 66.6% having a medium to low plasticity. The water content varies between 12% and 31%. The direct shear strength test shows that the cohesions values range from 41 KPa to 77 KPa and the internal friction angle values range widely from 12° to 27°. A statistical approach was taken to determine the most important factors responsible for the decrease of the cohesion and friction angle which are in charge of slope failure. For this, a correlation matrix of all soil properties was done. The coefficients of correlation show that the clay fraction is the most correlated parameter to the cohesion with an index of −0.872. Unfortunately, the internal friction angle is very low correlated to all geotechnical parameters. The clay fraction, as the most correlated to the cohesion, and the water content, which depends on rainfall (landslide triggering factor), were considered as two independent parameters for the establishment of a multiple linear and non-linear regression models of the cohesion. The multiple linear model showed that the cohesion decrease with the increase of water conten...
Arabameri, A, Pradhan, B & Rezaei, K 2019, 'Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS', Journal of Environmental Management, vol. 232, pp. 928-942.
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© 2018 Elsevier Ltd Every year, gully erosion causes substantial damage to agricultural land, residential areas and infrastructure, such as roads. Gully erosion assessment and mapping can facilitate decision making in environmental management and soil conservation. Thus, this research aims to propose a new model by combining the geographically weighted regression (GWR) technique with the certainty factor (CF) and random forest (RF) models to produce gully erosion zonation mapping. The proposed model was implemented in the Mahabia watershed of Iran, which is highly sensitive to gully erosion. Firstly, dependent and independent variables, including a gully erosion inventory map (GEIM) and gully-related causal factors (GRCFs), were prepared using several data sources. Secondly, the GEIM was randomly divided into two groups: training (70%) and validation (30%) datasets. Thirdly, tolerance and variance inflation factor indicators were used for multicollinearity analysis. The results of the analysis corroborated that no collinearity exists amongst GRCFs. A total of 12 topographic, hydrologic, geologic, climatologic, environmental and soil-related GRCFs and 150 gully locations were used for modelling. The watershed was divided into eight homogeneous units because the importance level of the parameters in different parts of the watershed is not the same. For this purpose, coefficients of elevation, distance to stream and distance to road parameters were used. These coefficients were obtained by extracting bi-square kernel and AIC via the GWR method. Subsequently, the RF-CF integrated model was applied in each unit. Finally, with the units combined, the final gully erosion susceptibility map was obtained. On the basis of the RF model, distance to stream, distance to road and land use/land cover exhibited a high influence on gully formation. Validation results using area under curve indicated that new GWR–CF–RF approach has a higher predictive accuracy 0.967 (96.7%) than t...
Arabameri, A, Pradhan, B & Rezaei, K 2019, 'Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models', Geosciences Journal.
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© 2019, The Association of Korean Geoscience Societies and Springer-Verlag GmbH Germany, part of Springer Nature. Remote sensing is recognized as a powerful and efficient tool that provides a comprehensive view of large areas that are difficult to access, and also reduces costs and shortens the timing of projects. The purpose of this study is to introduce effective parameters using remote sensing data and subsequently predict gully erosion using statistical models of Density Area (DA) and Information Value (IV), and data mining based Random Forest (RF) model and their ensemble. The aforementioned models were employed at the Tororud-Najarabad watershed in the northeastern part of Semnan province, Iran. For this purpose, at first using various resources, the map of the distribution of the gullies was prepared with the help of field visits and Google Earth images. In order to analyse the earth's surface and extraction of topographic parameters, a digital elevation model derived from PALSAR (Phased Array type L-band Synthetic Aperture Radar) radar data with a resolution of 12.5 meters was used. Using literature review, expert opinion and multi-collinearity test, 15 environmental parameters were selected with a resolution of 12.5 meters for the modelling. Results of RF model indicate that parameters of NDVI (normalized difference vegetation index), elevation and land use respectively had the highest effect on the gully erosion. Several techniques such as area under curve (AUC), seed cell area index (SCAI), and Kappa coefficient were used for validation. Results of validation indicated that the combination of bivariate (IV and DA models) with the RF data-mining model has increased their performance. The prediction accuracy of AUC and Kappa values in DA, IV and RF are (0.745, 0.782, and 0.792) and (0.804, 0.852, and 0.860) and these values in ensemble models of DA-RF and IV-RF are (0.845, and 0.911) and (0.872, and 0.951) respectively. Results of SCAI show that ensemble...
Areerachakul, N, Sakulkhaemaruethai, S, Johir, MAH, Kandasamy, J & Vigneswaran, S 2019, 'Photocatalytic degradation of organic pollutants from wastewater using aluminium doped titanium dioxide', Journal of Water Process Engineering, vol. 27, pp. 177-184.
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© 2018 Elsevier Ltd The objective of this research was to study the performance of batch and continuous recirculating reactor to photo-degrade dye and synthetic wastewater. Here, Aluminium (Al) was used as the doped metal. The commercially available TiO2 P-25 and Al (NO3)3 was used as a Ti-precursor and doping agent, respectively, via the impregnation method. Various parameters such as the concentration of the doping agent, and calcination temperature were studied. The TiO2 nanocrystal doped with Al was characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), and thermogravimetric analyzer (TGA). The photocatalytic performance of Al-doped nanoparticle was quantified by the degradation of methylene blue (MB) solution under a visible light irradiation condition. Its performance was compared against undoped-nano-TiO2. The results showed that Al(NO3)3 solution with a concentration of 0.25% and volume of 100 cm/ml, and calcined at 300 ๐C for 4 h, was the optimum condition of Al-doped nano-TiO2. Furthermore, the highest pseudo-first-order kinetic rate was 0.096 where the doped Al(NO3)3 of 0.75 w/v was used in the batch reactor. The Al-doped nano-TiO2 that was obtained has the potential for use as a photocatalyst for degradation organics pollutant from wastewater under the visible light irradiation. The highest removal of organic pollutants from synthetic wastewater was 75% using TiO2 P-25 alone at 2 g/L dosage. In addition, the removal of organic pollutant by TiO2/doped with Al was 80% at a dosage of 0.5 g/L and was 85% at a dosage of 1 g/L.
Argha, A, Su, SW & Celler, BG 2019, 'Static output feedback fault tolerant control using control allocation scheme', International Journal of Robust and Nonlinear Control, vol. 29, no. 1, pp. 98-116.
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© 2018 John Wiley & Sons, Ltd. This paper describes two novel schemes for fault tolerant control using robust suboptimal static output feedback design methods. These schemes can also be employed as actuator redundancy management for overactuated uncertain linear systems. In contrast to many existing methods in the literature that assume the control input matrix (i) is not of full-rank such that it can be factorized into two matrices and (ii) it does not involve uncertainty, these schemes can be applied to systems whose control input matrix cannot be factorized and/or involve uncertainty. The so-called virtual control, in these schemes, is calculated using suboptimal H 2 -based static output feedback design schemes constructed to be robust against uncertainties emanating from inherent input matrix uncertainty and visibility of the control allocator to the controller. Then, using two proposed control allocation schemes (fixed and on-line), the obtained virtual control signal is redistributed among remaining (redundant or nonfaulty) set of actuators. As the proposed schemes are modular-based, they can be employed as real-time fault tolerant control schemes with no need to reconfigure the controller in the case of actuator faults or failures. The effectiveness of the proposed schemes is discussed and compared with numerical examples.
Argha, A, Su, SW, Savkin, A & Celler, B 2019, 'Design of optimal sliding-mode control using partial eigenstructure assignment', International Journal of Control, pp. 1-13.
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© 2017 Informa UK Limited, trading as Taylor & Francis Group This paper describes a new framework for the design of a sliding surface for a given system while multi-channel (Formula presented.) performances of the closed-loop system are under control. In contrast to most of the current sliding surface design schemes, in this new method, the level of control effort required to maintain sliding is penalised. The proposed method for the design of optimal sliding mode control is implemented in two stages. In the first stage, a state feedback gain is derived using a linear matrix inequality (LMI)-based scheme that can assign a number of the closed-loop eigenvalues to a known value whilst satisfying performance specifications. The sliding function matrix related to the particular state feedback derived in the first stage is obtained in the second stage by using one of the two different methods developed for this goal. The proposed theory is evaluated by using numerical examples including the problem of steady-state output tracking via a state-feedback SMC for flight control.
Ashournejad, Q, Hosseini, A, Pradhan, B & Hosseini, SJ 2019, 'Hazard zoning for spatial planning using GIS-based landslide susceptibility assessment: a new hybrid integrated data-driven and knowledge-based model', Arabian Journal of Geosciences, vol. 12, no. 4.
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© 2019, Saudi Society for Geosciences. The popular use of geographical information system (GIS) technology in landslide assessment ensures good, accurate and fast results with less cost and manages mass movements and identifies safe areas for the development of new settlements. In the literature, different methods have been applied in landslide susceptibility assessment using various models. In these studies, the methods are either expert knowledge-based or data-driven approaches. These two approaches produce different outcomes based on their specific characteristics. Thus, the simultaneous use of a set of methods using fusion techniques at the decision-level improves the results. This study aims to investigate and identify a landslide zonation model by combining different methods in GIS and integrate the results (incorporated in the decision level) to achieve optimum accuracy. In this study, landslide hazard mapping was performed using knowledge-based models such as simple additive weighting (SAW) and fuzzy gamma operator and data-driven models namely radial basis function link network (RBFLN) and probabilistic neural network (PNN). Evaluation results indicate that data-driven methods have better performance than knowledge-based methods based on expert opinion. The SAW method also has a better result than other knowledge-based methods, such as fuzzy gamma. The assessment results of the randomly selected landslide control points indicate that PNN, SAW, RBFLN and fuzzy gamma have accuracies of 82.3, 69.62, 65.39 and 61.26% in landslide zoning, respectively. Meanwhile, weighted mean, maximum, median and minimum were used to incorporate the results in decision-level fusion; results show improved accuracies of 70.53, 83.5, 67.39 and 60.27% for each criterion, respectively.
Aydogdu, MO, Chou, J, Altun, E, Ekren, N, Cakmak, S, Eroglu, M, Osman, AA, Kutlu, O, Oner, ET, Avsar, G, Oktar, FN, Yilmaz, I & Gunduz, O 2019, 'Production of the biomimetic small diameter blood vessels for cardiovascular tissue engineering', International Journal of Polymeric Materials and Polymeric Biomaterials, pp. 1-13.
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© 2018 Taylor & Francis A novel biomimetic vascular graft scaffolds were produced by electrospinning method with the most superior characteristics to be a proper biomimetic small diameter blood vessel using Polycaprolactone(PCL), Ethyl Cellulose(EC) and Collagen Type-1 were used to create the most convenient synergy of a natural and synthetic polymer to achieve similarity to native small diameter blood vessels. Scanning Electron Microscopy(SEM), Fourier Transform Infrared Spectroscopy(FTIR), Differential Scanning Calorimetry Analysis(DSC), tensile measurement tests, and in-vitro and in-vivo applications were performed. Results indicated significant properties such as having 39.33 nm minimum, 104.98 nm average fiber diameter, 3.2 MPa young modulus and 135% relative cell viability.
Azeez, OS, Pradhan, B, Shafri, HZM, Shukla, N, Lee, C & Rizeei, HM 2019, 'Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks', Applied Sciences (Bucureşti), vol. 9, no. 2.
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Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model’s predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas.
Azizivahed, A, Arefi, A, Ghavidel Jirsaraie, S, Shafie-khah, M, Li, L, Zhang, J & Catalao, JPS 2019, 'Energy Management Strategy in Dynamic Distribution Network Reconfiguration considering Renewable Energy Resources and Storage', IEEE Transactions on Sustainable Energy, pp. 1-1.
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Babayan, M, Mazraeh, AE, Yari, M, Niazi, NA & Saha, SC 2019, 'Hydrogen production with a photovoltaic thermal system enhanced by phase change materials, Shiraz, Iran case study', Journal of Cleaner Production, vol. 215, pp. 1262-1278.
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© 2019 Elsevier Ltd Whereas Photovoltaic Thermal Systems (PVT), Phase Change Materials (PCM) and Proton Exchange Membrane (PEM) electrolyzer have been thoroughly studied individually, the effects of their combination need to be more investigated. The current study proposed a new PVT system integrated with PCM and PEM electrolyzer to produce hydrogen in a hydrogen fuel filling station. Based on the energy and exergy balance equations, a mathematical model is developed to analyse the effects of different types of PV and PCM sets on the thermal and electrical performances. Variations in the temperature of system components, generated electricity, hydrogen production as well as the energy/exergy amounts and efficiencies with time are presented for different effective parameters. Based on the obtained results, we found that PV type is one of the most dominant parameters of the system. PCM utilization improves the electrical, thermal energies and exergy efficiencies. The highest daily amount of produced hydrogen is obtained for 16th August 2018 with mono-crystalline semitransparent PV and 120 kg of RT35 PCM type (88.71 gr/day). While the hydrogen production for the same PVT system without PCM is 5.32% less than the case with PCM. Moreover, the maximum diurnal energy efficiency is obtained 35.04% for mono-crystalline semitransparent PV and RT35 PCM during the summer, while the maximum daily exergy efficiency of 15.17% is achieved for the integration of mono-crystalline semitransparent PV and RT28 PCM type in the winter.
Bah, AO, Qin, PY, Ziolkowski, RW, Guo, YJ & Bird, TS 2019, 'A Wideband Low-Profile Tightly Coupled Antenna Array with a Very High Figure of Merit', IEEE Transactions on Antennas and Propagation.
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IEEE A wideband, low-profile, tightly coupled antenna array with a simple feed network is presented. The dipole and feed network in each unit cell are printed on both sides of a single RT/DuroidTM 6010 substrate with a relative dielectric constant of 10.2. The feed network, composed of meandered impedance transformer and balun sections, is designed based on Klopfenstein tapered microstrip lines. The wide angle impedance matching is empowered by a novel wideband metasurface superstrate. For the optimum design, scanning to 70° along the E-plane is obtained together with a very high array figure of merit, PA = 2.84. The H-plane scan extends to 55°. The broadside impedance bandwidth is 5.5:1 (0.80 – 4.38) GHz with an active VSWR value ≤ 2. The overall height of the array above the ground plane is 0.088 λL, where λL is the wavelength at the lowest frequency of operation. A prototype was fabricated and tested to confirm the design concepts.
Barker, RA, Eager, D & Sharwood, LN 2019, 'Ensuring safety in public playgrounds is everybody's business.', The Medical journal of Australia, vol. 210, no. 1, pp. 9-11.e1.
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Ben, X, Gong, C, Zhang, P, Jia, X, Wu, Q & Meng, W 2019, 'Coupled Patch Alignment for Matching Cross-view Gaits', IEEE Transactions on Image Processing, pp. 1-1.
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Ben, X, Gong, C, Zhang, P, Yan, R, Wu, Q & Meng, W 2019, 'Coupled Bilinear Discriminant Projection for Cross-view Gait Recognition', IEEE Transactions on Circuits and Systems for Video Technology.
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IEEE A problem that hinders good performance of general gait recognition systems is that the appearance features of gaits are more affected-prone by views than identities, especially when the walking direction of the probe gait is different from the register gait. This problem cannot be solved by traditional projection learning methods, because these methods can learn only one projection matrix, thus for the same subject, it cannot transfer cross-view gait features into similar ones. This paper presents an innovative method to overcome this problem by aligning gait energy images (GEIs) across views with the Coupled bilinear discriminant projection (CBDP). Specifically, CBDP generates the aligned gait matrix features for two views with two sets of bilinear transformation matrices, so that the original GEIs’ spatial structure information can be preserved. By iteratively maximizing the ratio of inter-class distance metric to intra-class distance metric, CBDP can learn the optimal matrix subspace where GEIs across views are aligned in both horizontal and vertical coordinates. Therefore, CBDP is also able to avoid the under-sample problem. We also theoretically prove that the upper and lower bounds of the objective function sequence of CBDP are both monotonically increasing, so the convergence of CBDP is demonstrated. In terms of accuracy, the comparative experiments on the CASIA(B) and OU-ISIR gait databases show that our method is superior to the state-of-the-art cross-view gait recognition methods. More impressively, the encouraging performance is obtained by our method even in matching a lateral-view gait with a frontal-view gait.
Bennett, J, Froggett, L, Kenning, G, Manley, J & Muller, L 2019, 'Memory Loss and Scenic Experience: An Arts Based Investigation', Forum : Qualitative Social Research, vol. 20, no. 1.
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Best, G, Cliff, OM, Patten, T, Mettu, RR & Fitch, R 2019, 'Dec-MCTS: Decentralized planning for multi-robot active perception', International Journal of Robotics Research, vol. 38, no. 2-3, pp. 316-337.
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© The Author(s) 2018. We propose a decentralized variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimize its own actions by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of their search trees, which are used to update the joint distribution using a distributed optimization approach inspired by variational methods. Our method admits any objective function defined over robot action sequences, assumes intermittent communication, is anytime, and is suitable for online replanning. Our algorithm features a new MCTS tree expansion policy that is designed for our planning scenario. We extend the theoretical analysis of standard MCTS to provide guarantees for convergence rates to the optimal payoff sequence. We evaluate the performance of our method for generalized team orienteering and online active object recognition using real data, and show that it compares favorably to centralized MCTS even with severely degraded communication. These examples demonstrate the suitability of our algorithm for real-world active perception with multiple robots.
Bhowmick, S, Saha, SC, Qiao, M & Xu, F 2019, 'Transition to a chaotic flow in a V-shaped triangular cavity heated from below', International Journal of Heat and Mass Transfer, vol. 128, pp. 76-86.
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© 2018 Elsevier Ltd Natural convection in a V-shaped cavity heated from below and cooled from top is investigated owing to its extensive presence in industrial systems and in nature such as in a valley. Two dimensional numerical simulation is performed for natural convection in the cavity using a Finite Volume Method. A wide range of Rayleigh numbers of Ra = 100to 108for the aspect ratio of A = 0.5 and the Prandtl number of Pr = 0.71 is considered. A set of supercritical bifurcations in a transition to a chaotic flow are described, which include a Pitchfork bifurcation from symmetric to asymmetric state and a Hopf bifurcation from steady to unsteady state. It is found that the Pitchfork bifurcation occurs between Ra = 7.5 × 103and 7.6 × 103and the Hopf bifurcation occurs between Ra = 1.5 × 107and 1.6 × 107. Additionally, a further bifurcation from periodic to chaotic state occurs between Ra = 5 × 107and 6 × 107. The power spectral density, the phase space trajectory and the largest Lyapunov exponent of unsteady flows in the transition to a chaotic state have been described. Further, heat transfer in the cavity is calculated and the corresponding dependence on the Rayleigh number is discussed and quantified.
Biglari, S, Le, TYL, Tan, RP, Wise, SG, Zambon, A, Codolo, G, De Bernard, M, Warkiani, M, Schindeler, A, Naficy, S, Valtchev, P, Khademhosseini, A & Dehghani, F 2019, 'Simulating Inflammation in a Wound Microenvironment Using a Dermal Wound-on-a-Chip Model.', Advanced healthcare materials, vol. 8, no. 1, p. e1801307.
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Considerable progress has been made in the field of microfluidics to develop complex systems for modeling human skin and dermal wound healing processes. While microfluidic models have attempted to integrate multiple cell types and/or 3D culture systems, to date they have lacked some elements needed to fully represent dermal wound healing. This paper describes a cost-effective, multicellular microfluidic system that mimics the paracrine component of early inflammation close to normal wound healing. Collagen and Matrigel are tested as materials for coating and adhesion of dermal fibroblasts and human umbilical vein endothelial cells (HUVECs). The wound-on-chip model consists of three interconnecting channels and is able to simulate wound inflammation by adding tumor necrosis factor alpha (TNF-α) or by triculturing with macrophages. Both the approaches significantly increase IL-1β, IL-6, IL-8 in the supernatant (p < 0.05), and increases in cytokine levels are attenuated by cotreatment with an anti-inflammatory agent, Dexamethasone. Incorporation of M1 and M2 macrophages cocultured with fibroblasts and HUVECs leads to a stimulation of cytokine production as well as vascular structure formation, particularly with M2 macrophages. In summary, this wound-on-chip system can be used to model the paracrine component of the early inflammatory phase of wound healing and has the potential for the screening of anti-inflammatory compounds.
Biradar, J, Banerjee, S, Shankar, R, Ghosh, P, Mukherjee, S & Fatahi, B 2019, 'Response of square anchor plates embedded in reinforced soft clay subjected to cyclic loading', GEOMECHANICS AND ENGINEERING, vol. 17, no. 2, pp. 165-173.
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Blanco-Mesa, F, León-Castro, E, Merigó, JM & Xu, Z 2019, 'Bonferroni means with induced ordered weighted average operators', International Journal of Intelligent Systems, vol. 34, no. 1, pp. 3-23.
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© 2018 Wiley Periodicals, Inc. The induced ordered weighted average is an averaging aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum. This paper presents some new generalizations by using Bonferroni means (BM) forming induced BM. The main advantage of this approach is the possibility of reordering the results according to complex ranking processes based on order-inducing variables. The work also presents some additional extensions by using the weighted ordered weighted average, immediate weights, and hybrid averages. Some further generalizations with generalized and quasi-arithmetic means are also developed to consider a wide range of particular cases including quadratic and geometric aggregations. The article also considers the applicability of the new approach in-group decision-making developing an application in sales forecasting.
Bliuc, D, Tran, T, van Geel, T, Adachi, JD, Berger, C, van den Bergh, J, Eisman, JA, Geusens, P, Goltzman, D, Hanley, DA, Josse, RG, Kaiser, S, Kovacs, CS, Langsetmo, L, Prior, JC, Nguyen, TV, Center, JR & CaMOS Research Group 2019, 'Mortality risk reduction differs according to bisphosphonate class: a 15-year observational study.', Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA.
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In this prospective cohort of 6120 participants aged 50+, nitrogen-bisphosphonates but not non-nitrogen bisphosphonates were associated with a significant 34% mortality risk reduction compared to non-treated propensity score matched controls. These findings open new avenues for research into mechanistic pathways. INTRODUCTION:Emerging evidence suggests that bisphosphonates (BP), first-line treatment of osteoporosis, are associated with reduced risks for all-cause mortality. This study aimed to determine the association between different BP types and mortality risk in participants with or without a fracture. METHODS:A prospective cohort study of users of different BPs matched to non-users by propensity score (age, gender, co-morbidities, fragility fracture status) and time to starting the BP medication from the population-based Canadian Multicentre Osteoporosis Study from nine Canadian centres followed from 1995 to 2013. Mortality risk for bisphosphonate users vs matched non-users was assessed using pairwise multivariable Cox proportional hazards models. RESULTS:There were 2048 women and 308 men on BP and 1970 women and 1794 men who did not receive medication for osteoporosis. The relationship between BP and mortality risk was explored in three separate 1:1 propensity score-matched cohorts of BP users and no treatment (etidronate, n = 599, alendronate, n = 498, and risedronate n = 213). Nitrogen BP (n-BP) (alendronate and risedronate) was associated with lower mortality risks [pairwise HR, 0.66 (95% CI, 0.48-0.91)] while the less potent non-n-BP, etidronate, was not [pairwise HR: 0.89 (95% CI, 0.66-1.20)]. A direct comparison between n-BP and etidronate (n = 340 pairs) also suggested a better survival for n-BP [paired HR, 0.47 (95%CI, (95% CI, 031-0.70)] for n-BP vs. etidronate]. CONCLUSION:Compared to no treatment, nitrogen but not non-nitrogen bisphosphonates appear to be associated with better survival.
Bryce, T, Far, H & Gardner, A 2019, 'Barriers to Career Advancement for Female Engineers in Australia’s Civil Construction Industry and Recommended Solutions', Australian Journal of Civil Engineering.
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This study explores the challenges that have emerged from the outdated and inflexible workplace culture of the civil construction industry, and how it is affecting female engineers and women in other functional site roles. The study primarily explores issues such as the strong culture of long work hours, the perception of staff who pursue work-life balance, and the perception of part time and flexible working options within the industry. The study was conducted over three phases where members of the industry answered questionnaires on the workplace issues listed above. The first phase aimed to document a female perspective on the construction workplace culture while the second phase focused on the perceptions of the same respondents regarding the industry’s attitude to work-life balance and whether any of the aspects of workplace culture has discouraged them from staying in the industry. The final phase included men and women in management and employer roles to determine their views on part time and flexible working options within the industry. The results of the study are presented and discussed before recommendations are offered for contractor organisations, their employers and staff. The recommendations have been addressed in a way that a gradual culture change can be accepted and acted on throughout the entire workplace.
Bykerk, L, Quin, P & Liu, D 2019, 'A Method for Selecting the Next Best Angle-of-Approach for Touch-Based Identification of Beam Members in Truss Structures', IEEE Sensors Journal, pp. 1-1.
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Cagno, E, Moschetta, D & Trianni, A 2019, 'Only non-energy benefits from the adoption of energy efficiency measures? A novel framework', Journal of Cleaner Production, vol. 212, pp. 1319-1333.
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© 2018 Elsevier Ltd Industrial energy efficiency has been widely recognized as a major contributor to the reduction of greenhouse gases emissions and improvement of industrial competitiveness. Nevertheless, a broad set of studies have pointed out the existence of barriers limiting the adoption of promising Energy Efficiency Measures (EEMs). Recently, scholars have shown the relevance of the so-called “non-energy benefits” (NEBs) coming from the adoption of EEMs for overcoming those barriers. Still, the existence of such benefits has been pointed out from specific studies and manuals for practitioners, but an overall framework describing them in terms of savings and benefits, as well as technical and management implications, is missing yet. Moreover, a considerable part of the scholars and of the practitioners just focuses on the identification and definition of the positive benefits deriving from these measures after they have been completely adopted, thus neglecting to describe the full set of both positive and negative effects occurring also during the implementation phase. Thus, starting from a literature review of scientific as well as practitioners’ studies, we have proposed a novel framework and characterization of the relevant items to be considered by an industrial decision-maker when deciding whether to adopt an EEM considering both the implementation and service phases. Hence, by taking this perspective, we have tested and validated the framework and the characterization in a two-step process: firstly, considering a set of EEMs well diffused and adopted in industry; secondly, investigating benefits and losses in ad-hoc selected manufacturing companies. Finally, considerations and implications are drawn from the preliminary validation and suggestion for further research are proposed, for both industrial decision-making as well as policy-making purposes.
Cao, X, Qiu, B & Xu, G 2019, 'BorderShift: toward optimal MeanShift vector for cluster boundary detection in high-dimensional data', Pattern Analysis and Applications, pp. 1-13.
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© 2018 Springer-Verlag London Ltd., part of Springer Nature We present a cluster boundary detection scheme that exploits MeanShift and Parzen window in high-dimensional space. To reduce the noises interference in Parzen window density estimation process, the kNN window is introduced to replace the sliding window with fixed size firstly. Then, we take the density of sample as the weight of its drift vector to further improve the stability of MeanShift vector which can be utilized to separate boundary points from core points, noise points, isolated points according to the vector models in multi-density data sets. Under such circumstance, our proposed BorderShift algorithm doesn’t need multi-iteration to get the optimal detection result. Instead, the developed Shift value of each data point helps to obtain it in a liner way. Experimental results on both synthetic and real data sets demonstrate that the F-measure evaluation of BorderShift is higher than that of other algorithms.
Chen, B, Huang, J & Ji, JC 2019, 'Control of flexible single-link manipulators having Duffing oscillator dynamics', Mechanical Systems and Signal Processing, vol. 121, pp. 44-57.
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© 2018 Elsevier Ltd Unwanted vibrations caused by the commanded motions lower the positioning accuracy and degrade the control performance of flexible link manipulators. Much work has been devoted to the dynamics and control of flexible link manipulators. However, there are few studies dedicated to the flexible link manipulators having Duffing oscillator dynamics. This paper develops a new model for a flexible single-link manipulator by assuming large mechanical impedance in the drives or large inertia of the motor hub and by considering the flexibility of the single-link manipulator. The derived model includes an infinite number of uncoupled Duffing oscillators by ignoring the modal coupling effects. Two new methods are presented for controlling the vibrations of the flexible manipulator governed by Duffing oscillators. One is designed for the single-mode Duffing oscillator, and the other is for the multi-mode Duffing oscillators. A comparison of these two methods is made using the results of numerical simulations and experimental measurements. Experimental investigations are also performed on a flexible single-link manipulator to validate the dynamic behavior of Duffing oscillators and the effectiveness of the new control methods.
CHEN, J, SU, S & WANG, X 2019, 'Towards Privacy-Preserving Location Sharing over Mobile Online Social Networks', IEICE TRANSACTIONS on Information and Systems, vol. 102, pp. 133-146.
Chen, S, Wang, Y, Lin, CT, Ding, W & Cao, Z 2019, 'Semi-supervised feature learning for improving writer identification', Information Sciences, vol. 482, pp. 156-170.
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© 2019 Elsevier Inc. Data augmentation is typically used by supervised feature learning approaches for offline writer identification, but such approaches require a mass of additional training data and potentially lead to overfitting errors. In this study, a semi-supervised feature learning pipeline is proposed to improve the performance of writer identification by training with extra unlabeled data and the original labeled data simultaneously. Specifically, we propose a weighted label smoothing regularization (WLSR) method for data augmentation, which assigns a weighted uniform label distribution to the extra unlabeled data. The WLSR method regularizes the convolutional neural network (CNN) baseline to allow more discriminative features to be learned to represent the properties of different writing styles. The experimental results on well-known benchmark datasets (ICDAR2013 and CVL) showed that our proposed semi-supervised feature learning approach significantly improves the baseline measurement and perform competitively with existing writer identification approaches. Our findings provide new insights into offline writer identification.
Chen, W, Pradhan, B, Li, S, Shahabi, H, Rizeei, HM, Hou, E & Wang, S 2019, 'Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis', Natural Resources Research.
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© 2019, International Association for Mathematical Geosciences. Groundwater is a vital water source in the rural and urban areas of developing and developed nations. In this study, a novel hybrid integration approach of Fisher’s linear discriminant function (FLDA) with rotation forest (RFLDA) and bagging (BFLDA) ensembles was used for groundwater potential assessment at the Ningtiaota area in Shaanxi, China. A spatial database with 66 groundwater spring locations and 14 groundwater spring contributing factors was prepared; these factors were elevation, aspect, slope, plan and profile curvatures, sediment transport index, stream power index, topographic wetness index, distance to roads and streams, land use, lithology, soil and normalized difference vegetation index. The classifier attribute evaluation method based on the FLDA model was implemented to test the predictive competence of the mentioned contributing factors. The area under curve, confidence interval at 95%, standard error, Friedman test and Wilcoxon signed-rank test were used to compare and validate the success and prediction competence of the three applied models. According to the achieved results, the BFLDA model showed the most prediction competence, followed by the RFLDA and FLDA models, respectively. The resulting groundwater spring potential maps can be used for groundwater development plans and land use planning.
Chen, W, Yan, X, Zhao, Z, Hong, H, Bui, DT & Pradhan, B 2019, 'Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China)', Bulletin of Engineering Geology and the Environment, vol. 78, no. 1, pp. 247-266.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naïve Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landslide locations were identified within the study area using historical reports, aerial photographs, and extensive field surveys. All the landslides were randomly separated into two parts with a ratio of 70/30 for training and validation purposes. Second, 12 landslide conditioning factors were prepared for landslide susceptibility modeling, including slope aspect, slope angle, plan curvature, profile curvature, elevation, distance to faults, distance to rivers, distance to roads, lithology, NDVI (normalized difference vegetation index), land use, and rainfall. Third, the correlations between the conditioning factors and the occurrence of landslides were analyzed using normalized frequency ratios. A multicollinearity analysis of the landslide conditioning factors was carried out using tolerances and variance inflation factor (VIF) methods. Feature selection was performed using the chi-squared statistic with a 10-fold cross-validation technique to assess the predictive capabilities of the landslide conditioning factors. Then, the landslide conditioning factors with null predictive ability were excluded in order to optimize the landslide models. Finally, the trained KLR, NB, and RBFNetwork models were used to construct landslide susceptibility maps. The receiver operating characteristics (ROC) curve, the area under the curve (AUC), and several statistical measures, such as accuracy (ACC), F-measure, mean absolute error (MAE), and root mean squared error (RMSE), were used for the assessment, validation, and comparison of the resulting models in order to choose the best model in this study. The validation results s...
Chen, X, Lai, CY, Fang, F, Zhao, HP, Dai, X & Ni, BJ 2019, 'Model-based evaluation of selenate and nitrate reduction in hydrogen-based membrane biofilm reactor', Chemical Engineering Science, vol. 195, pp. 262-270.
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© 2018 Elsevier Ltd A biofilm model was developed to describe the simultaneous NO 3− and SeO 42− reduction in a H 2 -based membrane biofilm reactor (MBfR). Model calibration and validation was conducted using the experimental data of a reported H 2 -based MBfR. With a good level of identifiability, the SeO 42− affinity constant and the SeO 32− affinity constant were estimated at 9.80 ± 0.51 g Se m −3 and 1.83 ± 0.38 g Se m −3 , respectively. The model was then applied to evaluate the effects of key operating conditions on the single-stage H 2 -based MBfR and the role of reactor configuration through comparing two-stage to single-stage MBfR systems. The results showed that (i) high SeO 42− or low NO 3− concentration in the influent favored the growth of selenate-reducing bacteria (SeRB) and therefore benefited the Se removal, (ii) the influent dissolved oxygen slightly inhibited the Se removal through enhancing the aerobic microbial respiration on H 2 , (iii) the H 2 supply should be controlled at a proper level to avoid SeRB suppression and H 2 wastage, (iv) thin biofilm should be avoided to ensure a protected niche for SeRB and therefore a promising Se removal, and (v) the two-stage MBfR configuration offered relatively higher efficiency in removing Se and NO 3− simultaneously under the same loading condition.
Chen, Z, Zhang, B, Zhang, N, Du, H & Kong, G 2019, 'Optimal preview position control for shifting actuators of automated manual transmission', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 233, no. 2, pp. 440-452.
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© IMechE 2018. This paper is concerned with the problem of position tracking control for the motor-driven gear-shift actuating mechanism for electro-mechanical automated manual transmissions (AMT). It is well known that torque interruption is an inherent flaw of AMT. As shift duration directly affects the torque interruption interval, shift quality can be improved by minimizing the duration of the shift. To realize rapid and precise gear-shift control, an optimal discrete-time preview position control scheme is proposed. The proposed control scheme consists of state-feedback control, a discrete integrator, and preview feed-forward control. Instead of the traditional difference method, the state transformation method is utilized to construct the augmented error system such that the augmented system can be kept in a simple form. The H∞ performance index is employed to reduce the effect of the shifting load disturbance during the gear-shifting process. The controller gains are obtained by solving a linear matrix inequality (LMI). Simulation and test bench results all show that the proposed preview control algorithm has better dynamic response and adaptability under different loads compared to the optimal linear quadratic regulator (LQR) controller and the standard PID controller.
Chenari, RJ, Fatahi, B, Ghoreishi, M & Taleb, A 2019, 'Physical and numerical modelling of the inherent variability of shear strength in soil mechanics', Geomechanics and Engineering, vol. 17, no. 1, pp. 31-45.
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© 2019 Techno-Press, Ltd. In this study the spatial variability of soils is substantiated physically and numerically by using random field theory. Heterogeneous samples are fabricated by combining nine homogeneous soil clusters that are assumed to be elements of an adopted random field. Homogeneous soils are prepared by mixing different percentages of kaolin and bentonite at water contents equivalent to their respective liquid limits. Comprehensive characteristic laboratory tests were carried out before embarking on direct shear experiments to deduce the basic correlations and properties of nine homogeneous soil clusters that serve to reconstitute the heterogeneous samples. The tests consist of Atterberg limits, and Oedometric and unconfined compression tests. The undrained shear strength of nine soil clusters were measured by the unconfined compression test data, and then correlations were made between the water content and the strength and stiffness of soil samples with different consistency limits. The direct shear strength of heterogeneous samples of different stochastic properties was then evaluated by physical and numerical modelling using FISH code programming in finite difference software of FLAC 3D . The results of the experimental and stochastic numerical analyses were then compared. The deviation of numerical simulations from direct shear load-displacement profiles taken from different sources were discussed, potential sources of error was introduced and elaborated. This study was primarily to explain the mathematical and physical procedures of sample preparation in stochastic soil mechanics. It can be extended to different problems and applications in geotechnical engineering discipline to take in to account the variability of strength and deformation parameters.
Cheng, DL, Ngo, HH, Guo, WS, Chang, SW, Nguyen, DD & Kumar, SM 2019, 'Microalgae biomass from swine wastewater and its conversion to bioenergy.', Bioresource technology, vol. 275, pp. 109-122.
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Ever-increasing swine wastewater (SW) has become a serious environmental concern. High levels of nutrients and toxic contaminants in SW significantly impact on the ecosystem and public health. On the other hand, swine wastewater is considered as valuable water and nutrient source for microalgae cultivation. The potential for converting the nutrients from SW into valuable biomass and then generating bioenergy from it has drawn increasing attention. For this reason, this review comprehensively discussed the biomass production, SW treatment efficiencies, and bioenergy generation potentials through cultivating microalgae in SW. Microalgae species grow well in SW with large amounts of biomass being produced, despite the impact of various parameters (e.g., nutrients and toxicants levels, cultivation conditions, and bacteria in SW). Pollutants in SW can effectively be removed by harvesting microalgae from SW, and the harvested microalgae biomass elicits high potential for conversion to valuable bioenergy.
Cheng, H, Zhang, J, Wu, Q & An, P 2019, 'A computational model for stereoscopic visual saliency prediction', IEEE Transactions on Multimedia, vol. 21, no. 3, pp. 678-689.
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© 2018 IEEE. Depth information plays an important role in human vision as it provides additional cues that distinguish objects from their backgrounds. This paper explores depth information for analyzing stereoscopic saliency and presents a computational model that predicts stereoscopic visual saliency based on three aspects of human vision: 1) the pop-out effect; 2) comfort zones; and 3) background effects. Through an analysis of these three phenomena, we find that most of the stereoscopic saliency region can be explained. Our model comprises three modules, each describing one aspect of saliency distribution, and a control function that can be used to adjust the three models independently. The relationship between the three models is not mutually exclusive. One, two, or three phenomena may appear in one image. Therefore, to accurately determine which phenomena the image conforms to, we have devised a selection strategy that chooses the appropriate combination of models based on the content of the image. Our approach is implemented within a framework based on the multifeature analysis. The framework considers surrounding regions, color/depth contrast, and points of interest. The selection strategy can improve the performance of the framework. A series of experiments on two recent eye-tracking datasets shows that our proposed method outperforms several state-of-the-art saliency models.
Cheng, S, Dong, H, Yu, L, Zhang, D & Ji, J 2019, 'Consensus of Second-order Multi-agent Systems with Directed Networks Using Relative Position Measurements Only', International Journal of Control, Automation and Systems, vol. 17, no. 1, pp. 85-93.
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© 2019, Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature. This brief paper studies the consensus problem of second-order multi-agent systems when the agents’ velocity measurements are unavailable. Firstly, two simple consensus protocols which do not need velocity measurements of the agents are derived to guarantee that the multi-agent systems achieve consensus in directed networks. Secondly, a key constant which is determined by the complex eigenvalue of the nonsymmetric Laplacian matrix and an explicit expression of the consensus state are respectively developed based on matrix theory. The obtained results show that all the agents can reach consensus if the feedback parameter is bigger than the key constant. Thirdly, the theoretical analysis shows that the followers can track the position and velocity of the leader provided that the leader has a directed path to all other followers and the feedback parameter is bigger enough. Finally, numerical simulations are given to illustrate the effectiveness of the proposed protocols.
Chiniforush, AA, Akbarnezhad, A, Valipour, H & Malekmohammadi, S 2019, 'Moisture and temperature induced swelling/shrinkage of softwood and hardwood glulam and LVL: An experimental study', Construction and Building Materials, vol. 207, pp. 70-83.
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Choi, J, Dorji, P, Shon, HK & Hong, S 2019, 'Applications of capacitive deionization: Desalination, softening, selective removal, and energy efficiency', Desalination, vol. 449, pp. 118-130.
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© 2018 Elsevier B.V. Capacitive deionization (CDI) has attracted a great attention as a promising desalination technology, and studies on CDI have increased significantly in the last ten years. However, there have been no guidelines for developing strategies involving CDI technology for specific applications. Therefore, our work presents a critical review of the recent advances in CDI to meet the technical requirements of various applicable areas, with an emphasis on hybrid systems. This paper first summarizes the major developments made on novel electrode materials for CDI for brackish water desalination. Then, CDI and reverse osmosis (RO) integrated systems are critically reviewed for both ultrapure water production and wastewater treatment. Additionally, the applicability of CDI on various industrial processes is discussed, covering two distinct topics: (1) water softening and (2) selective removal of valuable heavy metals and nutrients (nitrate/phosphate). Lastly, recent improvements on the energy efficiency of CDI processes are delineated, specifically focusing on energy recovery and hybridization with energy producing technology, such as reverse electrodialysis (RED) and microbial fuel cells (MFC). This review paper is expected to share the practical experience of CDI applications as well as to provide guidelines for electrode material development for each specific application.
Cui, H, Xu, F, Saha, SC & Liu, Q 2019, 'Transient free convection heat transfer in a section-triangular prismatic enclosure with different aspect ratios', International Journal of Thermal Sciences, vol. 139, pp. 282-291.
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© 2019 Elsevier Masson SAS Free convection studies in a section-triangular prismatic enclosure with different aspect ratios (depth-width ratios, A) is conducted using three-dimensional numerical modeling approach. The Rayleigh number (Ra) covers a broad range from 10 0 to 10 7 . Transient free convection is characterized under top cooled and bottom heated boundary conditions. The flow structure of transverse rolls and longitudinal rolls is described. The critical Rayleigh numbers for the transition of the flow from driven by the baroclinic to Rayleigh-Bénard instability and from a steady to an unsteady state have been obtained for different aspect ratios. Free convection in the section-triangular prismatic enclosure could be divided into three regimes, which are presented in a Ra-A space. The quantitative relationship between heat transfer and the aspect ratio as well as the Rayleigh number has been obtained numerically.
Cui, L, Wu, J, Pi, D, Zhang, P & Kennedy, P 2019, 'Dual Implicit Mining-Based Latent Friend Recommendation', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user-user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen-Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics.
Dackermann, U, Smith, WA, Alamdari, MM, Li, J & Randall, RB 2019, 'Cepstrum-based damage identification in structures with progressive damage', Structural Health Monitoring, vol. 18, no. 1, pp. 87-102.
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© The Author(s) 2018. This article aims at developing a new framework to identify and assess progressive structural damage. The method relies solely on output measurements to establish the frequency response functions of a structure using cepstrum-based operational modal analysis. Two different damage indicative features are constructed using the established frequency response functions. The first damage feature takes the residual frequency response function, defined as the difference in frequency response function between evolving states of the structure, and then reduces its dimension using principle component analysis; while in the second damage indicator, a new feature based on the area under the residual frequency response function curve is proposed. The rationale behind this feature lies in the fact that damage often affects a number of modes of the system, that is, it affects the frequency response function over a wide range of frequencies; as a result, this quantity has higher sensitivity to any structural change by combining all contributions from different frequencies. The obtained feature vectors serve as inputs to a novel multi-stage neural network ensemble designed to assess the severity of damage in the structure. The proposed method is validated using extensive experimental data from a laboratory four-girder timber bridge structure subjected to gradually progressing damage at various locations with different severities. In total, 13 different states of the structure are considered, and it is demonstrated that the new damage feature outperforms the conventional principle component analysis–based feature. The contribution of the work is threefold: first, the application of cepstrum-based operational modal analysis in structural health monitoring is further validated, which has potential for real-life applications where only limited knowledge of the input is available; second, a new damage feature is proposed and its superior performance is demonstrat...
Dang, C, Zhang, J, Kwong, C-P & Li, L 2019, 'Demand Side Load Management for Big Industrial Energy Users under Blockchain-Based Peer-to-Peer Electricity Market', IEEE Transactions on Smart Grid.
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Dang, LC, Dang, CC & Khabbaz, H 2019, 'Modelling of columns and fibre reinforced load transfer platform supported embankments', Proceedings of the Institution of Civil Engineers - Ground Improvement.
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This study proposes a novel ground modification technique utilising fibre reinforced load transfer platform (FRLTP) and deep cement mixing (DCM) columns supported (CS) embankment constructed over soft soils. An equivalent two-dimensional finite element model was developed to simulate the full geometry of a CS embankment reinforced without or with an FRLTP. A series of numerical analyses was firstly conducted on the proposed model for different improvement depths to assess the effectiveness of the introduction of FRLTP into the CS embankment system in terms of total and differential settlements, stress transfer mechanism and lateral displacement with depth. Subsequently, another extensive parametric study was conducted to further investigate the influence of the FRLTP key parameters including elastic deformation modulus, shear strength properties, and tensile strength on the embankment performance during construction and consolidation time. The numerical results show that the embankment with FRLTP can effectively diminish the total settlement and the lateral deformation of the embankment, meanwhile improve the stress concentration ratio and the embankment stability to a great extent. The findings of the extensive parametric study indicate that the FRLTP shear strength properties appear to be the most influence factors to be considered in the design procedure of a target CS-FRLTP-embankment system.
Darabi, H, Choubin, B, Rahmati, O, Torabi Haghighi, A, Pradhan, B & Kløve, B 2019, 'Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques', Journal of Hydrology, vol. 569, pp. 142-154.
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© 2018 Elsevier B.V. Flood risk mapping and modeling is important to prevent urban flood damage. In this study, a flood risk map was produced with limited hydrological and hydraulic data using two state-of-the-art machine learning models: Genetic Algorithm Rule-Set Production (GARP) and Quick Unbiased Efficient Statistical Tree (QUEST). The flood conditioning factors used in modeling were: precipitation, slope, curve number, distance to river, distance to channel, depth to groundwater, land use, and elevation. Based on available reports and field surveys for Sari city (Iran), 113 points were identified as flooded areas (with each flooded zone assigned a value of 1). Different conditioning factors, including urban density, quality of buildings, age of buildings, population density, and socio-economic conditions, were taken into account to analyze flood vulnerability. In addition, the weight of these conditioning factors was determined based on expert knowledge and Fuzzy Analytical Network Process (FANP). An urban flood risk map was then produced using flood hazard and flood vulnerability maps. The area under the receiver-operator characteristic curve (AUC-ROC) and Kappa statistic were applied to evaluate model performance. The results demonstrated that the GARP model (AUC-ROC = 93.5%, Kappa = 0.86) had higher performance accuracy than the QUEST model (AUC-ROC = 89.2%, Kappa = 0.79). The results also indicated that distance to channel, land use, and elevation played major roles in flood hazard determination, whereas population density, quality of buildings, and urban density were the most important factors in terms of vulnerability. These findings demonstrate that machine learning models can help in flood risk mapping, especially in areas where detailed hydraulic and hydrological data are not available.
De Carvalho Gomes, S, Zhou, JL, Li, W & Long, G 2019, 'Progress in manufacture and properties of construction materials incorporating water treatment sludge: A review', Resources, Conservation and Recycling, vol. 145, pp. 148-159.
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© 2019 Elsevier B.V. Water treatment sludge (WTS) management is a growing global problem for water treatment plants (WTPs) and governments. Considering the scarcity of raw materials in many parts of the planet and unique properties of WTS, extensive research has been conducted on the application of WTS in the production of construction materials such as roof tiles, bricks, lightweight aggregates, cement, concrete and geopolymers. This paper critically reviews the progress in the application of WTS in construction materials, by synthesizing results from recent studies. Research findings have revealed that incorporation of ≤10% alum-based sludge in ceramic bricks is satisfactory with a small reduction of mechanical performance. Using the iron-based sludge, the bricks presented better mechanical strength than the reference clay-bricks. Concerning WTS application in concrete, 5% replacement of cement or sand by WTS was considered as the ideal value for the application in a variety of structural and non-structural concrete without adverse effect on concrete mechanical performance. Furthermore, this paper discusses sludge-amended concrete in terms of durability, potential leaching of toxic elements and cost, and suggests topics for future research on the sustainable management of WTS.
Dekhtyar, A, Huffman Hayes, J, Hadar, I, Combs, E, Ferrari, A, Gregory, S, Horkoff, J, Levy, M, Nayebi, M, Paech, B, Payne, J, Primrose, M, Spoletini, P, Clarke, S, Brophy, C, Amyot, D, Maalej, W, Ruhe, G, Cleland-Huang, J & Zowghi, D 2019, 'Requirements engineering (RE) for social good: RE cares [requirements]', IEEE Software, vol. 36, no. 1.
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© 1984-2012 IEEE. As researchers and teachers and practitioners, we software types excel at multitasking. This, in part, led us to ask the question: Can one attend a software engineering conference and do something good for society? We found the answer to be a resounding yes. In this article, we present our first experience of running RE Cares, a conference collocated event. This event included a workshop, conference sessions, and a hackathon for developing an application to support emergency field activity for Mutual Aid Alberta, a nonprofit organization coordinating natural disaster responses in the Canadian province.
Ding, A, Lin, D, Zhao, Y, Ngo, HH, Guo, W, Bai, L, Luo, X, Li, G, Ren, N & Liang, H 2019, 'Effect of metabolic uncoupler, 2,4‑dinitrophenol (DNP) on sludge properties and fouling potential in ultrafiltration membrane process.', The Science of the total environment, vol. 650, no. Pt 2, pp. 1882-1888.
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Energy uncoupling technology was applied to the membrane process to control the problem of bio-fouling. Different dosages of uncoupler (2,4‑dinitrophenol, DNP) were added to the activated sludge, and a short-term ultrafiltration test was systematically investigated for analyzing membrane fouling potential and underlying mechanisms. Ultrafiltration membrane was used and made of polyether-sulfone with a molecular weight cut off (MWCO) of 150 kDa. Results indicated that low DNP concentration (15-30 mg/g VSS) aggravated membrane fouling because more soluble microbial products were released and then rejected by the membrane, which significantly increased cake layer resistance compared with the control. Conversely, a high dosage of DNP (45 mg/g VSS) retarded membrane fouling owing to the high inhibition of extracellular polymeric substances (proteins and polysaccharides) of the sludge, which effectively prevented the formation of cake layer on the membrane surface. Furthermore, analyses of fouling model revealed that a high dosage of DNP delayed the fouling model from pore blocking transition to cake filtration, whereas this transition process was accelerated in the low dosage scenario.
Ding, H, Ji, J & Chen, LQ 2019, 'Nonlinear vibration isolation for fluid-conveying pipes using quasi-zero stiffness characteristics', Mechanical Systems and Signal Processing, vol. 121, pp. 675-688.
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© 2018 Fluid-conveying pipes are always subjected to various excitations to cause unwanted vibrations. A quasi-zero stiffness system consisting of three linear springs is adopted as the nonlinear isolator to attenuate the transverse vibrations of fluid-conveying pipes induced by foundation excitations. A dynamic model of nonlinear forced vibration of the fluid-conveying pipe coupled with two nonlinear isolators is established for the nonlinear continuous system and validated by using two methods, Galerkin method and the finite difference method. The influence of the quasi-zero stiffness isolators on the vibration characteristics and vibration transmission of the pipe is investigated by analyzing the natural frequency, vibration mode, and nonlinear vibration response. The effects of flow speed of the fluid and the system parameters of the isolator are studied to evaluate the isolation performance. It is found that the quasi-zero stiffness isolator and fluid flow can shift several natural frequencies of vibration of the pipeline to the low-frequency region. When the linear stiffness of the vibration isolation is zero in the vertical direction, the first two modes of the bending vibration of the fluid-conveying pipe tend to become rigid mode. While achieving high-efficiency vibration isolation in the high-frequency region, the vibration in the low-frequency region is complicated. The flow speed of the fluid can deteriorate the performance of vibration isolation.
Ding, W, Lin, C-T & Cao, Z 2019, 'Deep Neuro-Cognitive Co-Evolution for Fuzzy Attribute Reduction by Quantum Leaping PSO With Nearest-Neighbor Memeplexes.', IEEE Transactions on Cybernetics.
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Attribute reduction with many patterns and indicators has been regarded as an important approach for large-scale data mining and machine learning tasks. However, it is extremely difficult for researchers to inadequately extract knowledge and insights from multiple overlapping and interdependent fuzzy datasets from the current changing and interconnected big data sources. This paper proposes a deep neuro-cognitive co-evolution for fuzzy attribute reduction (DNCFAR) that contains a combination of quantum leaping particle swarm optimization with nearest-neighbor memeplexes. A key element of DNCFAR resides in its deep neuro-cognitive cooperative co-evolution structure, which is explicitly permitted to identify interdependent variables and adaptively decompose them in the same neuro-subpopulation, with minimizing the complexity and nonseparability of interdependent variables among different fuzzy attribute subsets. Next DNCFAR formalizes to the different types of quantum leaping particles with nearest-neighbor memeplexes to share their respective solutions and deeply cooperate to evolve the assigned fuzzy attribute subsets. The experimental results demonstrate that DNCFAR can achieve competitive performance in terms of average computational efficiency and classification accuracy while reinforcing noise tolerance. Furthermore, it can be well applied to clearly identify different longitudinal surfaces of infant cerebrum regions, which indicates its great potential for brain disorder prediction based on fMRI.
Djebbara, Z, Fich, LB, Petrini, L & Gramann, K 2019, 'Sensory-motor brain dynamics reflect architectural affordances'.
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Anticipating meaningful actions in the environment is an essential function of the brain. Such predictive mechanisms originate from the motor system and allow for inferring actions from environmental affordances, the potential to act within a specific environment. Using architecture, we provide a unique perspective to the abiding debate in cognitive neuroscience and philosophy on whether cognition depends on movement or is decoupled from our physical structure. To investigate cognitive processes associated with architectural affordances, we used a Mobile Brain/Body Imaging approach recording brain activity synchronized to head-mounted virtual reality. Participants perceived and acted upon virtual transitions ranging from non-passable to easily passable. We demonstrate that early sensory brain activity, upon revealing the environment and before actual movement, differed as a function of affordances. Additionally, movement through transitions was preceded by a motor-related negative component also depended on affordances. Our results suggest that potential actions afforded by an environment influence perception.
Dong, B, Xia, Z, Sun, J, Dai, X, Chen, X & Ni, B-J 2019, 'The inhibitory impacts of nano-graphene oxide on methane production from waste activated sludge in anaerobic digestion.', The Science of the total environment, vol. 646, pp. 1376-1384.
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The wide application of graphene oxide nanoparticles inevitably leads to their discharge into wastewater treatment plants and combination with the activated sludge. However, to date, it is largely unknown if the nano-graphene oxide (NGO) has potential impacts on the anaerobic digestion of waste activated sludge (WAS). Therefore, this work aims to fill the knowledge gap through comprehensively investigating the effects of NGO on carbon transformation and methane production in the anaerobic digestion of WAS. Biochemical methane potential tests demonstrated the methane production dropped with increasing NGO additions, the cumulative methane production decreasing by 7.6% and 12.6% at the NGO dosing rates of 0.054 mg/mg-VS and 0.108 mg/mg-VS, respectively. Model-based analysis indicated NGO significantly reduced biochemical methane potential, with the highest biochemical methane potential decrease being approximately 10% at the highest NGO dosing rate. Further experimental analysis suggested that the decreased methane production was firstly related to a decrease in soluble organic substrates availability during the process of sludge disintegration, potentially attributing to the strong absorption of organic substrates by NGO. Secondly, NGO significantly inhibited the methanogenesis by negatively affecting the corresponding enzyme activity (i.e. coenzyme F420), which could also resulted in a decreased methane production.
Dong, M, Wen, S, Zeng, Z, Yan, Z & Huang, T 2019, 'Sparse fully convolutional network for face labeling', Neurocomputing, vol. 331, pp. 465-472.
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© 2018 Elsevier B.V. This paper proposes a sparse fully convolutional network (FCN) for face labeling. FCN has demonstrated strong capabilities in learning representations for semantic segmentation. However, it often suffers from heavy redundancy in parameters and connections. To ease this problem, group Lasso regularization and intra-group Lasso regularization are utilized to sparsify the convolutional layers of the FCN. Based on this framework, parameters that correspond to the same output channel are grouped into one group, and these parameters are simultaneously zeroed out during training. For the parameters in groups that are not zeroed out, intra-group Lasso provides further regularization. The essence of the regularization framework lies in its ability to offer better feature selection and higher sparsity. Moreover, a fully connected conditional random fields (CRF) model is used to refine the output of the sparse FCN. The proposed approach is evaluated on the LFW face dataset with the state-of-the-art performance. Compared with a non-regularized FCN, the sparse FCN reduces the number of parameters by 91.55% while increasing the segmentation performance by 11% relative error reduction.
Dong, W, Li, W, Tao, Z & Wang, K 2019, 'Piezoresistive properties of cement-based sensors: Review and perspective', Construction and Building Materials, vol. 203, pp. 146-163.
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© 2019 Elsevier Ltd Cement-based sensors are increasingly used in smart concrete to self-sense and monitor the damages and cracks through the measurements of concrete electrical resistivity. The fundamental concepts, key components, manufacturing process, piezoresistivity measurements, and primary applications of cement-based sensors are reviewed in this paper. Various materials, mechanical and environmental factors affecting concrete piezoresistive properties are explicated. Some contradictory results from different studies are reported and discussed. Future perspectives of piezoresistive cement-based sensors are also delineated. The review reveals that there is an optimal conductor content, below which the sensor would perform more like plain concrete with high resistivity and low sensitivity, while excessively higher than which, the dispersion of the conductive phase could become difficult, thus increasing resistivity. The manufacturing process, such as the dispersion method of conductors and curing condition, plays a significant role in conductor distribution, matrix density and pore structure of the sensors, which, together with rheology of the sensor composite, consequently alters the piezoresistive properties of the sensors. In addition to responding to mechanical loading, cement-based piezoresistive sensor also has a great potential for monitoring behaviour of concrete under freeze-thaw cycling. It is expected that this review will provide not only an orientation for new researchers to explore and engage in related studies but also an insight for experienced researchers to perform transformational examinations into cement-based piezoresistive sensors.
Dong, X, Qiu, P, Lu, J, Cao, L & Xu, T 2019, 'Mining Top-k Useful Negative Sequential Patterns via Learning.', IEEE transactions on neural networks and learning systems.
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As an important tool for behavior informatics, negative sequential patterns (NSPs) (such as missing a medical treatment) are sometimes much more informative than positive sequential patterns (PSPs) (e.g., attending a medical treatment) in many applications. However, NSP mining is at an early stage and faces many challenging problems, including 1) how to mine an expected number of NSPs; 2) how to select useful NSPs; and 3) how to reduce high time consumption. To solve the first problem, we propose an algorithm Topk-NSP to mine the k most frequent negative patterns. In Topk-NSP, we first mine the top-k PSPs using the existing methods, and then we use an idea which is similar to top-k PSPs mining to mine the top-k NSPs from these PSPs. To solve the remaining two problems, we propose three optimization strategies for Topk-NSP. The first optimization strategy is that, in order to consider the influence of PSPs when selecting useful top-k NSPs, we introduce two weights, wP and wN, to express the user preference degree for NSPs and PSPs, respectively, and select useful NSPs by a weighted support wsup. The second optimization strategy is to merge wsup and an interestingness metric to select more useful NSPs. The third optimization strategy is to introduce a pruning strategy to reduce the high computational costs of Topk-NSP. Finally, we propose an optimization algorithm Topk-NSP⁺. To the best of our knowledge, Topk-NSP⁺ is the first algorithm that can mine the top-k useful NSPs. The experimental results on four synthetic and two real-life data sets show that the Topk-NSP⁺ is very efficient in mining the top-k NSPs in the sense of computational cost and scalability.
Dong, X, Yan, Y, Tan, M, Yang, Y & Tsang, IW 2019, 'Late Fusion via Subspace Search With Consistency Preservation.', IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol. 28, no. 1, pp. 518-528.
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In many real-world applications, data can be represented by multiple ways or multi-view features to describe various characteristics of data. In this sense, the prediction performance can be significantly improved by taking advantages of these features together. Late fusion, which combines the predictions of multiple features, is a commonly used approach to make the final decision for a test instance. However, it is ubiquitous that different features dispute the prediction on the same data with each other, leading to performance degeneration. In this paper, we propose an efficient and effective matrix factorization-based approach to fuse predictions from multiple sources. This approach leverages a hard constraint on the matrix rank to preserve the consistency of predictions by various features, and we thus named it as Hard-rank Constraint Matrix Factorization-based fusion (HCMF). HCMF can avoid the performance degeneration caused by the controversy of multiple features. Extensive experiments demonstrate the efficacy of HCMF for outlier detection and the performance improvement, which outperforms the state-of-the-art late fusion algorithms on many data sets.
Douglas, ANJ, Irga, PJ & Torpy, FR 2019, 'Determining broad scale associations between air pollutants and urban forestry: A novel multifaceted methodological approach.', Environmental pollution (Barking, Essex : 1987), vol. 247, pp. 474-481.
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Global urbanisation has resulted in population densification, which is associated with increased air pollution, mainly from anthropogenic sources. One of the systems proposed to mitigate urban air pollution is urban forestry. This study quantified the spatial associations between concentrations of CO, NO₂, SO₂, and PM₁₀ and urban forestry, whilst correcting for anthropogenic sources and sinks, thus explicitly testing the hypothesis that urban forestry is spatially associated with reduced air pollution on a city scale. A Land Use Regression (LUR) model was constructed by combining air pollutant concentrations with environmental variables, such as land cover type and use, to develop predictive models for air pollutant concentrations. Traffic density and industrial air pollutant emissions were added to the model as covariables to permit testing of the main effects after correcting for these air pollutant sources. It was found that the concentrations of all air pollutants were negatively correlated with tree canopy cover and positively correlated with dwelling density, population density and traffic count. The LUR models enabled the establishment of a statistically significant spatial relationship between urban forestry and air pollution mitigation. These findings further demonstrate the spatial relationships between urban forestry and reduced air pollution on a city-wide scale, and could be of value in developing planning policies focused on urban greening.
Du, B, Xinyao, T, Wang, Z, Zhang, L & Tao, D 2019, 'Robust Graph-Based Semisupervised Learning for Noisy Labeled Data via Maximum Correntropy Criterion.', IEEE transactions on cybernetics, vol. 49, no. 4, pp. 1440-1453.
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Semisupervised learning (SSL) methods have been proved to be effective at solving the labeled samples shortage problem by using a large number of unlabeled samples together with a small number of labeled samples. However, many traditional SSL methods may not be robust with too much labeling noisy data. To address this issue, in this paper, we propose a robust graph-based SSL method based on maximum correntropy criterion to learn a robust and strong generalization model. In detail, the graph-based SSL framework is improved by imposing supervised information on the regularizer, which can strengthen the constraint on labels, thus ensuring that the predicted labels of each cluster are close to the true labels. Furthermore, the maximum correntropy criterion is introduced into the graph-based SSL framework to suppress labeling noise. Extensive image classification experiments prove the generalization and robustness of the proposed SSL method.
Du, X, Nie, F, Wang, W, Yang, Y & Zhou, X 2019, 'Exploiting Combination Effect for Unsupervised Feature Selection by l2,0 Norm.', IEEE transactions on neural networks and learning systems, vol. 30, no. 1, pp. 201-214.
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In learning applications, exploring the cluster structures of the high dimensional data is an important task. It requires projecting or visualizing the cluster structures into a low dimensional space. The challenges are: 1) how to perform the projection or visualization with less information loss and 2) how to preserve the interpretability of the original data. Recent methods address these challenges simultaneously by unsupervised feature selection. They learn the cluster indicators based on the k nearest neighbor similarity graph, then select the features highly correlated with these indicators. Under this direction, many techniques, such as local discriminative analysis, nonnegative spectral analysis, nonnegative matrix factorization, etc., have been successfully introduced to make the selection more accurate. In this paper, we focus on enhancing the unsupervised feature selection in another perspective, namely, making the selection exploit the combination effect of the features. Given the expected feature amount, previous works operate on the whole features then select those of high coefficients one by one as the output. Our proposed method, instead, operates on a group of features initially then update the selection when a better group appears. Compared to the previous methods, the proposed method exploits the combination effect of the features by l2,0 norm. It improves the selection accuracy where the cluster structures are strongly related to a group of features. We conduct the experiments on six open access data sets from different domains. The experimental results show that our proposed method is more accurate than the recent methods which do not specially consider the combination effect of the features.
Duan, P, Mao, G, Liang, W & Zhang, D 2019, 'A Unified Spatio-Temporal Model for Short-Term Traffic Flow Prediction', IEEE Transactions on Intelligent Transportation Systems.
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IEEE This paper proposes a unified spatio-temporal model for short-term road traffic prediction. The contributions of this paper are as follows. First, we develop a physically intuitive approach to traffic prediction that captures the time-varying spatio-temporal correlation between traffic at different measurement points. The spatio-temporal correlation is affected by the road network topology, time-varying speed, and time-varying trip distribution. Distinctly different from previous black-box approaches to road traffic modeling and prediction, parameters of the proposed approach have physically intuitive meanings which make them readily amendable to suit changing road and traffic conditions. Second, unlike some existing techniques that capture the variation of spatio-temporal correlation by a complete re-design and calibration of the model, the proposed approach uses a unified model that incorporates the physical factors potentially affecting the variation of spatio-temporal correlation into a series of parameters. These parameters are relatively easy to control and adjust when road and traffic conditions change, thereby greatly reducing the computational complexity. Experiments using two sets of real traffic traces demonstrate that the proposed approach has superior accuracy compared with the widely used space-time autoregressive integrated moving average (STARIMA) and the back propagation neural network approaches, and is only marginally inferior to that obtained by constructing multiple STARIMA models for different times of the day, however, with a much reduced computational and implementation complexity.
Duan, W & Kirby, R 2019, 'Guided wave propagation in buried and immersed fluid-filled pipes: Application of the semi analytic finite element method', Computers and Structures, vol. 212, pp. 236-247.
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© 2018 Elsevier Ltd Developing an understanding of guided wave propagation in multi-layered systems has important applications in non-destructive evaluation. This article presents a general weighted residual formulation for guided wave propagation in fluid-filled pipes buried in an elastic solid, or immersed in a quiescent fluid. A one-dimensional semi-analytic finite element (SAFE) approach is combined with a perfectly matched layer (PML), to compute dispersion curves for different pipe applications. The speed and accuracy of this approach are compared against the scaled boundary finite element method (SBFEM) and it is shown that for a rod immersed in a liquid the two methods provide very similar computational speeds. The speed and accuracy of the model is then investigated for immersed and buried fluid-filled pipes, and it is shown that when discretising the PML no advantage in computational speed is found when using either quadratic or higher order spectral elements, provided the number of degrees of freedom in the PML is equivalent. Accordingly, it is shown that the SAFE-PML method is capable of obtaining accurately the modal characteristics of buried and immersed fluid-filled pipes, with computational speeds comparable to the SBFEM approach.
Eager, D & Hayati, H 2019, 'Additional injury prevention criteria for impact attenuation surfacing within children’s playgrounds', ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering.
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Ernesto Solanes, J, Gracia, L, Muñoz-Benavent, P, Valls Miro, J, Perez-Vidal, C & Tornero, J 2019, 'Robust Hybrid Position-Force Control for Robotic Surface Polishing', Journal of Manufacturing Science and Engineering, Transactions of the ASME, vol. 141, no. 1.
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© 2018 HMP Communications. All rights reserved. This work presents a hybrid position-force control of robots for surface polishing using task priority. The robot force control is designed using sliding mode ideas in order to benefit from its inherent robustness and low computational cost. In order to avoid the chattering drawback typically present in sliding mode control, several chattering-free controllers are evaluated and tested. A distinctive feature of the method is that the sliding mode force task is defined using not only equality constraints but also inequality constraints, which are satisfied using conventional and nonconventional sliding mode control, respectively. Moreover, a lower priority tracking controller is defined to follow the desired reference trajectory on the surface being polished. The applicability and the effectiveness of the proposed approach considering the mentioned chattering-free controllers are substantiated by experimental results using a redundant 7R manipulator.
Eskandari, M & Li, L 2019, 'Microgrid operation improvement by adaptive virtual impedance', IET Renewable Power Generation, vol. 13, no. 2, pp. 296-307.
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© The Institution of Engineering and Technology 2018.. Microgrids (MGs) are regarded as the best solution for optimal integration of the renewable energy sources into power systems. However, novel control strategies should be developed because of the distinct inherent feature of MG components in comparison to conventional power systems. Although the droop-based control method is adopted in the MG to share power among distributed generation units, its dependency to grid parameters makes its implementation not as convenient as that in conventional power systems. Virtual impedance has been proposed as the complementary part of droop control in MGs. In this study, adaptive virtual impedance is designed considering its effects on the system performance in the MG including: (i) decoupling active and reactive power control by making the grid X/R ratio high, (ii) maximum transferable power through the feeder, (iii) stability concern and (iv) precise reactive power sharing in different operating modes as well as smooth transition from connected mode to islanded mode (IM). To this end, a novel method is proposed to determine the reactive power reference of distributed generation (DG) units according to their contribution in reactive power sharing in IM. In addition, simulation in MATLAB/Simulink environment is conducted to assess the performance of the control system.
Fahmideh, M & Beydoun, G 2019, 'Big data analytics architecture design—An application in manufacturing systems', Computers and Industrial Engineering.
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© 2018 Elsevier Ltd Context: The rapid prevalence and potential impact of big data analytics platforms have sparked an interest amongst different practitioners and academia. Manufacturing organisations are particularly well suited to benefit from data analytics platforms in their entire product lifecycle management for intelligent information processing, performing manufacturing activities, and creating value chains. This needs a systematic re-architecting approach incorportaitng careful and thorough evaluation of goals for for integrating manufacturing legacy information systems with data analytics platforms. Furthermore, ameliorating the uncertainty of the impact the new big data architecture on system quality goals is needed to avoid cost blowout in implementation and testing phases. Objective: We propose an approach for goal-obstacle analysis and selecting suitable big data solution architectures that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of an architectural solution. Method: The approach employs goal-oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. It combines fuzzy logic to explore uncertainties in solution architectures and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems. Result: The approach brings two innovations to the state of the art of big data analytics platform adoption in manufacturing systems: (i) A goal-oriented modelling for exploring goals and obstacles in integrating manufacturing systems with data analytics platforms at the requirement level and (ii) An analysis of the architectural decisions under uncertainty. The efficacy of the approach is illustrated with a scenario of reengineering a hyper-connected ...
Fanos, AM & Pradhan, B 2019, 'A novel rockfall hazard assessment using laser scanning data and 3D modelling in GIS', Catena, vol. 172, pp. 435-450.
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© 2018 Elsevier B.V. Rockfall hazards occur widely in regions with steep terrain such as Kinta Valley, Malaysia. Rockfalls threaten urban areas and the transportation corridors that pass through such areas. This paper proposes a comprehensive rockfall hazard assessment strategy based on high-resolution laser scanning data (LiDAR), both airborne and terrestrial. It provides (1) rockfall source identification by developing a hybrid model based on a bagging neural network (BBNN), which is compared with various machine learning algorithms and ensemble models (bagging, boosting, voting) and a Gaussian mixture model; (2) 3D modelling of rockfall kinematic processes (trajectory distribution, frequency, velocity, kinetic energy, bounce height, impact location); and (3) hazard zonation based on spatial modelling in combination with an analytical hierarchy process (AHP) in a geographic information system (GIS). In addition, mitigation measures are suggested based on the modelling results. The proposed methodology was validated in three study areas to test the applicability and generalisability of the methods. The results show that the proposed hybrid model can accurately identify rockfall source areas at the regional scale. It achieved a 97% training accuracy and 5-fold cross-validation area under curve (AUC) value of 0.96. The mechanical parameters of the developed 3D model were calibrated with an accuracy of 97%, 93% and 95% for Gunung Lang, Gua Tambun and Gunung Rapat areas, respectively. In addition, the proposed spatial model effectively delineates areas at risk of rockfalls. This method provides a comprehensive understanding of rockfall hazards that can assist authorities to develop proper management and protection of urban areas and transportation corridors.
Far, H 2019, 'Advanced Computation Methods for Soil Structure Interaction Analysis of Structures Resting on Soft Soils', International Journal of Geotechnical Engineering.
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Adopting the most accurate and realistic modelling technique and computation method for treatment of dynamic soil-structure interaction effects in seismic analysis and design of structures resting on soft soil deposits is one of the most discussed and challenging issues in the field of seismic design and re-qualification of different structures. In this study, a comprehensive critical review has been carried out on available and well-known modelling techniques and computation methods for dynamic soil-structure interaction analysis. Discussing and comparing the advantages and disadvantages of employing each method, in this study, the most precise and reliable modelling technique as well as computation method have been identified and proposed to be employed in studying dynamic soil-structure interaction analysis of structures resting on soft soil deposits.
Far, H 2019, 'Dynamic Behaviour of Unbraced Steel Frames Resting on Soft Ground', Steel Construction, vol. 12, no. 1, pp. 1-6.
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Far, H & Far, C 2019, 'Experimental Investigation on Creep Behaviour of Composite Sandwich Panels Constructed from Polystyrene/Cement-Mixed Cores and Thin Cement Sheet Facings', Australian Journal of Structural Engineering, vol. 20, no. 1, pp. 63-73.
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Composite sandwich panels have gradually become more popular due to their typical benefits including strength, weight, ease of handling, durability, versatility, thermal and acoustic properties. Many researchers are aware of these benefits and have undertaken detailed research and publicised large amounts of scientific papers on composite sandwich panels. With the variety of loads that could possibly be applied to a structure, often the in service life behaviour is significant. Reviewing whether it will function correctly and without excessive deformations is a factor that can sometimes impact the performance. Polystyrene/cement mixed cores and thin cement sheet facings composite sandwich panels are Australian products made of cement-polystyrene beaded mixture encapsulated between two thick cement board sheets. The creep and creep recovery properties of these sandwich panels are relatively unknown. Therefore, in this study, in order to understand the creep and creep recovery behaviour of those sandwich panels, a series of experimental tests have been performed and the outcomes have been explained and discussed. Based on the results of this study, values for immediate recovery, creep recovery and irrecoverable creep strain are determined and proposed. In addition, typical creep and creep recovery design curves have been developed and presented for practical applications in structural engineering.
Fatahi, B 2019, 'Editorial', Proceedings of the Institution of Civil Engineers: Ground Improvement, vol. 172, no. 1, pp. 1-2.
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Fu, J, Liu, Q, Liufu, K, Deng, Y, Fang, J & Li, Q 2019, 'Design of bionic-bamboo thin-walled structures for energy absorption', Thin-Walled Structures, vol. 135, pp. 400-413.
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© 2018 Elsevier Ltd Bio-inspired engineering design has drawn considerable attention in the recent years for its great structural and mechanical features. This study aimed to explore the energy absorption characteristics of a novel bionic-bamboo tube (BBT) structure subjected to axial crushing. The tubes with six different cross-sectional configurations were devised with inspiration of bamboo microstructure. The effects of rib shape and rib number were analyzed by using the finite element code LS-DYNA. The numerical results indicated that the BBT structures with the rib shape of “X” and the rib number of six exhibited the best crashworthiness. To further improve the energy absorption capabilities of these BBT structures, the multiobjective optimization was employed with respect to design variables of configurational structure, such as the rib angle of the “X” shaped cross-section, center distance and rib thickness. The response surface method (RSM) and multiobjective particle swarm optimization (MOPSO) algorithm were adopted to maximize specific energy absorption (SEA) while minimizing peak crushing force (PCF). The optimization results demonstrated that compared to the baseline design, the SEA value of the optimized BBT structure was further increased by 6.84% without sacrificing in peak crushing force.
Fujioka, T, Hoang, AT, Ueyama, T & Nghiem, LD 2019, 'Integrity of reverse osmosis membrane for removing bacteria: new insight into bacterial passage Electronic supplementary information (ESI) available. See DOI: 10.1039/c8ew00910d', ENVIRONMENTAL SCIENCE-WATER RESEARCH & TECHNOLOGY, vol. 5, no. 2, pp. 239-245.
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Gao, J, Luo, Z, Li, H & Gao, L 2019, 'Topology optimization for multiscale design of porous composites with multi-domain microstructures', Computer Methods in Applied Mechanics and Engineering, vol. 344, pp. 451-476.
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Gao, J, Luo, Z, Li, H, Li, P & Gao, L 2019, 'Dynamic multiscale topology optimization for multi-regional micro-structured cellular composites', Composite Structures, vol. 211, pp. 401-417.
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In this paper, a new dynamic multiscale topology optimization method for cellular composites with multi-regional material microstructures is proposed to improve the structural performance. Firstly, a free-material distribution optimization method (FMDO) is developed to generate the overall configuration for the discrete element densities distributed within a multi-regional pattern. The macrostructure is divided into several sub regions, and each of them consists of a number of elements but with the same densities. Secondly, a dynamic topology optimization formulation is developed to perform the concurrent design of the macrostructure and material microstructures, subject to the multi-regional distributed element densities. A parametric level set method is employed to optimize the topologies of the macrostructure and material microstructures, with the effective macroscopic properties evaluated by the homogenization. In the numerical implementation, the quasi-static Ritz vector (QSRV) method is incorporated into the finite element analysis so as to reduce the computational cost in numerical analysis, and some kinematical connectors are introduced to make sure the connectivity between adjacent material microstructures. Finally, 2D and 3D numerical examples are tested to demonstrate the effectiveness of the proposed dynamic multiscale topology optimization method for the material-structural composites.
Gao, P, Walker, PD, Liu, H, Zhou, S & Xiang, C 2019, 'Application of an adaptive tuned vibration absorber on a dual lay-shaft dual clutch transmission powertrain for vibration reduction', Mechanical Systems and Signal Processing, vol. 121, pp. 725-744.
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© 2018 Elsevier Ltd This paper investigates the application of adaptive tuned vibration absorbers (ATVAs) to lightly damped automotive powertrains. To achieve this, a vibration characteristic analysis of a dual lay-shaft dual clutch transmission (DCT)-equipped powertrain system was performed, including a natural frequency evaluation and sensitivity studies. Transient responses due to gear changes and variations of both the gear ratio and engine speed were also evaluated. The results demonstrate that the impact of ATVAs on transient responses during gear changes is minimal, suggesting that these short-duration transients are outside the scope of application for ATVAs. However, as excitation forces originate from the engine, speed has a significant impact on excitation frequency. Furthermore, the gear ratios significantly influence the engine operating range and natural frequency in any particular gear. Therefore, variations of engine speeds and gear ratios are the main influencing factors affecting the dominant excitation frequency. Consequently, the relationships among the system natural frequencies, the dominant external excitation frequency and the inherent frequency of the ATVA are comprehensively studied. By investigating the relationships among the dominant external excitation frequency and system natural frequencies and the system sensitivity analysis results, the installation position, stiffness range and optimal frequency tuning scheme of the ATVA can be determined. The results were applied to a DCT powertrain with continuously changing engine speeds and an ATVA with corresponding variable stiffness, and a frequency tuning scheme is installed on the powertrain. By comparing the simulation results, the effectiveness and reliability of the presented method can be verified.
Gao, P, Xiang, C, Liu, H, Walker, P & Zhou, H 2019, 'Vibration reduction performance parameters matching for adaptive tunable vibration absorber', Journal of Intelligent Material Systems and Structures, vol. 30, no. 2, pp. 198-212.
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© The Author(s) 2018. Based on the principle of vibration absorber, an adaptive tunable vibration absorber with the magnetorheological elastomer as the core intelligent component was designed to eliminate variable frequency vibration of the powertrain system. The simulation of the magnetic circuit of the adaptive tunable vibration absorber was carried out to ensure the magnetic field generated by the design with a closed magnetic circuit could meet the requirement. The transient dynamic simulation analysis of the adaptive tunable vibration absorber shows that the natural frequency of the vibration absorber could well follow the external excitation signal. The natural vibration sensitivity analysis of the 4-degree-of-freedom dynamic system of the powertrain system was performed to obtain the key moment of inertia that affects the natural frequencies of each stage and then the installation position of the adaptive tunable vibration absorber corresponding to external excitation near the resonant frequency band could be determined. Subsequently, taking the third natural vibration as an example, an adaptive tunable vibration absorber with corresponding variable-stiffness range was installed on the key moment of inertia affecting the vibration of this order. The transient changes of each natural vibration frequency of the powertrain system with the adaptive tunable vibration absorber were studied and then the best starting and stopping frequencies were determined. According to the optimal frequency tuning scheme, the variation range of the storage modulus required for the magnetorheological elastomer material in the limited magnetic field range was obtained. The relationship between the controllable current and the torsional stiffness of the vibration absorber is established. Aiming at the vibration problem near the third-order natural vibration, the best vibration reduction performance parameters were matched to the adaptive tunable vibration absorber. Finally, a time...
Gao, X, Du, J, Zhang, T & Guo, YJ 2019, 'High-T c Superconducting Fourth-Harmonic Mixer Using a Dual-Band Terahertz On-Chip Antenna of High Coupling Efficiency', IEEE Transactions on Terahertz Science and Technology, vol. 9, no. 1, pp. 55-62.
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© 2019 IEEE. This paper presents a dual-band on-chip antenna-coupled high-T c superconducting (HTS) Josephson-junction subterahertz (THz) fourth-harmonic mixer. The antenna utilizes a couple of different structured twin slots to enable the resonant radiations at two frequencies, and integrates a well-designed coplanar waveguide network for achieving good radiation coupling and signal isolation characteristics. The electromagnetic simulations show that coupling efficiencies as high as -4 and -3.5 dB are achieved for the 160- and 640-GHz operating frequency bands, respectively. Based on this dual-band antenna, a 640-GHz HTS fourth-harmonic mixer is developed and characterized in a range of operating temperatures. The mixer exhibits a measured conversion gain of around -18 dB at 20 K and -22 dB at 40 K, respectively. The achieved intermediate frequency bandwidth is larger than 23 GHz. These are the best results reported for HTS harmonic mixers at comparable sub-THz frequency bands to date.
Garshasbi, S, Mohammadi, Y, Graf, S, Garshasbi, S & Shen, J 2019, 'Optimal learning group formation: A multi-objective heuristic search strategy for enhancing inter-group homogeneity and intra-group heterogeneity', Expert Systems with Applications, vol. 118, pp. 506-521.
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© 2018 In modern education systems, plenty of research suggests that clustering the learners into optimal learning groups based on their multiple characteristics is a determining effort in enhancing the effectiveness of collaborative learning. Although there have been several evidences on developing and implementing appropriate computational tools to handle classification processes in expert and intelligent systems, the effectiveness and accuracy of optimal grouping algorithms are still worth improving. For instance, the majority of grouping processes in collaborative learning environments is orchestrated through single-objective optimization algorithms, which need to be revisited due to some intrinsic limitations. In this paper, we propose a novel algorithm capable of properly addressing a variety of optimization problems in optimal learning group formation processes. To this end, a multi-objective version of Genetic Algorithms, i.e. Non-dominated Sorting Genetic Algorithm, NSGA-II, was successfully implemented and applied to improve the performance and accuracy of optimally formed learning groups. In contrast to the previous related works applying single-objective algorithms, the main advantage of our work is simultaneous satisfaction of multiple targets predefined for the formation of optimal learning groups, especially the inter-homogeneity and intra-heterogeneity of each learning group, which significantly enhance both effectiveness and accuracy of optimal grouping processes in the underlying intelligent systems. Challenging the proposed optimization algorithms, both single- and multi-objective optimizers, with a similar grouping problem, clearly proved that the single-objective optimization technique has limited control and sensitivity to the quality of individual groups. Contrary to single-objective optimization techniques, which are mainly governed by adjusting the quality of the groups altogether in average, the proposed multi-objective algorithm not onl...
Gaviria-Marin, M, Merigó, JM & Baier-Fuentes, H 2019, 'Knowledge management: A global examination based on bibliometric analysis', Technological Forecasting and Social Change, vol. 140, pp. 194-220.
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© 2018 Knowledge management (KM) is a field of research that has gained wide acceptance in the scientific community and management literature. This article presents a bibliometric overview of the academic research on KM in the business and management areas. Various bibliometric methods are used to perform this overview, including performance analysis and science mapping of the KM field. The performance analysis uses a series of bibliometric indicators, such as the h-index, productivity and citations. In addition, the VOSviewer software is used to map the bibliographic material. Science mapping uses co-citations and the concurrency of keywords. References were obtained from the Web of Science database. We identified and classified the most relevant research in the field according to journals, articles, authors, institutions and countries. The results show that research in this field has increased significantly in the last ten years and that the USA is the most influential country in all aspects in this field. It is important to consider, however, that science continues to advance in this and in all fields and that data rapidly change over time. Therefore, this paper fulfills an informational role that shows that most of the fundamental research of KM is in business and management areas.
Ghadi, MJ, Ghavidel, S, Rajabi, A, Azizivahed, A, Li, L & Zhang, J 2019, 'A review on economic and technical operation of active distribution systems', Renewable and Sustainable Energy Reviews, pp. 38-53.
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© 2019 Elsevier Ltd Along with the advent of restructuring in power systems, considerable integration of renewable energy resources has motivated the transition of traditional distribution networks (DNs) toward new active ones. In the meanwhile, rapid technology advances have provided great potentials for future bulk utilization of generation units as well as the energy storage (ES) systems in the distribution section. This paper aims to present a comprehensive review of recent advancements in the operation of active distribution systems (ADSs) from the viewpoint of operational time-hierarchy. To be more specific, this time-hierarchy consists of two stages, and at the first stage of this time-hierarchy, four major economic factors, by which the operation of traditional passive DNs is evolved to new active DNs, are described. Then the second stage of the time-hierarchy refers to technical management and power quality correction of ADSs in terms of static, dynamic and transient periods. In the end, some required modeling and control developments for the optimal operation of ADSs are discussed. As opposed to previous review papers, potential applications of devices in the ADS are investigated considering their operational time-intervals. Since some of the compensating devices, storage units and generating sources may have different applications regarding the time scale of their utilization, this paper considers real scenario system operations in which components of the network are firstly scheduled for the specified period ahead; then their deviations of operating status from reference points are modified during three time-intervals covering static, dynamic and transient periods.
Ghavidel Jirsaraie, S, Jabbari Ghadi, M, Vahed, AA, Aghaei, J, Li, L & Zhang, J 2019, 'Risk-Constrained Bidding Strategy for a Joint Operation of Wind Power and Compressed Air Energy Storage Aggregators', IEEE Transactions on Sustainable Energy, pp. 1-1.
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Gill, AQ & Chew, E 2019, 'Configuration information system architecture: Insights from applied action design research', Information and Management.
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© 2018 Elsevier B.V. One of the critical information systems that enables service resilience is the service configuration information system (CiS). The fundamental challenge for organisations is the effective designing and implementation of the CiS architecture. This paper addresses this important research problem and reports insights from a completed applied action design research (ADR) project in an Australian financial services organisation. This paper aims to provide guidance to researchers and practitioners contemplating ADR, rooted in the organisational context, for practice-oriented academia-industry collaborative research. This research also contributes in terms of the CiS reference architecture design knowledge and demonstrates the applicability of the ADR method.
Giri, P, Kharkovsky, S, Zhu, X, Clark, SM, Taheri, S & Samali, B 2019, 'Characterization of carbon fiber reinforced polymer strengthened concrete and gap detection with a piezoelectric-based sensory technique', Structural Health Monitoring, vol. 18, no. 1, pp. 172-179.
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© The Author(s) 2018. In this article, a piezoelectric-based sensory technique is proposed for detection of the gap between surfaces of a carbon fiber reinforced polymer plate and a concrete specimen and characterization of shrinkage of early-age concrete. The proposed technique uses the propagation properties of the guided waves in the carbon fiber reinforced polymer plate excited and received by piezoelectric transducers attached to an external surface of the carbon fiber reinforced polymer–strengthened concrete specimen. Measurements are conducted with fresh and hardened early-age concrete specimens and two carbon fiber reinforced polymer plates at different gaps. A piezoelectric actuator is excited using a sine burst signal, and the generated wave is received by a sensor after propagation along the specimen. The received signal at different gap values is used to detect a gap. To quantify the gap, damage indices, including correlation coefficient, peak-to-peak amplitude of resultant signal, and root-mean-square deviation, are used. The shrinkage of concrete is detected and predicted by comparing the damage indices at different gaps with the indices at different stages of early-age concrete. The proposed technique is relatively simple method using small transducers. It is one-sided, non-destructive, and cost-effective solution for gap detection and concrete characterization.
Golhani, K, Balasundram, SK, Vadamalai, G & Pradhan, B 2019, 'Selection of a Spectral Index for Detection of Orange Spotting Disease in Oil Palm (Elaeis guineensis Jacq.) Using Red Edge and Neural Network Techniques', Journal of the Indian Society of Remote Sensing.
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© 2019, Indian Society of Remote Sensing. Spectral screening can play an important role in successful detection of viroid-infected oil palm seedlings from nursery stage prior to transplanting into the field. Coconut cadang–cadang viroid (CCCVd) is the main causal agent of orange spotting (OS) disease. OS disease is an emerging disease in Malaysian plantation. In this study, a glasshouse experiment was conducted with fifteen CCCVd-inoculated and five healthy oil palm seedlings in the growing season of 2015. Spectral screening was performed using a hyperspectral spectroradiometer, Analytic Spectral Device HandHeld 2 (325–1075 nm). The red edge, a steep gradient in reflectance between red and near-infrared bands (680–780 nm), was used for selection of red edge bands. A maximum point (i.e., 700 nm) and minimum point (i.e., 768 nm) of red edge were selected from healthy and inoculated spectra. Shifts of red edge inflection point from healthy to inoculated spectra were also studied. Four well-known spectral indices, namely simple ratio, red edge normalized difference vegetation index, two-band enhanced vegetation index 2 (EVI2), and chlorophyll index red edge, were evaluated using selected red edge bands. The multilayer perceptron neural network model was used to establish a nonlinear relationship between selected spectral bands and each spectral index. EVI2 was selected as a best spectral index which resulted in zero errors at the training, testing, and validation datasets. The highest coefficient of correlation (r = 1) was recorded between spectral bands (input values) and EVI2 (target values).
Guo, K & Guo, Y 2019, 'Key parameter design and analysis of flux reversal linear rotary permanent magnet actuator', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2002-2011 IEEE. Flux reversal linear rotary permanent magnet actuator (FR-LRPMA) is a two-degree-of-freedom actuator with two ferromagnetic (Fe) poles and two permanent magnet (PM) poles mounted on the surface of each stator pole. The flux linkage waveform of the actuator is more sinusoidal than that of the traditional topology, which are analyzed by the ideal linear model of one stator section of the proposed actuator. In order to reduce the amplitudes of the cogging torque and detent force, a key space gap parameter of the FR-LRPMA between the Fe pole and PM pole is studied in the circumferential and axial directions. The expressions of cogging torque and detent force are derived by the magnetomotive force analytical method, which are used to obtain the optimal space gap parameter value. The electromagnetic characteristics of the actuator are analyzed by the finite-element method. The amplitudes of cogging torque and detent force are reduced and the back electromotive force waveform is more sinusoidal than that of the original topology, which are verified by the experiment.
Guo, L, Zhu, H & Abbosh, A 2019, 'Phase reconfigurable microwave power divider', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 66, no. 1, pp. 21-25.
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© 2004-2012 IEEE. A reconfigurable power divider (PD) that can operate either in an in-phase mode or out-of-phase mode is presented. To that end, the novel concept of using tunable phase shifter, which is a reflection-type loaded coupled lines, to emulate an effective variable length transmission line (TL) is utilized. The proposed PD uses a quarter-wavelength TL, a 100 Ω isolation resistor and two tunable phase shifters. The presented theoretical analysis shows that by properly selecting the parameters of the phase shifter, it can be used to approximate the performance of variable length TL. To validate the design, a prototype of dimensions 50 mm × 25 mm is built, using Rogers RO3010 substrate, and tested. The results indicate that the device can operate as an in-phase and out-of-phase by using suitable biasing voltages. Across the band 0.9-1.1 GHz, the device has more than 12 dB return loss at all the ports and more than 15 dB isolation between the two output ports with less than 5° phase deviation for both of the in-phase and out-of-phase states.
Guo, T, Pan, S, Zhu, X & Zhang, C 2019, 'CFOND: Consensus Factorization for Co-Clustering Networked Data', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Networked data are common in domains where instances are characterized by both feature values and inter-dependency relationships. Finding cluster structures for networked instances and discovering representative features for each cluster represent a special co-clustering task usefully for many real-world applications, such as automatic categorization of scientific publications and finding representative key-words for each cluster. To date, although co-clustering has been commonly used for finding clusters for both instances and features, all existing methods are focused on instance-feature values, without leveraging valuable topology relationships between instances to help boost co-clustering performance. In this paper, we propose CFOND, a consensus factorization based framework for co-clustering networked data. We argue that feature values and linkages provide useful information from different perspectives, yet they are not always consistent and therefore need to be carefully aligned for best clustering results. In the paper, we advocate a consensus factorization principle, which simultaneously factorizes information from three aspects: network topology structures, instance-feature content relationships, and feature-feature correlations. The consensus factorization ensures that the final cluster structures are consistent across information from the three aspects with minimum errors. CFOND enjoys sound theoretical basis and proved convergence, and its performance is validated on real-world networks.
Hadzhiev, Y, Qureshi, HK, Wheatley, L, Cooper, L, Jasiulewicz, A, Van Nguyen, H, Wragg, JW, Poovathumkadavil, D, Conic, S, Bajan, S, Sik, A, Hutvàgner, G, Tora, L, Gambus, A, Fossey, JS & Müller, F 2019, 'A cell cycle-coordinated Polymerase II transcription compartment encompasses gene expression before global genome activation.', Nature communications, vol. 10, no. 1, p. 691.
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Most metazoan embryos commence development with rapid, transcriptionally silent cell divisions, with genome activation delayed until the mid-blastula transition (MBT). However, a set of genes escapes global repression and gets activated before MBT. Here we describe the formation and the spatio-temporal dynamics of a pair of distinct transcription compartments, which encompasses the earliest gene expression in zebrafish. 4D imaging of pri-miR430 and zinc-finger-gene activities by a novel, native transcription imaging approach reveals transcriptional sharing of nuclear compartments, which are regulated by homologous chromosome organisation. These compartments carry the majority of nascent-RNAs and active Polymerase II, are chromatin-depleted and represent the main sites of detectable transcription before MBT. Transcription occurs during the S-phase of increasingly permissive cleavage cycles. It is proposed, that the transcription compartment is part of the regulatory architecture of embryonic nuclei and offers a transcriptionally competent environment to facilitate early escape from repression before global genome activation.
Han, F, Zhuo, J, Liu, N, Liu, Y, Liu, H & Liu, QH 2019, 'Fast solution of electromagnetic scattering for 3-D inhomogeneous anisotropic objects embedded in layered uniaxial media by the BCGS-FFT method', IEEE Transactions on Antennas and Propagation, vol. 67, no. 3, pp. 1748-1759.
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© 1963-2012 IEEE. In this paper, the stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) method is used to solve the electromagnetic scattering problem for 3-D inhomogeneous uniaxial anisotropic objects embedded in a layered uniaxial medium. The optical axis of the background medium is perpendicular to the layer boundaries. However, the optical axis of the scatterer can be rotated through any angles. The electric field integral equation (EFIE) is formulated in the layered uniaxial anisotropic background medium. The volumetric roof-top function is used for the basis function as well as the testing function. The dyadic Green's functions (DGFs) for both the electric field and the magnetic vector potential are used to solve the EFIE, and the results as well as the performance are compared. Several numerical simulations are carried out to validate the accuracy and efficiency of the proposed method. The major new contribution of this paper is to extend the BCGS-FFT method to accommodate the uniaxial anisotropy of the layered background medium. Therefore, the DGFs for the layered uniaxial medium are evaluated before solving the EFIE. In addition, the DGF for the electric field is also used to solve the 3-D scattering problem through BCGS-FFT iterations, which is different from the previous work.
Han, L, Zhou, M, Jia, W, Dalil, Z & Xu, X 2019, 'Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model', Information Sciences, vol. 476, pp. 491-504.
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© 2018 Elsevier Inc. An effective security strategy for Wireless Sensor Networks (WSNs) is imperative to counteract security threats. Meanwhile, energy consumption directly affects the network lifetime of a wireless sensor. Thus, an attempt to exploit a low-consumption Intrusion Detection System (IDS) to detect malicious attacks makes a lot of sense. Existing Intrusion Detection Systems can only detect specific attacks and their network lifetime is short due to their high energy consumption. For the purpose of reducing energy consumption and ensuring high efficiency, this paper proposes an intrusion detection model based on game theory and an autoregressive model. The paper not only improves the autoregressive theory model into a non-cooperative, complete-information, static game model, but also predicts attack pattern reliably. The proposed approach improves on previous approaches in two main ways: (1) it takes energy consumption of the intrusion detection process into account, and (2) it obtains the optimal defense strategy that balances the system's detection efficiency and energy consumption by analyzing the model's mixed Nash equilibrium solution. In the simulation experiment, the running time of the process is regarded as the main indicator of energy consumption of the system. The simulation results show that our proposed IDS not only effectively predicts the attack time and the next targeted cluster based on the game theory, but also reduces energy consumption.
Han, SF, Jin, W, Yang, Q, El-Fatah Abomohra, A, Zhou, X, Tu, R, Chen, C, Xie, GJ & Wang, Q 2019, 'Application of pulse electric field pretreatment for enhancing lipid extraction from Chlorella pyrenoidosa grown in wastewater', Renewable Energy, pp. 233-239.
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© 2018 Elsevier Ltd Lipid extraction is a key step of biodiesel production from microalgae, however, the application of traditional extraction methods was limited due to the difficulties of cell disruption as well as solvent toxicity. In this work, pretreatment method using pulsed electric field (PEF), was primarily applied to lipid extraction process from microalgae Chlorella pyrenoidosa grown in wastewater. After the pretreatment with PEF, the yields of fatty acid methyl esters from C.pyrenoidosa was 12.0% higher than traditional pretreatment with ultrasonic. The results indicated that PEF was an effective method for cell disruption. Fluorescence staining and scanning electron microscopy showed that the integrity of the cell membrane of microalgae was damaged under pulsed electric field, which enhanced the penetration of solvents and lipid extraction.
Han, Y, Deng, Y, Cao, Z & Lin, CT 2019, 'An interval-valued Pythagorean prioritized operator-based game theoretical framework with its applications in multicriteria group decision making', Neural Computing and Applications.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Multicriteria decision-making process explicitly evaluates multiple conflicting criteria in decision making. The conventional decision-making approaches assumed that each agent is independent, but the reality is that each agent aims to maximize personal benefit which causes a negative influence on other agents’ behaviors in a real-world competitive environment. In our study, we proposed an interval-valued Pythagorean prioritized operator-based game theoretical framework to mitigate the cross-influence problem. The proposed framework considers both prioritized levels among various criteria and decision makers within five stages. Notably, the interval-valued Pythagorean fuzzy sets are supposed to express the uncertainty of experts, and the game theories are applied to optimize the combination of strategies in interactive situations. Additionally, we also provided illustrative examples to address the application of our proposed framework. In summary, we provided a human-inspired framework to represent the behavior of group decision making in the interactive environment, which is potential to simulate the process of realistic humans thinking.
Handayani, K, Krozer, Y & Filatova, T 2019, 'From fossil fuels to renewables: An analysis of long-term scenarios considering technological learning', Energy Policy, pp. 134-146.
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© 2018 The Author(s) This study analyses a diffusion of renewable energy in an electricity system accounting for technological learning. We explore long-term scenarios for capacity expansion of the Java-Bali electricity system in Indonesia, considering the country's renewable energy targets. We apply the Long-range Energy Alternative Planning (LEAP) model with an integration of technological learning. Our results reveal that, at the medium and high pace of technological learning, the total costs of electricity production to achieve the long-term renewable energy target are 4–10% lower than the scenario without considering technological learning. With respect to technology, solar PV and wind become competitive with other types of renewables and nuclear. Moreover, the fulfilment of the renewable energy targets decreases CO2 emissions by 25% compared to the reference scenario. Implications of our results indicate that energy policies should focus on the early deployment of renewables, upgrading the grid capacity to accommodate variable renewable energy, and enabling faster local learning.
Hassan, M, Liu, D & Xu, D 2019, 'A Two-Stage Approach to Collaborative Fiber Placement through Coordination of Multiple Autonomous Industrial Robots', Journal of Intelligent and Robotic Systems.
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He, L, Lu, Z, Pan, L, Zhao, H, Li, X & Zhang, J 2019, 'Optimal economic and emission dispatch of a microgrid with a combined heat and power system', Energies, vol. 12, no. 4.
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© 2019 by the authors. With the rapid development of the new concept of energy internet, electric power systems often need to be investigated together with thermal energy systems. Additionally, to reduce pollution from gas emissions, it is very important to study the economic and emission dispatch of integrated electrical and heating systems. Hence, this paper proposes a multi-objective optimization dispatch model for a microgrid (MG) with a combined heat and power (CHP) system. This CHP-based MG system consists of a CHP unit, a wind turbine, a PV system, a fuel cell, an electric boiler, an electric storage, and a heat storage. It can exchange electricity with the distribution network and exchange heat with the district heating network. Minimum economic cost and minimum environmental cost are considered as the two objectives for the operation of this CHP-based MG system. To solve the two objective optimization problem, the multi-objective bacterial colony chemotaxis algorithm is utilized to obtain the Pareto optimal solution set, and the optimal solution is chosen by the Technique for Order of Preference by Similarity to Ideal Solution method. Finally, numerical case studies demonstrate the effectiveness of proposed model and method for the optimal economic and emission dispatch of the CHP-based MG system.
He, LX, Wu, C & Li, J 2019, 'Post-earthquake evaluation of damage and residual performance of UHPSFRC piers based on nonlinear model updating', Journal of Sound and Vibration, vol. 448, pp. 53-72.
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© 2019 Elsevier Ltd This paper presents an innovative approach for damage and residual performance evaluation of ultra-high performance steel fiber reinforced concrete (UHPSFRC) piers after earthquakes utilizing low-level vibration tests. A nonlinear fiber section element model is constructed in OpenSees to simulate the hysteretic behavior of a UHPSFRC bridge pier. Experimental data from a UHPSFRC column is utilized to verify the accuracy of the nonlinear numerical model. Based on the nonlinear fiber section element model, a new technique of nonlinear finite element model updating involving two updating stages is developed. This new method is designed to incorporate the maximum and minimum strains of section fibers as the updating parameters. By forming the objective function from the modal information, the damage parameters related to the nonlinear material model can be updated by solving the constrained optimization problem. To validate the efficiency of this updating approach, it has been applied to a numerically simulated UHPSFRC pier. With using the updated nonlinear finite element model, the residual axial loading capacity and post-seismic performance of the UHPSFRC pier are examined. The numerical results indicate that the updated nonlinear finite element model can be used not only to assess the current damage state of the UHPSFRC pier but also to predict its future performance after an earthquake. Finally, the noise effect on the proposed method is also investigated. The results reveal that the post-earthquake evaluation approach for UHPSFRC piers based on this study's updating algorithm is robust to noise.
He, T, Zhu, J, Lu, DDC & Zheng, L 2019, 'Modified Model Predictive Control for Bidirectional Four-Quadrant EV Chargers with Extended Set of Voltage Vectors', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 7, no. 1, pp. 274-281.
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© 2013 IEEE. This paper presents a modified model predictive control (MMPC) for bidirectional power flow control between the electric vehicle (EV) chargers and the main grid. In contrast to the conventional finite control set MPC which selects an optimal switching state from eight possible voltage vectors, the proposed MMPC is based on the application of an optimal voltage vector chosen from an extended set of 20 modulated voltage vectors with a fixed duty ratio. To reduce the computational burden introduced by the increased number of voltage sets, a preselection algorithm is developed for the MMPC method. Six voltage vectors are preselected from the 20 sectors. Due to the increased number of the voltage space vectors, the grid currents and active and reactive power performance can be improved by using the proposed MMPC scheme. Both the conventional and proposed methods are compared through experimental test results of a two-level three-phase off-board EV charger.
Hodges, J, Attia, T, Arukgoda, J, Kang, C, Cowden, M, Doan, L, Ranasinghe, R, Abdelatty, K, Dissanayake, G & Furukawa, T 2019, 'Multistage bayesian autonomy for high-precision operation in a large field', Journal of Field Robotics, vol. 36, no. 1, pp. 183-203.
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© 2018 Wiley Periodicals, Inc. This paper presents a generalized multistage bayesian framework to enable an autonomous robot to complete high-precision operations on a static target in a large field. The proposed framework consists of two multistage approaches, capable of dealing with the complexity of high-precision operation in a large field to detect and localize the target. In the multistage localization, locations of the robot and the target are estimated sequentially when the target is far away from the robot, whereas these locations are estimated simultaneously when the target is close. A level of confidence (LOC) for each detection criterion of a sensor and the associated probability of detection (POD) of the sensor are defined to make the target detectable with different LOCs at varying distances. Differential entropies of the robot and target are used as a precision metric for evaluating the performance of the proposed approach. The proposed multistage observation and localization approaches were applied to scenarios using an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV). Results with the UGV in simulated environments and then real environments show the effectiveness of the proposed approaches to real-world problems. A successful demonstration using the UAV is also presented.
Hossain, N, Mahlia, TMI, Zaini, J & Saidur, R 2019, 'Techno-economics and Sensitivity Analysis of Microalgae as Commercial Feedstock for Bioethanol Production', Environmental Progress and Sustainable Energy.
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© 2019 American Institute of Chemical Engineers The foremost purpose of this techno-economic analysis (TEA) modeling was to predict a harmonized figure of comprehensive cost analysis for commercial bioethanol generation from microalgae species in Brunei Darussalam based on the conventional market scenario. This model was simulated to set out economic feasibility and probabilistic assumption for large-scale implementations of a tropical microalgae species, Chlorella vulgaris, for a bioethanol plant located in the coastal area of Brunei Darussalam. Two types of cultivation systems such as closed system (photobioreactor—PBR) and open pond approaches were anticipated for a total approximate biomass of 220 t year−1 on 6 ha coastal areas. The biomass productivity was 56 t ha−1 for PBR and 28 t ha−1 for pond annually. The plant output was 58.90 m3 ha−1 for PBR and 24.9 m3 ha−1 for pond annually. The total bioethanol output of the plant was 57,087.58 gal year−1 along with the value added by-products (crude bio-liquid and slurry cake). The total production cost of this project was US$2.22 million for bioethanol from microalgae and total bioethanol selling price was US$2.87 million along with the by-product sale price of US$1.6 million. A sensitivity analysis was conducted to forecast the uncertainty of this conclusive modeling. Different data sets through sensitivity analysis also presented positive impacts of economical and environmental views. This TEA model is expected to be initialized to determine an alternative energy and also minimize environmental pollution. With this current modeling, microalgal-bioethanol utilization mandated with gasoline as well as microalgae cultivation, biofuel production integrated with existing complementary industries, are strongly recommended for future applications. © 2019 American Institute of Chemical Engineers Environ Prog, 2019.
Hossain, N, Zaini, J, Mahlia, TMI & Azad, AK 2019, 'Elemental, morphological and thermal analysis of mixed microalgae species from drain water', Renewable Energy, vol. 131, pp. 617-624.
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© 2018 Elsevier Ltd In this study, Stigonematales sp. microalgae were collected from drain water and characterized for its’ morphological edifice, elemental composition, thermal condition and energy generation capacity by using scanning electron microscopy, energy dispersive X-ray, thermogravimetric analyzer and bomb calorimeter, respectively. Scanning electron micrographs revealed the top view of microalgae and ash pellet with carbon coated specimens at low voltage (5.0 kV) through the secondary electron image detector. Elemental analysis revealed all the major and minor constituents of this microalgae species and its’ ash in terms of dry weight (%) and atomic weight (%). Thermogravimetric analysis was conducted at heating rate, 10 °C/min and this experimental results determined moisture content, volatile matter, ash content and fixed carbon of the sample with 4.5%, 35%, 39.5% and 21%, respectively. Microalgae powder blended with bituminous coal by 75%, 50% and 25% measured calorific value 14.07 MJ/kg, 19.88 MJ/kg and 26.42 MJ/kg, respectively. Microalgae (75%) -coal (25%) blend showed excellent amount of energy content, 24.59 MJ/kg. Microalgae blended with coal unveiled an outstanding outcome with elevation of the volatile matter and drop of the ash content. Optimization of microalgae-coal blend in large-scale application can initiate bright future in renewable energy exploration.
Howard, D, Macsween, K, Edwards, GC, Desservettaz, M, Guérette, EA, Paton-Walsh, C, Surawski, NC, Sullivan, AL, Weston, C, Volkova, L, Powell, J, Keywood, MD, Reisen, F & (Mick) Meyer, CP 2019, 'Investigation of mercury emissions from burning of Australian eucalypt forest surface fuels using a combustion wind tunnel and field observations', Atmospheric Environment, pp. 17-27.
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© 2018 Environmental cycling of the toxic metal mercury (Hg) is ubiquitous, and still not completely understood. Volatilisation and emission of mercury from vegetation, litter and soil during burning represents a significant return pathway for previously-deposited atmospheric mercury. Rates of such emission vary widely across ecosystems as they are dependent on species-specific uptake of atmospheric mercury as well as fire return frequencies. Wildfire burning in Australia is currently thought to contribute between 1 and 5% of the global total of mercury emissions, yet no modelling efforts to date have utilised local mercury emission factors (mass of emitted mercury per mass of dry fuel) or local mercury emission ratios (ratio of emitted mercury to another emitted species, typically carbon monoxide). Here we present laboratory and field investigations into mercury emission from burning of surface fuels in dry sclerophyll forests, native to the temperate south-eastern region of Australia. From laboratory data we found that fire behaviour — in particular combustion phase — has a large influence on mercury emission and hence emission ratios. Further, emission of mercury was predominantly in gaseous form with particulate-bound mercury representing <1% of total mercury emission. Importantly, emission factors and emission ratios with respect to carbon monoxide and carbon dioxide, from both laboratory and field data all show that gaseous mercury emission from biomass burning in Australian dry sclerophyll forests is currently overestimated by around 60%. Based on these results, we recommend a mercury emission factor of 28.7 ± 8.1 μg Hg kg −1 dry fuel, and emission ratio of gaseous elemental mercury relative to carbon monoxide of 0.58 ± 0.01 × 10 −7 , for estimation of mercury release from the combustion of Australian dry sclerophyll litter.
Hu, J, Liang, B & Qiu, X 2019, 'High-efficiency collimation of airborne sound through a single deep-subwavelength aperture in an ultra-thin planar plate', Applied Physics Express, vol. 12, no. 2.
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© 2019 The Japan Society of Applied Physics. We propose a mechanism for high-efficiency collimation of airborne sound through an ultra-thin planar structure perforated with a single deep-subwavelength aperture by combining a zigzag-shaped structure with arrays of subwavelength resonators to increase the equivalent refractive index and eliminate high-order diffractive waves simultaneously. Numerical simulations demonstrate that the proposed mechanism enables remarkably enhanced transmission and strong collimation of the transmitted wave, which propagates for a strikingly long distance exceeding 125 wavelengths. Due to its capabilities and flexibility, the proposed design opens up possibilities for novel compact acoustic-steering devices and may have far-reaching impacts in diverse applications such as acoustic communications and loudspeaker design.
Hua, J, Huang, M, Huang, W & Zhao, C 2019, 'Applying Graph Centrality Metrics in Visual Analytics of Scientific Standard Datasets', Symmetry, vol. 11, no. 1, pp. 30-30.
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Graphs are often used to model data with a relational structure and graphs are usually visualised into node-link diagrams for a better understanding of the underlying data. Node-link diagrams represent not only data entries in a graph, but also the relations among the data entries. Further, many graph drawing algorithms and graph centrality metrics have been successfully applied in visual analytics of various graph datasets, yet little attention has been paid to analytics of scientific standard data. This study attempts to adopt graph drawing methods (force-directed algorithms) to visualise scientific standard data and provide information with importance ‘ranking’ based on graph centrality metrics such as Weighted Degree, PageRank, Eigenvector, Betweenness and Closeness factors. The outcomes show that our method can produce clear graph layouts of scientific standard for visual analytics, along with the importance ‘ranking’ factors (represent via node colour, size etc.). Our method may assist users with tracking various relationships while understanding scientific standards with fewer relation issues (missing/wrong connection etc.) through focusing on higher priority standards.
Huang, L, Liu, Z, Wu, C & Liang, J 2019, 'The scattering of plane P, SV waves by twin lining tunnels with imperfect interfaces embedded in an elastic half-space', Tunnelling and Underground Space Technology, vol. 85, pp. 319-330.
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© 2018 Elsevier Ltd A viscous-slip interface model is employed to simulate the contact between the tunnels lining and the surrounding rock, and the scattering of P, SV waves by twin shallowly buried lining tunnels is investigated with the indirect boundary integral equation method (IBIEM). The amplification effect of the dynamic stress concentration of the lining and the surface displacement near the tunnels is examined. It is evident that the slipping-stiffness coefficient and viscosity coefficient at the lining-surrounding rock interface have a significant influence on the dynamic stress distribution and the nearby surface displacement response of the lining tunnel, while the influence characteristics strongly depend on the incident wave type, frequency and angle. Under the incidence of low frequency wave, as a whole, with the increase of the sliding stiffness, the hoop stress increases gradually for plane P and SV waves; while in the resonance frequency (the incident wave frequency is consistent with the natural frequency of the soil column above the tunnels), specially for high-frequency band, the dynamic stress concentration effect is more significant for smaller sliding stiffness. With the increase of viscosity coefficient, the dynamic stress concentration factor inside the lining gradually decreases. Also, the tunnels with viscous-slip interfaces have a more significant amplification effect on the nearby surface displacement amplitude. Moreover, the hoop stress of the twin tunnels may be obviously larger than that of single tunnel in most cases. The dynamic analysis of the underground structure under the actual strong dynamic loading should consider the influence of the slip effect between the lining and surrounding rock interface.
Huang, S 2019, 'A review of optimisation strategies used in simultaneous localisation and mapping', Journal of Control and Decision, vol. 6, no. 1, pp. 61-74.
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Huang, S, Kang, Z, Tsang, IW & Xu, Z 2019, 'Auto-weighted multi-view clustering via kernelized graph learning', Pattern Recognition, vol. 88, pp. 174-184.
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© 2018 Datasets are often collected from different resources or comprised of multiple representations (i.e., views). Multi-view clustering aims to analyze the multi-view data in an unsupervised way. Owing to the efficiency of uncovering the hidden structures of data, graph-based approaches have been investigated widely for various multi-view learning tasks. However, similarity measurement in these methods is challenging since the construction of similarity graph is impacted by several factors such as the scale of data, neighborhood size, choice of similarity metric, noise and outliers. Moreover, nonlinear relationships usually exist in real-world datasets, which have not been considered by most existing methods. In order to address these challenges, a novel model which simultaneously performs multi-view clustering task and learns similarity relationships in kernel spaces is proposed in this paper. The target optimal graph can be directly partitioned into exact c connected components if there are c clusters. Furthermore, our model can assign ideal weight for each view automatically without additional parameters as previous methods do. Since the performance is often sensitive to the input kernel matrix, the proposed model is further extended with multiple kernel learning ability. With the proposed joint model, three subtasks including construct the most accurate similarity graph, automatically allocate optimal weight for each view and find the cluster indicator matrix can be simultaneously accomplished. By this joint learning, each subtask can be mutually enhanced. Experimental results on benchmark datasets demonstrate that our model outperforms other state-of-the-art multi-view clustering algorithms.
Huang, X, An, P, Cao, F, Liu, D & Wu, Q 2019, 'Light-field compression using a pair of steps and depth estimation', Optics Express, vol. 27, no. 3, pp. 3557-3573.
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Huang, Y, Ng, ECY, Yam, YS, Lee, CKC, Surawski, NC, Mok, WC, Organ, B, Zhou, JL & Chan, EFC 2019, 'Impact of potential engine malfunctions on fuel consumption and gaseous emissions of a Euro VI diesel truck', Energy Conversion and Management, pp. 521-529.
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© 2019 Elsevier Ltd Although new vehicles are designed to comply with specific emission regulations, their in-service performance would not necessarily achieve them due to wear-and-tear and improper maintenance, as well as tampering or failure of engine control and exhaust after-treatment systems. In addition, there is a lack of knowledge on how significantly these potential malfunctions affect vehicle performance. This study was therefore conducted to simulate the effect of various engine malfunctions on the fuel consumption and gaseous emissions of a 16-tonne Euro VI diesel truck using transient chassis dynamometer testing. The simulated malfunctions included those that would commonly occur in the intake, fuel injection, exhaust after-treatment and other systems. The results showed that all malfunctions increased fuel consumption except for the malfunction of EGR fully closed which reduced fuel consumption by 31%. The biggest increases in fuel consumption were caused by malfunctions in the intake system (16%–43%), followed by the exhaust after-treatment (6%–30%), fuel injection (4%–24%) and other systems (6%–11%). Regarding pollutant emissions, the effect of engine malfunctions on HC and CO emissions was insignificant, which remained unchanged or even reduced for most cases. An exception was EGR fully open which increased HC and CO emissions by 343% and 1124%, respectively. Contrary to HC and CO emissions, NO emissions were significantly increased by malfunctions. The largest increases in NO emissions were caused by malfunctions in the after-treatment system, ranging from 38% (SCR) to 1606% (DPF pressure sensor). Malfunctions in the fuel injection system (24%–1259%) and intercooler (438%–604%) could also increase NO emissions markedly. This study demonstrated clearly the importance of having properly functioning engine control and exhaust after-treatment systems to achieve the required performance of fuel consumption and pollutant emissions.
Huang, Y, Porter, A, Zhang, Y, Lian, X & Guo, Y 2019, 'An assessment of technology forecasting: Revisiting earlier analyses on dye-sensitized solar cells (DSSCs)', Technological Forecasting and Social Change.
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Huang, Y, Surawski, NC, Organ, B, Zhou, JL, Tang, OHH & Chan, EFC 2019, 'Fuel consumption and emissions performance under real driving: Comparison between hybrid and conventional vehicles.', Science of the Total Environment, vol. 659, pp. 275-282.
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Hybrid electric vehicles (HEVs) are perceived to be more energy efficient and less polluting than conventional internal combustion engine (ICE) vehicles. However, increasing evidence has shown that real-driving emissions (RDE) could be much higher than laboratory type approval limits and the advantages of HEVs over their conventional ICE counterparts under real-driving conditions have not been studied extensively. Therefore, this study was conducted to evaluate the real-driving fuel consumption and pollutant emissions performance of HEVs against their conventional ICE counterparts. Two pairs of hybrid and conventional gasoline vehicles of the same model were tested simultaneously in a novel convoy mode using two portable emission measurement systems (PEMSs), thus eliminating the effect of vehicle configurations, driving behaviour, road conditions and ambient environment on the performance comparison. The results showed that although real-driving fuel consumption for both hybrid and conventional vehicles were 44%-100% and 30%-82% higher than their laboratory results respectively, HEVs saved 23%-49% fuel relative to their conventional ICE counterparts. Pollutant emissions of all the tested vehicles were lower than the regulation limits. However, HEVs showed no reduction in HC emissions and consistently higher CO emissions compared to the conventional ICE vehicles. This could be caused by the frequent stops and restarts of the HEV engines, as well as the lowered exhaust gas temperature and reduced effectiveness of the oxidation catalyst. The findings therefore show that while achieving the fuel reduction target, hybridisation did not bring the expected benefits to urban air quality.
Huang, Y, Zhong, Y, Wu, Q, Dutkiewicz, E & Jiang, T 2019, 'Cost-Effective Foliage Penetration Human Detection under Severe Weather Conditions based on Auto-Encoder/Decoder Neural Network', IEEE Internet of Things Journal.
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IEEE Military surveillance events and rescue activities are vital missions for the Internet-of-things. To this end, foliage penetration for human detection plays an important role. However, although the feasibility of that mission has been validated, we observe that it still cannot performs promisingly under severe weather conditions such as rainy, foggy, and snowy days. Therefore, in this paper, experiments are conducted under severe weather conditions based on a proposed deep learning approach. We present an Auto-Encoder/Decoder (Auto-ED) deep neural network that can learn the deep representation and conduct classification task concurrently. Since the property of cost-effective, the device-free sensing (DFS) techniques are used to address human detection in our case. As we pursue the signal-based mission, two components are involved in the proposed Auto-ED approach. First, an encoder is utilized that encode signal-based inputs into higher dimensional tensors by fractionally-strided convolution operations. Then, a decoder is leveraged with convolution operations to extract deep representations and learn the classifier simultaneously. To verify the effectiveness of the proposed approach, we compare it with several machine learning approaches under different weather conditions. Also, a simulation experiment is conducted by adding Additive White Gaussian Noise (AWGN) to the original target signals with different Signal to Noise Ratios (SNRs). Experimental results demonstrate that the proposed approach can best tackle the challenge of human detection under severe weather conditions in the high-clutter foliage environment, which indicates its potential application values in the near future.
Imdad, U, Asif, M, Ahmad, M, Sohaib, O, Hanif, M & Chaudary, M 2019, 'Three Dimensional Point Cloud Compression and Decompression Using Polynomials of Degree One', Symmetry, vol. 11, no. 2, pp. 209-209.
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Irga, P, Pettit, T, Irga, R, Paull, N, Douglas, A & Torpy, F 2019, 'Does plant species selection in functional active green walls influence VOC phytoremediation efficiency?', Environmental Science and Pollution Research.
Jafari, M, Malekjamshidi, Z & Zhu, J 2019, 'A magnetically coupled multi-port, multi-operation-mode micro-grid with a predictive dynamic programming-based energy management for residential applications', International Journal of Electrical Power & Energy Systems, vol. 104, no. January 2019, pp. 784-796.
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This paper presents the development of a residential micro-grid topology based on a combination of common magnetic and electrical buses. The magnetic bus interfaces two low voltage dc buses linking a PV and a fuel cell to a high voltage dc bus connected to a grid-tied single-phase bidirectional inverter. A battery is used to store the surplus energy of the system and stabilise the dc voltage of the fuel cell bus. A synchronised bus voltage balance (SBVB) technique is used to reduce the conduction losses and increase the soft switching operation range of the converters. To improve the maximum power point tracking (MPPT) performance and system efficiency, appropriate control techniques and compensation blocks are designed. The proposed micro-grid is able to operate in multiple grid-connected and off-grid operation modes according to a predictive 2D dynamic programming-based energy management. A mode selection and transition strategy is developed to select the appropriate operation mode and smooth the mode transition. A detailed study of the micro-grid including steady-state operation, small signal modelling, controller design, and energy management is presented. A prototype of the system is developed, and experimental tests are conducted for an energy management scenario.
Jafari, M, Malekjamshidi, Z & Zhu, J 2019, 'Copper Loss Analysis of a Multi-winding High-frequency Transformer for a Magnetically-coupled Residential Micro-grid', IEEE Transactions on Industry Applications, vol. 55, no. 1, pp. 283-297.
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Improvements in characteristics of magnetic materials and switching devices have provided the feasibility of replacing the electrical buses with high-frequency magnetic links in small-scale micro-grids. This can effectively reduce the number of voltage conversion stages, size and cost of the micro-grid, and isolate the sources and loads. To optimally design the magnetic link, an accurate evaluation of copper loss of the windings considering both the current waveforms and parasitic effects are required. This paper studies the copper loss analysis of a three-winding high-frequency magnetic link for residential micro-grid applications. Due to the non-sinusoidal nature of the voltages and currents, the loss analysis is carried out on a harmonic basis taking into account the variations of phase shift, duty ratio and amplitude of the waveforms. The high-frequency parasitic phenomena including the skin and proximity effects are taken into account. The maximum and minimum copper loss operating conditions of the magnetic link and their dependency on the phase shift angle and the duty ratio of the connected waveforms are studied. A prototype of the micro-grid including the magnetic link is developed to validate the theoretical analysis, evaluate the micro-grid efficiency and perform the loss breakdown.
Jafari, M, Malekjamshidi, Z, Lu, DDC & Zhu, J 2019, 'Development of a Fuzzy-Logic-Based Energy Management System for a Multi-Port Multi-Operation Mode Residential Smart Micro-grid', IEEE Transactions on Power Electronics.
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IEEE In this paper a grid-tied residential smart micro-grid topology is proposed which integrates energies of a PV, a fuel cell and a battery bank to supply the local loads through a combination of electric and magnetic buses. In contrast to multiple-converter based micro-grids with a common electric bus, using a multi-port converter with a common magnetic bus can effectively reduce the number of voltage conversion stages, size and cost of the micro-grid and isolates the conversion ports. The resultant topology utilizes a centralized system level control which leads to the faster and more flexible energy management. The proposed micro-grid is able to operate in multiple grid-connected and off-grid operation modes. A fuzzy controlled energy management unit is designed to select the appropriate operation mode considering both real-time and long-term-predicted data of the system. A mode transition process is designed to smooth the mode variation by using a state transition diagram and bridging modes. To improve the micro-grid operation performance, appropriate control techniques such as synchronized bus-voltage balance are used. A prototype of the proposed micro-grid is developed and experimentally tested for three different energy management scenarios. Energy distribution and energy cost analysis are performed to validate the proposed control method.
Jahandari, S, Saberian, M, Tao, Z, Mojtahedi, SF, Li, J, Ghasemi, M, Rezvani, SS & Li, W 2019, 'Effects of saturation degrees, freezing-thawing, and curing on geotechnical properties of lime and lime-cement concretes', Cold Regions Science and Technology, vol. 160, pp. 242-251.
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© 2019 Elsevier B.V. There are very limited researches carried out to investigate the influence of saturation degrees, freezing-thawing, and curing times on geotechnical properties of lime concrete (LC) and lime-cement concrete (LCC) due to the capillary action and changes in groundwater table. Subsequently, the primary goal of this research is to investigate the influence of these parameters on mechanical properties of LC and LCC using unconfined compression tests, namely uniaxial compressive strength (UCS), stress-strain behavior, deformability index (I D ), secant modulus (E S ), failure strain, bulk modulus (K), resilient modulus (M R ), brittleness index (I B ), and shear modulus (G). At first, the mechanical and chemical characteristics of the utilized materials were measured. Then, samples were made with an optimal amount of cement, lime, coarse-grained soil, fine-grained soil, and water. The samples were then exposed to saturation points extending from 0 to 100% after 14, 28, 45 and 60 curing days. Then, to consider the effect of amount of saturation on the mechanical properties, UCS tests were performed on some of the samples. Other LCC specimens were exposed to freezing-thawing conditions to consider the effect of this phenomenon on the mechanical properties as well. The results of more than 250 UCS tests demonstrated that the curing times significantly affected the strength of LC and LCC specimens. Moreover, it is not ideal and logical to utilize LC and LC columns at a profundity underneath or near the groundwater level, though it is reasonable to adopt LCC and LCC columns at a profundity beneath or near the groundwater level because of the immaterial effect of degrees of saturation on LCC. In addition, this study showed that extending the curing period and diminishing the saturation degree would increase the strength and mechanical properties of the LCC specimens. The results of freezing-thawing demonstrated a negligible increase in the strength of t...
Jamil, S, Loganathan, P, Listowski, A, Kandasamy, J, Khourshed, C & Vigneswaran, S 2019, 'Simultaneous removal of natural organic matter and micro-organic pollutants from reverse osmosis concentrate using granular activated carbon.', Water research, vol. 155, pp. 106-114.
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Although reverse osmosis produces high quality reusable water from wastewater the rejected concentrate (ROC) poses potentially serious health hazards to non-target species. This is especially the case when it is disposed into aquatic environments due to the presence of high concentrations of dissolved natural organics, micro-organic pollutants (MOPs) and other pollutants. In batch and column studies we found that granular activated carbon (GAC) was very effective in simultaneously removing dissolved organic carbon (DOC) and 18 MOPs from ROC. The amounts of all DOC fractions adsorbed (0.01-3 mg/g) were much higher than those of the MOPs (0.01-2.5 μg/g) mainly because ROC contained larger concentrations of DOC fractions than MOPs. However, the partition coefficient which is a measure of the adsorbability was higher for most of the MOPs (0.21-21.6 L/g) than for the DOC fractions (0.01-0.45 L/g). The amount of DOC fraction adsorbed was in the order: humics > low molecular weights > building blocks > biopolymers (following mostly their concentrations in ROC). The partition coefficient was in the order: low molecular weigth nuetrals > humics > building blocks > biopolymers. The MOPs were classified into four groups based on their hydrophobicity (log Kow) and charge. The four positively charged MOPs with high hydrophobicity had the highest amounts adsorbed and partition coefficient, with 95-100% removal in the GAC column. The MOPs that are negatively charged, regardless of their hydrophobicity, had the lowest amounts adsorbed and partition coefficient with 73-94% removal.
Jamshidi Chenari, R, Alaie, R & Fatahi, B 2019, 'Constrained Compression Models for Tire-Derived Aggregate-Sand Mixtures Using Enhanced Large Scale Oedometer Testing Apparatus', Geotechnical and Geological Engineering.
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© 2018, Springer Nature Switzerland AG. Tire derived aggregates have recently been in wide use both in industry and engineering applications depending on the size and the application sought. Five different contents of tire derived aggregates (TDA) were mixed with sand thoroughly to ensure homogeneity. A series of large scale oedometer experiments were conducted to investigate the compressibility properties of the mixtures. Tire shreds content, TDA aspect ratio, skeletal relative density and overburden pressure are studied parameters. Constrained deformation modulus and coefficient of earth pressure at rest are measured parameters. All tests were conducted at seven overburden pressure levels. It was concluded that deformability of TDA-sand mixture increases with soft inclusion. Overburden pressure and skeletal relative density are also important parameters which render more rigidity and less lateral earth pressure coefficient accordingly. TDA size or aspect ratio was shown to have minor effect at least for the constrained strain conditions encountered in current study. An EPR-based parametric study and also sensitivity analyses based on cosine amplitude method revealed quantitative evaluation of the relative importance of each input parameter in varying deformation and lateral earth pressure coefficient as the outputs.
Javdanian, H & Pradhan, B 2019, 'Assessment of earthquake-induced slope deformation of earth dams using soft computing techniques', Landslides.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Evaluating behavior of earth dams under dynamic loads is one of the most important problems associated with the initial design of such massive structures. This study focuses on prediction of deformation of earth dams due to earthquake shaking. A total number of 103 real cases of deformation in earth dams due to earthquakes that has occurred over the past years were gathered and analyzed. Using soft computing methods, including feed-forward back-propagation and radial basis function based neural networks, two models were developed to predict slope deformations in earth dams under variant earthquake shaking. Earthquake magnitude (Mw), yield acceleration ratio (ay/amax), and fundamental period ratio (Td/Tp) were considered as the most important factors contributing to the level of deformation in earth dams. Subsequently, a sensitivity analysis was conducted to assess the performance of the proposed model under various conditions. Finally, the accuracy of the developed soft computing model was compared with the conventional relationships and models to estimate seismic deformations of earth dams. The results demonstrate that the developed neural model can provide accurate predictions in comparison to the available practical charts and recommendations.
Jeong, SY, Chang, SW, Ngo, HH, Guo, W, Nghiem, LD, Banu, JR, Jeon, B-H & Nguyen, DD 2019, 'Influence of thermal hydrolysis pretreatment on physicochemical properties and anaerobic biodegradability of waste activated sludge with different solids content.', Waste management (New York, N.Y.), vol. 85, pp. 214-221.
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The influence of thermal hydrolysis pretreatment (THP) on physicochemical properties (pH, total solids, volatile solids, chemical oxygen demand, total nitrogen, ammonium nitrogen, volatile fatty acids, viscosity, and cell morphology) and anaerobic biodegradability of highly concentrated waste activated sludge (WAS) with TS content ranging from 1 to 7% was evaluated at different temperatures ranging from 100 to 220 °C. The biomethane potential (BMP) of the WAS was systematically analyzed and evaluated. Images of its cellular structure were also analyzed. The results indicated that THP is a useful method for solubilizing volatile solids and enhancing CH4 production regardless of the TS content of the WAS feed. The ultimate CH4 production determined from the BMP analysis was 313-348 L CH4/kg VS (72.6-74.1% CH4) at the optimum THP temperature of 180 °C. The results showed that THP could improve both the capacity and efficiency of anaerobic digestion, even at a high TS content, and could achieve the dual purpose of sludge reduction and higher energy recovery.
Jiang, X, Pan, S, Long, G, Xiong, F, Jiang, J & Zhang, C 2019, 'Cost-sensitive Parallel Learning Framework for Insurance Intelligence Operation', IEEE Transactions on Industrial Electronics.
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IEEE Recent advancements in artificial intelligence (AI) are providing the insurance industry with new opportunities to create tailored solutions and services based on newfound knowledge of consumers, and the execution of enhanced operations and business functions. However, insurance data is heterogeneous, and imbalanced class distribution with low frequency and high dimensions presents four major challenges to machine learning in real-world business. Traditional machine learning algorithms can typically only be applied to standard data sets, which are normally homogeneous and balanced. In this paper, we focus on an efficient cost-sensitive parallel learning framework (CPLF) to enhance insurance operations with a deep learning approach that does not require pre-processing. Our approach comprises a novel, unified, end-to-end cost-sensitive parallel neural network that learns real-world heterogeneous data. A specifically-designed cost-sensitive matrix then automatically generates a robust model for learning minority classifications, and the parameters of both the cost-sensitive matrix and the hybrid neural network are alternately but jointly optimized during training. We also study the CPLF-based architecture for a real-world insurance intelligence operation system, and demonstrate fraud detection experiments on this system. The results of comparative experiments on real-world insurance data sets reflecting actual business cases demonstrate the effectiveness of our design.
Ju, M, Ding, C, Guo, YJ & Zhang, D 2019, 'Remote Sensing Image Haze Removal Using Gamma-Correction-Based Dehazing Model', IEEE Access, vol. 7, pp. 5250-5261.
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© 2013 IEEE. Haze is evident in most remote sensing (RS) images, particularly for the RS scenes captured in inclement weather, which severely hinders image interpretation. In this paper, two simple yet effective visibility restoration formulas are proposed for RGB-channel RS (RRS) images and multi-spectral RS (MSRS) images, respectively. More specifically, a robust gamma-correction-based dehazing model (RGDM) is first defined, which can better address the non-uniform illumination problem in hazy images. Then, the scene albedo restoration formula (SARF) used for the RRS images is obtained by imposing the existing prior knowledge on this RGDM, which enables us to simultaneously eliminate the interferences of haze and non-uniform illumination. In subsequence, according to Rayleigh's law, an expanded restoration formula (E-SARF) is further developed for MSRS data. Using the proposed E-SARF, the spatially varying haze in each band can be thoroughly removed without using any extra information. The experiments are conducted on the challenging RRS and MSRS images, including images with non-uniform illumination, non-uniform haze distribution, and heavy haze. The results reveal that the SARF and the E-SARF are superior to most other state-of-the-art techniques in terms of both the recover quality and the implementation efficiency.
Ju, M, Ding, C, Zhang, D & Guo, YJ 2019, 'BDPK: Bayesian Dehazing Using Prior Knowledge', IEEE Transactions on Circuits and Systems for Video Technology.
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IEEE Atmospheric scattering model (ASM) has been widely used in hazy image restoration. However, the recovered albedo might deviate from the real scene once the input hazy image cannot fully satisfy the model’s assumptions such as the homogeneous atmosphere and even illumination. In this paper, we break these limitations and redefine a more reliable atmospheric scattering model (RASM) that is extremely adaptable for various practical scenarios. Benefiting from RASM, a simple yet effective Bayesian dehazing algorithm (BDPK) is further proposed based on the prior knowledge. Our strategy is to convert the single image dehazing problem into a maximum a-posteriori probability (MAP) one that can be approximated as an optimization function using the existing priori constraints. To efficiently solve this optimization function, the alternating minimizing technique (AMT) is introduced, which enables us to directly restore the scene albedo. Experiments on a number of challenging images reveal the power of BDPK on removing haze and verify its superiority over several state-of-the-art techniques in terms of quality and efficiency.
Kacprzyk, J, Yager, RR & Merigo, JM 2019, 'Towards human-centric aggregation via ordered weighted aggregation operators and linguistic data summaries: A new perspective on zadeh's inspirations', IEEE Computational Intelligence Magazine, vol. 14, no. 1, pp. 16-30.
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© 2005-2012 IEEE. This work presents a new perspective on how Zadeh's ideas related to fuzzy logic and computing with words have influenced the crucial issue of information aggregation and have led to what may be called a human-centric aggregation. We indicate a need to develop tools and techniques to reflect some fine shades of meaning regarding what can be considered the very purpose of human-centric aggregation, notably stated by various modalities in natural language specifications, in particular the usuality. We advocate the use of the ordered weighted average (OWA) operator, which is a formidable tool that can easily be tailored to a user?s intention as to the purpose and method of aggregation, generalizing many simple and natural aggregation types, such as the arithmetic mean, maximum and minimum, and probability. We show some of the most representative extensions and generalizations, including the induced OWA, the generalized OWA, the probabilistic OWA, and the OWA distance. We show their use in the basic case of the aggregation of numerical values and in social choice (voting) results. Then, we claim that linguistic data summaries in Yager?s sense can be considered an »ultimately human consistent» form of human-centric aggregation and show how the OWA operators can be used therein.
Kaljahi, MA, Palaiahnakote, S, Anisi, MH, Idris, MYI, Blumenstein, M & Khan, MK 2019, 'A scene image classification technique for a ubiquitous visual surveillance system', Multimedia Tools and Applications, pp. 1-28.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature The concept of smart cities has quickly evolved to improve the quality of life and provide public safety. Smart cities mitigate harmful environmental impacts and offences and bring energy-efficiency, cost saving and mechanisms for better use of resources based on ubiquitous monitoring systems. However, existing visual ubiquitous monitoring systems have only been developed for a specific purpose. As a result, they cannot be used for different scenarios. To overcome this challenge, this paper presents a new ubiquitous visual surveillance mechanism based on classification of scene images. The proposed mechanism supports different applications including Soil, Flood, Air, Plant growth and Garbage monitoring. To classify the scene images of the monitoring systems, we introduce a new technique, which combines edge strength and sharpness to detect focused edge components for Canny and Sobel edges of the input images. For each focused edge component, a patch that merges nearest neighbor components in Canny and Sobel edge images is defined. For each patch, the contribution of the pixels in a cluster given by k-means clustering on edge strength and sharpness is estimated in terms of the percentage of pixels. The same percentage values are considered as a feature vector for classification with the help of a Support Vector Machine (SVM) classifier. Experimental results show that the proposed technique outperforms the state-of-the-art scene categorization methods. Our experimental results demonstrate that the SVM classifier performs better than rule and template-based methods.
Kang, Y, Xie, H, Li, B, Zhang, J, Hao Ngo, H, Guo, W, Guo, Z, Kong, Q, Liang, S, Liu, J, Cheng, T & Zhang, L 2019, 'Performance of constructed wetlands and associated mechanisms of PAHs removal with mussels', Chemical Engineering Journal, vol. 357, pp. 280-287.
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© 2018 Elsevier B.V. The role of mussels in laboratory-scale constructed wetlands (CWs) where wastewater was contaminated with five typical polycyclic aromatic hydrocarbons (PAHs) (three 3-ring PAHs and two 4-ring PAHs) was investigated in this study. The CWs performance and PAHs removal mechanisms were well studied. Results indicated that these five PAHs improved removal efficiencies of NO 3 -N but in the case of NH 4 -N accumulation occurred. Of the five added PAHs, the 4-ring PAHs were more refractory with higher concentrations in effluent than 3-ring PAHs. By monitoring the five PAHs concentration in water, mussels had excellent removal efficiency of the five PAHs (97%). According to the mass balance calculation, mussels promoted plant uptake of five PAHs, contributing 15.2% of five PAHs removal in CWs. The PAHs could also accumulate in mussels through ingested substrate. Thus, mussels presented a positive correlation with five PAHs purification at a depth of 0–10 cm in substrate, which was 34.7 μg/kg lower than the control group. Due to the purification and enhanced aerated degradation, mussels performed better in removing 3-ring PAHs in substrate, decreasing 8.3% for 3-ring PAHs compared to the control. A positive correlation between five PAHs addition and nirS, nrfA genes was observed. However, PAHs showed negative impact on nitrifying bacteria (amoA). The significant correlations between mussels and five PAHs made it possible to improve CWs for PAHs treatment with aquatic faunas.
Karmokar, DK, Chen, SL, Bird, TS & Guo, YJ 2019, 'Single-Layer Multi-Via Loaded CRLH Leaky-Wave Antennas for Wide-Angle Beam Scanning With Consistent Gain', IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 2, pp. 313-317.
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© 2018 IEEE. Achieving continuous backward-to-forward wide-angle beam scanning with consistent gain by employing composite right/left-handed (CRLH) leaky-wave antennas (LWAs) is reported. For structural and design simplicity, a single-layer one-dimensional structure is considered in which each unit cell consists of a patch shorted centrally to the ground plane. It was found that, when using only one via at the center of a unit cell, a continuous beam scan requires a large-diameter via when all other parameters remain unchanged. To eliminate this limitation while maintaining a single-layer structure, novel unit cells are proposed using multiple vias in each unit cell. An LWA design for continuous beam scan with three vias in each unit cell is investigated, and the results show good performance and design flexibility. A prototype antenna has been realized, and the measured results show that the antenna can scan the radiation beam continuously in a wide range, from -60° to +66° with a consistent gain. The measured gain variation within the scan range is only 2.9 dB, and the 3 dB gain bandwidth is 58.6%.
Katz, A, Shon, HK, Chekli, L & Kim, J-H 2019, 'TiO₂-Coated Optical Fibres for Groundwater Remediation.', Journal of nanoscience and nanotechnology, vol. 19, no. 2, pp. 1086-1089.
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In this study, polyethylene glycol (PEG) was tested as an alternative polymer to improve the coating of TiO₂ particles onto optical fibres. The addition of PEG helped dispersing effectively the particles in solution to control their deposition and therefore achieving better properties of the coating film. Results showed that PEG increased the effectiveness of the coating and the prepared fibres showed better performance for the removal of methylene blue (MB). This was attributed to the morphological changes induced by PEG. EDX mapping of the fibre surface showed that the addition of PEG lead to a better coverage of the fibre surface; increasing the active surface area for subsequent photocatalytic degradation. This study also showed that the light intensity, pH and initial concentration of MB have a significant influence. Finally, it was demonstrated that the coatings using PEG were better ordered and structured; showing a distinct layer-by-layer deposition.
Keerthisinghe, TP, Nguyen, LN, Kwon, EE & Oh, S 2019, 'Antiseptic chlorhexidine in activated sludge: Biosorption, antimicrobial susceptibility, and alteration of community structure', Journal of Environmental Management, vol. 237, pp. 629-635.
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© 2019 Elsevier Ltd Chlorhexidine (CHX) is a broad-spectrum antimicrobial, which may pose environmental health risks. This study examined the removal potential and the mechanisms regulating the fate of CHX in activated sludge (AS). Bioreactors inoculated with AS removed 74 ± 8% and 81 ± 6% of CHX at steady state while receiving 0.5 and 1 mg/L CHX, respectively. Analysis of the removal pathways showed that biosorption, rather than biological breakdown or other abiotic losses, largely (>70%) regulated the removal of CHX. 16S rRNA gene-based analysis revealed that CHX selected for Luteolibacter (4.3–10.1-fold change) and Runella (6.2–14.1-fold change) with potential multi-drug resistance mechanisms (e.g., efflux pumps). In contrast, it significantly reduced core members (Comamonadaceae and Flavobacteriaceae) of AS, playing a key role in contaminant removal and floc formation directly associated with the performance of WWTPs (e.g., wastewater effluent quality). Antimicrobial susceptibility testing showed that 0.4–1.3 mg/L of CHX can be sublethal to AS. Our work provided new insights into the fate of CHX in urban waste streams and the potential toxicity and effects on the structure and function of AS, which has practical implications for the management of biological WWTPs treating CHX.
Khan, MA, Ngo, HH, Guo, W, Liu, Y, Nghiem, LD, Chang, SW, Nguyen, DD, Zhang, S, Luo, G & Jia, H 2019, 'Optimization of hydraulic retention time and organic loading rate for volatile fatty acid production from low strength wastewater in an anaerobic membrane bioreactor.', Bioresource technology, vol. 271, pp. 100-108.
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This study aims to investigate the production of volatile fatty acids (VFAs) from low strength wastewater at various hydraulic retention time (HRT) and organic loading rate (OLR) in a continuous anaerobic membrane bioreactor (AnMBR) using glucose as carbon source. This experiment was performed without any selective inhibition of methanogens and the reactor pH was maintained at 7.0 ± 0.1. 48, 24, 18, 12, 8 and 6 h-HRTs were applied and the highest VFA concentration was recorded at 8 h with an overall VFA yield of 48.20 ± 1.21 mg VFA/100 mg CODfeed. Three different ORLs were applied (350, 550 and 715 mg CODfeed) at the optimum 8 h-HRT. The acetic and propanoic acid concentration maximums were (1.1845 ± 0.0165 and 0.5160 ± 0.0141 mili-mole/l respectively) at 550 mg CODfeed. The isobutyric acid concentration was highest (0.3580 ± 0.0407 mili-mole/l) at 715 mg CODfeed indicating butyric-type fermentation at higher organic loading rate.
Khan, SA, Islam, MR, Guo, Y & Zhu, J 2019, 'A New Isolated Multi-Port Converter With Multi-Directional Power Flow Capabilities for Smart Electric Vehicle Charging Stations', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2018 IEEE. If the batteries are charged by clean renewable energy sources, electric vehicles (EVs) can have zero gas emission, contributing greatly toward the preservation of the green environment. In a smart micro-grid, EVs together with other distributed energy storage units can be used to supply electricity to the loads during the peak hours so as to minimize the effects of the load shedding and improve the quality of electricity. To achieve these goals, an isolated hybrid multi-port converter is required to control the power flows and balance the energy among renewable energy sources, EVs, and the grid. In this paper, a new isolated multi-port converter is proposed, which can control the power flow in multiple directions. The converter is modeled in the matlab/Simulink software environment and this validates the technology with a laboratory prototype test platform. The modeling, implementation, and results are discussed comprehensively.
Khan, SA, Islam, MR, Guo, Y & Zhu, J 2019, 'An Amorphous Alloy Magnetic-Bus-Based SiC NPC Converter with Inherent Voltage Balancing for Grid-Connected Renewable Energy Systems', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2002-2011 IEEE. This paper presents an amorphous alloy magnetic-bus-based neutral point clamped (NPC) converter for grid-connected renewable generation systems. In the proposed system, the amorphous alloy high-frequency high-power density multi-winding magnetic bus generates balanced dc supplies for the five-level (5L) NPC converter for high-quality power conversion. Compared to the traditional NPC converter topologies, the proposed magnetic-bus-based architecture does not require any control circuit for voltage balancing of the series connected capacitors. The magnetic bus inherently overcomes galvanic isolation issues and may reduce the size of the boosting inductor. In this paper, a finite control set model predictive control algorithm is derived to control the grid-connected 5L-NPC inverter for multilevel voltage synthesizing, while achieving the user-defined active and reactive power values. To verify the proposed concept, a simulation model is developed and analyzed in MATLAB/Simulink environment. To validate the technology, a scale d-down prototype test platform is developed in the laboratory with silicon carbide switching devices, which achieves high blocking voltage, low power dissipation, high switching frequency, and high-Temperature operation. Based on the simulation and the experimental results, it is expected that the proposed converter will have a great potential for widespread application in renewable generation systems including superconducting generator-based wind turbines.
Kheshti, Z, Azodi Ghajar, K, Altaee, A & Kheshti, MR 2019, 'High-Gradient Magnetic Separator (HGMS) combined with adsorption for nitrate removal from aqueous solution', Separation and Purification Technology, pp. 650-659.
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© 2018 Elsevier B.V. This paper investigates the adsorption of nitrate anions from aqueous solutions on ammonium-functionalized magnetic mesoporous silica. The adsorbent was prepared via two-step coating process of silica on magnetic core (Fe3O4). The resultant structure was modified by 3-aminopropyl triethoxysilane (APTES), and finally acidified in HCl solution to convert the grafted amino groups to ammonium ones. Field-emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), vibration sample magnetometer (VSM), Energy-dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FT-IR), and N2 adsorption/desorption were used to characterize the obtained samples. Experimental results showed that several factors affected the uptake behavior such as pH, contact time, and initial concentration of nitrate. The amount of sorbent loading were examined and the adsorbent shows great adsorption capacity for NO3¯ (ca.51.28 mg g−1 at 25 °C). The nitrate loaded multifunctional microsphere can be easily regenerated with NaOH solution. The separation of multifunctional magnetic microspheres from solution by novel high gradient magnetic separation (HGMS), using the collection of rods, was also investigated in details. Contrast to other methods based on filter and batch conditions, large volumes of water can be easily handled by the new designed HGMS due to the decreasing pressure drop and retention times. The effect of a set of two different experimental variables, i.e. flowrate and magnetic field strength, were investigated to identify the best working conditions for the separation of adsorbent from treated water. The most efficient backwash system was offered to reuse the magnetic particles, too. The removal efficiency of NO3¯ from solution was around 86.24% by the constructed HGMS under the optimal experimental conditions of 7.5 mL s −1 flowrate and: 3.49 mT magnitude of the magnetic field.
Khosoussi, K, Giamou, M, Sukhatme, GS, Huang, S, Dissanayake, G & How, JP 2019, 'Reliable Graphs for SLAM', International Journal of Robotics Research, vol. 38, no. 2-3, pp. 260-298.
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© The Author(s) 2019. Estimation-over-graphs (EoG) is a class of estimation problems that admit a natural graphical representation. Several key problems in robotics and sensor networks, including sensor network localization, synchronization over a group, and simultaneous localization and mapping (SLAM) fall into this category. We pursue two main goals in this work. First, we aim to characterize the impact of the graphical structure of SLAM and related problems on estimation reliability. We draw connections between several notions of graph connectivity and various properties of the underlying estimation problem. In particular, we establish results on the impact of the weighted number of spanning trees on the D-optimality criterion in 2D SLAM. These results enable agents to evaluate estimation reliability based only on the graphical representation of the EoG problem. We then use our findings and study the problem of designing sparse SLAM problems that lead to reliable maximum likelihood estimates through the synthesis of sparse graphs with the maximum weighted tree connectivity. Characterizing graphs with the maximum number of spanning trees is an open problem in general. To tackle this problem, we establish several new theoretical results, including the monotone log-submodularity of the weighted number of spanning trees. We exploit these structures and design a complementary greedy–convex pair of efficient approximation algorithms with provable guarantees. The proposed synthesis framework is applied to various forms of the measurement selection problem in resource-constrained SLAM. Our algorithms and theoretical findings are validated using random graphs, existing and new synthetic SLAM benchmarks, and publicly available real pose-graph SLAM datasets.
Kirby, R & Duan, W 2019, 'Guided wave propagation in cylindrical ducts with elastic walls enclosing a fluid moving with a uniform velocity', Wave Motion, vol. 85, pp. 1-9.
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© 2018 Elsevier B.V. Theoretical models for elastic wave propagation in fluid filled ducts normally neglects mean fluid flow. However, in many engineering applications the velocity of the fluid may influence the modal characteristics of the duct, for example in gas pipelines, turbomachinery applications and ventilation systems. Accordingly, the influence of a mean uniform fluid flow on acoustically driven duct wall vibration is analysed here for a cylindrical geometry. The semi-analytic finite element method is used to couple the elastodynamic wave equation for the duct wall to the convected wave equation for sound propagation in a uniform fluid flow. A one dimensional finite element approach is described and this is used to find the coupled eigenmodes for the duct. Under certain conditions, a uniform mean flow is seen to significantly affect the phase speed for different eigenmodes, and it is shown that this may cause energy to transfer from the fluid to the surrounding wall at frequencies much lower than those seen without mean flow. This behaviour has the potential to increase sound radiation from ducts at lower frequencies when mean flow is present.
Ko, LW, Lu, YC, Bustince, H, Chang, YC, Chang, Y, Ferandez, J, Wang, YK, Sanz, JA, Pereira Dimuro, G & Lin, CT 2019, 'Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface', IEEE Computational Intelligence Magazine, vol. 14, no. 1, pp. 96-106.
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© 2005-2012 IEEE. Brain-computer interface technologies, such as steady-state visually evoked potential, P300, and motor imagery are methods of communication between the human brain and the external devices. Motor imagery-based brain-computer interfaces are popular because they avoid unnecessary external stimuli. Although feature extraction methods have been illustrated in several machine intelligent systems in motor imagery-based brain-computer interface studies, the performance remains unsatisfactory. There is increasing interest in the use of the fuzzy integrals, the Choquet and Sugeno integrals, that are appropriate for use in applications in which fusion of data must consider possible data interactions. To enhance the classification accuracy of brain-computer interfaces, we adopted fuzzy integrals, after employing the classification method of traditional brain-computer interfaces, to consider possible links between the data. Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based brain-computer interface system. Ten volunteers performed a motor imagery-based brain-computer interface experiment, and we acquired electroencephalography signals simultaneously. The multimodal fuzzy fusion-based brain-computer interface system enhanced performance compared with traditional brain-computer interface systems. Furthermore, when using the motor imagery-relevant electroencephalography frequency alpha and beta bands for the input features, the system achieved the highest accuracy, up to 78.81% and 78.45% with the Choquet and Sugeno integrals, respectively. Herein, we present a novel concept for enhancing brain-computer interface systems that adopts fuzzy integrals, especially in the fusion for classifying brain-computer interface commands.
Kordestani, MD, Naghibi, SA, Hashemi, H, Ahmadi, K, Kalantar, B & Pradhan, B 2019, 'Groundwater potential mapping using a novel data-mining ensemble model', Hydrogeology Journal, vol. 27, no. 1, pp. 211-224.
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© 2018, The Author(s). Freshwater scarcity is an ever-increasing problem throughout the arid and semi-arid countries, and it often results in poverty. Thus, it is necessary to enhance understanding of freshwater resources availability, particularly for groundwater, and to be able to implement functional water resources plans. This study introduces a novel statistical approach combined with a data-mining ensemble model, through implementing evidential belief function and boosted regression tree (EBF-BRT) algorithms for groundwater potential mapping of the Lordegan aquifer in central Iran. To do so, spring locations are determined and partitioned into two groups for training and validating the individual and ensemble methods. In the next step, 12 groundwater-conditioning factors (GCFs), including topographical and hydrogeological factors, are prepared for the modeling process. The mentioned factors are employed in the application of the EBF model. Then, the EBF values of the GCFs are implemented as input to the BRT algorithm. The results of the modeling process are plotted to produce spring (groundwater) potential maps. To verify the results, the receiver operating characteristics (ROC) test is applied to the model’s output. The findings of the test indicated that the areas under the ROC curves are 75 and 82% for the EBF and EBF-BRT models, respectively. Therefore, it can be inferred that the combination of the two techniques could increase the efficacy of these methods in groundwater potential mapping.
Kumar, P, Beck, D, Galeev, R, Thoms, JAI, Talkhoncheh, MS, de Jong, I, Unnikrishnan, A, Baudet, A, Subramaniam, A, Pimanda, JE & Larsson, J 2019, 'HMGA2 promotes long-term engraftment and myeloerythroid differentiation of human hematopoietic stem and progenitor cells.', Blood advances, vol. 3, no. 4, pp. 681-691.
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Identification of determinants of fate choices in hematopoietic stem cells (HSCs) is essential to improve the clinical use of HSCs and to enhance our understanding of the biology of normal and malignant hematopoiesis. Here, we show that high-mobility group AT hook 2 (HMGA2), a nonhistone chromosomal-binding protein, is highly and preferentially expressed in HSCs and in the most immature progenitor cell subset of fetal, neonatal, and adult human hematopoiesis. Knockdown of HMGA2 by short hairpin RNA impaired the long-term hematopoietic reconstitution of cord blood (CB)-derived CB CD34+ cells. Conversely, overexpression of HMGA2 in CB CD34+ cells led to overall enhanced reconstitution in serial transplantation assays accompanied by a skewing toward the myeloerythroid lineages. RNA-sequencing analysis showed that enforced HMGA2 expression in CD34+ cells induced gene-expression signatures associated with differentiation toward megakaryocyte-erythroid and myeloid lineages, as well as signatures associated with growth and survival, which at the protein level were coupled with strong activation of AKT. Taken together, our findings demonstrate a key role of HMGA2 in regulation of both proliferation and differentiation of human HSPCs.
Kuruneru, STW, Saha, SC, Sauret, E & Gu, YT 2019, 'Transient heat transfer and non-isothermal particle-laden gas flows through porous metal foams of differing structure', Applied Thermal Engineering, pp. 888-903.
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© 2019 Elsevier Ltd Multiphase solid-gas flows and particulate fouling in porous media are omnipresent in many environmental and industrial applications. However, the underlying mechanisms and transport behaviour of these complex fluids coupled with heat transfer effects are poorly understood. Moreover, the complexity of multiphase non-isothermal solid-gas transport is further compounded by the fact that thermal solid-gas transport through porous metal foams is not established. This paper numerically investigates non-isothermal solid-gas flows through an idealized metal foam heat exchanger based on a cylindrical ligament and a Weaire-Phelan model. We delve into the dynamics and heat transfer profiles of solid-gas flows immersed in porous metal foams based on various solid foulant properties. The foulant thermal behaviour based on various metal foam ligament wall temperatures is investigated. It is found that the foulant temperature profiles vary with time and the temperature profiles also vary with increasing wall temperature. Moreover, the Weaire-Phelan (WP) model can be used as an alternative to the real metal foam sample. The heat transfer between the foulant, wall, and fluid play a pivotal role in the alteration of the temperature profiles. The developed numerical methodology and the WP geometry may serve as a stepping-stone to address pertinent issues such as indoor environmental studies and optimization of compact heat exchangers.
Kutay, CM 2019, 'Language Located', Archives and Manuscripts.
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Laengle, S, Merigó, JM, Modak, NM & Yang, JB 2019, 'Bibliometrics in operations research and management science: a university analysis', Annals of Operations Research.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Many universities around the World have made important contributions in the field of operations research and management science. This article presents the most productive and influential universities between 1991 and 2015. For doing so, we use the Web of Science database in order to search for the information which is usually regarded as the most relevant for scientific research. The results show the country of origin of the leading universities being mainly from North America and Asia and especially from USA and China. The Centre National de la Recherche Scientifique (CNRS) of France is the most productive university while the Massachusetts Institute of Technology (MIT) of USA is the most influential one. The temporal evolution shows that USA is trailing its dominancy while China progressing quickly. The evaluation also reveals that Asian universities outperform North American universities during the last 5 years.
Lai, Y, Li, Y, Cao, H, Long, J, Wang, X, Li, L, Li, C, Jia, Q, Teng, B, Tang, T, Peng, J, Eglin, D, Alini, M, Grijpma, DW, Richards, G & Qin, L 2019, 'Osteogenic magnesium incorporated into PLGA/TCP porous scaffold by 3D printing for repairing challenging bone defect.', Biomaterials, vol. 197, pp. 207-219.
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Bone defect repair is a challenging clinical problem in musculoskeletal system, especially in orthopaedic disorders such as steroid associated osteonecrosis (SAON). Magnesium (Mg) as a biodegradable metal with properly mechanical properties has been investigating for a long history. In this study, Mg powder, poly (lactide-co-glycolide) (PLGA), β-tricalcium phosphate (β-TCP) were the elements to formulate a novel porous PLGA/TCP/Mg (PTM) scaffolds using low temperature rapid prototyping (LT-RP) technology. The physical characterization of PTM scaffold and Mg ions release were analyzed in vitro. The osteogenic and angiogenic properties of PTM scaffolds, as well as the biosafety after implantation were assessed in an established SAON rabbit model. Our results showed that the PTM scaffold possessed well-designed bio-mimic structure and improved mechanical properties. Findings of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and micro-computed tomography (micro CT)-based angiography indicated that PTM scaffold could increase blood perfusion and promote new vessel ingrowth at 4 weeks after surgery, meanwhile, a plenty of newly formed vessels with well-architective structure were observed at 8 weeks. Correspondingly, at 12 weeks after surgery, micro-CT, histological and mechanical properties analysis showed that PTM could significant enhance new bone formation and strengthen newly formed bone mechanical properties. The mean bone volume in PTM group was 56.3% greater than that in PT group. Biosafety assessments from 0 to 12 weeks after implantation did not induce increase in serum Mg ions concentration, and immune response, liver and kidney function parameters were all at normal level. These findings suggested that the PTM scaffold had both osteogenic and angiogenic abilities which were synergistic effect in enhancing new bone formation and strengthen newly formed bone quality in SAON. In summary, PTM scaffolds are promising composite biomaterials for re...
Lan, T, Liu, N, Liu, Y, Han, F & Liu, QH 2019, '2-D Electromagnetic Scattering and Inverse Scattering from Magnetodielectric Objects Based on Integral Equation Method', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 67, no. 2, pp. 1346-1351.
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Lapko, Y, Trianni, A, Nuur, C & Masi, D 2019, 'In Pursuit of Closed-Loop Supply Chains for Critical Materials: An Exploratory Study in the Green Energy Sector', Journal of Industrial Ecology, vol. 23, no. 1, pp. 182-196.
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© 2018 by Yale University A closed-loop supply chain (CLSC) is considered not only an important solution for ensuring sustainable exploitation of materials, but also a promising strategy for securing long-term availability of materials. The latter is especially highlighted in the materials criticality discourse. Critical raw materials (CRMs), being exposed to supply disruptions, create an uncertain operational environment for many industries, particularly for green energy technologies that employ multiple CRMs. However, recycling rates of CRMs are very low and engagement of companies in CLSC for CRM is limited. This study examines factors influencing CLSC for CRM development in photovoltaic panels and wind turbine technologies. The aim is to analyze how the factors manifest themselves in different companies along the supply chain and to identify enabling and bottleneck conditions for implementation of CLSC for CRM. The novelty of the study is twofold: the focus on material rather than product flows, and examination of factors from a multiactor perspective. The evidence obtained suggests that the manufacturing companies and reverse supply-chain operators engaged in the study take different perspectives (product vs. material) regarding development of CLSC for CRM and thus emphasize different factors. The findings underline the need for interactions between supply-chain actors, a sound competitive environment for recycling processes, and investment in technologies and infrastructure development if CLSC for CRM is to be developed. The paper provides implications for practitioners and policy makers for implementation of CLSC for CRM, and suggests prospects for further research.
Lashkari, B, Rezazadeh, J, Farahbakhsh, R & Sandrasegaran, K 2019, 'Crowdsourcing and Sensing for Indoor Localization in IoT - a Review', IEEE Sensors Journal, vol. 19, no. 7, pp. 2408-2434.
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IEEE Among the emerging concepts in the context of IoT, crowdsourcing and crowdsensing are known as two critical building blocks on the intersection point of things and humanbased techniques. Although contribution and involvement of people is a key factor in both crowdsensing and crowdsourcing systems which provides reliability and data quality, level of human contribution is what makes crowd-powered systems different. To scrutinize this difference it is considered that user intervention in crowd-based schemes can be either implicit or explicit. Since one of the most important applications of crowdsourcing and crowdsensing systems is providing location services in indoor environments, in this work we study the level of user contribution in available crowd-powered techniques and propose a classification for crowd-powered indoor localization solutions to clarify which crowds-based approach is utilized in each indoor localization solution because in many cases the distinction is not clear and often leads to misconception. Dependency on site survey process is considered as another distinction point in our proposed classification. Hence we consider indoor localization solutions with implicit user participation and latent mobile utilization as crowdsensing indoor localization systems. Respectively, indoor localization solutions with explicit user participation and manifest mobile utilization are classified as crowdsourcing indoor localization systems.
Le, A, Le, H, Nguyen, L & Phan, M 2019, 'An efficient adaptive hierarchical sliding mode control strategy using neural networks for 3D overhead cranes', International Journal of Automation and Computing.
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Le, AT, Tran, LC, Huang, X, Jay Guo, Y & Vardaxoglou, JYC 2019, 'Frequency-domain characterization and performance bounds of ALMS loop for RF self-interference cancellation', IEEE Transactions on Communications, vol. 67, no. 1, pp. 682-692.
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© 1972-2012 IEEE. Analog least mean square (ALMS) loop is a promising method to cancel self-interference (SI) in in-band full-duplex (IBFD) systems. In this paper, the steady state analyses of the residual SI powers in both analog and digital domains are firstly derived. The eigenvalue decomposition is then utilized to investigate the frequency domain characteristics of the ALMS loop. Our frequency domain analyses prove that the ALMS loop has an effect of amplifying the frequency components of the residual SI at the edges of the signal spectrum in the analog domain. However, the matched filter in the receiver chain will reduce this effect, resulting in a significant improvement of the interference suppression ratio (ISR). It means that the SI will be significantly suppressed in the digital domain before information data detection. This paper also derives the lower bounds of ISRs given by the ALMS loop in both analog and digital domains. These lower bounds are joint effects of the loop gain, tap delay, number of taps, and transmitted signal properties. The discovered relationship among these parameters allows the flexibility in choosing appropriate parameters when designing the IBFD systems under given constraints.
Lee, JS & Filatova, T 2019, 'Dimension reduction of multivariate outputs of socio-environmental agent-based models', Ecological Complexity.
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© 2018 Often, socio-environmental agent-based models (ABMs) are driven by a host of parameters, and their outputs are similarly multidimensional or vastly high-dimensional. While this complex data and its inter-relationships may be rendered tractable, the task is far from trivial. In this paper, we study the multidimensional outcome space of the socio-environmental, land-use RHEA ABM (Risks and Hedonics in an Empirical Agent-based Land Market), specifically the inter-distances among the outcome measures, and their reducibility using several well-known dimension reduction techniques, variants of multidimensional scalings. In testing the efficacy of several reduction algorithms, we temporally characterize the model's reducibility while exposing changes in behavior across a wide parameter space that can signal sudden or gradual shifts and possible critical transitions. Our findings reveal that the ABM's signature reducibility trends exhibit idiosyncrasies and unexpected non-linearity as well as discontinuities. These non-linearities and discontinuities are indicative of both gradual and sudden shifts, signaling potential propensity to internal perturbations induced by parameter settings. Additionally, we related the outcome space via their inter-distances to the multidimensional input parameter space, effectively assessing outcome “reducibility to model controls”. This analysis reveals that the model's sensitivity to parameters is not only temporally dependent, but also can be partitioned by them, some of which suppress variability in this reduction to model controls, raising questions regarding the extent and structure of endogeneity that yields the distinct temporal trends in the relationship between inputs and outputs and their connections to outcome reducibility.
Lee, S, Choi, J, Park, YG, Shon, H, Ahn, CH & Kim, SH 2019, 'Hybrid desalination processes for beneficial use of reverse osmosis brine: Current status and future prospects', Desalination, pp. 104-111.
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© 2018 Elsevier B.V. As water shortage has increasingly become a serious global problem, desalination using seawater reverse osmosis (SWRO) is considered as a sustainable source of potable water sources. However, a major issue on the SWRO desalination plant is the generation of brine that has potential adverse impact due to its high salt concentration. Accordingly, it is necessary to develop technologies that allow environmentally friendly and economically viable management of SWRO brines. This paper gives an overview of recent research works and technologies to treat SWRO brines for its beneficial use. The treatment processes have been classified into two different groups according to their final purpose: 1) technologies for producing fresh water and 2) technologies for recovering energy. Topics in this paper includes membrane distillation (MD), forward osmosis (FO), pressure-retarded osmosis (PRO), reverse electrodialysis (RED) as emerging tools for beneficial use of SWRO brine. In addition, a new approach to simultaneously recover water and energy from SWRO brine is introduced as a case study to provide insight into improving the sustainability of seawater desalination.
Lees, T, Elliott, JL, Gunning, S, Newton, PJ, Rai, T & Lal, S 2019, 'A systematic review of the current evidence regarding interventions for anxiety, PTSD, sleepiness and fatigue in the law enforcement workplace.', Industrial health.
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Law enforcement is inherently stressful, and police officers are particularly vulnerable to mental and physical disorders. As such, researchers are currently assessing intervention strategies that may combat or manage these psychological, physical and mental issues. To review most recent information regarding anxiety, PTSD, and sleepiness and fatigue and identify the interventions and treatments proposed to overcome work related stressors and associated mental illnesses inflicting law enforcement officers. The EMBASE, OVID MEDLINE and PsycINFO databases were canvassed for articles investigating anxiety, post-traumatic stress disorder, sleepiness, and fatigue. Initial article selections were made based on title, whilst final inclusion was informed by a full critical appraisal with respect to the primary and secondary effects. The systematic search returned 363 records, of which 183 were unique. Following screening, 43 records were included in the final review. The included literature assessed the efficacy of several interventions, and provided a number of recommendations regarding interventions, and policy. Moreover, literature indicates that police officers benefit from interventions targeting work-related stress and potential psychological disorders, if these interventions are continuous. Furthermore, larger controlled studies are required to further elucidate the benefits of psychosocial intervention in law enforcement.
Lei, G, Liu, C, Li, Y, Chen, D, Guo, Y & Zhu, J 2019, 'Robust Design Optimization of a High-Temperature Superconducting Linear Synchronous Motor Based on Taguchi Method', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2002-2011 IEEE. This paper investigates the efficient robust design and optimization of a high-Temperature superconducting (HTS) linear synchronous motor by using the Taguchi parameter design approach. The manufacturing tolerances of the HTS magnets, primary iron core and the air gap are considered in the robust design to ensure that the optimal design is less sensitive to these uncertainties. To overcome the disadvantages of the conventional Taguchi parameter design approach, a sequential Taguchi robust optimization method is presented for improvement of the motor performance and manufacturing quality. The proposed method is efficient because it holds the advantages of both Taguchi method and sequential optimization strategy. It can significantly increase the average thrust and decrease the thrust ripple of the investigated HTS linear synchronous motor.
Lei, G, Wang, T, Zhu, J & Guo, Y 2019, 'Robust Multiobjective and Multidisciplinary Design Optimization of Electrical Drive Systems', China Electrotechnical Society Transactions on Electrical Machines and Systems, vol. 2, no. 4, pp. 409-416.
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Li, G, Hu, J, Li, Y & Zhu, J 2019, 'An Improved Model Predictive Direct Torque Control Strategy for Reducing Harmonic Currents and Torque Ripples of Five Phase Permanent Magnet Synchronous Motors', IEEE Transactions on Industrial Electronics.
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IEEE Five phase permanent magnet synchronous motors (PMSMs) offer merits of high fault tolerant capability and high torque per rms ampere, and thus, are suitable for applications like aerospace and electric vehicles (EVs). However, the complex machine model causes difficulties in controller design. Besides, having 32 voltage vectors with various effects on currents and torque, the selection of the optimal switching state becomes a challenge to achieve a performance trade-off. This paper proposes an improved model predictive direct torque control (MPDTC) strategy consisting of a quadratic evaluation method (QEM) and a harmonic voltage elimination method (HVEM). In QEM, the preliminary vector is firstly chosen from the vectors of the outer decagon according to a cost function for torque and flux regulation. This preliminary vector, composed of three sets of different amplitudes, is further synthesized according to the error between the actual toque/flux and the references. In this way, the optimal voltage vector can be obtained without significantly increasing the computational burden. In HVEM, by subtracting the harmonics voltage component from the vector determined previously in QEM, the final voltage vector is obtained for mitigating stator harmonic currents. The proposed control strategy is compared with the conventional MPDTC approach. The results confirm the effectiveness of the proposed methods with good steady-state performance while maintaining quick dynamic responses.
Li, H, Wang, J, Li, R & Lu, H 2019, 'Novel analysis–forecast system based on multi-objective optimization for air quality index', Journal of Cleaner Production, vol. 208, pp. 1365-1383.
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© 2018 Elsevier Ltd The air quality index (AQI) is an important indicator of air quality. Owing to the randomness and non-stationarity inherent in AQI, it is still a challenging task to establish a reasonable analysis–forecast system for AQI. Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to improve both aspects simultaneously, leading to unsatisfactory results. In this study, a novel analysis–forecast system is proposed that consists of complexity analysis, data preprocessing, and optimize–forecast modules and addresses the problems of air quality monitoring. The proposed system performs a complexity analysis of the original series based on sample entropy and data preprocessing using a novel feature selection model that integrates a decomposition technique and an optimization algorithm for removing noise and selecting the optimal input structure, and then forecasts hourly AQI series by utilizing a modified least squares support vector machine optimized by a multi-objective multi-verse optimization algorithm. Experiments based on datasets from eight major cities in China demonstrated that the proposed system can simultaneously obtain high accuracy and strong stability and is thus efficient and reliable for air quality monitoring.
Li, H, Wang, TQ, Huang, X & Guo, YJ 2019, 'Adaptive AoA and Polarization Estimation for Receiving Polarized mmWave Signals', IEEE Wireless Communications Letters.
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IEEE This paper proposes a novel hybrid dual-polarized antenna array which exploits two orthogonally-collocated dipoles to capture the full power of a polarized millimeter wave signal. To maximize the received signal-to-noise ratio (SNR), we study the adaptive angle-of-arrival and polarization state estimation, and develop a differential beam tracking algorithm and a cross-correlation-to-power ratio polarization tracking algorithm for interleaved hybrid dual-polarized arrays. Simulation results verify the superior performance of the proposed algorithms, and confirm the significant improvement of SNR obtained by using the proposed array and algorithms.
Li, H, Wang, TQ, Huang, X, Zhang, JA & Guo, YJ 2019, 'Low-Complexity Multiuser Receiver for Massive Hybrid Array mmWave Communications', IEEE Transactions on Communications, pp. 1-1.
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Li, J, Zhu, X, Law, SS & Samali, B 2019, 'Indirect bridge modal parameters identification with one stationary and one moving sensors and stochastic subspace identification', Journal of Sound and Vibration, vol. 446, pp. 1-21.
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© 2019 Elsevier Ltd A new indirect strategy is proposed to estimate the bridge modal parameters from the dynamic responses of two vehicles using stochastic subspace identification technique. The effect of ambient excitation, such as ongoing traffic, is simulated as white-noise excitation at the bridge supports. The state-space model of the vehicle-bridge interaction system is derived for a single-degree-of-freedom quarter-car model and the bridge deck modeled as a simply-supported Euler-Bernoulli beam. Bridge modal frequencies can be estimated accurately from the vehicle responses. Two instrumented vehicles are required to estimate the bridge mode shapes, with one serving as a fixed reference sensor and the other as a moving sensor. The measured accelerations from the vehicles are divided into segments and each pair of signal segments forms a state-space identification problem. Local mode shape value from each signal segment can be estimated using the reference-based SSI method. A rescaling on the local mode shape values is applied to construct the global mode shapes. Effects of the bridge surface roughness, measurement noise and vehicle properties on the mode shape identification are also numerically studied. A vehicle-bridge interaction model in the laboratory serves for the experimental validation of the proposed strategy. Both numerical and experimental results show that the proposed method can estimate the bridge modal parameters with acceptable accuracy.
Li, N, Fu, X, Zhu, J, Lin, M, Yang, G, Kong, Y & Hao, L 2019, 'Hybrid-Excited Series Permanent Magnet Axial Field Flux Switching Memory Machine', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2002-2011 IEEE. This paper presents a novel axial field flux switching memory (AFFSM) machine, which integrates series permanent magnet (SPM) and an additional dc field winding for improving the flux regulation capability. The configuration and the operation principle of the SPM-AFFSM machine are presented. The design considerations for the SPM and the dc field winding are discussed. The voltage regulation capability and torque-speed characteristics of the SPM-AFFSM machine are investigated by 3-D FEA. Finally, a prototype is developed for experimental verification. The measured results verify structural validity and the finite-element predictions.
Li, Q, Zhong, J, Cao, Z & Li, X 2019, 'Optimizing streaming graph partitioning via a heuristic greedy method and caching strategy', Optimization Methods and Software.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Graph partitioning is an important method for accelerating large distributed graph computation. Streaming graph partitioning is more efficient than offline partitioning, and it has been developed continuously in the application of graph partitioning in recent years. In this work, we first introduce a heuristic greedy streaming partitioning method and show that it outperforms the state-of-the-art streaming partitioning methods, leading to exact balance and fewer cut edges. Second, we propose a cache structure for streaming partitioning, called an adjacent edge structure, which can improve the partition efficiency several times on a single commodity type computer without affecting the partition quality. Regardless as to whether the memory capacity is limited (local cache) or not (global cache), our strategy can also improve the partition quality by restreaming partitioning. Taking linear weight greedy streaming algorithm as an example, the experimental results on 19 real-world graphs show that the average partitioning time of the new method is 4.9 times faster than that of the original method, which proves the effectiveness and superiority of the cache structure mentioned in this paper.
Li, Q, Zhong, J, Li, Q, Wang, C & Cao, Z 2019, 'A community merger of optimization algorithm to extract overlapping communities in networks', IEEE Access, vol. 7, pp. 3994-4005.
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© 2018 IEEE. A community in networks is a subset of vertices primarily connecting internal components, yet less connecting to the external vertices. The existing algorithms aim to extract communities of the topological features in networks. However, the edges of practical complex networks involving a weight that represents the tightness degree of connection and robustness, which leads a significant influence on the accuracy of community detection. In our study, we propose an overlapping community detection method based on the seed expansion strategy applying to both the unweighted and the weighted networks, called OCSE. First, it redefines the edge weight and the vertex weight depending on the influence of the network topology and the original edge weight, and then selects the seed vertices and updates the edges weight. Comparisons between OCSE approach and existing community detection methods on synthetic and real-world networks, the results of the experiment show that our proposed approach has the significantly better performance in terms of the accuracy.
Li, X, Liu, Y, Xu, Q, Liu, X, Huang, X, Yang, J, Wang, D, Wang, Q, Liu, Y & Yang, Q 2019, 'Enhanced methane production from waste activated sludge by combining calcium peroxide with ultrasonic: Performance, mechanism, and implication.', Bioresource technology, vol. 279, pp. 108-116.
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This study reported a novel and high-efficient pretreatment method for anaerobic digestion, i.e., combining calcium peroxide (CaO2) with ultrasonic (US), by which not only the methane production was remarkably improved but also the removal of refractory organic contaminants was enhanced. Experimental results showed the optimum condition for methane production was achieved at 0.1 g CaO2/g VSS combined with US (1 W/ml, 10 min). Under this condition, the maximal methane yield of 211.90 ± 2.6 L CH4/kg VSS was obtained after 36 d of anaerobic digestion, which was respectively 1.36-fold, 1.19-fold and 1.26-fold of that from the control, solo US (1 W/ml, 10 min) and solo CaO2 (0.1 g/g VSS). Mechanism investigations revealed that CaO2 + US not only improved the disintegration of waste activated sludge (WAS) but also increased the proportion of biodegradable organic matters. Moreover, the total frequency of recalcitrant contaminants contained in WAS decreased significantly when CaO2 + US was applied.
Li, X, Zhao, L, Ji, W, Wu, Y, Wu, F, Yang, M-H, Tao, D & Reid, I 2019, 'Multi-Task Structure-Aware Context Modeling for Robust Keypoint-Based Object Tracking.', IEEE transactions on pattern analysis and machine intelligence, vol. 41, no. 4, pp. 915-927.
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In the fields of computer vision and graphics, keypoint-based object tracking is a fundamental and challenging problem, which is typically formulated in a spatio-temporal context modeling framework. However, many existing keypoint trackers are incapable of effectively modeling and balancing the following three aspects in a simultaneous manner: temporal model coherence across frames, spatial model consistency within frames, and discriminative feature construction. To address this problem, we propose a robust keypoint tracker based on spatio-temporal multi-task structured output optimization driven by discriminative metric learning. Consequently, temporal model coherence is characterized by multi-task structured keypoint model learning over several adjacent frames; spatial model consistency is modeled by solving a geometric verification based structured learning problem; discriminative feature construction is enabled by metric learning to ensure the intra-class compactness and inter-class separability. To achieve the goal of effective object tracking, we jointly optimize the above three modules in a spatio-temporal multi-task learning scheme. Furthermore, we incorporate this joint learning scheme into both single-object and multi-object tracking scenarios, resulting in robust tracking results. Experiments over several challenging datasets have justified the effectiveness of our single-object and multi-object trackers against the state-of-the-art.
Liang, H, Wang, H, Li, Q, Wang, J, Xu, G, Chen, J, Wei, JM & Yang, Z 2019, 'A general framework for learning prosodic-enhanced representation of rap lyrics', World Wide Web.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Learning and analyzing rap lyrics is a significant basis for many Web applications, such as music recommendation, automatic music categorization, and music information retrieval, due to the abundant source of digital music in the World Wide Web. Although numerous studies have explored the topic, knowledge in this field is far from satisfactory, because critical issues, such as prosodic information and its effective representation, as well as appropriate integration of various features, are usually ignored. In this paper, we propose a hierarchical attention variational a utoe ncoder framework (HAVAE), which simultaneously considers semantic and prosodic features for rap lyrics representation learning. Specifically, the representation of the prosodic features is encoded by phonetic transcriptions with a novel and effective strategy (i.e., rhyme2vec). Moreover, a feature aggregation strategy is proposed to appropriately integrate various features and generate prosodic-enhanced representation. A comprehensive empirical evaluation demonstrates that the proposed framework outperforms the state-of-the-art approaches under various metrics in different rap lyrics learning tasks.
Liang, J, Mondal, AK, Wang, DW & Iacopi, F 2019, 'Graphene-Based Planar Microsupercapacitors: Recent Advances and Future Challenges', Advanced Materials Technologies, vol. 4, no. 1.
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© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim The continuous development of integrated electronics such as maintenance-free biosensors, remote and mobile environmental sensors, wearable personal electronics, nanorobotics etc. and their continued miniaturization has led to an increasing demand for miniaturized energy storage units. Microsupercapacitors with graphene electrodes hold great promise as miniaturized, integrated power sources thanks to their fast charge/discharge rates, superior power performance, and long cycling stability. In addition, planar interdigitated electrodes also have the capability to reduce ion diffusion distances leading to a greatly improved electrochemical performance. Either as standalone power sources or complementing energy harvesting units, it is expected that graphene-based microsupercapacitors will play a key role as miniaturized power sources in electronic microsystems. This review highlights the recent development, challenges, and perspectives in this area, with an emphasis on the link between material and geometry design of planar graphene-based electrodes and their electrochemical performance and integrability.
Liang, J, Walker, PD, Ruan, J, Yang, H, Wu, J & Zhang, N 2019, 'Gearshift and brake distribution control for regenerative braking in electric vehicles with dual clutch transmission', Mechanism and Machine Theory, vol. 133, pp. 1-22.
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© 2018 Elsevier Ltd To alleviate the problem of limited driving range per charge in electric vehicles, a dual clutch transmission based regenerative braking power-on shifting control system is proposed and investigated in this paper. Power-on shifting refers to the shift process where the power flow between the wheel and the power source is not cut off and could be maintained around a desirable value. This character is more important for regenerative braking than the normal driving conditions as the regenerative braking force from the motor accounts for a large part of the total braking force. Due to the difference between the normal driving condition and the regenerative braking process, existing normal driving shifting control strategies, which could introduce significant torque interruption, cannot be directly applied for regenerative braking. As a result, the energy recovery capability and efficiency are compromised. To solve this problem, a power-on shifting control strategy for regenerative braking is proposed as well as an energy-safety oriented braking strategy. To demonstrate the effectiveness of the proposed system, mathematical models are built and dynamic responses of the transmission system during braking both in up-shift and down-shift processes are presented. Moreover, the efficiency and recovery capability improvements made by achieving power-on shifting during regenerative braking are verified through a typical deceleration driving cycle and a specially designed daily deceleration scenario.
Liang, T, Chen, L, Wu, J, Xu, G & Wu, Z 2019, 'SMS: A Framework for Service Discovery by Incorporating Social Media Information', IEEE Transactions on Services Computing, pp. 1-1.
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With the explosive growth of services, including Web services, cloud services, APIs and mashups, discovering the appropriate services for consumers is becoming an imperative issue. The traditional service discovery approaches mainly face two challenges: 1) the single source of description documents limits the effectiveness of discovery due to the insufficiency of semantic information; 2) more factors should be considered with the generally increasing functional and nonfunctional requirements of consumers. In this paper, we propose a novel framework, called SMS, for effectively discovering the appropriate services by incorporating social media information. Specifically, we present different methods to measure four social factors (semantic similarity, popularity, activity, decay factor) collected from Twitter. Latent Semantic Indexing (LSI) model is applied to mine semantic information of services from meta-data of Twitter Lists that contains them. In addition, we assume the target query-service matching function as a linear combination of multiple social factors and design a weight learning algorithm to learn an optimal combination of the measured social factors. Comprehensive experiments based on a real-world dataset crawled from Twitter demonstrate the effectiveness of the proposed framework SMS, through some compared approaches.
Lin, C, Sun, S, Walker, P & Zhang, N 2019, 'Accelerated adaptive second order super-twisting sliding mode observer', IEEE Access.
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OAPA An accelerated second order super twisting sliding mode observer with an adaptive gain is proposed for a typical nonlinear system. The key contribution of this algorithm is that the rate of convergence of observation error is accelerated remarkably by introducing "system damping". Chattering issue is attenuated with satisfactory performance compared to conventional sliding mode observer. Furthermore adaptive gain can vary with deviation between the trajectory and sliding mode switching manifold dynamically so that overshoot can be reduced. The novel observer is proven mathematically to be convergent in a finite time. Finally, an example of nonlinear system is given to verify the performance.
Lin, C, Sun, S, Yi, J, Walker, P & Zhang, N 2019, 'Accelerated adaptive super twisting sliding mode observer-based drive shaft torque estimation for electric vehicle with automated manual transmission'.
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Lin, CT, Chiu, C-Y, Singh, AK, King, J-T & Wang, YK 2019, 'A Wireless Multifunctional SSVEP-Based Brain Computer Interface Assistive System', IEEE Transactions on Cognitive and Developmental Systems.
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IEEE Several kinds of brain-computer interface (BCI) systems have been proposed to compensate for the lack of medical technology for assisting patients who lose the ability to use motor functions to communicate with the outside world. However, most of the proposed systems are limited by their non-portability, impracticality and inconvenience because of the adoption of wired or invasive electroencephalography (EEG) acquisition devices. Another common limitation is the shortage of functions provided because of the difficulty of integrating multiple functions into one BCI system. In this study, we propose a wireless, non-invasive and multifunctional assistive system which integrates steady state visually evoked potential (SSVEP)-based BCI and a robotic arm to assist patients to feed themselves. Patients are able to control the robotic arm via the BCI to serve themselves food. Three other functions: video entertainment, video calling, and active interaction are also integrated. This is achieved by designing a functional menu and integrating multiple subsystems. A refinement decision-making mechanism is incorporated to ensure the accuracy and applicability of the system. Fifteen participants were recruited to validate the usability and performance of the system. The averaged accuracy and information transfer rate (ITR) achieved is 90.91% and 24.94 bit per min respectively. The feedback from the participants demonstrates that this assistive system is able to significantly improve the quality of daily life.
Lin, L, Xu, L, Huang, Y, Xiang, Y & He, X 2019, 'On exploiting priority relation graph for reliable multi-path communication in mobile social networks', Information Sciences, vol. 477, pp. 490-507.
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© 2018 Elsevier Inc. A mobile social network (MSN) consists of certain amount of mobile users with social characteristics, and it provides data delivery concerning social relationships between mobile users. In MSN, ordinary people contact each other more frequently if they have more social features in common. In this paper, we apply a new topology structure–priority relation graph (PRG) to evaluate the data delivery routing in MSN on the system-level. By using the natural order of nodes’ representation, the diameter, the regular degree and the multi-path technology, we determine the priority relation graph-based social feature routing (PRG-SFR) algorithm to find disjointed multi-paths in MSN. Here, the multi-path technology can be exploited for ensuring that, between each pair of sender and receiver, the important information can be delivered through a highly reliable path. Then we calculate the tolerant ability of ‘faults’ and estimate the availability of MSN on the theoretical level. Finally, we analyze the efficiency of PRG-SFR algorithm from the numerical standpoint in terms of fault tolerance, forwarding number, transmission time and delivery rate. Moreover, we make comparisons between PRG-SFR algorithm and certain state-of-the-art technologies.
Lin, X, Far, H & Saleh, A 2019, 'Structural Behaviour and Mechanical Properties of Welded Steel I-Girders with Corrugated Webs', International Journal of Steel Structures.
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Steel I girders with corrugated webs are appropriate alternatives for normal flat-web girders in steel structures since they provide lighter and smaller beam features in steel design. Based on the existing literature, the corrugated web beams (CWBs) provide many advantages for structural applications. In this study, a series of numerical analyses have been performed in order to investigate the structural behaviour of steel I girders with corrugated web profile and to compare their mechanical performance with normal welded beams. Theory of Ultimate Limit State design has been adopted in accordance with AS4100 (1998) along with considering geometric and material non-linearity in the numerical analyses in SAP2000. Comparing the results of the numerical investigation, merits of using corrugated welded beams (CWBs) over normal welded beams (WBs) have become apparent. Moreover, investigations regarding force-displacement relationship and buckling analysis of the webs were carried out and presented to further validate the advantages of using corrugated web beams. CWBs have been used in some parts of Australia without detailed information about their mechanical properties. Thus, based on the outcomes of this study, CWB table for dimensions and cross sectional properties has been developed and proposed for practical applications.
Lin, Y, Zheng, Z, Zhang, H, Gao, C & Yang, Y 2019, 'Bayesian query expansion for multi-camera person re-identification', Pattern Recognition Letters.
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© 2018 Elsevier B.V. Person re-identification (re-ID) is challenging because pedestrians may exhibit distinct appearance under different cameras. Given a query image, previous methods usually output the person retrieval results directly, which may perform badly due to the limited information provided by the single query image. To mine more query information, we add an expansion step to post-process the initial ranking list. The intuition is that a true match in the gallery may be difficult to be found by the query alone, but it can be easily retrieved by other true matches in the initial ranking list. In this paper, we propose the Bayesian Query Expansion (BQE) method to generate a new query with information from the initial ranking list. The Bayesian model is used to predict true matches in the gallery. We apply pooling on the features of these “true matches” to get a single vector, i.e., the expanded new query, with which the retrieval process is performed again to obtain the final results. We evaluate BQE with various feature extraction methods and distance metric learning methods on four large-scale re-ID datasets. We observe consistent improvement over all the baselines and report competitive performances compared with the state-of-the-art results.
Lin, Z, Zhou, L & Qiu, X 2019, 'A composite objective measure on subjective evaluation of speech enhancement algorithms', Applied Acoustics, vol. 145, pp. 144-148.
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© 2018 Elsevier Ltd Speech enhancement algorithms is to improve speech quality, naturalness and intelligibility by eliminating the background noise and improving signal to noise ratio. There are several objective measures predicting the quality of noisy speech enhanced by noise suppression algorithms, and different objective measures capture different characteristics of the degraded signal. In this paper, the multiple linear regression analysis is used to obtain a composite measure which has high correlation with subjective tests, and the performance of several speech enhancement algorithms under car noise conditions is compared. The uncertainty of the results of the proposed measures on different speech enhancement algorithms is analyzed, and the reliability of the results is discussed.
Linares-Mustarós, S, Ferrer-Comalat, JC, Corominas-Coll, D & Merigó, JM 2019, 'The ordered weighted average in the theory of expertons', International Journal of Intelligent Systems, vol. 34, no. 3, pp. 345-365.
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© 2018 Wiley Periodicals, Inc. This work presents a data-fusion mathematical object that incorporates the optimism level of a decision-making agent. The new fusion object is constructed by extending the ordered weighted averaging (OWA) operator in the process of creating an experton. The main advantage of this approach is that it can represent the attitudinal character of the decision maker in the construction of the experton. Therefore, this approach represents a new method for addressing multiperson problems by using optimistic and pessimistic perspectives. The work presents different practical examples based on the absolute hierarchical relationships of the “minimum of the bottom end of the intervals,” “minimum of the top end of the intervals,” and “minimum size of the interval.” The work also considers a wide range of particular cases of the OWA-experton, including the minimum experton, the maximum experton, the average experton, and the olympic experton. In addition, the study presents software for the calculation of OWA-expertons. Finally, the paper ends with an application in business decision-making regarding the calculation of expected benefits.
Ling, Y, Wang, K, Li, W, Shi, G & Lu, P 2019, 'Effect of slag on the mechanical properties and bond strength of fly ash-based engineered geopolymer composites', Composites Part B: Engineering, vol. 164, pp. 747-757.
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© 2019 Recently, the concept of engineered cementitious composites (ECCs) has been extended to the creation of engineered geopolymer composites (EGCs). Although showing similar mechanical characteristics (e. g., strain hardening and multiple cracking) to conventional ECC, the strength of existing EGC is generally low, and this sometimes restrains its applications. In the present study, a low-calcium (Class F) fly ash-based, polyvinyl alcohol (PVA) fiber reinforced EGC was developed and further modified by a ground-granulated blast-furnace slag (slag). The slag was used to replace the fly ash at content of 0%, 10%, 20%, and 30% (by weight). The effects of the slag on the mechanical properties (e.g., compressive strength, modulus of elasticity, uniaxial tensile behavior, flexural bending strength, and pullout bond strength) of the EGCs were investigated. The results revealed that all EGCs studied exhibited a strain/deflection hardening behavior under tension/flexure, and all slag replacements for fly ash enhanced strength-related properties but reduced ductility-related properties of the EGCs. The EGC mix with 20% slag replacement for fly ash (FA-20%S) had 102.3 MPa compressive strength, 6.8 MPa tensile strength, and 6.2 MPa bond strength, while the EGC mix with no slag (FA-0%S) had 72.6 MPa compressive strength, 4.7 MPa tensile strength, and 3.5 MPa bond strength at 28 days. These strength enhancements were mainly attributed to the improved density of the EGC matrix and the bond between the matrix and fiber. There are close relationships between the bond strength and other strengths, especially the tensile and flexural strengths, of the EGCs.
Liu, C, Wang, S, Wang, Y, Lei, G, Guo, O & Zhu, J 2019, 'Development of a New Flux Switching Transverse Flux Machine with the Ability of Linear Motion', China Electrotechnical Society Transactions on Electrical Machines and Systems, vol. 2, no. 4, pp. 384-391.
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Liu, J, Wu, C, Li, C, Dong, W, Su, Y, Li, J, Cui, N, Zeng, F, Dai, L, Meng, Q & Pang, J 2019, 'Blast testing of high performance geopolymer composite walls reinforced with steel wire mesh and aluminium foam', Construction and Building Materials, vol. 197, pp. 533-547.
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© 2018 Elsevier Ltd Two blast tests were conducted to study the blast resistance of high performance geopolymer composite walls reinforced with steel wire mesh (SWM) and aluminium foam (AF). Conventional reinforced concrete (CRC) walls were also tested as control specimens. In total seven walls were tested under different blast loading conditions. The first blast test was conducted on one 2260 mm × 2260 mm × 150 mm SWM reinforced, one 2260 mm × 1000 mm × 150 mm SWM reinforced and one 2260 mm × 1000 mm × 150 mm combined SWM and AF reinforced high performance geopolymer composite walls under 50 kg TNT explosives at a standoff distance of 2.3 m. The second blast test was conducted on one 2260 mm × 2260 mm × 150 mm SWM reinforced and one 2260 mm × 2260 mm × 150 mm combined SWM and AF reinforced high performance geopolymer composite walls under 100 kg TNT explosives on the ground at the same standoff distance. Blast tests were also performed on two 2260 mm × 2260 mm × 150 mm CRC walls under such two designed explosions to compare their behaviours with reinforced high performance geopolymer composite walls. LVDT (linear variable differential transformer) devices were used to record the deflection histories and pressure sensors were used to measure the airblast pressure histories. The testing results indicated that the combined SWM and AF reinforced high performance geopolymer composite walls had a better blast resistance than the CRC walls, and the SWM reinforced high performance geopolymer composite wall was superior to both.
Liu, J, Wu, C, Li, J, Fang, J, Su, Y & Shao, R 2019, 'Ceramic balls protected ultra-high performance concrete structure against projectile impact–A numerical study', International Journal of Impact Engineering, vol. 125, pp. 143-162.
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© 2018 Elsevier Ltd Ceramic materials have excellent mechanical properties such as light weight, great hardness and high compressive strength. In this paper, a numerical study is conducted to investigate the response of ceramic balls protected ultra-high performance concrete (UHPC) targets against the high-velocity rigid projectile impact using the coupled smoothed particle hydrodynamics-finite element (SPH-FE) method in LS-DYNA. Based on the validated numerical models, parametric studies are performed to explore the effect of diameter, spatial arrangement and material type of ceramic balls as well as the impact position on the dynamic performance of UHPC targets, and then perforation and ballistic limits of ceramic balls protected UHPC targets are obtained. Compared with other UHPC slabs at the striking velocities from 500 m/s to 850 m/s, UHPC slabs protected with 6-layer hex-pack arranged ceramic balls with the diameter of 20 mm is most effective in terms of reducing the depth of penetration (DOP). In addition, the utilization of ceramic balls is economical in protective structures since the damaged ceramic balls can be replaced and undamaged ceramic balls are reusable.
Liu, T, Sun, G, Fang, J, Zhang, J & Li, Q 2019, 'Topographical design of stiffener layout for plates against blast loading using a modified ant colony optimization algorithm', Structural and Multidisciplinary Optimization, vol. 59, no. 2, pp. 335-350.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The stiffened plates are of demonstrable advantages and potential in offering high resistance to such extreme loading scenarios as blast. Since the distribution of the stiffeners has considerable effect on their performance, its design signifies an important topic of research. However, existing research has mainly focused on empirical design, and the configurations were largely experience based, which limits structural explosion-proof capacity. In order to improve the performance of stiffened plates against blast loading, we introduced here two new structural configurations of stiffened plates. In this study, the modified ant colony optimization (MACO) algorithm which introduces the mass constraint factor to the pheromone update function and integrates the idea of crossover and mutation was used to design the subjected to given working conditions. Specifically, material distribution of stiffeners is taken to be the design variables, and minimization of the maximum deflection of the center point of the plate to be the design objective under predetermined mass constraints. Compared with the baseline structure, the optimal designs largely improved the explosion-proof performance through distributing stiffener topology on the plates. The results showed that the optimum designs all present the reinforcement stiffeners to link with the fixed boundaries against the deformation. Moreover, the optimum designs placed more reinforcement materials in the central regions instead of four angles, and with the increase of the mass fraction, the reinforcement placement gradually extends from the center to the edges. The proposed method and new topological configurations are expected to provide some insights into design for novel protective structures.
Liu, W, Shen, X, Du, B, Tsang, IW, Zhang, W & Lin, X 2019, 'Hyperspectral Imagery Classification via Stochastic HHSVMs.', IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol. 28, no. 2, pp. 577-588.
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Hyperspectral imagery (HSI) has shown promising results in real-world applications. However, the technological evolution of optical sensors poses two main challenges in HSI classification: 1) the spectral band is usually redundant and noisy and 2) HSI with millions of pixels has become increasingly common in real-world applications. Motivated by the recent success of hybrid huberized support vector machines (HHSVMs), which inherit the benefits of both lasso and ridge regression, this paper first investigates the advantages of HHSVM for HSI applications. Unfortunately, the existing HHSVM solvers suffer from prohibitive computational costs on large-scale data sets. To solve this problem, this paper proposes simple and effective stochastic HHSVM algorithms for HSI classification. In the stochastic settings, we show that with a probability of at least , our algorithms find an -accurate solution using iterations. Since the convergence rate of our algorithms does not depend on the size of the training set, our algorithms are suitable for handling large-scale problems. We demonstrate the superiority of our algorithms by conducting experiments on large-scale binary and multiclass classification problems, comparing to the state-of-the-art HHSVM solvers. Finally, we apply our algorithms to real HSI classification and achieve promising results.
Liu, W, Xu, D, Tsang, IW & Zhang, W 2019, 'Metric Learning for Multi-Output Tasks.', IEEE transactions on pattern analysis and machine intelligence, vol. 41, no. 2, pp. 408-422.
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Multi-output learning with the task of simultaneously predicting multiple outputs for an input has increasingly attracted interest from researchers due to its wide application. The k nearest neighbor ([Formula: see text]) algorithm is one of the most popular frameworks for handling multi-output problems. The performance of [Formula: see text] depends crucially on the metric used to compute the distance between different instances. However, our experiment results show that the existing advanced metric learning technique cannot provide an appropriate distance metric for multi-output tasks. This paper systematically studies how to efficiently learn an appropriate distance metric for multi-output problems with provable guarantee. In particular, we present a novel large margin metric learning paradigm for multi-output tasks, which projects both the input and output into the same embedding space and then learns a distance metric to discover output dependency such that instances with very different multiple outputs will be moved far away. Several strategies are then proposed to speed up the training and testing time. Moreover, we study the generalization error bound of our method for three learning tasks, which shows that our method converges to the optimal solutions. Experiments on three multi-output learning tasks (multi-label classification, multi-target regression, and multi-concept retrieval) validate the effectiveness and scalability of the proposed method.
Liu, X, Iftikhar, N, Huo, H, Li, R & Nielsen, PS 2019, 'Two approaches for synthesizing scalable residential energy consumption data', Future Generation Computer Systems, vol. 95, pp. 586-600.
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© 2019 Elsevier B.V. Many fields require scalable and detailed energy consumption data for different study purposes. However, due to privacy issues, it is often difficult to obtain sufficiently large datasets. This paper proposes two different methods for synthesizing fine-grained energy consumption data for residential households, namely a regression-based method and a probability-based method. They each use a supervised machine learning method, which trains models with a relatively small real-world dataset and then generates large-scale time series based on the models. This paper describes the two methods in details, including data generation process, optimization techniques, and parallel data generation. This paper evaluates the performance of the two methods, which compare the resulting consumption profiles with real-world data, including patterns, statistics, and parallel data generation in the cluster. The results demonstrate the effectiveness of the proposed methods and their efficiency in generating large-scale datasets.
Liu, X, Xu, Q, Wang, D, Wu, Y, Yang, Q, Liu, Y, Wang, Q, Li, X, Li, H, Zeng, G & Yang, G 2019, 'Unveiling the mechanisms of how cationic polyacrylamide affects short-chain fatty acids accumulation during long-term anaerobic fermentation of waste activated sludge.', Water research, vol. 155, pp. 142-151.
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Cationic polyacrylamide, a flocculation powder widely used in wastewater pretreatment and sludge dewatering, was highly accumulated in waste activated sludge. However, its effect on short-chain fatty acids (SCFAs) accumulation from anaerobic fermentation of waste activated sludge has not been investigated. This work therefore aims to deeply unveil how cationic polyacrylamide affects SCFAs production, through both long-term and batch tests using either real waste activated sludge or synthetic wastewaters as fermentation substrates. Experimental results showed that the presence of cationic polyacrylamide not only significantly decreased the accumulation of SCFAs but also affected the composition of individual SCFA. The concentration of SCFAs decreased from 3374.7 to 2391.7 mg COD/L with cationic polyacrylamide level increasing from 0 to 12 g/kg of total suspended solids, whereas the corresponding percentage of acetic acid increased from 45.2% to 55.5%. The mechanism studies revealed that although cationic polyacrylamide could be partially degraded to produce SCFAs during anaerobic fermentation, cationic polyacrylamide and its major degradation metabolite, polyacrylic acid, inhibited all the sludge solubilization, hydrolysis, acidogenesis, acetogenesis and homoacetogenesis processes to some extents. As a result, the accumulation of SCFAs in the cationic polyacrylamide added systems decreased rather than increased. However, the inhibition to acetogenesis and homoacetogenesis was slighter than that to acidogenesis, leading to an increase of acetic acid to total SCFAs. It was further found that cationic polyacrylamide had stronger ability to adhere to protein molecules surface, which inhibited the bioconversion of proteins more severely. Illumina MiSeq sequencing analyses showed that cationic polyacrylamide decreased microbial community diversity, altered community structure and changed activities of key enzymes responsible for SCFAs accumulation.
Liu, X, Xu, Q, Wang, D, Yang, Q, Wu, Y, Li, Y, Fu, Q, Yang, F, Liu, Y, Ni, B-J, Wang, Q & Li, X 2019, 'Thermal-alkaline pretreatment of polyacrylamide flocculated waste activated sludge: Process optimization and effects on anaerobic digestion and polyacrylamide degradation.', Bioresource technology, vol. 281, pp. 158-167.
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Deterioration of anaerobic digestion can occur with the presence of polyacrylamide (PAM) in waste activated sludge, and little information on mitigating this deterioration is currently available. In this study, simultaneous mitigation of PAM negative effects and improvement of methane production was accomplished by thermal-alkaline pretreatment. Under the optimized pretreatment conditions (i.e., 75 °C, pH 11.0 for 17.5 h), the biochemical methane potential of PAM-flocculated sludge increased from 100.5 to 210.8 mL/g VS and the hydrolysis rate increased from 0.122 to 0.187 d-1. Mechanism investigations revealed that the pretreatment not only broke the large firm floccules, improved the degradation of PAM, but also facilitated the release of biodegradable organics from sludge, which thereby provided better growth environment and enough nutrients to anaerobic microbes for methane production. The activities of key enzymes responsible for methane production and PAM degradation were greatly improved in pretreated reactor, with the accumulation of acrylamide being avoided.
Liu, X, Xu, Q, Wang, D, Yang, Q, Wu, Y, Yang, J, Liu, Y, Wang, Q, Ni, BJ, Li, X, Li, H & Yang, G 2019, 'Enhanced Short-Chain Fatty Acids from Waste Activated Sludge by Heat-CaO 2 Advanced Thermal Hydrolysis Pretreatment: Parameter Optimization, Mechanisms, and Implications', ACS Sustainable Chemistry and Engineering, vol. 7, no. 3, pp. 3544-3555.
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© 2019 American Chemical Society. In the present work,heat-CaO 2 advanced thermal hydrolysis pretreatment was applied for enhancing fermentative short-chain fatty acids (SCFAs) production from waste activated sludge (WAS). Various pretreatment conditions including heating temperatures, CaO 2 doses, and times were optimized. Simulation and experimental results showed that the optimal pretreatment conditions were a temperature of 67.4 °C, CaO 2 of 0.12 g/g VSS, and time of 19 h, under which the maximum SCFAs yield reached to 336.5 mg COD/g VSS after 5 days of fermentation, with the percentage of acetic acid accounted for 70.1%. Mechanism investigations exhibited that CaO 2 and heat pretreatment caused positive synergy on sludge solubilization and SCFAs production. Compared with the control, heat pretreatment, and CaO 2 addition alone, the heat-CaO 2 pretreatment not only facilitated the organic released from WAS but also increased the proportion of biodegradable organic matters, which thereby providing more organics for subsequent SCFA production. It was found that the heat-CaO 2 pretreatment improved the activities of both hydrolytic and acid-forming enzymes while it inhibited the coenzymes of methanogens during the fermentation process. In addition, heat-CaO 2 pretreatment and subsequent fermentation worked well in removal of refractory organic pollutants and pathogens contained in WAS. Further analysis indicated that the heat-CaO 2 pretreatment can be used as an effective method for both valuable carbon source recovery and refractory pollutant removal in the WAS treatment process.
Liu, XP, Zhang, GQ, Lu, J & Zhang, JQ 2019, 'Risk assessment using transfer learning for grassland fires', Agricultural and Forest Meteorology, vol. 269-270, pp. 102-111.
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© 2019 A new direction of risk assessment research in grassland fire management is data-driven prediction, in which data are collected from particular regions. Since some regions have rich datasets that can easily generate knowledge for risk prediction, and some have no data available, this study addresses how we can leverage the knowledge learned from one grassland risk assessment to assist with a current assessment task. In this paper, we first introduce the transfer learning methodology to map and update risk maps in grassland fire management, and we propose a new grassland fire risk analysis method. In this study, two major grassland areas (Xilingol and Hulunbuir) in northern China are selected as the study areas, and five representative indicators (features) are extracted from grassland fuel, fire climate, accessibility, human and social economy. Taking Xilingol as the source domain (where sufficient labelled data are available) and Hulunbuir as the target domain (which contains insufficient data but requires risk assessment/prediction), we then establish the mapping relationship between grassland fire indicators and the degrees of grassland fire risk by using a transfer learning method. Finally, the fire risk in the Hulunbuir grassland is assessed using the transfer learning method. Experiments show that the prediction accuracy reached 87.5% by using the transfer learning method, representing a significant increase over existing methods.
Liu, Y, Lan, C, Blumenstein, M & Li, J 2019, 'Bi-level error correction for PacBio long reads', IEEE/ACM Transactions on Computational Biology and Bioinformatics.
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IEEE The latest sequencing technologies such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines can generate long reads at the length of thousands of nucleic bases which is much longer than the reads at the length of hundreds generated by Illumina machines. However, these long reads are prone to much higher error rates, for example 15%, making downstream analysis and applications very difficult. Error correction is a process to improve the quality of sequencing data. Hybrid correction strategies have been recently proposed to combine Illumina reads of low error rates to fix sequencing errors in the noisy long reads with good performance. In this paper, we propose a new method named Bicolor, a bi-level framework of hybrid error correction for further improving the quality of PacBio long reads. At the first level, our method uses a de Bruijn graph-based error correction idea to search paths in pairs of solid < formula > < tex > $k$ < /tex > < /formula > -mers iteratively with an increasing length of < formula > < tex > $k$ < /tex > < /formula > -mer. At the second level, we combine the processed results under different parameters from the first level. In particular, a multiple sequence alignment algorithm is used to align those similar long reads, followed by a voting algorithm which determines the final base at each position of the reads. We compare the superior performance of Bicolor with three state-of-the-art methods on three real data sets. Results demonstrate that Bicolor always achieves the highest identity ratio. Bicolor also achieves a higher alignment ratio ( < formula > < tex > $ & #x003E; 1.3\%$ < /tex > < /formula > ) and a higher number of aligned reads than the current methods on two data sets. On the third data set, our method is closely competitive to the current methods in terms of number of aligned reads and genome coverage. The C++ source codes of our algorithm are freely available at https://github.com/yuansliu/Bicolor.
Liu, Y, Ngo, HH, Guo, W, Peng, L, Wang, D & Ni, B 2019, 'The roles of free ammonia (FA) in biological wastewater treatment processes: A review.', Environment international, vol. 123, pp. 10-19.
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Free ammonia (FA) can pose inhibitory and/or biocidal effects on a variety of microorganisms involved in different biological wastewater treatment process, which is widely presented in wastewater treatment plants (WWTPs) due to the high levels of ammonium in the systems. This review article gives the up-to-date status on several essential roles of FA in biological wastewater treatment processes: the impacts of FA, mechanisms of FA roles, modeling of FA impacts, and implications of FA for wastewater treatment. Specifically, the impacts of FA on both wastewater and sludge treatment lines were firstly summarized, including nitrification, denitrification, anaerobic ammonium oxidation (Anammox), enhanced biological phosphorus removal and anaerobic processes. The involved mechanisms were then analyzed, which indicated FA inhibition can slow specific microbial activities or even reconfigure the microbial community structure, likely due to negative impacts of FA on intracellular pH, specific enzymes and extracellular polymeric substances (EPS), thus causing cell inactivation/lysis. Mathematical models describing the impact of FA on both wastewater and sludge treatment processes were also explored to facilitate process optimization. Finally, the key implications of FA were identified, that is FA can be leveraged to substantially enhance the biodegradability of secondary sludge, which would further improve biological nutrient removal and enhance renewable energy production.
Liu, Y, Xu, W, Zhu, J & Blaabjerg, F 2019, 'Sensorless Control of Standalone Brushless Doubly Fed Induction Generator Feeding Unbalanced Loads in a Ship Shaft Power Generation System', IEEE Transactions on Industrial Electronics, vol. 66, no. 1, pp. 739-749.
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© 1982-2012 IEEE. The standalone brushless doubly fed induction generator (BDFIG) has demonstrated excellent energy-saving performance in ship shaft power generation applications. As a standalone system, it exhibits unbalanced terminal voltages and poor performance under unbalanced loads. However, the existing control scheme of grid-connected BDFIGs cannot be directly applied to stabilize the amplitude and frequency of terminal voltage when the rotor speed and electrical load vary. This paper presents a new sensorless control scheme for the standalone BDFIG under unbalanced load conditions in the ship shaft power generation system. A second-order generalized integrator-based quadrature signal generator is introduced to realize the rotor speed observer for the standalone BDFIG feeding unbalanced loads. The compensation method of negative-sequence power winding voltage is proposed to eliminate the negative-sequence component of the unbalanced power winding voltage. Comprehensive experiments are carried out on a prototype BDFIG with and without the compensation of negative-sequence power winding voltage. The good performance of the proposed sensorless control scheme is verified by the experimental test results.
Liu, Z, Yang, X, Yang, Y, Wang, K & Mao, G 2019, 'DATS: Dispersive Stable Task Scheduling in Heterogeneous Fog Networks', IEEE Internet of Things Journal.
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IEEE Fog computing has risen as a promising architecture for future Internet of Things (IoT), 5G and embedded artificial intelligence (AI) applications with stringent service delay requirements along the cloud to things continuum. For a typical fog network consisting of heterogeneous fog nodes (FNs) with different computing resources and communication capabilities, how to effectively schedule complex computation tasks to multiple FNs in the neighborhood to achieve minimal service delay is a fundamental challenge. To tackle this problem, a new concept named processing efficiency (PE) is first defined to incorporate computing resources and communication capacities. Further, to minimize service delay in heterogeneous fog networks, a scalable, stable and decentralized algorithm, namely dispersive stable task scheduling (DATS), is proposed and evaluated, which consists of two key components: (i) a PE-based progressive computing resources competition (PCRC) and (ii) a QoE-oriented synchronized task scheduling (STS). Theoretical proofs and simulation results show that the proposed DATS algorithm can achieve effective tradeoff between computing resources and communication capabilities, thus significantly reducing service delay in heterogeneous fog networks.
Liu, Z, Zhang, H, Cheng, A, Wu, C & Yang, G 2019, 'Seismic Interaction between a Lined Tunnel and a Hill under Plane SV Waves by IBEM', International Journal of Structural Stability and Dynamics, vol. 19, no. 2.
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© 2019 World Scientific Publishing Company. This paper investigates the dynamic interaction between a lined tunnel and a hill under plane SV waves using the indirect boundary element method (IBEM), with the displacement and stress characteristics of the system presented in frequency domain. The IBEM has several unique advantages such as reducing calculation dimension, automatically satisfying the infinite radiation condition, etc. The numerical results indicated that the dynamic response of the tunnel-hill system is strongly dependent on incident wave characteristics, geometrical and material properties of the lined tunnel, as well as the topography of the hill. For a dimension ratio between the hill and tunnel of less than 10.0, the lined tunnel has large amplification or deamplification effect on the dynamic response of the hill. Correspondingly, the hill also greatly amplifies the displacement and stress concentration of the tunnel especially in the lower-frequency range, due to the complicated interference effect among the reflected waves and diffracted waves induced by the tunnel and hill. Also demonstrated is that the displacement and stress amplitude spectrums highly depend on the incident frequency and the space location, and there exist multiple peaks and troughs in the spectrum curve with the peaks usually appearing in the low-frequency range. Thus, for the seismic safety assessment of a hill slope or hill tunnel in practice, the dynamic interaction within the tunnel-hill system should be taken into consideration.
Llanos-Herrera, GR & Merigo, JM 2019, 'Overview of brand personality research with bibliometric indicators', Kybernetes.
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© 2018, Emerald Publishing Limited. Purpose: The purpose of this paper is to present a global view of the research that has been conducted regarding brand personality by using the Core Collection of the Web of Science (WoS) as a reference. The main bibliometric indicators considered are number of articles, number of citations, main authors, principal journals, institutions, countries and keywords. Design/methodology/approach: Through a bibliometric investigation, this paper performs an analysis of investigations of brand personality that have been conducted to date. In particular, the analysis focuses on the papers that have generated the greatest impact in the scientific community, the journals that have given the most attention to this concept and the authors who have most strongly influenced the academic world in this field. The analysis reveals a series of relationships between the bases of knowledge considered for different authors and journals and the structure of those relationships based on the keywords considered in each contribution. Findings: This analysis allows to obtain a general and impartial view of brand personality research, and it reveals the most relevant contributions to the academic world in terms of authors, journals, institutions, countries and keywords. The analysis shows that the concept under study seems to still be in an early stage of development and there may well be an important amount of development ahead. Although there have been important contributions to this field, work is still required to consolidate this knowledge. Research limitations/implications: The information provided pertains to a relatively specific subject but is still general when considered within the context of this topic and thus leaves aside elements that could greatly enrich the analysis. However, this work presents some important guidelines for conducting in-depth academic research and publication. Practical implications: This work identifies the most product...
Lu, P, Liu, T, Ni, BJ, Guo, J, Yuan, Z & Hu, S 2019, 'Growth kinetics of Candidatus ‘Methanoperedens nitroreducens’ enriched in a laboratory reactor', Science of the Total Environment, vol. 659, pp. 442-450.
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© 2018 Recently it has been shown that Candidatus ‘Methanoperedens nitroreducens’, an anaerobic methanotrophic archaea (ANME), can reduce nitrate to nitrite using electrons derived from anaerobic oxidation of methane. In this study, the growth kinetics of ‘M. nitroreducens’ enriched in a laboratory reactor were studied. In the experimental concentration range (up to 16 mg CH 4 L −1 ), anaerobic oxidation of methane by ‘M. nitroreducens’ was found to comply with first order kinetic model with a rate constant of 0.019 ± 0.006 h −1 and a biomass-specific rate constant of 0.04–0.14 L h −1 g −1 VSS. Meanwhile, the nitrate reduction to nitrite was well described by the Monod-type kinetic model with an affinity constant for nitrate of 2.1 ± 0.4 mg N L −1 , which is slightly higher than, but comparable to, that of most known denitrifying bacteria. This is the first time that the growth kinetics of ‘M. nitroreducens’ have been experimentally studied. The applicability of the kinetic model reported herein to this organism or similar organisms in natural or engineering systems requires further investigation.
Lu, Y, Fang, J, Guo, Z & Zhang, J 2019, 'Distributed Transmit Beamforming for UAV to Base Communications', China Communications, vol. 16, no. 1, pp. 15-25.
Lu, Z, Zhao, B, He, L, Zhang, D & Zhang, J 2019, 'Security level classification based on power system partitioning', IET Generation, Transmission and Distribution.
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Lu, ZH, Lun, PY, Li, W, Luo, Z, Li, Y & Liu, P 2019, 'Empirical model of corrosion rate for steel reinforced concrete structures in chloride-laden environments', Advances in Structural Engineering, vol. 22, no. 1, pp. 223-239.
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© The Author(s) 2018. The corrosion rate of reinforcing steel is an important factor to determine the corrosion propagation of reinforced concrete structures in the chloride-laden environments. Since the corrosion rate of reinforcing steel is affected by several coupled parameters, the efficient prediction of which remains challenging. In this study, a total of 156 experimental data on corrosion rate from the literature were collected and compared. Seven empirical models for predicting the corrosion rate were reviewed and investigated using the collected experimental data. Based on the investigations, a new empirical model is proposed for predicting the corrosion rate in corrosion-affected reinforced concrete structures considering parameters including concrete resistivity, temperature, relative humidity, corrosion duration and concrete chloride content. The comparison between the experimental data and those predicted using the new empirical model demonstrates that the new model gives a good prediction of the corrosion rate. Furthermore, the uncertainty and probability characteristics of these empirical models are also investigated. It is found that the probability distributions of the model errors can be described as lognormal, normal, Weibull or Gumbel distributions. As a result, the new empirical model can provide an efficient prediction of the corrosion rate of reinforcing steel, and the model error analysis results can be utilized for reliability-based service life prediction of reinforced concrete structures under chloride-laden environments.
Luo, Y, Wen, Y, Liu, T & Tao, D 2019, 'Transferring Knowledge Fragments for Learning Distance Metric from a Heterogeneous Domain.', IEEE transactions on pattern analysis and machine intelligence, vol. 41, no. 4, pp. 1013-1026.
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The goal of transfer learning is to improve the performance of target learning task by leveraging information (or transferring knowledge) from other related tasks. In this paper, we examine the problem of transfer distance metric learning (DML), which usually aims to mitigate the label information deficiency issue in the target DML. Most of the current Transfer DML (TDML) methods are not applicable to the scenario where data are drawn from heterogeneous domains. Some existing heterogeneous transfer learning (HTL) approaches can learn target distance metric by usually transforming the samples of source and target domain into a common subspace. However, these approaches lack flexibility in real-world applications, and the learned transformations are often restricted to be linear. This motivates us to develop a general flexible heterogeneous TDML (HTDML) framework. In particular, any (linear/nonlinear) DML algorithms can be employed to learn the source metric beforehand. Then the pre-learned source metric is represented as a set of knowledge fragments to help target metric learning. We show how generalization error in the target domain could be reduced using the proposed transfer strategy, and develop novel algorithm to learn either linear or nonlinear target metric. Extensive experiments on various applications demonstrate the effectiveness of the proposed method.
Lyu, B, Hoang, DT & Yang, Z 2019, 'User Cooperation in Wireless-Powered Backscatter Communication Networks', IEEE Wireless Communications Letters, pp. 1-1.
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Lyu, B, Qin, L, Lin, X, Chang, L & Yu, JX 2019, 'Supergraph Search in Graph Databases via Hierarchical Feature-Tree', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 2, pp. 385-400.
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© 1989-2012 IEEE. Supergraph search is a fundamental problem in graph databases that is widely applied in many application scenarios. Given a graph database and a query-graph, supergraph search retrieves all data-graphs contained in the query-graph from the graph database. Most existing solutions for supergraph search follow the pruning-and-verification framework, which prune false answers based on features in the pruning phase and perform subgraph isomorphism testings on the remaining graphs in the verification phase. However, they are not scalable to handle large-sized data-graphs and query-graphs due to three drawbacks. First, they rely on a frequent subgraph mining algorithm to select features which is expensive and cannot generate large features. Second, they require a costly verification phase. Third, they process features in a fixed order without considering their relationships to the query-graph. In this paper, we address the three drawbacks and propose new indexing and query processing algorithms. In indexing, we select features directly from the data-graphs without expensive frequent subgraph mining. The features form a feature-tree that contains all-sized features and both the cost sharing and pruning power of the features are considered. In query processing, we propose a new algorithm, where the order to process features is query-dependent by considering both the cost sharing and the pruning power. We explore two optimization strategies to further improve the algorithm efficiency. The first strategy applies a lightweight graph compression technique and the second strategy optimizes the inclusion of answers. We further introduce how to efficiently maintain the index incrementally when the graph database is updated dynamically. Moreover, we propose an approximation approach to significantly reduce the computational cost for large data-graphs and/or query-graphs while preserving a high result quality. Finally, we conduct extensive performance studies on ...
Ma, C, Tsang, IW, Shen, F & Liu, C 2019, 'Error Correcting Input and Output Hashing.', IEEE transactions on cybernetics, vol. 49, no. 3, pp. 781-791.
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Most learning-based hashing algorithms leverage sample-to-sample similarities, such as neighborhood structure, to generate binary codes, which achieve promising results for image retrieval. This type of methods are referred to as instance-level encoding. However, it is nontrivial to define a scalar to represent sample-to-sample similarity encoding the semantic labels and the data structure. To address this issue, in this paper, we seek to use a class-level encoding method, which encodes the class-to-class relationship, to take the semantic information of classes into consideration. Based on these two encodings, we propose a novel framework, error correcting input and output (EC-IO) coding, which does class-level and instance-level encoding under a unified mapping space. Our proposed model contains two major components, which are distribution preservation and error correction. With these two components, our model maps the input feature of samples and the output code of classes into a unified space to encode the intrinsic structure of data and semantic information of classes simultaneously. Under this framework, we present our hashing model, EC-IO hashing (EC-IOH), by approximating the mapping space with the Hamming space. Extensive experiments are conducted to evaluate the retrieval performance, and EC-IOH exhibits superior and competitive performances comparing with popular supervised and unsupervised hashing methods.
Mahamedi, B, Eskandari, M, Fletcher, JE & Zhu, J 2019, 'Sequence-Based Control Strategy with Current Limiting for the Fault Ride-Through of Inverter-Interfaced Distributed Generators', IEEE Transactions on Sustainable Energy.
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IEEE Three-phase three-leg inverters comprise the majority of inverters that are integrated into power grids. They can develop positive- and negative-sequence components under unbalanced conditions including asymmetrical faults, but not zero sequence. This indicates that a sequence-based control strategy is required for a comprehensive control of inverter currents and voltages, hence positive- and negative-sequence components must be controlled separately with their own dedicated controllers. Given this critical need, a sequence-based control technique for grid-forming and grid-feeding inverter-interfaced distributed generators (IIDG) is proposed. As a necessary part of the fault ride-through (FRT) capability of IIDGs, a current limiter is developed which can limit inverter currents at a pre-defined threshold for both balanced and unbalanced faults/voltage sags. Whilst the current regulators work in the synchronous reference frame with simple PI controllers, the limiter works in natural reference frame and thus treats each phase current separately, which results in current limiting at the threshold for all phases. The advantage of the proposed limiter is that it virtually acts instantaneously such that the inverter current can be limited from the start of the disturbance. The simulation results in MATLAB/SIMULINK reveal promising performance of the proposed control strategy with the proposed current limiting.
Maharaj, S, Lees, T & Lal, S 2019, 'Negative Mental States and Their Association to the Cognitive Function of Nurses', Journal of Psychophysiology.
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© 2018 Hogrefe Publishing. Nurses' inherently stressful occupation leaves them at a higher risk of developing negative mental states (stress, anxiety, and depression). However, research examining the effect of negative mental states on these health professionals' cognitive performance is sparse. Thus, the present study aimed to assess the link between negative mental states and cognitive performance in nurses (n = 53). Negative mental state data was obtained using the Depression Anxiety Stress Scale, brain activity was measured using electroencephalography, and finally, cognitive performance was assessed using the Cognistat and the Mini-Mental State Examination. Significant negative correlations (p <.05) were observed between anxiety and attention, and all three negative mental states and memory performance. Electroencephalographic changes indicated that increases in anxiety were significantly associated (p <.05) with decreases in gamma reactivity at fronto-central sites. The current study suggests that higher levels of negative mental states are associated with domain-specific cognitive impairments, and variations in gamma reactivity; possibly reflecting less optimal cortical functioning.
Maharaj, S, Lees, T & Lal, S 2019, 'Prevalence and Risk Factors of Depression, Anxiety, and Stress in a Cohort of Australian Nurses', International Journal of Environmental Research and Public Health, vol. 16, no. 1, pp. 1-10.
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Nurses remain at the forefront of patient care. However, their heavy workload as a career can leave them overworked and stressed. The demanding nature of the occupation exposes nurses to a higher risk of developing negative mental states such as depression, anxiety, and stress. Hence, the current study aimed to assess the prevalence and risk factors of these mental states in a representative sample of Australian nurses. The Depression Anxiety Stress Scale was administered to 102 nurses. Information about demographic and work characteristics were obtained using lifestyle and in-house designed questionnaires. Prevalence rates of depression, anxiety, and stress were found to be 32.4%, 41.2%, and 41.2% respectively. Binominal logistic regressions for depression and stress were significant (p = 0.007, p = 0.009). Job dissatisfaction significantly predicted a higher risk of nurses developing symptoms of depression and stress respectively (p = 0.009, p = 0.011). Poor mental health among nurses may not only be detrimental to the individual but may also hinder professional performance and in turn, the quality of patient care provided. Further research in the area is required to identify support strategies and interventions that may improve the health and wellbeing of nursing professionals and hence the quality of care delivered.
Mahmudul, HM, Rasul, MG, Akbar, D & Mofijur, M 2019, 'Opportunities for solar assisted biogas plant in subtropical climate in Australia: A review', Energy Procedia, vol. 160, pp. 683-690.
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Makhdoom, I, Abolhasan, M, Abbas, H & Ni, W 2019, 'Blockchain's adoption in IoT: The challenges, and a way forward', Journal of Network and Computer Applications.
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Makhdoom, I, Abolhasan, M, Lipman, J, Liu, RP & Ni, W 2019, 'Anatomy of Threats to The Internet of Things', Communications Surveys and Tutorials, IEEE Communications Society.
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Mandal, R, Roy, PP, Pal, U & Blumenstein, M 2019, 'Bag-of-visual-words for signature-based multi-script document retrieval', Neural Computing and Applications, pp. 1-25.
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© 2018 The Natural Computing Applications Forum An end-to-end architecture for multi-script document retrieval using handwritten signatures is proposed in this paper. The user supplies a query signature sample, and the system exclusively returns a set of documents that contain the query signature. In the first stage, a component-wise classification technique separates the potential signature components from all other components. A bag-of-visual-words powered by SIFT descriptors in a patch-based framework is proposed to compute the features and a support vector machine (SVM)-based classifier was used to separate signatures from the documents. In the second stage, features from the foreground (i.e., signature strokes) and the background spatial information (i.e., background loops, reservoirs etc.) were combined to characterize the signature object to match with the query signature. Finally, three distance measures were used to match a query signature with the signature present in target documents for retrieval. The ‘Tobacco’ (The Legacy Tobacco Document Library (LTDL). University of California, San Francisco, 2007. http://legacy.library.ucsf.edu/) document database and an Indian script database containing 560 documents of Devanagari (Hindi) and Bangla scripts were used for the performance evaluation. The proposed system was also tested on noisy documents, and the promising results were obtained. A comparative study shows that the proposed method outperforms the state-of-the-art approaches.
Mao, M, Lu, J, Han, J & Zhang, G 2019, 'Multiobjective e-commerce recommendations based on hypergraph ranking', Information Sciences, vol. 471, pp. 269-287.
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© 2018 Recommender systems are emerging in e-commerce as important promotion tools to assist customers to discover potentially interesting items. Currently, most of these are single-objective and search for items that fit the overall preference of a particular user. In real applications, such as restaurant recommendations, however, users often have multiple objectives such as group preferences and restaurant ambiance. This paper highlights the need for multi-objective recommendations and provides a solution using hypergraph ranking. A general User–Item–Attribute–Context data model is proposed to summarize different information resources and high-order relationships for the construction of a multipartite hypergraph. This study develops an improved balanced hypergraph ranking method to rank different types of objects in hypergraph data. An overall framework is then proposed as a guideline for the implementation of multi-objective recommender systems. Empirical experiments are conducted with the dataset from a review site Yelp.com, and the outcomes demonstrate that the proposed model performs very well for multi-objective recommendations. The experiments also demonstrate that this framework is still compatible for traditional single-objective recommendations and can improve accuracy significantly. In conclusion, the proposed multi-objective recommendation framework is able to handle complex and changing demands for e-commerce customers.
Mehvar, S, Filatova, T, Sarker, MH, Dastgheib, A & Ranasinghe, R 2019, 'Climate change-driven losses in ecosystem services of coastal wetlands: A case study in the West coast of Bangladesh', Ocean and Coastal Management, vol. 169, pp. 273-283.
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© 2018 The Authors Climate change is globally recognized as one of the key drivers of degradation of coastal wetland ecosystems, causing considerable alteration of services provided by these habitats. Quantifying the physical impacts of climate change on these services is therefore of utmost importance. Yet, practical work in this field is fragmented and scarce in current literature, especially in developing countries which are likely to suffer most from the adverse climate change impacts. Using a coherent scenario-based approach that combines assessment of physical impacts with economic valuation techniques, here we quantify potential climate change driven losses in the value of wetland ecosystems services due to relative sea-level rise (RSLR)-induced inundation in the vulnerable Western coastal area of Bangladesh in 2100. The results show a small inundation area in 2100 under the three IPCC climate scenarios of RCP2.6 (with 0.25 m of RSLR), RCP6.0 (with 1.18 m of RSLR), and RCP8.5 (with 1.77 m of RSLR) for the coastal wetland ecosystems including the Sundarbans mangrove forest, neritic system and aquaculture ponds. In all scenarios, RSLR will drive a loss in the total value of ecosystem services such as provision of raw materials, and food provision, ranging from US$ 0–1 million to US$ 16.5–20 million, respectively. The outcomes of this study reveal that RSLR-induced inundation on its own, is unlikely to be a major threat to the wetland ecosystems in Western coast of Bangladesh. This would suggest that other climate change impacts such as coastal erosion, increase in frequency of cyclone events, and sea temperature rise might be the likely primary drivers of change in the value of wetland ecosystems services in this area.
Meng, Q, Wu, C, Su, Y, Li, J, Liu, J & Pang, J 2019, 'A study of steel wire mesh reinforced high performance geopolymer concrete slabs under blast loading', Journal of Cleaner Production, vol. 210, pp. 1150-1163.
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© 2018 Elsevier Ltd In this study, a novel green construction material, high performance alkali-activated geopolymer concrete is introduced. Both numerically and experimentally investigations were conducted on a new type of structural slabs made of steel wire mesh reinforced geopolymer concrete against close-in ground surface explosion. Steel rebar reinforced conventional concrete slabs are also studied to compare the results. The experimental investigation was conducted to study the slab damage mechanism. It is found that the steel wire mesh reinforced geopolymer concrete slab showed less damage and fragmentation under 50 kg Trinitrotoluene (TNT) blast load within 3 m, 5 m and 7 m distances as compared to the C30 concrete slab. Numerical analysis was then conducted to further investigate the slab dynamic responses. Combining the steel wire mesh reinforcement with geopolymer concrete can help increase the blast resistance capacity leading to promising and environmental friendly structural protective design.
Merigó, JM, Cobo, MJ, Laengle, S, Rivas, D & Herrera-Viedma, E 2019, 'Twenty years of Soft Computing: a bibliometric overview', Soft Computing, vol. 23, no. 5, pp. 1477-1497.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The journal Soft Computing was launched in 1997, and it is dedicated to promote advancements in soft computing theories, which includes fuzzy sets theory, neural networks, evolutionary computation, probabilistic reasoning and hybrid theories. 2017 marks the 20th anniversary of the journal. Motivated by this anniversary, this study presents a bibliometric analysis of the current publications in the journal in order to identify the leading trends ruling the journal. The paper also develops a mapping analysis of the bibliographic material by using the visualization of similarities viewer software. The results show that researchers from all over the world publish regularly in the journal. Soft Computing is growing significantly during the last years, becoming one of the leading journals in the field.
Miao, Z, Yu, J, Ji, J & Zhou, J 2019, 'Multi-objective region reaching control for a swarm of robots', Automatica, vol. 103, pp. 81-87.
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© 2019 Elsevier Ltd This paper is concerned with the multi-objective region reaching control for a swarm of robots which are formulated by Lagrangian dynamics. Two distributed multi-objective region reaching control protocols are proposed for the networked robotic systems under directed acyclic topology, and a unifying methodology is presented to perform the convergence analysis for the robotic systems with static and moving target regions. The control strategy is developed by using the potential energy function approach, and the specified shapes of the various desired regions are constructed by selecting appropriate objective functions. In this control strategy, a network of a large number of robots evolves into multiple groups, and the robots in each group only require communicating with their neighbors. Thus, the proposed control strategy is effective for multi-objective region reaching control for a swarm of robots in practical applications. Finally, simulation examples are given to show the validity of the theoretical results.
Miller, HD, Akbarnezhad, A, Mesgari, S & Foster, SJ 2019, 'Performance of oxygen/argon plasma-treated steel fibres in cement mortar', Cement and Concrete Composites, vol. 97, pp. 24-32.
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© 2018 Elsevier Ltd Steel fibres are used widely to control initiation and growth of cracks in concrete. However, the bond between steel fibres and the cement matrix in steel fibre-reinforced concrete (SFRC) is almost always purely physical. A viable approach to reduce cracking in fibre-reinforced concrete is to supplement the physical bond between fibres and cement matrix with a relatively uniform chemical bond along the fibres’ surface. However, despite the promising results reported for other types of fibres, little attention has been paid to chemical surface treatment of steel fibres to improve their ability to bond with concrete. In this study, an oxygen/argon plasma treatment process is investigated as a potential technique to improve the bond between steel fibres and a cementitious matrix. Several variations in treatment parameters are made in order to identify the optimal treatment conditions. The results of X-ray Photoelectron Spectroscopy and surface energy measurements confirm that plasma treatment can significantly increase the fibres’ surface energy and consequently the strength of the fibre-cement bond. The improved bond between steel fibres and concrete is confirmed by the results of fibre pull-out tests, as well as the reduced average crack size observed in restrained drying shrinkage tests. Moreover, the results indicate considerable decrease in the volume of permeable voids in SFRC due to plasma treatment of steel fibres.
Mishra, AK, Tripathy, AK, Puthal, D & Yang, LT 2019, 'Analytical Model for Sybil Attack Phases in Internet of Things', IEEE Internet of Things Journal.
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IEEE The sybil attack in IoT commonly aims the sensing domain that may impose serious threat to the devices both in perception and communication layer. The singularity of the sybil attack is a sybil node that publish multiple identities of legitimate devices. It is highly essential to learn the behavior and predict possible actions of a sybil attacker while devising a defense mechanism for it. This paper provides a comprehensive characteristic analysis of sybil attack in IoT. Based on the nature of the task performed during this attack, it is classified into 3 phases as compromise, deployment, and launching phase. The compromise phase is modeled as an automaton with attacker state transition as a markov chain model. A heuristic is also proposed for selection criteria of an attacker to compromise a node. In the deployment phase of the attack, an algorithm based on K-mean clustering is proposed to group compromised identities and deploy the sybil node for corresponding identities without violating the set of adjacent nodes. In the launching phase, the process of replacing sybil identities either over time or on detection is modeled using age replacement policy. The results depict that the proposed model effectively visualize the behavior of a sybil attacker in challenging environments of Internet of Things.
Mo, W, Walker, PD & Zhang, N 2019, 'Dynamic analysis and control for an electric vehicle with harpoon-shift synchronizer', Mechanism and Machine Theory, vol. 133, pp. 750-766.
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© 2018 In order to improve shifting quality and the overall efficiency of electric vehicles, this paper presents a multi-gear electric vehicle powertrain system with a Harpoon-shift synchronizer as an alternative to the traditional cone clutch synchronizer. An original dynamic model of the powertrain integrating the model of the Harpoon-shift mechanism is established to investigate the transient responses of driveline system during gear shifting process. In addition, a shifting control method based on a special step function is proposed to improve the driving comfort. Upshift and downshift simulation results validate the effectiveness of the proposed mathematic model and control strategy. Then the torque spring stiffness of Harpoon-shift for each gear is optimized to reduce the shifting jerk. Furthermore, the influences of rotating inertia and speed difference on the shifting jerk of vehicle are quantitatively analyzed. Simulation results demonstrate that the jerk reduces considerably during engagement using the optimized torque springs, which shows the significant advantages of the proposed Harpoon-shift synchronizer compared to traditional synchronizers.
Modak, NM, Merigó, JM, Weber, R, Manzor, F & Ortúzar, JDD 2019, 'Fifty years of Transportation Research journals: A bibliometric overview', Transportation Research Part A: Policy and Practice, vol. 120, pp. 188-223.
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© 2018 Elsevier Ltd Transportation Research (TR) was established in 1967 with the vision of promoting multi-disciplinary (economics, engineering, sociology, psychology) research on transport systems. The journal has continuously expanded its wings becoming a world-leading journal, now publishing research work through six parts, A to F, respectively addressing Policy and Practice, Methodological, Emerging Technologies, Transport and Environment, Logistics and Transportation Review, and Traffic Psychology and Behaviour. This study aims to celebrate the first half century of the journal through a bibliometric study of the publications on all six parts between 1967 and 2016. It uses the most reliable database for academic research, the Web of Science Core Collection, to identify the leading trends in all TR journals in terms of impact, topics, authors, universities, and countries. Moreover, it uses the Visualization of Similarities (VOS) viewer software to analyse bibliographic coupling, co-citation, citation, co-authorship, and co-occurrence of keywords.
Mofijur, M, Rasul, M, Hassan, NMS & Uddin, MN 2019, 'Investigation of exhaust emissions from a stationary diesel engine fuelled with biodiesel', Energy Procedia, vol. 160, pp. 791-797.
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Mofijur, M, Rasul, MG, Hassan, NMS & Nabi, MN 2019, 'Recent Development in the Production of Third Generation Biodiesel from Microalgae', Energy Procedia, vol. 156, pp. 53-58.
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Moghaddam, F, Sirivivatnanon, V & Vessalas, K 2019, 'The effect of fly ash fineness on heat of hydration, microstructure, flow and compressive strength of blended cement pastes', Case Studies in Construction Materials, vol. 10.
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© 2019 In this paper, an experimental study on the effect of fly ash fineness on the heat of hydration, microstructure, flow and compressive strength of blended cement pastes was carried out and evaluated against control cement paste. Fly ashes with different fineness: classified fly ash, run-of-station fly ash and grounded run-of-station fly ash; with a median particle size of 17.4, 11.3 and 5.7 μm, respectively, from the same power station source in Australia were used to partially replace Portland cement at 20% and 40% by weight of cement using a fixed water-to-binder ratio of 0.40. Results of this study showed that the cumulative heat of hydration of blended cement paste decreased as fly ash content in blended cement paste was increased. For a given cement replacement level, blended cement paste containing finer fly ash released more heat of hydration when compared to coarser fly ash. Moreover, increasing the fineness of fly ash resulted in a higher consumption of calcium hydroxide at 7 and 28 days reflecting pozzolanic reactivity and, thus, a denser microstructure than blended pastes containing coarser fly ash as revealed by the X-ray diffraction (XRD), scanning electron microscopy (SEM) and compressive strength results. In addition, the incorporation of fly ash in the blended pastes led to the introduction of an additional hydration peak in the heat evolution curve possibly due to the late activation of fly ash by calcium hydroxide renewing the C3A reaction and converting ettringite to monosulfate. The flow of the freshly blended cement pastes was also found to improve slightly with increasing fineness of the fly ash. In addition, the hardened blended cement pastes containing 20% ground run-of-station fly ash showed comparable compressive strength with the control cement pastes at both 7 and 28 days mainly due to the higher fineness of the ground run-of-station fly ash and increased reactivity compared to coarser grade fly ash.
Mojaddadi Rizeei, H, Pradhan, B & Saharkhiz, MA 2019, 'Urban object extraction using Dempster Shafer feature-based image analysis from worldview-3 satellite imagery', International Journal of Remote Sensing, vol. 40, no. 3, pp. 1092-1119.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. A detailed and up-to-date land use of the urban environment is essentially required in many applications. Very high-resolution (VHR), Multispectral Scanner System (MSS) Worldview-3 (WV-3) satellite imagery provides detailed information on urban characteristics, which should be professionally mined. In this research, WV-3 was processed by machine learning (ML) methods to extract the most accurate urban features. Fuze-Go panchromatic sharpening in conjunction with atmospheric and topographic correction was initially utilized to increase the image quality and colour contrast. Three image analysis approaches including, current pixel-based image analysis (PBIA), object-based image analysis (OBIA) and new feature-based image analysis (FBIA) were implemented on WV-3 image. The k-nearest neighbour (k-NN), Naive Bayes (NB), support vector machine (SVM) classifiers were represented by PBIA, the Decision Tree (DT) classifier was examined as OBIA and the Dempster–Shafer (DS) fusion classifier was manifested for the first time as FBIA. In order to engage DS as FBIA, four types of Belief Masses, namely, Precision, Recall, Overall Accuracy, and kappa coefficient (ĸ) were implemented and compared to assign the most likelihood urban features. All the applied classifiers were also trained on the first site and then tested on another site to examine the transferability. The accuracy, reliability, and computational time of all classifiers were examined by confusion matrix and McNemar assessment. Results show improvements on the detailed urban extraction obtained using the proposed FBIA with 92.2% overall accuracy in compared with PBIA and OBIA. The FBIA result of urban extraction is more consistent when transferred to another study area and consumes much lesser time than OBIA. Also, the precision mass belief measurement achieved highest efficiency regarding receiver operating characteristic (ROC) curve rate.
Moktadir, MA, Ali, SM, Paul, SK & Shukla, N 2019, 'Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh', Computers and Industrial Engineering, vol. 128, pp. 1063-1075.
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Recently, big data (BD) has attracted researchers and practitioners due to its potential usefulness in decision-making processes. Big data analytics (BDA) is becoming increasingly popular among manufacturing companies as it helps gain insights and make decisions based on BD. However, there many barriers to the adoption of BDA in manufacturing supply chains. It is therefore necessary for manufacturing companies to identify and examine the nature of each barrier. Previous studies have mostly built conceptual frameworks for BDA in a given situation and have ignored examining the nature of the barriers to BDA. Due to the significance of both BD and BDA, this research aims to identify and examine the critical barriers to the adoption of BDA in manufacturing supply chains in the context of Bangladesh. This research explores the existing body of knowledge by examining these barriers using a Delphi-based analytic hierarchy process (AHP). Data were obtained from five Bangladeshi manufacturing companies. The findings of this research are as follows: (i) data-related barriers are most important, (ii) technology-related barriers are second, and (iii) the five most important components of these barriers are (a) lack of infrastructure, (b) complexity of data integration, (c) data privacy, (d) lack of availability of BDA tools and (e) high cost of investment. The findings can assist industrial managers to understand the actual nature of the barriers and potential benefits of using BDA and to make policy regarding BDA adoption in manufacturing supply chains. A sensitivity analysis was carried out to justify the robustness of the barrier rankings.
Nanda, A, Nanda, P, He, X, Jamdagni, A & Puthal, D 2019, 'A hybrid encryption technique for Secure-GLOR: The adaptive secure routing protocol for dynamic wireless mesh networks', Future Generation Computer Systems.
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As we progress in into a digital era where most aspects of our life depend upon a network of computers,it is essential to focus on digital security. Each component of a network, be it a physical network, virtualnetwork or social network requires security when transmitting data. Hence the dynamic wireless meshnetwork must also deploy high levels of security as found in current legacy networks. This paper presentsa secure Geo-Location Oriented Routing (Secure-GLOR) protocol for wireless mesh networks, whichincorporates a hybrid encryption scheme for its multilevel security framework. The hybrid encryptiontechnique improves the network’s overall performance compared to the basic encryption by using acombination of symmetric key as well as asymmetric key encryption. Using the combination of the twoencryption schemes, the performance of the network can be improved by reducing the transmitted datasize, reduced computational overhead and faster encryption–decryption cycles. In this paper discussedmultiple encryption schemes for both symmetric and asymmetric encryption, compare their performancein various experimental scenarios. Proposed security scheme achieves better performance based on theresults obtained with most viable options for our network model.
Nathan, KS, Ghosh, S, Siwakoti, YP & Long, T 2019, 'A New DC-DC Converter for Photovoltaic Systems: Coupled-Inductors Combined Cuk-SEPIC Converter', IEEE Transactions on Energy Conversion.
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IEEE An enhanced DC-DC converter is proposed in this paper, based on the combination of the Cuk and SEPIC converters, which is well-suited for solar photovoltaic (PV) applications. The converter uses only one switch (which is ground-referenced, so simple gate drive circuitry may be used), yet provides dual outputs in the form of a bipolar DC bus. The bipolar output from the DC-DC converter is able to send power to the grid via any inverter with a unipolar or bipolar DC input, and leakage currents can be eliminated if the latter type is used without using lossy DC capacitors in the load current loop. The proposed converter uses integrated magnetics cores to couple the input and output inductors, which significantly reduces the input current ripple and hence greatly improves the power extracted from the solar PV system. The design methodology along with simulation, experimental waveforms, and efficiency measurements of a 4 kW DC-DC converter are presented to prove the concept of the proposed converter. Further, a 1 kW inverter is also developed to demonstrate the converter's grid-connection potential.
Nguyen, A, Chan, THT & Zhu, X 2019, 'Special Issue: Real World Application of SHM in Australia', Structural Health Monitoring, vol. 18, no. 1, pp. 3-4.
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Nguyen, AQ, Nguyen, LN, Phan, HV, Galway, B, Bustamante, H & Nghiem, LD 2019, 'Effects of operational disturbance and subsequent recovery process on microbial community during a pilot-scale anaerobic co-digestion', International Biodeterioration and Biodegradation, vol. 138, pp. 70-77.
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© 2019 This study investigated changes in microbial community structure and composition in response to operational disturbance and subsequent process recovery by inoculum addition. Amplicon sequencing of 16S rRNA and mcrA marker genes on the Illumina Miseq platform was used for microbial community analysis. The results show that imbalance among core microbial groups caused volatile fatty acid accumulation and subsequent deteriorated biogas production (decreased by 45% of daily volume) and methane content (<49%). Operational disturbance led to the enrichment of hydrolytic and fermentative bacteria (accounted for >57% of the total abundance) and reduction of acetogenic and methanogenic microbes (they accounted for <9% and <3% of the total abundance, respectively). Acetogens and methanogens were replenished by inoculum addition to recover digester performance. Although digester performances were similar in stable (prior to disturbance) and post recovery phases, the microbial community did not return to the original state, suggesting the existence of functional redundancy in the community.
Nguyen, H, Khabbaz, H & Fatahi, B 2019, 'A numerical comparison of installation sequences of plain concrete rigid inclusions', Computers and Geotechnics, vol. 105, pp. 1-26.
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Soil displacement induced when installing controlled modulus columns (CMC) as ground reinforcement could affect the columns installed close by. Realising numerical analyses may provide useful insights, this paper describes a numerical approach to investigate how groups of CMC installed in different sequences could affect columns installed previously. Coupled consolidation analyses in large strain mode and incorporating soil-CMC interaction were carried out using the three-dimensional finite difference software package FLAC3D. The CMCs were modelled using advanced non-linear Hoek-Brown material with a tensile yield criterion while soils with a typical profile were characterised using the modified Cam Clay and the elastic-perfectly plastic material with a Mohr-Coulomb yield criterion. Where possible, the predicted responses of ground surrounding the CMCs were compared to a number of existing analytical methods. Predictions revealed that lateral soil movement and soil heave near existing CMCs induced by installing new CMCs towards the existing CMCs were approximately 15% and 25% greater than corresponding predictions when a reverse installation sequence was adopted. The maximum excess pore water pressures, induced near existing columns due to installing new columns towards the existing ones, were almost twice more than those caused by the reverse sequence of installation. Moreover, the predicted bending moments generated in the existing columns induced by installing new columns towards the existing CMCs were almost 22% greater than the corresponding values when the reverse installation sequence was adopted. This shows the importance of selecting an appropriate installation sequence in the CMC construction process as well as considering the initial stress field and bending moments in the surrounding soil and CMCs, respectively when designing embankments on improved soft soils.
Nguyen, L, Valls Miro, J & Qiu, X 2019, 'Multilevel B-Splines based Learning Approach for Sound Source Localization', IEEE Sensors Journal.
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Nguyen, LN & Oh, S 2019, 'Impacts of antiseptic cetylpyridinium chloride on microbiome and its removal efficiency in aerobic activated sludge', International Biodeterioration & Biodegradation, vol. 137, pp. 23-29.
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This study evaluated short- and long-term exposure of activated sludge (AS) microbiome to cetylpyridinium chloride (CPC), a quaternary ammonium compound widely used as biocidal additive or cationic surfactant. Toxicity assay in batch mode showed that CPC (50 μg L−1) inhibited cell growth. However, in a continuous reactor, CPC concentration in the range of 50–500 μg L−1 did not result in any observable impact on the biological activities of the AS microbiome. Similarly, 16S rRNA gene-based community profiling revealed that CPC had no observable impact on microbial diversity. At the phylogenetic structure, Rhodobacter (15 ± 7% of the total) and Asticcacaulis (9 ± 3%) were the only two phyla with increasing population in the 500 μg L−1-exposed reactors. This was also supported by an observation of no major change in the community structure. The reactors could remove >60% of CPC at initial concentrations of 50–500 μg L−1, primarily by adsorption and biodegradation. The enrichment of Rhodobacter and Asticcacaulis genus might contribute to CPC biodegradation and emerge as a potential microbial niche for the removal of CPC.
Nguyen, LN, Johir, MAH, Commault, A, Bustamante, H, Aurisch, R, Lowrie, R & Nghiem, LD 2019, 'Impacts of mixing on foaming, methane production, stratification and microbial community in full-scale anaerobic co-digestion process.', Bioresource technology, vol. 281, pp. 226-233.
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This study investigated the impact of mixing on key factors including foaming, substrate stratification, methane production and microbial community in three full scale anaerobic digesters. Digester foaming was observed at one plant that co-digested sewage sludge and food waste, and was operated without mixing. The lack of mixing led to uneven distribution of total chemical oxygen demand (tCOD) and volatile solid (VS) as well as methane production within the digester. 16S rRNA gene-based community analysis clearly differentiated the microbial community from the top and bottom. By contrast, foaming and substrate stratification were not observed at the other two plants with internal circulation mixing. The abundance of methanogens (Methanomicrobia) at the top was about four times higher than at the bottom, correlating to much higher methane production from the top verified by ex-situ biomethane assay, causing foaming. This result is consistent with foaming potential assessment of digestate samples from the digester.
Nguyen, LN, Nghiem, LD, Pramanik, BK & Oh, S 2019, 'Cometabolic biotransformation and impacts of the anti-inflammatory drug diclofenac on activated sludge microbial communities', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 657, pp. 739-745.
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Nguyen, LN, Nghiem, LD, Pramanik, BK & Oh, S 2019, 'Cometabolic biotransformation and impacts of the anti-inflammatory drug diclofenac on activated sludge microbial communities.', The Science of the total environment, vol. 657, pp. 739-745.
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This study evaluated the removal of diclofenac (DCF) in activated sludge and its long-term exposure effects on the function and structure of the microbial community. Activated sludge could remove <50% of 50 μg/L DCF. The removal decreased significantly to below 15% when DCF concentrations increased to 500 and 5000 μg/L. Quantitative assessment of the fate of DCF showed that its main removal routes were biodegradation (21%) and adsorption (7%), with other abiotic removals being insignificant (<5%). The biodegradation occurred through cometabolic mechanisms. DCF exposure in the range of 50-5000 μg/L did not disrupt the major functions of the activated sludge ecosystem (e.g. biomass yield and heterotrophic activity) over two months of DCF exposure. Consistently, 16S rRNA gene-based community analysis revealed that the overall community diversity (e.g. species richness and diversity) and structure of activated sludge underwent no significant alterations. The analysis did uncover a significant increase in several genera, Nitratireductor, Asticcacaulis, and Pseudacidovorax, which gained competitive advantages under DCF exposure. The enrichment of Nitratireductor, Asticcacaulis, and Pseudacidovorax genus might contribute to DCF biodegradation and emerge as a potential microbial niche for the removal of DCF.
Nguyen, LTN, Eager, D & Nguyen, H 2019, 'The relationship between compression garments and electrocardiogram signals during exercise and recovery phase.', Biomedical engineering online, vol. 18, no. 1, p. 27.
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BACKGROUND:The direction of the current research was to investigate whether electrocardiogram (ECG) signals have been impacted by using compression garments during exercise and recovery phase. Each subject is non-athletes, conducted two running tests, wearing either non-compression garments (NCGs) or compression garments (CGs) throughout experiments and 2-h of the recovery phase. Experiment 1 (number of participants (n) = 8; 61.4 ± 13.7 kg, 25.1 ± 3.8 years, 165.9 ± 8.3 cm) focused on the exercising phase while Experiment 2 (n = 14; 60.9 ± 12.0 kg, 24.7 ± 4.5 years, 166.0 ± 7.6 cm) concentrated on the recovery phase. Electrocardiogram (ECG) data were collected through wearable biosensors. RESULTS:The results demonstrated a significant difference between compression garments and non-compression garments at the end of the tests and from 90 min onwards during the recovery phase (p < 0.05). Corrected QT (QTc), ST interval and heart rate (HR) indicated the significant difference between NCGs and CGs. CONCLUSION:Based on the findings, the utilization of compression garments showed a positive influence in non-athletes based on the quicker recovery in HR, ST, and QTc.
Nguyen, MN, Nguyen, LD, Duong, TQ & Tuan, HD 2019, 'Real-time optimal resource allocation for embedded UAV communication systems', IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 225-238.
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© 2012 IEEE. We consider device-to-device (D2D) wireless information and power transfer systems using an unmanned aerial vehicle (UAV) as a relay-assisted node. As the energy capacity and flight time of UAVs is limited, a significant issue in deploying UAV is to manage energy consumption in real-time application, which is proportional to the UAV transmit power. To tackle this important issue, we develop a real-time resource allocation algorithm for maximizing the energy efficiency by jointly optimizing the energy-harvesting time and power control for the considered (D2D) communication embedded with UAV. We demonstrate the effectiveness of the proposed algorithms as running time for solving them can be conducted in milliseconds.
Nguyen, TT, Ngo, HH, Guo, W, Wang, XC, Ren, N, Li, G, Ding, J & Liang, H 2019, 'Implementation of a specific urban water management - Sponge City.', The Science of the total environment, vol. 652, pp. 147-162.
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Climate change, rapid urbanization and inappropriate urban planning policies in many countries have resulted in urban water-related problems, such as flooding disasters, water pollution and water shortages. To tackle these issues, the specific urban water management strategy known as Sponge City has been implemented in China since 2013. This is a complex method and one involving many challenges. This paper critically assesses the approaches associated with conventional urban water management. The Sponge City concept and its adoption are then scrutinized to comprehensively assess the limitations and opportunities. It emerges that Sponge City has four main principles, these being: urban water resourcing, ecological water management, green infrastructures, and urban permeable pavement. The uncertainties in Sponge City design and planning, and financial insufficiencies are the most serious problems that can risk the failure of the Sponge City concept. While significant barriers exist, the opportunities for implementing a Sponge City are evident. To obtain multi-ecosystem services of Sponge City, it should be implemented at the watershed scales and be flexible, depending on different decision levels or catchment characteristics. It is essential to apply an intelligent decision-making mechanism and consider the need for close cooperation between various agencies with which the central government can work. A suitable sized and harmonious Sponge City, ensuring a good balance between socio-economic development and environmental conservation, is the ideal.
Ni, B-J, Huang, Q-S, Wang, C, Ni, T-Y, Sun, J & Wei, W 2019, 'Competitive adsorption of heavy metals in aqueous solution onto biochar derived from anaerobically digested sludge.', Chemosphere, vol. 219, pp. 351-357.
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Heavy metals often coexist in contaminated wastewater systems and their competitive behavior could affect the adsorption capacity of biochar. Till now, the competitive adsorption of heavy metals by biochar derived from anaerobically digested sludge has never been reported. In this work, biochar from anaerobically digested sludge was synthesized and characterized to explore the competitive behavior of widely co-existed Pb(II) and Cd(II). The mutual effects and inner mechanisms of their adsorption on studied biochar were systematically investigated via single-metal and binary-metals systems. In single-metal system, the biochar exhibited much higher adsorption capacity for Pb(II) compared to that for Cd(II). The maximum adsorption capacities of Pb(II) and Cd(II) based on single-component adsorption isotherm were 0.75 and 0.55 mmoL/g, respectively, which were much higher than those reported biochars from different materials. In binary-metals system, the Cd(II) adsorption on biochar was severely inhibited, while the uptake of Pb(II) was not affected significantly. The results of binary-components adsorption isotherm clearly demonstrated the competitive adsorption between two metals occurred as well as the preference of biochar for Pb(II) compared to Cd(II). FTIR and metal characteristics analysis results revealed that Pb(II) had exactly the same adsorption sites with Cd(II), but Pb(II) has a greater affinity than Cd(II), thereby exhibiting a competitive advantage in the coexisting system.
Pettit, T, Irga, PJ & Torpy, FR 2019, 'The in situ pilot-scale phytoremediation of airborne VOCs and particulate matter with an active green wall', Air Quality, Atmosphere and Health, pp. 33-44.
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© 2019, Springer Nature B.V. Atmospheric pollutant phytoremediation technologies, such as potted plants and green walls, have been thoroughly tested in lab-scale experiments for their potential to remove air pollutants. The functional value of these technologies, however, is yet to be adequately assessed in situ, in ‘high value’ environments, where pollutant removal will provide the greatest occupant health benefits. Air pollution in countries such as China is a significant public health issue, and efficient air pollution control technologies are needed. This work used pilot-scale trials to test the capacity of potted plants, a passive green wall and an active green wall (AGW) to remove particulate matter (PM) and total volatile organic compounds (TVOCs) from a room in a suburban residential house in Sydney, Australia, followed by an assessment of the AGW’s potential to remove these pollutants from a classroom in Beijing. In the residential room, compared to potted plants and the passive green wall, the AGW maintained TVOCs at significantly lower concentrations throughout the experimental period (average TVOC concentration 72.5% lower than the control), with a similar trend observed for PM. In the classroom, the AGW reduced the average TVOC concentration by ~ 28% over a 20-min testing period compared to levels with no green wall and a filtered HVAC system in operation. The average ambient PM concentration in the classroom with the HVAC system operating was 101.18 μg/m3, which was reduced by 42.6% by the AGW. With further empirical validation, AGWs may be implemented to efficiently clean indoor air through functional reductions in PM and TVOC concentrations.
Pou, J, Perez, MA & Aguilera, RP 2019, 'Modular multilevel converters', IEEE Transactions on Industrial Electronics, vol. 66, no. 3, pp. 2204-2206.
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Pu, L, Chen, X, Mao, G, Xie, Q & Xu, J 2019, 'Chimera: An Energy-efficient and Deadline-aware Hybrid Edge Computing Framework for Vehicular Crowdsensing Applications', IEEE Internet of Things Journal.
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IEEE In this paper, we propose Chimera, a novel hybrid edge computing framework, integrated with the emerging edge cloud radio access network, to augment network-wide vehicle resources for future large-scale vehicular crowdsensing applications, by leveraging a multitude of cooperative vehicles and the virtual machine (VM) pool in the edge cloud via the control of the application manager deployed in the edge cloud. We present a comprehensive framework model and formulate a novel multi-vehicle and multi-task offloading problem, aiming at minimizing the energy consumption of network-wide recruited vehicles serving heterogeneous crowdsensing applications, and meanwhile reconciling both application deadline and vehicle incentive. We invoke Lyapunov optimization framework to design TaskSche, an online task scheduling algorithm, which only utilizes the current system information. As the core components of the algorithm, we propose a task workload assignment policy based on graph transformation and a knapsack-based VM pool resource allocation policy. Rigorous theoretical analyses and extensive trace-driven simulations indicate that our framework achieves superior performance (e.g., 20% – 68% energy saving without overstepping application deadlines for network-wide vehicles compared with vehicle local processing) and scales well for a large number of vehicles and applications.
Puthal, D 2019, 'Lattice-modelled Information Flow Control of Big Sensing Data Streams for Smart Health Application', IEEE Internet of Things Journal.
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IEEE Internet of Things (IoT) provides a promising opportunity to build powerful data analytics systems with real time event detection for smart health, and therefore wearable IoT has become a rising source of big data streams for smart health, for which security needs to be assured by detecting real-time event to avoid malicious activities, and meanwhile to control the information leakage of big sensing data streams. I refer to this as an information flow control problem. To address this problem, this paper proposes a static lattice model for information flow control over big sensing data streams. I initialize two static lattices i.e. sensor lattice for wearable sensors and user lattice for users, and then static lattices aim to process the flow control model faster, because I am dealing with high volume and velocity of data streams. The experimental evaluation and results of the information flow model show that it can excellently handle the incoming big data streams with low latency and buffer requirement.
Puthal, D & Mohanty, SP 2019, 'Proof of Authentication: IoT-Friendly Blockchains', IEEE Potentials, vol. 38, no. 1, pp. 26-29.
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© 2018 IEEE. This article introduces the concept of proof of authentication (PoAh) for the lightweight implementation of blockchains in the Internet of Things (IoT). The PoAh can replace existing consensus algorithms, such as proof of work (PoW), proof of stake (PoS), and proof of activity (PoA), for resource- and energyconstrained infrastructures, such as the IoT.
Puthal, D, Ranjan, R, Nanda, A, Nanda, P, Jayaraman, PP & Zomaya, AY 2019, 'Secure authentication and load balancing of distributed edge datacenters', Journal of Parallel and Distributed Computing, vol. 124, pp. 60-69.
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© 2018 Edge computing is an emerging research area to incorporate cloud computing into edge network devices. An Edge datacenter, also referred to as EDC, processes data streams and user requests in real-time and is therefore used to decrease the latency and congestion in the network. EDC is usually setup as a distributed system and is accordingly placed between the cloud datacenter and the data source. These EDCs work as an intermediate layer in the fog hierarchy between IoT and Cloud datacenter. EDC's are aided by load balancers, responsible for distributing the workload amongst multiple EDC, in order to optimize resource utilization and response time. The load balancers make sure that the workload is equally divided amongst the available EDCs to avoid over loading of some EDCs while other remain idle as this directly impacts the user response and real-time event detection. Given the fact that EDCs are deployed in remote environments, the need for secure authentication is of major importance. In this paper we propose a novel load balancing technique that enables EDC authentication as well as identification of idle EDCs for better load balancing. The proposed load balancing technique is also compared with existing approaches and proves to be more efficient in locating EDC's with less workload. In addition to the improved efficiency, the proposed scheme also strengthens the security of the network by incorporating destination EDC authentication.
Putra, N, Rawi, S, Amin, M, Kusrini, E, Kosasih, EA & Indra Mahlia, TM 2019, 'Preparation of beeswax/multi-walled carbon nanotubes as novel shape-stable nanocomposite phase-change material for thermal energy storage', Journal of Energy Storage, vol. 21, pp. 32-39.
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© 2018 Elsevier Ltd Development of phase-change material (PCM) as thermal energy storage for building envelopes is promising for energy utilization. However, there are two major drawbacks of PCM application, which are low thermal conductivity and high-volume reduction due to phase-change transition. One solution is to develop a shape-stabilized phase-change material (SSPCM) as a composite that is able to prevent leakage during the transition from solid to liquid. Therefore, the objective of this study is to prepare beeswax/multi-walled carbon nanotubes as form-stable nanocomposite phase-change material for thermal energy storage, based on previously unattempted methods. Beeswax was being used as PCM because of its high latent heat and multi-walled carbon nanotubes (MWCNTs) as supporting material with high thermal conductivity. There are three types of MWCNTs applied in this research: pristine MWCNTs, ball-milled MWCNTs and acid-treated MWCNTs. Beeswax/CNT composite samples were prepared with ratios of 5 and 20 wt%. Composite samples were tested from structure modification and thermal performance, including latent heat, sensible heat, melting point, solidifying point, thermal conductivity, and thermal-cycle testing for up to 300 cycles. Experimental results showed that thermal conductivity of novel shape-stable nanocomposite PCM increased by a factor of 2 and there was no significant phase transition in the melting or solidifying temperature. The high heat storage capability and thermal conductivity of nanocomposite PCM enable it to be a potential material for thermal energy storage in practical applications.
Qiao, M, Yu, J, Bian, W, Li, Q & Tao, D 2019, 'Adapting Stochastic Block Models to Power-Law Degree Distributions.', IEEE transactions on cybernetics, vol. 49, no. 2, pp. 626-637.
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Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new variant of SBM, termed power-law degree SBM (PLD-SBM), by introducing degree decay variables to explicitly encode the varying degree distribution over all nodes. With an exponential prior, it is proved that PLD-SBM approximately preserves the scale-free feature in real networks. In addition, from the inference of variational E-Step, PLD-SBM is indeed to correct the bias inherited in SBM with the introduced degree decay factors. Furthermore, experiments conducted on both synthetic networks and two real-world datasets including Adolescent Health Data and the political blogs network verify the effectiveness of the proposed model in terms of cluster prediction accuracies.
Qiu, W, Sun, J, Fang, M, Luo, S, Tian, Y, Dong, P, Xu, B & Zheng, C 2019, 'Occurrence of antibiotics in the main rivers of Shenzhen, China: Association with antibiotic resistance genes and microbial community.', The Science of the total environment, vol. 653, pp. 334-341.
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The occurrence and distribution of antibiotics were investigated in surface water and sediment collected from the main rivers of Shenzhen, China. Total concentrations of 20 selected antibiotics ranged from 36.510 to 1075.687 ng L-1 (mean 244.992 ng L-1) in 31 water samples and from 28.124 to 2728.810 ng g-1 (mean 680.169 ng g-1) in 31 sediment samples. Notably, STZ and SDZ were the dominant antibiotics in both water and sediment as their higher concentrations compared with the other compounds. Furthermore, comprehensive profiling of antibiotic resistance genes (ARGs) and microbial community was performed to gain an understanding of the evolution and dissemination of ARGs in microbial communities caused by the occurrence of antibiotics in sediment samples from Maozhou River. As a result, the sul1 gene was found to be the most abundant ARG and Proteobacteria was the most abundant microorganism in all the samples (37.4-51.7%), followed by Bacteroidetes (15.3-18.4%). Statistical analysis figured out the relations among antibiotics, ARGs and microbial community. A specific conclusion could be drawn from the positive correlations among the bla_d gene, Fusobacteria, and sulfamethoxazole. It suggests that antibiotics may be positively linked to the expression of ARGs in certain bacteria, and thus high reproduction would occur within the bacterial community. Overall, the widespread distribution of ARGs underscores the need for further research on the mechanism of antibiotics influence as emerging contaminants in the environment and the associated risks to microbial community.
Ramadan-Jradi, R & Paryani, S 2019, 'The Impact of Flipped Learning on Student Performance and Engagement: A Systematic Literature Review', International Journal for Learning and Teaching, vol. 5, no. 1.
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Rao, P, Zhao, L, Chen, Q & Nimbalkar, S 2019, 'Three-dimensional slope stability analysis incorporating coupled effects of pile reinforcement and reservoir drawdown', International Journal of Geomechanics, vol. 19, no. 4.
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© 2019 American Society of Civil Engineers. In pile-reinforced dams and bank slopes, the antislide effect of piles and drawdown of reservoirs are two aspects that could significantly affect the slope stability. However, existing studies have incorporated these two factors separately, albeit not in tandem. Moreover, stability assessment of these earth structures is usually performed ignoring the three-dimensional (3D) effect. To address these issues, the kinematic approach of limit analysis is adopted in this technical note for evaluating slope stability based on the 3D rotational failure mechanism. In addition, the coupled effects of pile reinforcement and water drawdown are considered. The analysis is performed for four types of drawdown cases. The results demonstrate that the optimal pile location undergoes significant change during the external drawdown process, while the effect of the declining water level on slope stability follows the similar pattern for varying pile locations.
Rasouli, H & Fatahi, B 2019, 'A novel cushioned piled raft foundation to protect buildings subjected to normal fault rupture', Computers and Geotechnics, vol. 106, pp. 228-248.
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© 2018 Elsevier Ltd Recent earthquake events have shown that besides the earthquake forces, interaction between the fault rupture and structure could cause a lot of damage to the surface and underground structures. Field observations have revealed a need to design structures for fault induced loading in regions with active faults. In this present study, three-dimensional numerical modelling using ABAQUS finite element software is used to study the interactive mechanism of normal fault rupture with a 20-story moment-resisting building frame sitting on a raft, connected piled raft, and cushioned piled raft foundations. The performance of a foundation-structure system is examined by considering geotechnical and structural performance objectives such as structural inter-story drift, raft displacement, and the bending moment and shear forces within the raft and piles. In order to improve the geotechnical and structural performance of foundations and buildings, a new foundation system with cushioned piles below the raft is proposed because of its superior performance with regards to raft rocking and permanent structural inter-story drifts under normal fault rupture. This proposed foundation system also curtailed the bending moments induced in the piles.
Rehman, J, Sohaib, O, Asif, M & Pradhan, B 2019, 'Applying systems thinking to flood disaster management for a sustainable development', International Journal of Disaster Risk Reduction, pp. 101101-101101.
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Ren, C, Lyu, X, Ni, W, Tian, H & Liu, RP 2019, 'Distributed Online Learning of Fog Computing under Nonuniform Device Cardinality', IEEE Internet of Things Journal, vol. 6, no. 1, pp. 1147-1159.
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© 2014 IEEE. Processing data around the point of capture, fog computing can support computationally demanding Internet-of-Things (IoT) services. Distributed online optimization is important given the size of IoT, but challenging due to time variations of random traffic and nonuniform connectivity (or cardinality) of edge servers and IoT devices. This paper presents a distributed online learning approach to asymptotically minimizing the time-average cost of fog computing in the absence of the a-priori knowledge on traffic randomness, for light-weight, and delay-tolerant application scenarios. Stochastic gradient descent is exploited to decouple the optimizations between time slots. A graph matching problem is then formulated for every time slot by decoupling and unifying the nonuniform cardinalities, and solved in a distributed manner by developing a new linear (1/2)-approximation method. We prove that the optimality loss resulting from the distributed approximate graph matching method can be compensated and diminish by increasing the learning time. Corroborated by simulations, the proposed distributed online learning is asymptotically optimal and superior to the state of the art in terms of throughput and energy efficiency.
Richards, D, Ranjbartabar, H, Bilgin, A & Kutay, C 2019, 'First Impressions Count! The Role of the Human’s Emotional State on Rapport Established with an Empathic versus Neutral Virtual Therapist', IEEE Transactions on Affective Computing, pp. 1-3.
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Intelligent virtual agents are being endowed with empathic behaviours to perform roles such as virtual therapists. Studies often evaluate the level of rapport established, but do not measure the therapeutic benefit and the relative advantage of empathic versus neutral behaviours. We have created two virtual (empathic/neutral) therapists. Our experiment with 63 participants consisted of one within-subjects (empathic/neutral) and one between-subjects (order) factors. Regardless of the virtual therapist used, improvements in baseline emotion were reported after the first interaction (time one) and further improvement after the second interaction (time two). Our study reveals that if the human initially expresses strong emotional feeling for a problem they are facing, rapport will be higher for the empathic therapist and the level of rapport established at the first meeting will persist regardless of whether the second encounter used empathic or neutral dialogue. Conversely, participants experiencing low emotional feeling reported greater rapport with the neutral therapist, and that level of rapport persisted in the second encounter with the alternative therapist. This study shows that an empathic agent will not necessarily build more rapport or deliver better emotional outcomes than a neutral agent. Further studies are needed to determine when tailoring and complex behaviours are justified.
Roy, S 2019, 'Fundamental noisy multiparameter quantum bounds.', Scientific reports, vol. 9, no. 1, p. 1038.
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Quantum multiparameter estimation involves estimating multiple parameters simultaneously and can be more precise than estimating them individually. Our interest here is to determine fundamental quantum limits to the achievable multiparameter estimation precision in the presence of noise. We first present a lower bound to the estimation error covariance for a noisy initial probe state evolving through a noiseless quantum channel. We then present a lower bound to the estimation error covariance in the most general form for a noisy initial probe state evolving through a noisy quantum channel. We show conditions and accordingly measurements to attain these estimation precision limits for noisy systems. We see that the Heisenberg precision scaling of 1/N can be achieved with a probe comprising N particles even in the presence of noise. In fact, some noise in the initial probe state or the quantum channel can serve as a feature rather than a bug, since the estimation precision scaling achievable in the presence of noise in the initial state or the channel in some situations is impossible in the absence of noise in the initial state or the channel. However, a lot of noise harms the quantum advantage achievable with N parallel resources, and allows for a best precision scaling of [Formula: see text]. Moreover, the Heisenberg precision limit can be beaten with noise in the channel, and we present a super-Heisenberg precision limit with scaling of 1/N2 for optimal amount of noise in the channel, characterized by one-particle evolution operators. Furthermore, using γ-particle evolution operators for the noisy channel, where γ > 1, the best precision scaling attainable is 1/N2γ, which is otherwise known to be only possible using 2γ-particle evolution operators for a noiseless channel.
Ryu, S, Naidu, G, Hasan Johir, MA, Choi, Y, Jeong, S & Vigneswaran, S 2019, 'Acid mine drainage treatment by integrated submerged membrane distillation-sorption system.', Chemosphere, vol. 218, pp. 955-965.
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Acid mine drainage (AMD), an acidic effluent characterized by high concentrations of sulfate and heavy metals, is an environmental and economic concern. The performance of an integrated submerged direct contact membrane distillation (DCMD) - zeolite sorption system for AMD treatment was evaluated. The results showed that modified (heat treated) zeolite achieved 26-30% higher removal of heavy metals compared to natural untreated zeolite. Heavy metal sorption by heat treated zeolite followed the order of Fe > Al > Zn > Cu > Ni and the data fitted well to Langmuir and pseudo second order kinetics model. Slight pH adjustment from 2 to 4 significantly increased Fe and Al removal rate (close to 100%) due to a combination of sorption and partial precipitation. An integrated system of submerged DCMD with zeolite for AMD treatment enabled to achieve 50% water recovery in 30 h. The integrated system provided a favourable condition for zeolite to be used in powder form with full contact time. Likewise, heavy metal removal from AMD by zeolite, specifically Fe and Al, mitigated membrane fouling on the surface of the hollow fiber submerged membrane. The integrated system produced high quality fresh water while concentrating sulfuric acid and valuable heavy metals (Cu, Zn and Ni).
Sánchez-Orgaz, EM, Denia, FD, Baeza, L & Kirby, R 2019, 'Numerical mode matching for sound propagation in silencers with granular material', Journal of Computational and Applied Mathematics, vol. 350, pp. 233-246.
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© 2018 Elsevier B.V. This work presents an efficient numerical approach based on the combination of the mode matching technique and the finite element method (FEM) to model the sound propagation in silencers containing granular material and to evaluate their acoustic performance through the computation of transmission loss (TL). The methodology takes into account the presence of three-dimensional (3D) waves and the corresponding higher order modes, while reducing the computational expenditure of a full 3D FEM calculation. First, the wavenumbers and transversal pressure modes associated with the silencer cross section are obtained by means of a two-dimensional FEM eigenvalue problem, which allows the consideration of arbitrary transversal geometries and material heterogeneities. The numerical approach considers the possibility of using different filling levels of granular material, giving rise to cross sections with abrupt changes of properties located not only in the usual central perforated passage, but also in the transition between air and material, that involves a significant change in porosity. After solving the eigenvalue problem, the acoustic fields (acoustic pressure and axial velocity) are coupled at geometric discontinuities between ducts through the compatibility conditions to obtain the complete solution of the wave equation and the acoustic performance (TL). The granular material is analysed as a potential alternative to the traditional dissipative silencers incorporating fibrous absorbent materials. Sound propagation in granular materials can be modelled through acoustic equivalent properties, such as complex and frequency dependent density and speed of sound. TL results computed by means of the numerical approach proposed here show good agreement with full 3D FEM calculations and experimental measurements. As expected, the numerical mode matching outperforms the computational expenditure of the full 3D FEM approach. Different configurations have be...
Sarikaya, A & Erkmen, R 2019, 'A plastic-damage model for concrete under compression', International Journal of Mechanical Sciences, vol. 150, pp. 584-593.
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A phenomenological model for plain concrete under compression is formulated within the framework of the coupled elastoplastic-damage theory. Phenomenological elastoplastic-damage models have been widely used for concrete because of their capability of representing both the permanent inelastic deformations and the degra- dation of material moduli beyond the elastic range. The essential contribution introduced in this paper is the proposed partitioning of the strain tensor within the coupled elastoplastic-damage framework which simplifies the selection of the failure surface and the potential function. Proposed partitioning permits the use of single failure criterion and single potential surface that are effective for both damage and plasticity models during inelastic deformations. Therefore, the coupled elastoplastic-damage model can be easily calibrated to fit the ob- served concrete behaviour based on well-established non-associated plasticity rules for concrete. The proposed approach also simplifies the numerical procedure by eliminating iterations that is required to equilibrate the stresses in plastic and damage components of the model. The numerical implementation is explained, and the results predicted by the model are compared with experimental data provided in the literature.
Sarker, PC, Islam, MR, Guo, Y, Zhu, J & Lu, HY 2019, 'State-of-The-Art Technologies for Development of High Frequency Transformers with Advanced Magnetic Materials', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2002-2011 IEEE. With the development of advanced soft magnetic materials of high-saturation flux density and low specific core loss and semiconductor power devices, the high-frequency transformer (HFT) has received significant attention in recent years for its widespread emerging applications. The optimal design of high-power-density HFTs for high-performance energy conversion systems is, however, a multiphasic problem that needs special considerations on various aspects such as core material selection, minimization of parasitic components, and thermal management. This paper presents a comprehensive review on advancement of soft magnetic materials for high-power-density magnetic devices and advanced technologies for characterizations and optimal design of HFTs. The future research and development trends are also discussed.
Sattarian, M, Rezazadeh, J, Farahbakhsh, R & Bagheri, A 2019, 'Indoor navigation systems based on data mining techniques in internet of things: a survey', Wireless Networks, vol. 25, no. 3, pp. 1385-1402.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Internet of Things (IoT) is turning into an essential part of daily life, and numerous IoT-based scenarios will be seen in future of modern cities ranging from small indoor situations to huge outdoor environments. In this era, navigation continues to be a crucial element in both outdoor and indoor environments, and many solutions have been provided in both cases. On the other side, recent smart objects have produced a substantial amount of various data which demands sophisticated data mining solutions to cope with them. This paper presents a detailed review of previous studies on using data mining techniques in indoor navigation systems for the loT scenarios. We aim to understand what type of navigation problems exist in different IoT scenarios with a focus on indoor environments and later on we investigate how data mining solutions can provide solutions on those challenges.
Seifollahi, S, Bagirov, A, Zare Borzeshi, E & Piccardi, M 2019, 'A simulated annealing‐based maximum‐margin clustering algorithm', Computational Intelligence, vol. 2018, pp. 1-19.
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Maximum‐margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing‐based algorithm that is able to mitigate the issue of local minima in the maximum‐margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k‐means++ and SVM at each step of the annealing process. More precisely, k‐means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms.
Shakibayifar, M, Hassannayebi, E, Mirzahossein, H, Taghikhah, F & Jafarpur, A 2019, 'An intelligent simulation platform for train traffic control under disturbance', International Journal of Modelling and Simulation.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. Railway disturbance management is inherently a multi-objective optimization problem that concerns both the operators’ cost and passenger’s service level. This study proposes a multi-objective simulation-based optimization framework to effectively manage the train conflicts after the occurrences of a disturbance caused by a temporary line blockage. The simulation model enhanced with a dynamic priority dispatching rule in order to speed up the optimization procedure. A multi-objective variable neighborhood search meta-heuristic is proposed to solve the train rescheduling model. The obtained Pareto optimal solutions for disturbance management model support the decision maker to find a trade-off between both user and operator viewpoints. The proposed approach has been validated on a set of disruption scenarios covering a large part of the Iranian rail network. The computational results prove that the proposed model can generate good-quality timetables with the minimum passenger delay and deviation from the initial timetable. The outcomes indicate that the developed simulation-based optimization approach has substantial advantages in producing practical solution quickly when compared to currently accepted solutions. Abbreviation: MOVNS: multi-objective variable neighbourhood search; DES: discrete-event simulation; SO: simulation-optimization; AG: Alternative Graph; FCFS: First Come First Served; MIP: mixed integer programming; MILP: mixed-integer linear programming; B&B: branch and bound algorithm; VND: Variable Neighborhood Descent; NSGA-II: Non-dominated Sorting Genetic Algorithm–II; CD: crowding distance; DP: dynamic priority; EDD: earliest due date first; SRTT: shortest remaining traveling time; LST: least slack time first.
Shakor, P, Nejadi, S, Paul, G & Malekmohammadi, S 2019, 'Review of emerging additive manufacturing technologies in 3D printing of cementitious materials in the construction industry', Frontiers in Built Environment, vol. 4, pp. 1-17.
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Shakor, P, Nejadi, S, Paul, G, Sanjayan, J & Nazari, A 2019, 'MECHANICAL PROPERTIES OF THE CEMENT-BASED MATERIALS AND EFFECT OF ELEVATED TEMPERATURE ON 3D PRINTING MORTAR SPECIMENS IN INKJET 3D PRINTING', ACI Materials Journal.
Shao, R, Wu, C, Su, Y, Liu, Z, Liu, J, Chen, G & Xu, S 2019, 'Experimental and numerical investigations of penetration resistance of ultra-high strength concrete protected with ceramic balls subjected to projectile impact', Ceramics International, vol. 45, no. 6, pp. 7961-7975.
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© 2019 Elsevier Ltd and Techna Group S.r.l. Ceramic materials characterized by high hardness, high inherent strength, low density and excellent dimensional stability have been extensively applied in the design of high-performance and lightweight protective structures to resist the high-speed projectile impact. In order to study the anti-penetration capability of ceramic balls protected ultra-high strength concrete (CB-UHSC), high-speed projectile impact tests were conducted at striking velocities of 545 m/s, 679 m/s, and 809 m/s to investigate the impact performance of ceramic balls, projectiles, and the protected UHSC. The experimental results indicated the effectiveness and economy of ceramic balls in resisting the high-speed projectile impact. Numerical studies were then conducted to reproduce the projectile penetration process within CB-UHSC targets with the assistance of LS-DYNA. Based on the validated numerical models, impact resistance and ballistic deviation of projectiles, as well as the energy evolution between projectiles and targets, were further investigated to comprehensively understand the impact performance of this newly designed protective structure under projectile penetration.
Shi, X, Zhu, J, Li, L & Lu, DDC 2019, 'Model-Predictive-Based Duty Cycle Control with Simplified Calculation and Mutual Influence Elimination for AC/DC Converter', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 7, no. 1, pp. 504-514.
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© 2013 IEEE. The single-vector-based model-predictive-based direct power control (MPDPC) is commonly used for control of three-phase full-bridge ac/dc converters, but it could only select the best switching vector to be implemented. Due to the limited number of voltage vectors in a two-level three-phase converter, the sampling frequency needs to be high to achieve an acceptable performance. Also, it bears variable switching frequency that causes spread spectrum nature of harmonics. In this paper, a three-vector-based simplified model predictive duty cycle control (SMPDCC) is proposed. The adjacent two nonzero vectors are selected by evaluating the effects of each vector pairs based on the revised cost function. The duration calculation is simplified compared with the conventional predictive duty cycle control (CPDCC) by allocating a control period in reciprocal proportion with the corresponding cost function value of the selected vectors. Besides, the negative duration issue of CPDCC could be completely avoided and the mutual influence elimination ability could be realized. A comparative study with MPDPC and CPDCC has been conducted to verify the superiority of the proposed scheme by the simulation and experimental results. It shows that the SMPDCC has the advantages of lower power ripple, fixed switching frequency, lower total harmonic distortion, and mutual influence elimination ability.
Shi, Z, Sun, X, Cai, Y, Yang, Z, Lei, G, Guo, Y & Zhu, J 2019, 'Torque Analysis and Dynamic Performance Improvement of a PMSM for EVs by Skew Angle Optimization', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2018 IEEE. In this paper, a permanent magnet synchronous machine (PMSM) for electric vehicles (EVs) is studied. Since EVs need to face some low speed road conditions, it is necessary to drive the machine to maintain a stable torque at low speed. The stator skew slot is often adopted to reduce torque ripple; however, it declines the output torque at same time. Besides, the difference between positive rotation performance and negative rotation performance, which caused by the skew slot are often ignored. Through the finite element analysis, the cogging torque and dynamic performance of the PMSM at different skew angle are studied. Moreover, the different influence of slot skew angle on positive and negative rotation performance is studied. Then, the optimum skew angle of the PMSM is studied through comprehensive consideration. Finally, the cogging torque of the prototype is verified to be less than 2 N·m through the experiment.
Shukla, N, Tiwari, MK & Beydoun, G 2019, 'Next generation smart manufacturing and service systems using big data analytics', Computers and Industrial Engineering.
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Si, L, Eisman, JA, Winzenberg, T, Sanders, KM, Center, JR, Nguyen, TV & Palmer, AJ 2019, 'Microsimulation model for the health economic evaluation of osteoporosis interventions: study protocol.', BMJ open, vol. 9, no. 2, p. e028365.
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INTRODUCTION:Osteoporosis is a systemic skeletal disease that is characterised by reduced bone strength and increased fracture risk. Osteoporosis-related fractures impose enormous disease and economic burden to the society. Although many treatments and health interventions are proven effective to prevent fractures, health economic evaluation adds evidence to their economic merits. Computer simulation modelling is a useful approach to extrapolate clinical and economic outcomes from clinical trials and it is increasingly used in health economic evaluation. Many osteoporosis health economic models have been developed in the past decades; however, they are limited to academic use and there are no publicly accessible health economic models of osteoporosis. METHODS AND ANALYSIS:We will develop the Australian osteoporosis health economic model based on our previously published microsimulation model of osteoporosis in the Chinese population. The development of the model will follow the recommendations for the conduct of economic evaluations in osteoporosis by the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases and the US branch of the International Osteoporosis Foundation. The model will be a state-transition semi-Markov model with memory. Clinical parameters in the model will be mainly obtained from the Dubbo Osteoporosis Epidemiology Study and the health economic parameters will be collected from the Australian arm of the International Costs and Utilities Related to Osteoporotic Fractures Study. Model transparency and validates will be tested using the recommendations from Good Research Practices in Modelling Task Forces. The model will be used in economic evaluations of osteoporosis interventions including pharmaceutical treatments and primary care interventions. A user-friendly graphical user interface will be developed, which will connect the user to the calculation engine and the results will be genera...
Sick, N, Preschitschek, N, Leker, J & Bröring, S 2019, 'A new framework to assess industry convergence in high technology environments', Technovation.
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© 2018 Elsevier Ltd The process of convergence, from science and technology convergence to that of markets as well as entire industries can be witnessed in a range of different high technology environments such as IT and NanoBiotech. Although this phenomenon has been subject of analysis in an increasing number of studies, the notion of industry convergence – the final step of a full convergence process - still lacks a common definition. The missing conceptual definition of what industry convergence really is and how it can be assessed impedes both analyses and monitoring - let alone its anticipation. To address the missing conceptual definition of the final step in convergence, this paper seeks to develop a framework based on novel indicators that enable identifying and monitoring trends of industry convergence in high technology environments. Building on indicators in the domain of collaboration, a framework, which distinguishes different stages and types of industry convergence is developed. Subsequently, the newly developed framework is empirically illustrated in the area of stationary energy storage based on publicly available data. To this end, the full text database Nexis is used to conduct a search in news reports on collaborations in the domain of stationary energy storage. The study contributes to the growing body of convergence literature by providing a novel framework allowing the identification of not only industry convergence as the final step of the convergence process but also the classification of its type. Practical implications include an orientation for companies in converging environments on when and how to close the resulting technology and market competence gaps.
Silitonga, AS, Mahlia, TMI, Kusumo, F, Dharma, S, Sebayang, AH, Sembiring, RW & Shamsuddin, AH 2019, 'Intensification of Reutealis trisperma biodiesel production using infrared radiation: Simulation, optimisation and validation', Renewable Energy, pp. 520-527.
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© 2018 Elsevier Ltd Biodiesel production using intensification of methyl ester is becoming very important due to its considerably lower energy requirement and shorter reaction time in obtaining feedstock oil. The present study investigated utilisation of Reutealis trisperma oil to produce biodiesel. A Box-Behnken experimental design was used to optimise the transesterification process. The process variables were explored and the optimum methanol to oil molar ratio, catalyst concentration, reaction temperature, and reaction time were 8:1, 1.2 wt%, 64 °C and 68 min respectively and the corresponding methyl ester yield was 98.39%. The experiment was conducted in triplicate to validate the quadratic model. Results showed average methyl ester yield was 97.78%, which is close to the predicted value, indicating reliability of the model. Results also indicated that using infrared radiation method has many advantageous, such as less energy consumption as a result of deeper penetration of reactant mass which can improve mass transfer between the immiscible reactants in order to improve quality of biodiesel. The physicochemical properties of Reutealis trisperma methyl ester produced under optimum transesterification process variables were also measured and the properties fulfilled the fuel specifications as per ASTM D6751 and EN 14214 standards.
Siyari, P, Krunz, MM & Nguyen, D 2019, 'Distributed Power Control in Single-stream MIMO Wiretap Interference Networks with Full-duplex Jamming Receivers', IEEE Transactions on Signal Processing.
Skilodimou, HD, Bathrellos, GD, Chousianitis, K, Youssef, AM & Pradhan, B 2019, 'Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study', Environmental Earth Sciences, vol. 78, no. 2.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Multi-hazard assessment modeling comprises an essential tool in any plan that aims to mitigate the impact of future natural disasters. For a particular area they can be generated by combining assessment maps for different types of natural hazards. In the present study, the analytical hierarchy process (AHP) supported by a Geographical Information System (GIS) was utilized to initially produce assessment maps on hazards from landslides, floods and earthquakes and subsequently to combine them into a single multi-hazard map. Evaluation of the reliability of the proposed model predictions was performed through uncertainty analysis of the variables that we used for producing the final model. The drainage basin of Peneus (Pinios) River (Western Peloponnesus, Greece), an area that is prone to landslides, floods and seismic events, was selected for the implementation of the aforementioned approach. Our findings revealed that the high hazard zones are mainly distributed in the western and north-eastern part of the region under investigation. The calculated multi-hazard map, which corresponds to the potential urban development suitability map of the study area, was classified into five classes, namely of very low, low, moderate, high and very high suitability. The most suitable areas for urban development are distributed mostly in the eastern part, in agreement with the low and very low hazard level for the three considered natural hazards. In addition, by performing uncertainty analysis we showed that the spatial distribution of the suitability zones does not change significantly. Ultimately, the final map was verified using the actual inventory of landslides and floods that affected the study area. In this context, we showed that 80% of the landslide occurrences and all the recorded flood events fall within the boundaries of the moderate, low and very low suitability zones. Consequently, the predictive capaci...
Sohaib, O, Kang, K & Nurunnabi, M 2019, 'Gender-Based iTrust in E-Commerce: The Moderating Role of Cognitive Innovativeness', Sustainability, vol. 11, no. 1, pp. 1-16.
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Song, Z, Zhang, X, Ngo, HH, Guo, W, Song, P, Zhang, Y, Wen, H & Guo, J 2019, 'Zeolite powder based polyurethane sponges as biocarriers in moving bed biofilm reactor for improving nitrogen removal of municipal wastewater.', The Science of the total environment, vol. 651, no. Pt 1, pp. 1078-1086.
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This study aims to enhance nitrogen removal efficiency of a moving bed biofilm reactor (MBBR) by developing a new MBBR with zeolite powder-based polyurethane sponges as biocarriers (Z-MBBR). Results indicated the total nitrogen (TN) removal efficiency and simultaneous nitrification and denitrification (SND) performance in Z-MBBR were nearly 10% higher than those in the conventional MBBR with sponges as biocarriers (S-MBBR). About 84.2 ± 4.8% of TN was removed in Z-MBBR compared to 75.1 ± 6.8% in S-MBBR. Correspondingly, the SND performance in Z-MBBR and S-MBBR was 90.7 ± 4.1% and 81.7 ± 6.5%, respectively. The amount of biofilm attached to new biocarriers (0.470 ± 0.131 g/g carrier) was 1.3 times more than that of sponge carriers (0.355 ± 0.099 g/g carrier). Based on the microelectrode measurements and microbial community analysis, more denitrifying bacteria existed in the Z-MBBR system, and this can improve the SND performance. Consequently, this new Z-MBBR can be a promising option for a hybrid treatment system to better nitrogen removal from wastewater.
Stender, M, Oberst, S & Hoffmann, N 2019, 'Recovery of Differential Equations from Impulse Response Time Series Data for Model Identification and Feature Extraction', Vibration, vol. 2, no. 1, pp. 25-46.
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Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for (a) describing the dynamics and (b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and nonlinearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and how to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of nonlinearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.
Sun, HH, Ding, C, Zhu, H, Jones, B & Guo, YJ 2019, 'Suppression of Cross-Band Scattering in Multiband Antenna Arrays', IEEE Transactions on Antennas and Propagation.
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IEEE This paper presents a novel method of suppressing cross-band scattering in dual-band dual-polarized antenna arrays. The method involves introducing chokes into low-band (LB) elements to suppress high-band (HB) scattering currents. Experimental results show that by inserting LB-pass HB-stop chokes into LB radiators, suppression of induced HB currents on the LB elements is achieved. This greatly reduces the pattern distortion of the HB array caused by the presence of LB elements. The array considered is configured as two columns of HB antennas operating from 1.71 GHz to 2.28 GHz interleaved with a single column of LB antennas operating from 0.82 GHz to 1.0 GHz. The realized array with choked LB element has stable and symmetrical radiation in both high and low bands.
Sun, J, Dai, X, Wang, Q, van Loosdrecht, MCM & Ni, B-J 2019, 'Microplastics in wastewater treatment plants: Detection, occurrence and removal.', Water research, vol. 152, pp. 21-37.
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Microplastics have aroused increasing concern as they pose threats to aquatic species as well as human beings. They do not only contribute to accumulation of plastics in the environment, but due to absorption they can also contribute to spreading of micropollutants in the environment. Studies indicated that wastewater treatment plants (WWTPs) play an important role in releasing microplastics to the environment. Therefore, effective detection of the microplastics and understanding their occurrence and fate in WWTPs are of great importance towards microplastics control. In this review, the up-to-date status on the detection, occurrence and removal of microplastics in WWTPs are comprehensively reviewed. Specifically, the different techniques used for collecting microplastics from both wastewater and sewage sludge, and their pretreatment and characterization methods are reviewed and analyzed. The key aspects regarding microplastics occurrence in WWTPs, such as concentrations, total discharges, materials, shapes and sizes are summarized and compared. Microplastics removal in different treatment stages and their retention in sewage sludge are explored. The development of potential microplastics-targeted treatment technologies is also presented. Although previous researches in microplastics have undoubtedly improved our level of understanding, it is clear that much remains to be learned about microplastics in WWTPs, as many unanswered questions and thereby concerns still remain; some of these important future research areas are outlined. The key challenges appear to be to harmonize detection methods as well as microplastics mitigation from wastewater and sewage sludge.
Sun, X, Gui, G, Li, Y, Liu, RP & An, Y 2019, 'ResInNet: A Novel Deep Neural Network with Feature Reuse for Internet of Things', IEEE Internet of Things Journal, vol. 6, no. 1, pp. 679-691.
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© 2014 IEEE. Deep neural networks (DNNs) have widely used in various Internet-of-Things (IoT) applications. Pursuing superior performance is always a hot spot in the field of DNN modeling. Recently, feature reuse provides an effective means of achieving favorable nonlinear approximation performance in deep learning. Existing implementations utilizes a multilayer perception (MLP) to act as a functional unit for feature reuse. However, determining connection weight and bias of MLP is a rather intractable problem, since the conventional back-propagation learning approach encounters the limitations of slow convergence and local optimum. To address this issue, this paper develops a novel DNN considering a well-behaved alternative called reservoir computing, i.e., reservoir in network (ResInNet). In this structure, the built-in reservoir has two notable functions. First, it behaves as a bridge between any two restricted Boltzmann machines in the feature learning part of ResInNet, performing a feature abstraction once again. Such reservoir-based feature translation provides excellent starting points for the following nonlinear regression. Second, it serves as a nonlinear approximation, trained by a simple linear regression using the most representative (learned) features. Experimental results over various benchmark datasets show that ResInNet can achieve the superior nonlinear approximation performance in comparison to the baseline models, and produce the excellent dynamic characteristics and memory capacity. Meanwhile, the merits of our approach is further demonstrated in the network traffic prediction related to real-world IoT application.
Sun, Y, Nimbalkar, S & Chen, C 2019, 'Particle breakage of granular materials during sample preparation', Journal of Rock Mechanics and Geotechnical Engineering.
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© 2019 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences Particle breakage is commonly observed in granular materials when subjected to external loads. It was found that particle breakage would occur during both sample preparation and loading stages. However, main attention was usually paid to the particle breakage behaviour of samples during loading stage. This study attempts to explore the breakage behaviour of granular materials during sample preparation. Triaxial samples of rockfill aggregates are prepared by layered compaction method to achieve different relative densities. Extents of particle breakage based on the gradings before and after test are presented and analysed. It is found that particle breakage during sample preparation cannot be ignored. Gradings after test are observed to shift away from the initial grading. Aggregates with larger size that appear to break are more than the smaller-sized ones. Irrespective of the initial gradings, an increase in the extent of particle breakage with the increasing relative density is observed during sample preparation.
Talaei-Khoei, A & Wilson, JM 2019, 'Using time-series analysis to predict disease counts with structural trend changes', Information Processing and Management, vol. 56, no. 3, pp. 674-686.
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© 2018 This paper aims at time-series prediction of disease counts when the trend changes unexpectedly. There is a body of literature in time-series analysis that focuses on improving the prediction when a change in the trend is observed. Following this line of literature, a method was recently proposed that outperforms the previous proposals. To utilize this method for prediction of disease counts, the present study employs a commonly used method called local estimators for estimation near boundaries. This provides two advantages for disease count prediction. First, the prior method is limited to only smooth trend changes. However, the current study using local estimators also encounters abrupt changes. Taking both smooth and abrupt changes into account is important in predicting disease counts, because, for variety of different reasons, such as vaccination programs, the disease counts may encounter abrupt changes. Second, the current study, despite the previous approach, can also model uncertainty. The parameter uncertainty plays a significant role when predicting disease counts. Compared to current literature, the proposed method outperformed to predict counts for 12 diseases in Nevada. The paper demonstrates that the proposed methods improved the prediction Q-Score in comparison to both outbreak and structural change methods available in literature. Although this method has been specifically demonstrated for prediction of disease counts, it can be generalized for different applications. With the recent utilization of analytics techniques and the increased use of time-series analysis in information systems, the suggested method provides implications for the literature of information systems when predicting with structural trend changes in data.
Talaei-Khoei, A, Wilson, JM & Kazemi, S-F 2019, 'Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment.', JMIR public health and surveillance, vol. 5, no. 1, p. e11357.
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BACKGROUND:The literature in statistics presents methods by which autocorrelation can identify the best period of measurement to improve the performance of a time-series prediction. The period of measurement plays an important role in improving the performance of disease-count predictions. However, from the operational perspective in public health surveillance, there is a limitation to the length of the measurement period that can offer meaningful and valuable predictions. OBJECTIVE:This study aimed to establish a method that identifies the shortest period of measurement without significantly decreasing the prediction performance for time-series analysis of disease counts. METHODS:The data used in this evaluation include disease counts from 2007 to 2017 in northern Nevada. The disease counts for chlamydia, salmonella, respiratory syncytial virus, gonorrhea, viral meningitis, and influenza A were predicted. RESULTS:Our results showed that autocorrelation could not guarantee the best performance for prediction of disease counts. However, the proposed method with the change-point analysis suggests a period of measurement that is operationally acceptable and performance that is not significantly different from the best prediction. CONCLUSIONS:The use of change-point analysis with autocorrelation provides the best and most practical period of measurement.
Tan, X, Li, W, Zhao, M & Tam, VWY 2019, 'Numerical discrete-element method investigation on failure process of recycled aggregate concrete', Journal of Materials in Civil Engineering, vol. 31, no. 1.
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© 2018 American Society of Civil Engineers. This study numerically investigates the failure processes of recycled aggregate concrete (RAC) and natural aggregate concrete (NAC). A two-dimensional simulation based on a discrete-element method (DEM) is conducted with a universal distinct-element code (UDEC) program. RAC is modeled by a combination of rigid Voronoi blocks cemented to each other using contacts for interfaces. The determination procedure of contact microparameters is analyzed, and a series of microscopic contact parameters in different components of modeled recycled aggregate concrete (MRAC) is calibrated using nanoindentation results. The complete stress-strain curves, fracture process, and failure pattern of numerical model are verified by experimental results, proving its accuracy and validation. The initiation, propagation, and coalescence of microcracks and subsequent nonlinear deformation behaviors of cement mortar, modeled natural aggregate, and recycled aggregate concrete are captured through DEM numerical simulations and compared with digital image correlation (DIC) results. It is found that both the new interfacial transition zone and the old interfacial transition zone are the weak links in RAC, where most microcracks initiate and propagate into the cement mortar region. The failure behaviors of MRAC revealed by both experimental and numerical results can effectively provide insights into the failure mechanism and enhancement of RAC.
Tang, L, Wang, Y, Ding, X, Yin, H, Xiong, R & Huang, S 2019, 'Topological local-metric framework for mobile robots navigation: a long term perspective', Autonomous Robots, vol. 43, no. 1, pp. 197-211.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Long term mapping and localization are the primary components for mobile robots in real world application deployment, of which the crucial challenge is the robustness and stability. In this paper, we introduce a topological local-metric framework (TLF), aiming at dealing with environmental changes, erroneous measurements and achieving constant complexity. TLF organizes the sensor data collected by the robot in a topological graph, of which the geometry is only encoded in the edge, i.e. the relative poses between adjacent nodes, relaxing the global consistency to local consistency. Therefore the TLF is more robust to unavoidable erroneous measurements from sensor information matching since the error is constrained in the local. Based on TLF, as there is no global coordinate, we further propose the localization and navigation algorithms by switching across multiple local metric coordinates. Besides, a lifelong memorizing mechanism is presented to memorize the environmental changes in the TLF with constant complexity, as no global optimization is required. In experiments, the framework and algorithms are evaluated on 21-session data collected by stereo cameras, which are sensitive to illumination, and compared with the state-of-art global consistent framework. The results demonstrate that TLF can achieve similar localization accuracy with that from global consistent framework, but brings higher robustness with lower cost. The localization performance can also be improved from sessions because of the memorizing mechanism. Finally, equipped with TLF, the robot navigates itself in a 1 km session autonomously.
Tang, MC, Duan, Y, Wu, Z, Chen, X, Li, M & Ziolkowski, RW 2019, 'Pattern reconfigurable, vertically polarized, low-profile, compact, near-field resonant parasitic antenna', IEEE Transactions on Antennas and Propagation, vol. 67, no. 3, pp. 1467-1475.
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© 1963-2012 IEEE. A vertically polarized, low-profile, compact, near-field resonant parasitic antenna with pattern reconfigurability is demonstrated. The antenna has three dynamic end-fire states facilitated with only three p-i-n diodes. The radiation pattern in each state covers more than 120° in its azimuth plane and, hence, it achieves beam scanning that covers the entire azimuth plane. The antenna height and transverse size are, respectively, only 0.048λ 0 and 0.1λ 02 . Measured results, in good agreement with their simulated values, demonstrate that the antenna exhibits a 11% fractional impedance bandwidth, and a 6.6 dBi peak realized gain in all three of its pattern-reconfigurable states. Stable and high peak realized gain values are realized over its entire operational band surrounding 2.22 GHz.
Tang, MC, Wu, Z, Shi, T & Ziolkowski, RW 2019, 'Dual-Band, Linearly Polarized, Electrically Small Huygens Dipole Antennas', IEEE Transactions on Antennas and Propagation, vol. 67, no. 1, pp. 37-47.
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© 1963-2012 IEEE. Two electrically small, dual-band Huygens dipole antennas are reported. In both designs, two pairs of magnetic and electric near-field resonant parasitic (NFRP) elements are combined organically within an electrically small, low profile package. The NFRP elements are excited effectively using only one coaxial-fed-driven element. One dual-band Huygens system produces parallel, linearly polarized (LP) fields at its two operating frequencies. The other dual-band system produces two orthogonal LP fields. Additional parasitic elements are introduced to mitigate the mutual coupling effects between the pairs of NFRP elements. The measured values for prototypes of both antennas in the L-band demonstrate their electrically small size (ka< 1) and low profile (∼ 0.03λ0). They also confirm their broadside radiation and polarization performance characteristics, as well as the isolation between each operating frequency. Their fractional bandwidths, peak realized gains, front-to-back ratios, and radiation efficiencies are, respectively, 0.6%, > 2 dBi, > 10 dB, and > 60% at both frequencies. These dual-band systems would provide multifunctional performance in a variety of portable, compact wireless devices.
Tang, Z, Li, W, Hu, Y, Zhou, JL & Tam, VWY 2019, 'Review on designs and properties of multifunctional alkali-activated materials (AAMs)', Construction and Building Materials, vol. 200, pp. 474-489.
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© 2018 Elsevier Ltd The stream of research on alkali-activated materials (AAMs) has expanded rapidly during the last decades owing to the potential as a viable alternative to cement-based materials. In addition to the load-carrying function, AAMs have been integrated with other functions to develop advanced construction materials, namely multifunctional AAMs. Multifunctional AAMs are intelligent systems not only serve a basic structural function but also exhibit other functional properties or have the abilities to react upon external stimuli or disturbances. Materials of this kind have tremendous potential to enhance the mechanical performance and durability of structure, improve the reliability and longevity of infrastructure, as well as reduce life-cycle service and maintenance cost. These multifunctional properties are mainly achieved through materials composition design, incorporation of functional elements, or microstructure modification. This paper presents an overview on designs and properties of multifunctional AAMs covering the smart functions, mechanical functions, and electrical functions, and with special attention to their definition, principles, and current progress. Furthermore, the challenges in the research of multifunctional AAMs have been discussed, as well as the future directions to increase the innovation and engineering application of these materials and structures.
Tang, Z, Li, W, Ke, G, Zhou, JL & Tam, VWY 2019, 'Sulfate attack resistance of sustainable concrete incorporating various industrial solid wastes', Journal of Cleaner Production, vol. 218, pp. 810-822.
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© 2019 Elsevier Ltd Industrial solid wastes are inducing severe environmental problems, but the problem can be overcame by reusing them as construction materials. The sulfate resistances of sustainable concrete incorporating various solid waste materials, including waste glass powder (WGP), coal gangue powder (CGP) and fly ash (FA) were investigated in this study. Concrete mixes with different water to binder (w/b) ratios and containing various solid waste materials as partial replacement of Portland cement by ratios of 10%, 20%, and 30% were prepared. These mixes were immersed in the 5% Na 2 SO 4 solution for a total period of 22 months. The sulfate attack resistances were evaluated extensively based on visual appearance, mass change, compressive strength, splitting tensile strength, ultrasonic pulse velocity, mineralogy, and microstructure. The results indicate that regardless of the type and content of solid waste materials, the replacement of cement by solid waste materials exhibit a positive impact on the sulfate attack resistance. Under the same substitution level, WGP appear to be the most effective in offsetting the destructive effect of sulfate attack, followed by CGP and FA. Therefore, sustainable concrete incorporating solid waste materials can not only promote the recycling of solid waste, but also provide high sulfate attack resistance.
Tao, M, Li, Z, Cao, W, Li, X & Wu, C 2019, 'Stress redistribution of dynamic loading incident with arbitrary waveform through a circular cavity', International Journal for Numerical and Analytical Methods in Geomechanics.
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© 2019 John Wiley & Sons, Ltd. In actual geotechnical and civil engineering, dynamic stress concentrations around cavities generated by wave sources widely exist. In this study, based on the complex variable theory and Fourier transform method, the expression of the dynamic stress concentration factor (DSCF) around a circular cavity in infinite homogeneous media subjected to transient waves with arbitrary waveform is obtained. The relationships between both steady-state and transient DSCF and their waveform parameters are investigated quantitatively. The results indicate that a relatively large tensile stress is generated with low Poisson's ratio under steady-state incidence. Under the condition of transient incidence, the position of the wave peak has a minor effect on the DSCF in the case of small wavenumber, but it has a significant effect in the opposite case. It is found that when the wavenumber is high, such as 0.5, the stress response lags behind the stress wave. In addition, the closer the wave peak to the center of the waveform, the greater the potential damage of the transient incidence.
Taylor, CN & Bishop, AN 2019, 'Homogeneous functionals and Bayesian data fusion with unknown correlation', Information Fusion, vol. 45, pp. 179-189.
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© 2018 Information or data fusion concerns the aggregation, or combination, of probability measures. For example, in machine learning, statistics and signal processing, one may seek to ‘combine’ posterior distributions, [e.g. 1) Bayes classifiers or 2) posteriors over target states etc], arising from distinct but not necessarily independent sources. For example, sources might include partially disjoint trainers, or spatially distinct sensors correlated via state dependent measurements, etc. Data fusion is common in risk analysis where one is broadly interested in pooling expert opinions described by probability measures, and where it is often hard to assess and account for correlation among experts. The contribution of this work is the introduction of a broad class of data fusion rules that seek the combination of two (or more) probability distributions in the presence of non-zero, but unknown, correlation. We introduce rules that are improved in the sense that they are ‘closer’ to the true Bayesian result that would be computed if one could exploit knowledge of the correlation between the input distributions. We introduce these rules under the common algorithmic constraint of avoiding the so-called ‘double-counting’ of correlated information. The general framework proposed is based on homogeneous functionals. We examine the fusion performance and computational properties when using these functionals. We also consider distributed data fusion on (possibly) time-varying and incomplete network topologies and related convergence properties.
Terechovs, AKE, Ansari, AJ, McDonald, JA, Khan, SJ, Hai, FI, Knott, NA, Zhou, J & Nghiem, LD 2019, 'Occurrence and bioconcentration of micropollutants in Silver Perch (Bidyanus bidyanus) in a reclaimed water reservoir.', The Science of the total environment, vol. 650, no. Pt 1, pp. 585-593.
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This study examined the occurrence of 49 micropollutants in reclaimed water and Silver Perch (Bidyanus bidyanus) living in a reclaimed water reservoir. The numbers of micropollutants detected in reclaimed water, Silver Perch liver, and Silver Perch flesh were 20, 23, and 19, respectively. Concentrations of all micropollutants in reclaimed water, except benzotriazole, were well below the Australian Guideline for Recycled Water (AGRW) values for potable purposes. The concentration of benzotriazole in reclaimed water was 675 ± 130 ng/L while the AGRW value for this compound was 7 ng/L. Not all micropollutants detected in the water phase were identified in the Silver Perch flesh and liver tissues. Likewise, not all micropollutants detected in the Silver Perch flesh and liver were identified in the reclaimed water. In general, micropollutant concentrations in the liver were higher than in the flesh. Perfluorooctane sulfonate (PFOS) was detected at a trace level in reclaimed water well below the AGRW guideline value for potable purposes, but showed a high and medium bioconcentration factor in Silver Perch liver and flesh, respectively. In addition, the risk quotient for PFOS was medium and high when considering its concentration in Silver Perch liver and flesh, respectively. Results reported here highlight the need to evaluate multiple parameters for a comprehensive risk assessment. The results also single out PFOS as a notable contaminant of concern for further investigation.
To, VHP, Nguyen, TV, Bustamante, H & Vigneswaran, S 2019, 'Deleterious effects of soluble extracellular polymeric substances on polyacrylamide demand for conditioning of anaerobically digested sludge', Journal of Environmental Chemical Engineering, vol. 7, no. 2.
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© 2019 Elsevier Ltd. All rights reserved. High polyacrylamide (polymer) demand for conditioning of sludge, especially anaerobically digested sludge (ADS), is a major issue for the water industry. Currently, this problem is being investigated and the reasons for doing so are varied. It has been demonstrated that excess amounts of soluble extracellular polymeric substances (EPS) can lead to high polymer demand for conditioning. This study developed a simple and unique yet effective method for quantifying the contribution of soluble EPS to conditioning polymer demand. It did this by measuring absorbance at 191.5 nm wavelength of the supernatant derived from conditioned ADS. Experimental results confirmed that approximately 87 wt% of soluble EPS interacted with polyacrylamides during the conditioning process. Furthermore, they revealed that a specified amount of soluble EPS could not be removed by polymer flocculation despite high polymer dosage. This study concluded that about 86 wt% of the polyacrylamide used for conditioning was consumed solely by soluble EPS. These results confirm the important role of reducing this EPS fraction in ADS in order to curtail significant chemical costs for sludge conditioning and dewatering.
Torres Robles, A, Wiecek, E, Cutler, R, Drake, B, Benrimoj, SI, Fernandez-Llimos, F & Garcia Cardenas, M 2019, 'Using dispensing data to evaluate adherence implementation rates in community pharmacy', Frontiers in Pharmacology.
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Tran, HN, Nguyen, DT, Le, GT, Tomul, F, Lima, EC, Woo, SH, Sarmah, AK, Nguyen, HQ, Nguyen, PT, Nguyen, DD, Nguyen, TV, Vigneswaran, S, Vo, D-VN & Chao, H-P 2019, 'Adsorption mechanism of hexavalent chromium onto layered double hydroxides-based adsorbents: A systematic in-depth review', Journal of Hazardous Materials.
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Tran, NT & Khoury, S 2019, 'The challenges of using blood-based miRNAs in the clinic'.
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This preprint addresses the enormous challenges confronting researchers invested in serum miRNAs and their potential use as clinical biomarkers. We discuss some of the inherent technical obstacles and suggest several possible solutions. We hope to encourage all researchers to be united and collaborative in the pursuit of delivering clinically relevant biomarkers in the near future.
Tri, DQ, Mai Linh, NT, Thai, TH & Kandasamy, J 2019, 'Application of 1D–2D coupled modeling in water quality assessment: A case study in Ca Mau Peninsula, Vietnam', Physics and Chemistry of the Earth.
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© 2018 Elsevier Ltd A few 1D and 2D models are used to simulate and calculate the water level and water quality regarding four state variable (DO, NH4, NO3, and BOD) observations in the main rivers and coastal estuaries on the Ca Mau peninsula. This study calibrates and validates 1D and 2D models during the dry and flood seasons for 2014 and 2015, as well as assesses water quality in coastal estuaries during the dry and flood season of 2016 by using a 2D model. The calibration and validation results of the hydrodynamic 1D and 2D models show that there is a high degree of conformity regarding the phase and amplitude of water level at observing stations with mean absolute error (MAE) ranges from 0.05 m to 0.37 m. The RMSE–observation standard deviation ratio (RSR) vary from 0.12 to 0.64, and the percent bias (PBIAS) is from −8.9% to 3.2%. Calibration and validation of water quality parameter (DO, NH4, NO3, and BOD5 concentration) results have a high correlation coefficient during both the dry and flood season of 2014 and 2015. The standards, originating from the National Technical Regulation on Surface Water quality and on Coastal Water Quality, are used to evaluate pollutant concentrations in estuaries in the study area during the dry and flood seasons of 2016. The water quality parameters contain DO (4.6 mg/l–7.9 mg/l) and BOD (4.6 mg/l–10.7 mg/l) concentrations over the National Technical Regulation on Surface and Coastal Water Quality and the A1 limit with DO (>4 mg/l) and BOD5 (4 mg/l) on surface water quality for domestic water use in the dry and flood seasons. The calculated results will help managers make better plans for aquaculture and aquatic conservation zones in coastal estuaries in the future.
Trianni, A, Cagno, E, Bertolotti, M, Thollander, P & Andersson, E 2019, 'Energy management: A practice-based assessment model', Applied Energy, pp. 1614-1636.
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© 2018 Elsevier Ltd Industrial energy efficiency is crucial for energy cost saving and sustainable competitiveness, but its potential is not exploited due to several barriers. Previous literature has pointed out that, among the most effective means, energy management in industrial companies could bring a valuable contribution. Therefore, it is crucial to assess and evaluate the energy management status in an organisation so to undertake the most appropriate improvement actions. So far, literature has neither described the fundamental characteristics of energy management practices, nor specifically developed an assessment model to support industrial decision-makers. Stemming from those research gaps, the present work presents and discusses an innovative energy management assessment model based on a novel characterization of energy management practices. We validated and applied the model through case studies among large Italian and Swedish manufacturing companies, both proving the model to be able to thoroughly describe the energy management status and benchmarking the adoption level of energy management practices with respect to specific baselines. The model highlights both strengths and critical areas in an industrial company's energy management, thus offering a valuable support to drive further improvement activities. The work concludes with interesting suggestions for industrial decision-makers and policy-makers, sketching also some further research avenues.
Uddin, MN, Rahman, MA, Mofijur, M, Taweekun, J, Techato, K & Rasul, MG 2019, 'Renewable energy in Bangladesh: Status and prospects', Energy Procedia, vol. 160, pp. 655-661.
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Uddin, MN, Rahman, MA, Taweekun, J, Techato, K, Mofijur, M & Rasul, M 2019, 'Enhancement of biogas generation in up-flow sludge blanket (UASB) bioreactor from palm oil mill effluent (POME)', Energy Procedia, vol. 160, pp. 670-676.
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Uddin, MN, Taweekun, J, Techato, K, Rahman, MA, Mofijur, M & Rasul, MG 2019, 'Sustainable Biomass as an Alternative Energy Source: Bangladesh Perspective', Energy Procedia, vol. 160, pp. 648-654.
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Uddin, MN, Techato, K, Rasul, MG, Hassan, NMS & Mofijur, M 2019, 'Waste coffee oil: A promising source for biodiesel production', Energy Procedia, vol. 160, pp. 677-682.
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Vahedian, A, Shrestha, R & Crews, K 2019, 'Experimental and analytical investigation on CFRP strengthened glulam laminated timber beams: Full-scale experiments', Composites Part B: Engineering, vol. 164, pp. 377-389.
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Vakhshouri, B & Nejadi, S 2019, 'Empirical models and design codes in prediction of modulus of elasticity of concrete', Frontiers of Structural and Civil Engineering, vol. 13, no. 1, pp. 38-48.
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© 2018, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature. Modulus of Elasticity (MOE) is a key parameter in reinforced concrete design. It represents the stress-strain relationship in the elastic range and is used in the prediction of concrete structures. Out of range estimation of MOE in the existing codes of practice strongly affect the design and performance of the concrete structures. This study includes: (a) evaluation and comparison of the existing analytical models to estimating the MOE in normal strength concrete, and (b) proposing and verifying a new model. In addition, a wide range of experimental databases and empirical models to estimate the MOE from compressive strength and density of concrete are evaluated to verification of the proposed model. The results show underestimation of MOE of conventional concrete in majority of the existing models. Also, considering the consistency between density and mechanical properties of concrete, the predicted MOE in the models including density effect, are more compatible with the experimental results.
van Rijmenam, M, Erekhinskaya, T, Schweitzer, J & Williams, MA 2019, 'Avoid being the turkey: How big data analytics changes the game of strategy in times of ambiguity and uncertainty', Long Range Planning.
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In order for organisations to remain competitive in times of ambiguity and uncertainty, there is a need to detect and anticipate unknown unknowns, also called ‘black swans’. When these are ignored they may lead to competitive struggles. In this paper, we build on this view and suggest that big data analytics can provide necessary insights to help change strategy making. Research suggests that ambidextrous organisations should focus on developing and maintaining their dynamic capabilities. Following on from this, we take a dynamic capabilities perspective and propose a theoretical framework to explain the intricacies of big data analytics. This framework explains the ability of organisations to detect, anticipate and respond strategically in ambiguous and uncertain business environments. For a meta- synthesis of 101 cases of big data analytics, we employ a multi-method approach that incorporates Natural Language Processing, semantic analysis and case analysis, allowing extraction and analysis of structured information from unstructured data. Overall, we find evidence of big data analytics helping to detect, anticipate and respond to industry disruption. We offer six propositions about the relationships between the levels of data analytics capabilities and strategic dynamic capabilities. We find that descriptive data analytics improves the capability of an organisation to understand the business context (sensing) and that predictive data analytics aids in the realisation of business opportunities (seizing). This study contributes to an understanding of big data analytics as a dynamic organisational capability that supports strategic decision-making in times of ambiguity and uncertainty. We conclude by suggesting areas for further investigation, particularly in regard to the strategic application of prescriptive data analytics.
Van, HT, Nguyen, LH, Nguyen, VD, Nguyen, XH, Nguyen, TH, Nguyen, TV, Vigneswaran, S, Rinklebe, J & Tran, HN 2019, 'Characteristics and mechanisms of cadmium adsorption onto biogenic aragonite shells-derived biosorbent: Batch and column studies.', Journal of environmental management.
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Calcium carbonate (CaCO3)-enriched biomaterial derived from freshwater mussel shells (FMS) was used as a non-porous biosorbent to explore the characteristics and mechanisms of cadmium adsorption in aqueous solution. The adsorption mechanism was proposed by comparing the FMS properties before and after adsorption alongside various adsorption studies. The FMS biosorbent was characterized using nitrogen adsorption/desorption isotherm, X-ray diffraction, scanning electron microscopy with energy dispersive spectroscopy, Fourier-transform infrared spectroscopy, and point of zero charge. The results of batch experiments indicated that FMS possessed an excellent affinity to Cd(II) ions within solutions pH higher than 4.0. An increase in ionic strength resulted in a significant decrease in the amount of Cd(II) adsorbed onto FMS. Kinetic study demonstrated that the adsorption process quickly reached equilibrium at approximately 60 min. The FMS biosorbent exhibited the Langmuir maximum adsorption capacity as follows: 18.2 mg/g at 10 °C < 26.0 mg/g at 30 °C < 28.6 mg/g at 50 °C. The Cd(II) adsorption process was irreversible, spontaneous (-ΔG°), endothermic (+ΔH°), and more random (+ΔS°). Selective order (mmol/g) of metal cations followed as Pb2+ > Cd2+ > Cu2+ > Cr3+ > Zn2+. For column experiments, the highest Thomas adsorption capacity (7.86 mg/g) was achieved at a flow rate (9 mL/min), initial Cd(II) concentration (10 mg/L), and bed height (5 cm). The Cd(II) removal by FMS was regarded as non-activated chemisorption that occurred very rapidly (even at a low temperature) with a low magnitude of activation energy. Primary adsorption mechanism was surface precipitation. Cadmium precipitated in the primary (Cd,Ca)CO3 form with a calcite-type structure on the FMS surface. A crust of rhombohedral crystals on the substrate was observed by SEM. Freshwater mussel shells have the potential as a renewable adsorbent to remove cadmium from water.
Vincent Poor, H, Shi, Y, Tuan, HD, Savkin, AV & Duong, TQ 2019, 'Model predictive control for smart grids with multiple electric-vehicle charging stations', IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 2127-2136.
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© 2017 IEEE Next-generation power grids will likely enable concurrent service for residences and plug-in electric vehicles (PEVs). While the residence power demand profile is known and thus can be considered inelastic, the PEVs' power demand is only known after random PEV arrivals. PEV charging scheduling aims at minimizing the potential impact of the massive integration of PEVs into power grids to save service costs to customers while power control aims at minimizing the cost of power generation subject to operating constraints and meeting demand. This paper develops a model predictive control-based approach to address joint PEV charging scheduling and power control to minimize both PEV charging cost and energy generation cost in meeting both residence and PEV power demands. Unlike in related works, no assumptions are made about the probability distribution of PEVs' arrivals, knowledge of PEVs' future demand, or unlimited charging capacity of PEVs. The proposed approach is shown to achieve a globally optimal solution. Numerical results for IEEE benchmark power grids serving Tesla model S PEVs show the merit of this approach.
Vo, HNP, Ngo, HH, Guo, W, Nguyen, TMH, Liu, Y, Liu, Y, Nguyen, DD & Chang, SW 2019, 'A critical review on designs and applications of microalgae-based photobioreactors for pollutants treatment.', The Science of the total environment, vol. 651, no. Pt 1, pp. 1549-1568.
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The development of the photobioreactors (PBs) is recently noticeable as cutting-edge technology while the correlation of PBs' engineered elements such as modellings, configurations, biomass yields, operating conditions and pollutants removal efficiency still remains complex and unclear. A systematic understanding of PBs is therefore essential. This critical review study is to: (1) describe the modelling approaches and differentiate the outcomes; (2) review and update the novel technical issues of PBs' types; (3) study microalgae growth and control determined by PBs types with comparison made; (4) progress and compare the efficiencies of contaminants removal given by PBs' types and (5) identify the future perspectives of PBs. It is found that Monod model's shortcoming in internal substrate utilization is well fixed by modified Droop model. The corroborated data also remarks an array of PBs' types consisting of flat plate, column, tubular, soft-frame and hybrid configuration in which soft-frame and hybrid are the latest versions with higher flexibility, performance and smaller foot-print. Flat plate PBs is observed with biomass yield being 5 to 20 times higher than other PBs types while soft-frame and membrane PBs can also remove pharmaceutical and personal care products (PPCPs) up to 100%. Looking at an opportunity for PBs in sustainable development, the flat plate PBs are applicable in PB-based architectures and infrastructures indicating an encouraging revenue-raising potential.
Volkova, L, Roxburgh, SH, Surawski, NC, Meyer, CP & Weston, CJ 2019, 'Improving reporting of national greenhouse gas emissions from forest fires for emission reduction benefits: An example from Australia', Environmental Science and Policy, vol. 94, pp. 49-62.
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© 2018 Elsevier Ltd Forest fires are a significant contributor to global greenhouse gas (GHG) emissions. Accurate reporting of GHG emissions from forest fires requires development of detailed methodologies and country specific data for estimating emissions. In recent years, Australia has updated its national methodology for reporting GHG emissions from fires on temperate forested lands, using a Tier 2 approach of the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories. This involved refinement of the equation for estimating GHG emissions from fires provided in the Guidance, and the revision of country specific data which was derived from a comprehensive literature review. The refinements were key to transparent reporting and evaluation of the climatic impacts of mitigation actions such as forest fire management. In this paper we describe the steps required to develop a Tier 2 method in reporting fire emissions using this Australian example, the lessons learnt, and the steps required to reduce uncertainties in estimates. This paper may assist other countries seeking to estimate and report GHG emissions from forest fires by moving from the default Tier 1 method to Tier 2 using country-specific information.
Volpin, F, Chekli, L, Phuntsho, S, Ghaffour, N, Vrouwenvelder, JS & Shon, HK 2019, 'Optimisation of a forward osmosis and membrane distillation hybrid system for the treatment of source-separated urine', Separation and Purification Technology, pp. 368-375.
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© 2018 Elsevier B.V. The high concentration of nitrogen, phosphorous and potassium in human urine makes it a suitable raw material for fertiliser production. However, urine is often diluted with a significant amount of flushing water which increases the costs for the downstream nutrients recovery process. Re-using the water and the nutrients in the urine is paramount for enhancing the sustainability of our waste management system. In this work, a combination of forward osmosis (FO) and membrane distillation (MD) was used to extract distilled water from human urine. FO was chosen as MD pre-treatment to increase the overall nitrogen rejection and to prevent wetting of the MD membrane. The goal of this investigation was to tune the FO and MD operating parameters to reduce the nitrogen transport to the MD permeate. Urine pH, draw solution (DS) salt concentration and operating pressure were varied as a means to enhance the FO performances. On the other hand, feed temperature, nitrogen concentration and membrane characteristics were investigated to optimise the MD process. With 2.5 M NaCl as DS commercial FO membranes achieved a water flux between 31.5 and 28.7 L m−2 h−1 and a minimum nitrogen flux of 1.4 g L−1. An additional 33% reduction in the nitrogen transport was observed by applying minimal hydraulic pressure on the DS. However, this was also found to significantly reduce the net transmembrane water flux. Acidification of the feed was also beneficial for both FO and MD nitrogen rejection. Finally, we demonstrated that, by tuning the MD membrane porosity and thickness, higher MD permeate quality could be achieved. To conclude, the hybrid FO-MD process is expected to be an effective solution for the production of clean water and concentrated fertiliser from human urine. This double barrier separation process could be suitable for both water reclamation in space application and resource recovery in urban application.
Volpin, F, Heo, H, Hasan Johir, MA, Cho, J, Phuntsho, S & Shon, HK 2019, 'Techno-economic feasibility of recovering phosphorus, nitrogen and water from dilute human urine via forward osmosis.', Water research, vol. 150, pp. 47-55.
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Due to high phosphorus (P) and nitrogen (N) content, human urine has often proven to suitable raw material for fertiliser production. However, most of the urine diverting toilets or male urinals dilute the urine 2 to 10 times. This decreases the efficiency in the precipitation of P and stripping of N. In this work, a commercial fertiliser blend was used as forward osmosis (FO) draw solution (DS) to concentrate real diluted urine. During the concentration, the urea in the urine is recovered as it diffuses to the fertiliser. Additionally, the combination of concentrate PO43-, reverse Mg2+ flux from the DS and the Mg2+ presents in the flushing water, was able to recover the PO43- as struvite. With 50% concentrated urine, 93% P recovery was achieved without the addition of an external Mg2+. Concurrently, 50% of the N was recovered in the diluted fertiliser DS. An economic analysis was performed to understand the feasibility of this process. It was found that the revenue from the produced fertilisers could potentially offset the operational and capital costs of the system. Additionally, if the reduction in the downstream nutrients load is accounted for, the total revenue of the process would be over 5.3 times of the associated costs.
Vu, MN, Tran, NH, Tuan, HD, Nguyen, TV & Nguyen, DHN 2019, 'Optimal signaling schemes and capacities of non-coherent correlated MISO channels under per-antenna power constraints', IEEE Transactions on Communications, vol. 67, no. 1, pp. 190-204.
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© 1972-2012 IEEE. This paper investigates the optimal signaling schemes and capacities of non-coherent correlated multiple-input single-output (MISO) channels in fast Rayleigh fading. We consider both channels under per-antenna power constraints as well as channels under joint per-antenna and sum power constraints. For per-antenna power constraint channels, we first establish the convex and compact properties of the feasible sets, and demonstrate the existence of optimal input distribution and the uniqueness of optimal effective magnitude input distribution. By exploiting the solutions of a quadratic optimization problem, we show that the Kuhn-Tucker condition on the optimal inputs can be simplified to a single dimension. As a result, we can apply the Identity Theorem to show the discrete and finite nature of the optimal effective magnitude distribution, with a mass point located at the origin. By using this distribution, we then construct a finite and discrete optimal input vector distribution. The use of this input allows us to determine the capacity gain of MISO over SISO via the phase solutions of a constrained quadratic optimization problem on a sphere, which can be obtained using a proposed penalized optimization algorithm. We also extend the results to MISO channels subject to the joint per-antenna and sum power constraints. Under this consideration, it is shown that not all per-antenna constraints are active. While the finiteness and discreteness of the optimal effective magnitude and the optimal input vector distributions still hold, the optimal phases and the optimal power allocation among the transmit antennas need to be determined simultaneously via a quadratic optimization problem under inequality constraints. These solutions can finally be used to obtain the MISO capacity gain.
Wakefield, J, Tyler, J, Dyson, LE & Frawley, JK 2019, 'Implications of student-generated screencasts on final examination performance', Accounting and Finance.
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© 2017 AFAANZ. While educational technologies can play a vital role in students' active participation in introductory accounting subjects, learning outcome implications are less clear. We believe this is the first accounting education study examining the implications of student-generated screencast assignments. We find benefits in developing the graduate attributes of communication, creativity and multimedia skills, consistent with calls by the profession. Additionally, we find improvement in final examination performance related to the assignment topic, notably in lower performing students. The screencast assignment was optional, and the findings suggest a tailored approach to assignment design related to students' developmental needs is appropriate.
Wang, B, Yuan, X, Gao, X, Li, X & Tao, D 2019, 'A Hybrid Level Set With Semantic Shape Constraint for Object Segmentation.', IEEE transactions on cybernetics, vol. 49, no. 5, pp. 1558-1569.
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This paper presents a hybrid level set method for object segmentation. The method deconstructs segmentation task into two procedures, i.e., shape transformation and curve evolution, which are alternately optimized until convergence. In this framework, only one shape prior encoded by shape context is utilized to estimate a transformation allowing the curve to have the same semantic expression as shape prior, and curve evolution is driven by an energy functional with topology-preserving and kernelized terms. In such a way, the proposed method is featured by the following advantages: 1) hybrid paradigm makes the level set framework possess the ability of incorporating other shape-related techniques about shape descriptor and distance; 2) shape context endows one single prior with semanticity, and hence leads to the competitive performance compared to the ones with multiple shape priors; and 3) additionally, combining topology-preserving and kernelization mechanisms together contributes to realizing a more reasonable segmentation on textured and noisy images. As far as we know, we propose a hybrid level set framework and utilize shape context to guide curve evolution for the first time. Our method is evaluated with synthetic, healthcare, and natural images, as a result, it shows competitive and even better performance compared to the counterparts.
Wang, D, Wu, C, Huang, W & Zhang, Y 2019, 'Vibration investigation on fluid-structure interaction of AP1000 shield building subjected to multi earthquake excitations', Annals of Nuclear Energy, vol. 126, pp. 312-329.
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© 2018 Elsevier Ltd Fluid-structure interaction (FSI) between water and water tank of AP1000 nuclear power plant has always been a hot topic because the gravity water tank plays a key role in protecting structural safety in an emergency such as an earthquake. The main target of this study is to investigate the FSI effect on structural dynamic responses of AP1000 shield building with filled and empty water tanks and to explore the most reasonable height of water level for reducing seismic response under inputs of multi three-direction earthquake excitations. For this purpose, method of nonlinear FSI algorithm of finite element is employed based on ANSYS platform. The numerical procedure is validated by comparison with theoretical calculation and existing experimental results of fluid free vibration and structural seismic responses in the situation of El Centro wave. Based on the validated numerical model, a series of numerical simulations on seven partially-filled models in six natural and one artificial earthquake are carried out and corresponding results, such as peak acceleration & displacement, floor response spectrum and structural base shear, are studied comparatively in details. Discussions of this study show that the partially-filled shield building appears significant FSI effect, which generates great influence on structural dynamic characteristics and responses. Reasonable design of the water level can contribute to reducing structural responses and improving seismic safety.
Wang, D, Wu, C, Zhang, Y & Li, S 2019, 'Study on vertical vibration control of long-span steel footbridge with tuned mass dampers under pedestrian excitation', Journal of Constructional Steel Research, vol. 154, pp. 84-98.
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© 2018 Elsevier Ltd This paper aims to study crowd-induced vibration control of long-span steel footbridges with different dynamic characteristics by combining methods of site measurement and numerical simulation. Four kinds of footbridge models with sensitive vertical natural frequencies of easily generating human-bridge resonance under pedestrian loading are designed based on an actual steel footbridge. The numerical models are firstly validated by comparative investigation between the site measurement and the simulation. Detailed study on dynamic responses of the four footbridges with and without controlling systems of tuned mass damper (TMD) and multiple tuned mass damper (MTMD) is then conducted under 13 cases of crowd random excitations, rhythmic running and jumping excitations. Results show that the numerical simulation agrees well with the site measurement data. TMD system is found to be highly efficient in reducing vibration responses only when the excitation frequency is basically consistent with the structural natural frequency, which obviously limits the application of TMD system in footbridges as wider excitation frequency bandwidth is caused by human activities. MTMD system are demonstrated to be with higher vibration absorption robustness appearing predominant and stable capacity of reducing structural vibrations under all the crowd random and rhythmic excitations for the four footbridges.
Wang, D, Wu, C, Zhang, Y, Ding, Z & Chen, W 2019, 'Elastic-plastic behavior of AP1000 nuclear island structure under mainshock-aftershock sequences', Annals of Nuclear Energy, vol. 123, pp. 1-17.
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© 2018 Elsevier Ltd This paper studies dynamic responses of AP1000 nuclear island structure in strong earthquake sequences. A numerical model to simulate nuclear structural behaviors in earthquake is validated by comparison with data from a previous study on a nonlinear dynamic analysis of a reinforced concrete shield building. The validated numerical model is then used to carry out a series of parametric analyses with 112 computational cases so as to determine influence of strong aftershocks on structural elastic-plastic behavior considering input of three-dimensional ground motions. The results indicate that the influence of aftershocks on structural horizontal/vertical dynamic responses is very small in design basis earthquake sequences. However, the influence must be considered seriously in beyond-design basis earthquake sequences as values of RMVs (Ratio of Mean Value) deviating IPRs (Input Peak Ratio) obviously, which means structural dynamic responses are greatly changed in strong aftershocks. Damage aggravating effect induced by strong aftershocks can cause severe damage of structural members and it is found the greater the magnitude of aftershocks, the severer the aggravation effect. Although earthquake input energy is mostly dissipated by damping energy, plastic damage energy plays considerable role in strong aftershocks as it shares beyond 8 percent of the total input energy, which is 10 times more compared to design basis earthquake sequences.
Wang, D, Zhang, D, Xu, Q, Liu, Y, Wang, Q, Ni, BJ, Yang, Q, Li, X & Yang, F 2019, 'Calcium peroxide promotes hydrogen production from dark fermentation of waste activated sludge', Chemical Engineering Journal, vol. 355, pp. 22-32.
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© 2018 Elsevier B.V. Calcium peroxide (CaO2), one of versatile and harmless inorganic peroxy compounds, has been recently used as a highly effective oxidant to degrade pollutants in sludge and a method to achieve sludge resource utilization. However, its impact on hydrogen production has never been studied before. This investigation therefore aims to fill this knowledge gap. Results indicated that with CaO2increased from 0.05 to 0.25 g/g VSS (volatile suspended solids), the maximum hydrogen yield increased from 0.77 to 10.55 mL/g VSS. The mechanism studies revealed that CaO2accelerated the breakage and death of sludge cells. Excitation emission matrix (EEM) analyses further indicated that CaO2increased the biodegradability of the released substances, which could provide more biodegradable organics for the following reactions. Although CaO2caused inhibitions to some extents to all the tested microbes, its inhibition to hydrogen consumers was much severer than that to hydrolytic microbes and hydrogen producers. The enzyme analyses also demonstrated that the suppression of calcium peroxide to the activities of enzymes related to hydrogen consumption process was much severer than that to the activities of the activities of enzymes related to hydrogen production process. Further investigations exhibited that alkali, [rad]O2−and [rad]OH radicals, were the major intermediate products of CaO2decomposition. All of them were verified to contribute the increased hydrogen production, and their contributions were in the order of alkali > [rad]O2−> [rad]OH. This is the first work demonstrating that CaO2can enhance hydrogen production from WAS. The findings reported in our paper not only expand the application field of CaO2but also deepen the understanding of the CaO2-involved sludge fermentation process.
Wang, G, Ji, J & Zhou, J 2019, 'Practical stochastic synchronisation of coupled harmonic oscillators subjected to heterogeneous noises and its applications to electrical systems', IET Control Theory and Applications, vol. 13, no. 1, pp. 96-105.
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© The Institution of Engineering and Technology 2018. This study focuses on the practical stochastic synchronisation of coupled harmonic oscillators subjected to heterogeneous noises, where the dissipative and restorative couplings are no longer required in the connected network topologies. By employing the variational approach in combination with Lyapunov-like analysis, some simple yet generic practical synchronisation criteria are established in the sense of probability distribution and of mean square for coupled harmonic oscillator with directed network topology. Three main issues on stochastic synchronisation, including practical distribution synchronisation, stochastic distribution synchronisation, and practical mean square synchronisation, as well as their differences and relationships are fully addressed. The developed practical synchronisation criteria are then applied to a representative model of electrical systems which are composed of LC oscillators with linear time-invariant (LTI) resistors and inductors. Finally, numerical simulations are provided to show the effectiveness of the developed methods.
Wang, G, Lu, J, Choi, KS & Zhang, G 2019, 'A Transfer-Based Additive LS-SVM Classifier for Handling Missing Data', IEEE Transactions on Cybernetics.
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IEEE The performance of a classifier might greatly deteriorate due to missing data. Many different techniques to handle this problem have been developed. In this paper, we solve the problem of missing data using a novel transfer learning perspective and show that when an additive least squares support vector machine (LS-SVM) is adopted, model transfer learning can be used to enhance the classification performance on incomplete training datasets. A novel transfer-based additive LS-SVM classifier is accordingly proposed. This method also simultaneously determines the influence of classification errors caused by each incomplete sample using a fast leave-one-out cross validation strategy, as an alternative way to clean the training data to further improve the data quality. The proposed method has been applied to seven public datasets. The experimental results indicate that the proposed method achieves at least comparable, if not better, performance than case deletion, mean imputation, and k-nearest neighbor imputation methods, followed by the standard LS-SVM and support vector machine classifiers. Moreover, a case study on a community healthcare dataset using the proposed method is presented in detail, which particularly highlights the contributions and benefits of the proposed method to this real-world application.
Wang, G, Zhang, G, Choi, KS & Lu, J 2019, 'Deep Additive Least Squares Support Vector Machines for Classification With Model Transfer', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE The additive kernel least squares support vector machine (AK-LS-SVM) has been well used in classification tasks due to its inherent advantages. For example, additive kernels work extremely well for some specific tasks, such as computer vision classification, medical research, and some specialized scenarios. Moreover, the analytical solution using AK-LS-SVM can formulate leave-one-out cross-validation error estimates in a closed form for parameter tuning, which drastically reduces the computational cost and guarantee the generalization performance especially on small and medium datasets. However, AK-LS-SVM still faces two main challenges: 1) improving the classification performance of AK-LS-SVM and 2) saving time when performing a grid search for model selection. Inspired by the stacked generalization principle and the transfer learning mechanism, a layer-by-layer combination of AK-LS-SVM classifiers embedded with transfer learning is proposed in this paper. This new classifier is called deep transfer additive kernel least square support vector machine (DTA-LS-SVM) which overcomes these two challenges. Also, considering that imbalanced datasets are involved in many real-world scenarios, especially for medical data analysis, the deep-transfer element is extended to compensate for this imbalance, thus leading to the development of another new classifier iDTA-LS-SVM. In the hierarchical structure of both DTA-LS-SVM and iDTA-LS-SVM, each layer has an AK-LS-SVM and the predictions from the previous layer act as an additional input feature for the current layer. Importantly, transfer learning is also embedded to guarantee generalization consistency between the adjacent layers. Moreover, both iDTA-LS-SVM and DTA-LS-SVM can ensure the minimal leave-one-out error by using the proposed fast leave-one-out cross validation strategy on the training set in each layer. We compared the proposed classifiers DTA-LS-SVM and iDTA-LS-SVM with the traditional LS-SVM and SVM using ...
Wang, J, Tang, L, Zhao, L & Zhang, Z 2019, 'Efficiency investigation on energy harvesting from airflows in HVAC system based on galloping of isosceles triangle sectioned bluff bodies', Energy.
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Galloping-based piezoelectric energy harvester (GPEH) has been used in power generation from small-scale airflows for low-power devices such as Micro-Electromechanical Systems (MEMS) and wireless sensing electronics. The bluff body plays an important role for the onset of galloping. Existing literature regarding analytical and numerical analysis of GPEH has focused on designs incorporating bluff bodies with a variety of cross-sections, such as square, D-section and regular triangle. In this work, a GPEH with triangular cross-section bluff bodies with different vertex angles is investigated. The aerodynamic characteristics are determined by Computational Fluid Dynamics (CFD) and verified by experimental data. Subsequently, an aero-electro-mechanical model with piezoelectric coupling is established and numerically solved. Furthermore, a parametric study is performed to investigate the influence of electromechanical coupling on the GPEH's behavior, with a focus on the threshold wind speed, transverse displacement and power output. It is determined that with weak coupling, the obtuse angle β = 130° is the most preferred vertex angle. This is the first documented determination that an obtuse angled isosceles triangle could be used for efficient galloping energy harvesting. The findings provide a guideline for designing efficient GPEHs with triangular bluff bodies.
Wang, J, Zhang, N & Lu, H 2019, 'A novel system based on neural networks with linear combination framework for wind speed forecasting', Energy Conversion and Management, pp. 425-442.
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© 2018 Elsevier Ltd The absence of accurate and stable prediction of wind speed remains a major obstacle to the rational planning, scheduling, and maintenance of wind power generation. Currently, an extensive body of methods that aim to enhance the accuracy of wind speed prediction have been proposed. However, the majority of previous studies have tended to emphasize the structural improvement of individual forecasting models without considering the validity of data preprocessing. This can result in poor forecasting accuracy due to their failure to fully capture the effective information of the wind speed data. A new approach is proposed in this paper that successfully combines a data preprocessing technique with a linear combination method. Further, a new neural network framework is employed to determine the required combination weights to ensure improved prediction performance, thereby overcoming the drawback of the low accuracy of individual prediction models. Six wind speed datasets from Penglai are regarded as expository cases to analyze the forecasting validity and stability of the developed model. It can be concluded from the experiments that the combined forecasting system outperforms the individual models and the traditional linear combination models with higher accuracy and stronger stability.
Wang, K, Wang, P & Qiu, X 2019, 'Increasing speech intelligibility in monaural hearing by adding noise at the other ear', Applied Acoustics, vol. 146, pp. 50-55.
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© 2018 Elsevier Ltd Ambient environment noise can affect speech intelligibility in phone communication. This paper investigates the feasibility of increasing speech intelligibility in monaural hearing by adding noise at the other ear. The testing materials were generated by mixing the speech from the English Coordinate Response Measure corpus with three types of environmental noise, where 4 signals to noise ratios in the speech ear and 14 noise levels in the contralateral ear were included. The experimental results show that a proper level of contralateral noise can improve the speech intelligibility when the signal to noise ratio in the speech ear is lower than a certain level, but a large contralateral noise level has the opposite effect. A preliminary explanation for the phenomena is attempted by using a binaural loudness model and some psychoacoustic and physiological facts.
Wang, M, Xu, C, Chen, X, Hao, H, Zhong, L & Yu, S 2019, 'Differential privacy oriented distributed online learning for mobile social video prefetching', IEEE Transactions on Multimedia, vol. 21, no. 3, pp. 636-651.
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© 2019 IEEE The ever fast growing mobile social video traffic has motivated the urgent requirement of alleviating backbone pressures while ensuring the user-quality experience. Mobile video prefetching previously caches the future accessed videos at the edge, which has become a promising solution for traffic offloading and delay reduction. However, providing high performance prefetching still remains problematic in the presence of high dynamic mobile users' viewing behaviors and consecutive generated video content. Besides, given the fact that making prefetching decision requires viewing history that is sensitive, the increasing privacy issues should also be considered. In this paper, we propose a differential privacy oriented distributed online learning method for mobile social video prefetching (DPDL-SVP). Through a large-scale data analysis based on one of the most popular online social network sites, WeiBo.cn, we reveal that users' viewing behaviors have strong a relation with video preference, content popularity, and social interactions. We then formulate the prefetching problem as an online convex optimization based on these three factors. Furthermore, the problem is divided into two subproblems, and we implement a distributed algorithm separately to solve them with differential privacy. The performance bound of the proposed online algorithms is also theoretically proved. We conduct a series simulation based on real viewing traces to evaluate the performance of DPDL-SVP. Evaluation results show how our proposed algorithms achieve superior performance in terms of the prediction accuracy, delay reduction, and scalability.
Wang, Q, Gong, Y, Liu, S, Wang, D, Liu, R, Zhou, X, Nghiem, LD & Zhao, Y 2019, 'Free Ammonia Pretreatment to Improve Bio-hydrogen Production from Anaerobic Dark Fermentation of Microalgae', ACS Sustainable Chemistry and Engineering, vol. 7, no. 1, pp. 1642-1647.
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© 2018 American Chemical Society. Microalgae are third generation feedstocks for bio-hydrogen production to achieve a low carbon economy. Nevertheless, the bio-hydrogen production from microalgae is generally low. In this study, an innovative free ammonia (FA, i.e., NH 3 ) pretreatment technology was first demonstrated to improve bio-hydrogen production from the secondary effluent cultivated microalgae during the anaerobic dark fermentation experiments. Scanning electron microscopy revealed that FA pretreatment disrupted microalgae surface morphology. The soluble chemical oxygen demand (SCOD) release increased from 0.01 g SCOD/g VS microalgae (VS = volatile solids) to 0.05-0.07 g SCOD/g VS microalgae after FA pretreatment of 240-530 mg NH 3 -N/L for 1 day, indicating the enhanced microalgae solubilization. Dark fermentation bio-hydrogen potential experiments showed that bio-hydrogen production from microalgae was substantially improved following FA pretreatment of 240-530 mg NH 3 -N/L. The bio-hydrogen production potential and maximum bio-hydrogen production rate increased from 18.2 L H 2 /kg VS microalgae and 2.5 L H 2 /kg VS microalgae/d to 19.9-22.1 L H 2 /kg VS microalgae and 3.1-3.8 L H 2 /kg VS microalgae/d, respectively, after FA pretreatment of 240-530 mg NH 3 -N/L was implemented on the microalgae for 1 day. This FA technology follows a circular economic model because the required FA is from the FA rich dark fermentation liquid, which is a wastewater treatment waste.
Wang, R, Hao, Q, Feng, J, Wang, GC, Ding, H, Chen, D & Ni, B 2019, 'Enhanced separation of photogenerated charge carriers and catalytic properties of ZnO-MnO 2 composites by microwave and photothermal effect', Journal of Alloys and Compounds, pp. 418-427.
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© 2019 Elsevier B.V. To improve the solar energy utilization and photodegradation efficiency of ZnO and α-MnO 2 , ZnO/MnO 2 composite materials were prepared by a facile method. The materials are characterized by XRD, Raman spectra, X-ray photoelectron spectroscopy, scanning electronic microscopy, transmission electron microscopy, and UV–vis diffuse reflection spectroscopy. The photocatalytic activity and microwave-assisted photocatalytic activity of the composite are much higher than that of ZnO or α-MnO 2 . The main active species in the reaction progress were confirmed by electron paramagnetic resonance spectra and trapping experiments. According to the DFT calculation result and photothermal images, the enhanced catalytic activity is attributed to the photothermal and microwave-assisted effect. The addition of α-MnO 2 improves the absorption of light and microwave by the composite, which can further heat up the catalysts. As a result, the separation of photogenerated charge carriers is accelerated. Finally, a mechanism for the enhanced catalytic performance of the composite materials was proposed.
Wang, S, Wang, Y, Liu, C, Lei, G, Guo, Y & Zhu, J 2019, 'Comparative Study of Linear Superconductivity Machine with Different Stator and Winding Configurations', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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IEEE Compared with rotary electrical machine in linear drive applications, linear machine without rotary-linear motion conversion equipment can have higher force density and efficiency. Linear superconductivity machine (LSM) consists of high performance superconducting magnet instead of permanent magnet can offer very high force density and efficiency compared with other linear machines, such as linear induction machine, linear permanent magnet machine and so on. In this paper, LSM with different stators and winding configurations are investigated, specifically LSMs with concentrated winding or distributed winding and unilateral side stator or bilateral side stator. The electromagnetic parameters and performance of these LSMs are calculated and compared by using the finite element method (FEM), then the main difference of various design methods in LSM have been concluded.
Wang, TQ, Li, H & Huang, X 2019, 'Analysis and Mitigation of Clipping Noise in Layered ACO-OFDM Based Visible Light Communication Systems', IEEE Transactions on Communications.
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IEEE Due to the limited dynamic range of the off-theshelf electrical and optical components, deliberate digital clipping (DDC) is widely applied to optical orthogonal frequency division multiplexing (OFDM) based visible light communication systems. In this paper, we present a theoretical characterization of the layered asymmetrically clipped optical OFDM (ACO-OFDM) signals subject to peak clipping. We decouple a clipped L-layer ACO-OFDM symbol to L single-layer ACO-OFDM symbols, each corresponding to a layer, and show that these symbols are subject to symmetrical peak clippings at random levels. Using the Bussgang’s theorem, the resulting attenuation factors and variances of the additive noise associated with each layer are derived. It is shown that the clipping noise caused by the DDC mainly falls onto the first layer, and its impact is gradually reduced in the subsequent layers. In order to combat the clipping noise, a novel receiver based on decision aided reconstruction is proposed. Simulation results show that the proposed receiver can effectively mitigate the clipping noise, leading to significant improvement of bit error rates over the conventional receiver.
Wang, W, Wu, C & Liu, Z 2019, 'Compressive behavior of hybrid double-skin tubular columns with ultra-high performance fiber-reinforced concrete (UHPFRC)', Engineering Structures, vol. 180, pp. 419-441.
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© 2018 Elsevier Ltd This study presents the results of an experimental program on the compressive behavior of hybrid fiber-reinforced polymer (FRP)-concrete-steel double-skin tubular columns (DSTCs) with ultra-high performance fiber-reinforced concrete (UHPFRC). In total 40 specimens, including 32 hybrid DSTCs and eight FRP confined solid concrete (FCSC) specimens, were prepared and tested under axial compression. In addition to hybrid UHPFRC DSTCs, hybrid DSTCs with ultra-high performance concrete without steel fiber addition (UHPC), high-strength concrete (HSC), and normal-strength concrete (NSC) were also tested. The investigated parameters included the FRP tube thickness, steel tube thickness, void ratio, steel fiber addition, concrete type, UHPFRC-filling inside the steel tube, and the column type. The test results indicate that the hybrid UHPFRC DSTCs can exhibit highly ductile behavior when a thick FRP tube is used. However, due to the ultra-high strength and the dense microstructure of UHPFRC, the hybrid UHPFRC DSTCs are likely to exhibit more brittle behavior than the hybrid DSTCs with NSC and HSC. Even though a high confinement level is provided, sudden stress reduction or stress fluctuations can be observed for the UHPFRC in hybrid DSTCs. The influences of FRP tube thickness, void ratio, steel fiber addition, and UHPFRC-filling inside the steel tube on the compressive behavior of the hybrid UHPFRC DSTCs are significant, while the influence of steel tube thickness is insignificant. Moreover, when compared to the FCSC specimens, the presence of an inner void is beneficial for the compressive behavior of UHPFRC in the hybrid DSTCs, especially when a thick FRP tube is used. Furthermore, the performance of existing stress-strain model to predict the compressive behavior of UHPFRC in the hybrid DSTCs is investigated.
Wang, X, Liu, Y, Lu, J, Xiong, F & Zhang, G 2019, 'TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators', Future Generation Computer Systems, vol. 94, pp. 224-236.
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© 2018 Elsevier B.V. In recent years, an increase in group activities on websites has led to greater demand for highly-functional group recommender systems. The goal of group recommendation is to capture and distill the preferences of each group member into a single recommendation list that meets the needs of all group members. Existing aggregation functions perform well in harmonious and congruent scenarios, but tend not to generate satisfactory results when group members hold conflicting preferences. Moreover, most of current studies improve group recommendation only based on a single aggregation strategy and explicit trust information is still ignored in group recommender systems. Motivated by these concerns, this paper presents TruGRC, a novel Trust-aware Group Recommendation method with virtual Coordinators, that combines two different aggregation strategies: result aggregation and profile aggregation. As each individual's preferences are modeled, a virtual user is built as a coordinator to represent the profile aggregation strategy. This coordinator provides a global view of the preferences for all group members by interacting with each user to resolve conflicting preferences. Then, we also model the impact from group members to the virtual coordinator in accordance with personal social influence inferred by trust information on social networks. Group preferences can be easily generated by the average aggregation method under the effect of the virtual coordinator. Experimental results on two benchmark datasets with a range of different group sizes show that TruGRC method has significant improvements compared to other state-of-the-art methods.
Wang, X, Qin, L, Lin, X, Zhang, Y & Chang, L 2019, 'Leveraging set relations in exact and dynamic set similarity join', VLDB Journal.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Set similarity join, which finds all the similar set pairs from two collections of sets, is a fundamental problem with a wide range of applications. Existing works study both exact set similarity join and approximate similarity join problems. In this paper, we focus on the exact set similarity join problem. The existing solutions for exact set similarity join follow a filtering-verification framework, which generates a list of candidate pairs through scanning indexes in the filtering phase and reports those similar pairs in the verification phase. Though much research has been conducted on this problem, set relations have not been well studied on improving the algorithm efficiency through computational cost sharing. Therefore, in this paper, we explore the set relations in different levels to reduce the overall computational cost. First, it has been shown that most of the computational time is spent on the filtering phase, which can be quadratic to the number of sets in the worst case for the existing solutions. Thus, we explore index-level set relations to reduce the filtering cost while keeping the same filtering power. We achieve this by grouping related sets into blocks in the index and skipping useless index probes in joins. Second, we explore answer-level set relations to further improve the algorithm based on the intuition that if two sets are similar, their answers may have a large overlap. We derive an algorithm which incrementally generates the answer of one set from an already computed answer of another similar set rather than compute the answer from scratch to reduce the computational cost. In addition, considering that in real applications, the data are usually updated dynamically, we extend our techniques and design efficient algorithms to incrementally update the join result when any element in the sets is updated. Finally, we conduct extensive performance studies using 21 real datasets...
Wang, X, Song, B, Ni, W, Liu, RP, Guo, YJ, Niu, X & Zheng, K 2019, 'Group-based susceptible-infectious-susceptible model in large-scale directed networks', Security and Communication Networks, vol. 2019.
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© 2019 Xu Wang et al. Epidemic models trade the modeling accuracy for complexity reduction. This paper proposes to group vertices in directed graphs based on connectivity and carries out epidemic spread analysis on the group basis, thereby substantially reducing the modeling complexity while preserving the modeling accuracy. A group-based continuous-time Markov SIS model is developed. The adjacency matrix of the network is also collapsed according to the grouping, to evaluate the Jacobian matrix of the group-based continuous-time Markov model. By adopting the mean-field approximation on the groups of nodes and links, the model complexity is significantly reduced as compared with previous topological epidemic models. An epidemic threshold is deduced based on the spectral radius of the collapsed adjacency matrix. The epidemic threshold is proved to be dependent on network structure and interdependent of the network scale. Simulation results validate the analytical epidemic threshold and confirm the asymptotical accuracy of the proposed epidemic model.
Wang, X, Zha, X, Ni, W, Liu, RP, Guo, YJ, Niu, X & Zheng, K 2019, 'Survey on blockchain for Internet of Things', Computer Communications, vol. 136, pp. 10-29.
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© 2019 Elsevier B.V. The Internet of Things (IoT) is poised to transform human life and unleash enormous economic benefit. However, inadequate data security and trust of current IoT are seriously limiting its adoption. Blockchain, a distributed and tamper-resistant ledger, maintains consistent records of data at different locations, and has the potential to address the data security concern in IoT networks. While providing data security to the IoT, Blockchain also encounters a number of critical challenges inherent in the IoT, such as a huge number of IoT devices, non-homogeneous network structure, limited computing power, low communication bandwidth, and error-prone radio links. This paper presents a comprehensive survey on existing Blockchain technologies with an emphasis on the IoT applications. The Blockchain technologies which can potentially address the critical challenges arising from the IoT and hence suit the IoT applications are identified with potential adaptations and enhancements elaborated on the Blockchain consensus protocols and data structures. Future research directions are collated for effective integration of Blockchain into the IoT networks.
Wang, Y, Shuai, Y, Zhu, Y, Zhang, J & An, P 2019, 'Jointly learning perceptually heterogeneous features for blind 3D video quality assessment', Neurocomputing, vol. 332, pp. 298-304.
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© 2018 Elsevier B.V. 3D videos quality assessment (3D-VQA) is essential to various 3D video processing applications. However, it has not been well investigated on how to make use of perceptual multi-channel video information to improve 3D-VQA under different distortion categories and degrees, especially under asymmetrical distortions. In the paper, we propose a new blind 3D-VQA metric by jointly learning perceptually heterogeneous features. Firstly, a binocular spatio-temporal internal generative mechanism (BST-IGM) is proposed to decompose the views of 3D video into multi-channel videos. Then, we extract perceptually heterogeneous features by proposed multi-channel natural video statistics (MNVS) model, which are characterized 3D video information. Furthermore, a robust AdaBoosting Radial Basis Function (RBF) neural network is utilized to map the features to the overall quality of 3D video. On two benchmark databases, the extensive evaluations demonstrate that the proposed algorithm significantly outperforms several state-of-the-art quality metrics in term of prediction accuracy and robustness.
Wei, F, Yang, ZJ, Qin, PY, Guo, YJ, Li, B & Shi, XW 2019, 'A Balanced-to-Balanced In-Phase Filtering Power Divider with High Selectivity and Isolation', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 2, pp. 683-694.
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© 1963-2012 IEEE. In this paper, a balanced-to-balanced in-phase filtering power divider (FPD) is proposed, which can realize a two-way equal power division with high selectivity and isolation. The design of the proposed FPD is primarily based on microstrip/slotline transition structures and slotline T-junction. A U-type microstrip feed line integrated with a stepped-impedance slotline resonator is adopted at the input and output ports, which makes the differential-mode (DM) responses independent of the common-mode (CM) ones. Meanwhile, superior DM transmission and CM suppression are achieved intrinsically, thereby simplifying the design procedure significantly. By employing slotline resonators loaded with resistors, the isolation between the two output ports can be improved greatly. In addition, a DM passband with a sharp filtering performance is realized by introducing the microstrip stub-loaded resonators (SLRs). By changing the electrical length of the open stub of the SLR, the fractional bandwidth is controllable. In order to verify the feasibility of the proposed design method, two prototype circuits of the proposed FPDs with different bandwidths are fabricated and measured. Good agreement between the simulation and measurement results is observed.
Wei, W, Huang, Q-S, Sun, J, Wang, J-Y, Wu, S-L & Ni, B-J 2019, 'Polyvinyl Chloride Microplastics Affect Methane Production from the Anaerobic Digestion of Waste Activated Sludge through Leaching Toxic Bisphenol-A.', Environmental science & technology, vol. 53, no. 5, pp. 2509-2517.
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The retention of polyvinyl chloride (PVC) microplastics in sewage sludge during wastewater treatment raises concerns. However, the effects of PVC microplastics on methane production from anaerobic digestion of waste activated sludge (WAS) have never been documented. In this work, the effects of PVC microplastics (1 mm, 10-60 particles/g TS) on anaerobic methane production from WAS were investigated. The presence of 10 particles/g TS of PVC microplastics significantly ( P = 0.041) increased methane production by 5.9 ± 0.1%, but higher levels of PVC microplastics (i.e., 20, 40, and 60 particles/g TS) inhibited methane production to 90.6 ± 0.3%, 80.5 ± 0.1%, and 75.8 ± 0.2% of the control, respectively. Model-based analysis indicated that PVC microplastics at >20 particles/g TS decreased both methane potential (B0) and hydrolysis coefficient (k) of WAS. The mechanistic studies showed that bisphenol A (BPA) leaching from PVC microplastics was the primary reason for the decreased methane production, causing significant ( P = 0.037, 0.01, 0.004) inhibitory effects on the hydrolysis-acidification process. The long-term effects of PVC microplastics revealed that the microbial community was shifted in the direction against hydrolysis-acidification and methanation. In conclusion, PVC microplastic caused negative effects on WAS anaerobic digestion through leaching the toxic BPA.
Wei, X, Cheng, M, Zhu, J, Yang, H & Luo, R 2019, 'Finite-Set Model Predictive Power Control of Brushless Doubly Fed Twin Stator Induction Generator', IEEE Transactions on Power Electronics, vol. 34, no. 3, pp. 2300-2311.
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© 1986-2012 IEEE. This paper presents a finite-set model predictive power control (FS-MPPC) method for the brushless doubly fed twin stator induction generator (BDFTSIG) in variable speed constant frequency generation applications. The FS-MPPC controller is developed in a general reference frame from which all other reference frames can be deduced readily. The invariant feature of the predictive power model in various reference frames contributes to the reference frame-free characteristic of the developed FS-MPPC controller, enabling its application more flexible and universal. Besides, the arduous process of control winding flux estimation is avoided in the FS-MPPC controller by choosing state variables that are easy to be obtained. Moreover, the influence of rotor circuit that has long been neglected in the existing controllers for the brushless doubly fed induction machines is embedded within the predictive power model and inherently considered in the FS-MPPC controller, which contributes to accurate power control of the BDFTSIG. Furthermore, the feasibility and effectiveness of the developed FS-MPPC controller regarding different power levels and grid fault conditions are briefly discussed. Finally, numerical simulations and experimental tests are carried out, which demonstrates the effectiveness of the developed FS-MPPC controller.
Wen, D, Qin, L, Zhang, Y, Lin, X & Yu, JX 2019, 'I/O Efficient Core Graph Decomposition: Application to Degeneracy Ordering', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 1, pp. 75-90.
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© 1989-2012 IEEE. Core decomposition is a fundamental graph problem with a large number of applications. Most existing approaches for core decomposition assume that the graph is kept in memory of a machine. Nevertheless, many real-world graphs are too big to reside in memory. In this paper, we study I/O efficient core decomposition following a semi-external model, which only allows node information to be loaded in memory. We propose a semi-external algorithm and an optimized algorithm for I/O efficient core decomposition. To handle dynamic graph updates, we firstly show that our algorithm can be naturally extended to handle edge deletion. Then, we propose an I/O efficient core maintenance algorithm to handle edge insertion, and an improved algorithm to further reduce I/O and CPU cost. In addition, based on our core decomposition algorithms, we further propose an I/O efficient semi-external algorithm for degeneracy ordering, which is an important graph problem that is highly related to core decomposition. We also consider how to maintain the degeneracy order. We conduct extensive experiments on 12 real large graphs. Our optimal core decomposition algorithm significantly outperforms the existing I/O efficient algorithm in terms of both processing time and memory consumption. They are very scalable to handle web-scale graphs. As an example, we are the first to handle a web graph with 978.5 million nodes and 42.6 billion edges using less than 4.2 GB memory. We also show that our proposed algorithms for degeneracy order computation and maintenance can handle big graphs efficiently with small memory overhead.
Wen, S, Xiao, S, Yang, Y, Yan, Z, Zeng, Z & Huang, T 2019, 'Adjusting Learning Rate of Memristor-based Multilayer Neural Networks via Fuzzy Method', IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
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IEEE Back Propagation (BP) based on stochastic gradient descent (SGD) is the prevailing method to train multilayer neural networks (MNNs) with hidden layers. However, the existence of the physical separation between memory arrays and arithmetic module makes it inefficient and ineffective to implement BP in conventional digital hardware. Although CMOS may alleviate some problems of the hardware implementation of MNNs, synapses based on complementary metal-oxide-semiconductor (CMOS) cost too much power and areas in very large scale integrated (VLSI) circuits. As a novel device, memristor shows promises to overcome this shortcoming due to its ability to closely integrate processing and memory. This paper proposes a novel circuit for implementing a synapse based on a memristor and two MOSFET tansistors (p-type and n-type). Compared with a CMOS-only circuit, the proposed one reduced the area consumption by 92%-98%. In addition, we develop a fuzzy method for the adjustment of the learning rates of MNNs, which increases the learning accuracy by 2%-3% compared with a constant learning rate. Meanwhile, the fuzzy adjustment method is robust and insensitive to parameter changes due to the approximate reasoning. Furthermore, the proposed methods can be extended to memristor-based multi-layer convolutional neural network for complex tasks. The novel architecture behaves in a human-liking thinking process.
Wicks, M, Millar, GJ & Altaee, A 2019, 'Process simulation of ion exchange desalination treatment of coal seam gas associated water', Journal of Water Process Engineering, vol. 27, pp. 89-98.
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© 2018 Elsevier Ltd The aim of this investigation was to develop an ion exchange process for the remediation of coal seam gas (CSG) associated water to make it suitable for beneficial reuse. The hypothesis was that computational modelling could accelerate the selection of appropriate ion exchange desalination strategies. Hence, we applied AqMB water process engineering software to predict which combination of weak acid cation (WAC), strong acid cation (SAC), weak base anion (WBA) and strong base anion (SBA) resins were most appropriate. Simulation results revealed that both SAC/WBA and SAC/SBA resin combinations were unable to meet water beneficial reuse standards for conductivity (< 950 μS/cm) due to the presence of bicarbonate species (4973 and 1918 μS/cm, respectively). Thus, a degasser unit was necessary to remove the large concentrations (ca. 1328 mg/L) of dissolved carbon dioxide formed due to decomposition of bicarbonate/carbonate species under acidic conditions in the cation resin stages. pH adjustment of effluent from the preferred SBA resin with acid not only did not meet solution conductivity guidelines but also raised the concentration of chloride or sulphate ions to levels, which may be detrimental for crop growth. Addition of a WAC resin allowed production of high quality water (either SAC/SBA/WAC or WAC/SAC/SBA combinations). To comply with sodium adsorption ratio requirements for irrigating soil it was suggested to apply micronized gypsum to the treated water. Economic evaluation suggested the treated water cost was A$1003 (WAC/SAC/SBA) to A$1276 (SAC/SBA/WAC) per ML treated which was comparable to estimated costs for a reverse osmosis desalination system.
Woolfrey, J, Lu, W & Liu, D 2019, 'A Control Method for Joint Torque Minimization of Redundant Manipulators Handling Large External Forces', Journal of Intelligent and Robotic Systems: Theory and Applications.
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© 2019, The Author(s). In this paper, a control method is developed for minimizing joint torque on a redundant manipulator where an external force acts on the end-effector. Using null space control, the redundant task is designed to minimize the torque required to oppose the external force, and reduce the dynamic torque. Furthermore, the joint motion can be weighted to factor in physical constraints such as joint limits, collision avoidance, etc. Conventional methods for joint torque minimization only consider the internal dynamics of the manipulator. If external forces acting on the end-effector are inadvertently implemented in to these control methods this could lead to joint configurations that amplify the resulting joint torque. The proposed control method is verified through two different case studies. The first case study involves simulation of high-pressure blasting. The second is a simulation of a manipulator lifting and moving a heavy object. The results show that the proposed control method reduces overall joint torque compared to conventional methods. Furthermore, the joint torque is minimized such that there is potential for a manipulator to execute certain tasks beyond its nominal payload capacity.
Wu, C, Fang, J & Li, Q 2019, 'Multi-material topology optimization for thermal buckling criteria', Computer Methods in Applied Mechanics and Engineering, vol. 346, pp. 1136-1155.
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© 2018 Elsevier B.V. The structures in thermal environment often suffer from severe thermal expansion, potentially leading to buckling failure. This study aims to address this issue by proposing multi-material topology optimization for thermomechanical buckling problems. The density-based model with the rational approximation of material properties (RAMP) is adopted here for parameterization of multiple materials. The sensitivities of thermomechanical compliance and buckling are derived through the adjoint technique. The globally convergent version of the method of moving asymptotes (GCMMA) is employed to solve the non-monotonic topology optimization problem. In this study, two numerical examples are presented to illustrate the effectiveness of the proposed method, in which the total volume of multi-materials is minimized subject to thermoelastic compliance and buckling constraints. The examples exhibit significant difference in the final topologies for mechanical buckling and thermomechanical buckling optimization. The study demonstrates the importance of thermomechanical buckling criteria for the design of structures operating in a temperature-varying environment.
Wu, C, Gao, Y, Fang, J, Lund, E & Li, Q 2019, 'Simultaneous Discrete Topology Optimization of Ply Orientation and Thickness for Carbon Fiber Reinforced Plastic-Laminated Structures', Journal of Mechanical Design, Transactions of the ASME, vol. 141, no. 4.
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© 2019 by ASME. This study developed a discrete topology optimization procedure for the simultaneous design of ply orientation and thickness for carbon fiber reinforced plastic (CFRP)-laminated structures. A gradient-based discrete material and thickness optimization (DMTO) algorithm was developed by using casting-based explicit parameterization to suppress the intermediate void across the thickness of the laminate. A benchmark problem was first studied to compare the DMTO approach with the sequential three-phase design method using the free size, ply thickness, and stacking sequence of the laminates. Following this, the DMTO approach was applied to a practical design problem featuring a CFRP-laminated engine hood by minimizing overall compliance subject to volume-related and functional constraints under multiple load cases. To verify the optimized design, a prototype of the CFRP engine hood was created for experimental tests. The results showed that the simultaneous discrete topology optimization of ply orientation and thickness was an effective approach for the design of CFRP-laminated structures.
Wu, H, Walker, P, Wu, J, Liang, J, Ruan, J & Zhang, N 2019, 'Energy management and shifting stability control for a novel dual input clutchless transmission system', Mechanism and Machine Theory, vol. 135, pp. 298-321.
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© 2019 Elsevier Ltd A dual input clutchless transmission system based on automated manual transmission (AMT) structure is developed for pure electric vehicles. An energy management strategy (EMS) is proposed to determine the power distribution between two motors and the optimal gear state. A mathematical model is built to minimize the energy consumption of the motors at each instant based on the motor efficiency maps. However, the proposed EMS in line with other energy-oriented strategies often result in excessive gear shifts and compromised drivability. To avoid the undesired gear shift, a shifting stabilizer is built in the EMS objective function to improve the shift quality. Accordingly, to achieve a balance between the energy consumption and the drivability, a multi-objective optimization method is adopted to reduce the unnecessary shift events while minimizing energy consumption. Two driving cycles representing typical daily driving conditions are used to demonstrate the effectiveness of the proposed system in terms of energy efficiency and shifting stability.
Wu, J, Ruan, J, Zhang, N & Walker, PD 2019, 'An Optimized Real-Time Energy Management Strategy for the Power-Split Hybrid Electric Vehicles', IEEE Transactions on Control Systems Technology.
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IEEE This paper proposes a new real-time energy management strategy (R-EMS) to improve the fuel economy performance of the power-split hybrid electric vehicles (HEVs). Being different from most conventional optimization-based EMS, the R-EMS does not need priori information of the driving cycle and is used to the online control of HEV. The forward dynamic model of power-split powertrain is built based on the Prius MY10. At each instant, the proposed R-EMS tries to minimize the equivalent consumed power of the HEV, which is the weighted summation of gasoline power and battery output power. The equivalence factor of battery output power has a clear physical meaning that is the efficiency of gasoline energy transferred to battery energy. Another two coefficients are introduced to control the state of charge (SOC) of battery. By considering the engine torque and engine speed as two independent or dependent design variables, respectively, the 2-D R-EMS and 1-D R-EMS are formed. Several typical driving cycles are used to simulate the performance of the R-EMS, and the results show that the proposed R-EMS not only maintains the battery SOC but also saves the fuel consumption compared with the rule-based EMS.
Wu, K, Ni, W, Su, T, Liu, RP & Guo, YJ 2019, 'Efficient angle-of-arrival estimation of lens antenna arrays for wireless information and power transfer', IEEE Journal on Selected Areas in Communications, vol. 37, no. 1, pp. 116-130.
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© 1983-2012 IEEE. Antenna design and angle-of-arrival (AoA) estimation are critical to the efficiency of wireless information and power transfer. The AoA estimation is challenging for energy-efficient lens antenna arrays (LAAs), due to discrete sets of fixed discrete Fourier transform (DFT) beams. This paper presents a novel fast and accurate approach for the AoA estimation of LAAs. The key idea is that we prove the two differential outputs of three adjacent lens beams, referred to as 'DFT beam differences (DBDs),' that are the strongest at the two sides of an AoA. They are easy to identify and robust to noises, and their powers are proved to provide an accurate estimate of the AoA. Another important aspect is a new beam synthesis technique which produces different beam widths based on DFT beams and practical 1-bit phase shifts in real time. As a result, the angular region containing the AoA can exponentially narrow down, and the two strongest DBDs can be quickly identified. The proposed approach can operate in coupling with successive interference cancellation to estimate the AoAs of multiple paths. Simulations show that the proposed approach is able to outperform the state of the art by orders of magnitude in terms of accuracy. The power transfer efficiency can be dramatically improved.
Wu, P, Wu, C, Liu, Z & Hao, H 2019, 'Investigation of shear performance of UHPC by direct shear tests', Engineering Structures, vol. 183, pp. 780-790.
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© 2019 Elsevier Ltd Ultra-high performance concrete (UHPC) has been applied widely in modern structure construction. The outstanding mechanical properties of UHPC not only enable strong yet slim structural designs but also highlight its potential in protective structural constructions against extreme loads. In this study, the shear transfer behaviors of UHPC are investigated by push-off tests on Z-shaped specimens, investigating the influences of the microsteel fiber volume ratio and stirrup reinforcement ratio on the shear strength, shear slip, and shear crack width of UHPC. The test results indicate that using a microsteel fiber can enhance the shear strength of UHPC specimens. Within a reasonable range of the steel fiber volume ratio (optimum volume ratio ranges from 0% to 2.5% for microsteel fiber), the shear strength and shear slip of UHPC increase significantly, and the shear crack width reduces with an increasing steel fiber volume ratio. Additionally, the ductility, shear strength, and shear slip of UHPC increase significantly, and the shear crack width reduces with increasing stirrup ratio. Finally, the simplified empirical equations for the ultimate shear strengths of UHPC specimens are deduced, and indicate good agreement with the experimental results.
Wu, T, Lu, K, Zhu, J, Lei, G, Guo, Y & Tang, S 2019, 'Calculation of Eddy Current Loss in a Tubular Oscillatory LPMSM Using Computationally Efficient FEA', IEEE Transactions on Industrial Electronics.
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IEEE In some special oscillatory applications, especially those operated at high speeds, the eddy current loss of linear permanent magnet (PM) synchronous machine (LPMSM) should be fully considered because the loss might be large and concentrated in PMs during the oscillation period. This paper presents a loss analysis method based on computationally efficient finite element analysis (CE-FEA) for a 20 Hz oscillatory tubular LPMSM. Since the mover speed varies with time, an equally-divided model in 1/4 period is introduced to calculate the average PM eddy current loss. The flux density curves in PMs are calculated at 18 intervals by the CE-FEA, through which the change rate of magnetic flux density is analyzed, considering both the entering and leaving effects and coil end effects. The calculation results show that the eddy current loss is obviously concentrated in PMs near the two ends of coils. The calculation results at a speed of 3.6 m/s obtained by the CE-FEA and 2D&3D time-stepping FEAs are compared to validate the accuracy. Finally, the proposed method is validated by the experimental test results on a prototype LPMSM.
Wu, Y, Liu, T, Ling, SH, Szymanski, J, Zhang, W & Su, SW 2019, 'Air Quality Monitoring for Vulnerable Groups in Residential Environments Using a Multiple Hazard Gas Detector.', Sensors (Basel, Switzerland), vol. 19, no. 2.
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This paper presents a smart "e-nose" device to monitor indoor hazardous air. Indoor hazardous odor is a threat for seniors, infants, children, pregnant women, disabled residents, and patients. To overcome the limitations of using existing non-intelligent, slow-responding, deficient gas sensors, we propose a novel artificial-intelligent-based multiple hazard gas detector (MHGD) system that is mounted on a motor vehicle-based robot which can be remotely controlled. First, we optimized the sensor array for the classification of three hazardous gases, including cigarette smoke, inflammable ethanol, and off-flavor from spoiled food, using an e-nose with a mixing chamber. The mixing chamber can prevent the impact of environmental changes. We compared the classification results of all combinations of sensors, and selected the one with the highest accuracy (98.88%) as the optimal sensor array for the MHGD. The optimal sensor array was then mounted on the MHGD to detect and classify the target gases without a mixing chamber but in a controlled environment. Finally, we tested the MHGD under these conditions, and achieved an acceptable accuracy (70.00%).
Wu, Y, Wu, Z, Chu, H, Li, J, Ngo, HH, Guo, W, Zhang, N & Zhang, H 2019, 'Comparison study on the performance of two different gas-permeable membranes used in a membrane-aerated biofilm reactor.', The Science of the total environment, vol. 658, pp. 1219-1227.
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This study compared the performance of two different gas-permeable membranes, PVDF and PP membrane, in a membrane-aerated biofilm reactor (MABR). The surface characteristics and membrane pore blocking of these two membrane materials were studied utilizing AFM, SEM and CLSM. The PVDF membrane surface was more rough and hydrophilic, and possessed a better microbial affinity compared to PP. Furthermore, the MABR equipped with a PVDF membrane removed more COD (97.06 ± 0.97%) and TN (85.66 ± 0.87%) compared to the MABR with PP membrane (87.13 ± 0.87% and 71.13 ± 0.71%, respectively). As well, the PP membrane exhibited severe membrane pore blocking and had a lower oxygen transfer rate than the PVDF membrane. It is concluded that the PVDF membrane has potential as an aeration membrane material for MABRs.
Xiao, C, Zeng, J, Ni, W, Liu, RP, Su, X & Wang, J 2019, 'Delay Guarantee and Effective Capacity of Downlink NOMA Fading Channels', IEEE Journal on Selected Topics in Signal Processing.
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IEEE Non-orthogonal multiple access (NOMA) is promising for increasing connectivity and capacity. But there has been little consideration on the quality of service (QoS) of NOMA; let alone that in generic fading channels. This paper establishes closed-form upper bounds for the delay violation probability of downlink Nakagami-$m$ and Rician NOMA channels, by exploiting stochastic network calculus (SNC). The key challenge addressed is to derive the Mellin transforms of the service processes in the NOMA fading channels. The transforms are proved to be stable, and incorporated into the SNC to provide the closed-form upper bounds of the delay violation probability. The paper also applies the Mellin transforms to develop the closed-form expressions for the effective capacity of the NOMA fading channels, which measures the channel capacity under statistical delay guarantees. By further applying the min-max and max-min rules, two new power allocation algorithms are proposed to optimize the closed-form expressions, which can provide the NOMA users fairness in terms of delay violation probability and effective capacity. Simulation results substantiate the derived upper bounds of the delay violation probabilities and the effective capacity. The proposed power allocation algorithms are also numerically validated.
Xu, H & Ji, J 2019, 'Analytical-numerical studies on the stability and bifurcations of periodic motion in the vibro-impact systems with clearances', International Journal of Non-Linear Mechanics, vol. 109, pp. 155-165.
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© 2018 This paper investigates the stability and bifurcations of periodic solutions in three-degree-of-freedom vibro-impact systems based on the explicit critical criteria and the discontinuous mapping method. Firstly, a six-dimensional Poincaré map is established by taking the impact surface as the Poincaré section. The explicit criteria including eigenvalue assignment and transversality condition are applied to determine the bifurcation point of co-dimension one pitchfork bifurcation. The stability and direction of the bifurcation solution are then studied by using center manifold reduction theory and normal form approach. Secondly, the bifurcation points of co-dimension-two Hopf–Hopf interaction bifurcation and pitchfork–Neimark–Sacker bifurcation are determined by applying the explicit critical criteria, and the local dynamic behaviors are examined in the neighborhood of these co-dimension-two bifurcation points. Finally, a six-dimensional Poincaré map formed by choosing the constant phase angle as the Poincaré section is used to investigate the existence and stability of grazing bifurcation based on the piecewise compound normal form map. The causes of the discontinuous jump and the coexistence of attractors near the grazing periodic motion are explained for the three-degree-of-freedom vibro-impact system with a moving constraint.
Xu, J, Cao, Z, Wang, Y, Zhang, Y, Gao, X, Ahmed, MB, Zhang, J, Yang, Y, Zhou, JL & Lowry, GV 2019, 'Distributing sulfidized nanoscale zerovalent iron onto phosphorus-functionalized biochar for enhanced removal of antibiotic florfenicol', Chemical Engineering Journal, pp. 713-722.
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© 2018 Aggregation of nZVI and sulfur-modified nZVI (S-nZVI) can lower its reactivity with contaminants in water. To overcome this limitation, we synthesized biochar-supported nZVI and S-nZVI using a phosphate pretreatment of the biochar (pBC) to uniformly distribute the nZVI and S-nZVI onto the biochar support. The participation of phosphorus groups in the synthesis, and the good distribution of S-nZVI on the pBC were confirmed by FTIR, SEM, XRD, and XPS. Pretreatment of the biochar led to smaller well-dispersed S-nZVI compared to S-nZVI supported on untreated biochar. This increased the surface area of the S-nZVI and the reaction rate with the antibiotic florfenicol (FF). The removal rate of FF by pBC-S-nZVI was 4.3 times higher than that by unsupported S-nZVI. Even though FF strongly adsorbed to the pBC support, FF was fully degraded based on the mass balance results. Surface area normalized reaction rate constants (kSA) for FF removal by S-nZVI, BC-S-nZVI, and pBC-S-nZVI were similar, suggesting that the enhanced reactivity is due to the greater dispersion of S-nZVI on the treated biochar. These results provide a simple pretreatment method for dispersing nZVI or S-nZVI onto biochar supports.
Xu, JX, Li, HY, Zhang, XY, Yang, Y, Xue, Q & Dutkiewicz, E 2019, 'Compact Dual-Channel Balanced Filter and Balun Filter Based on Quad-Mode Dielectric Resonator', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 2, pp. 494-504.
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© 1963-2012 IEEE. In this paper, we propose a method for designing a dual-channel balanced filter and a dual-channel balun filter based on the quad-mode dielectric resonator (DR) for the first time. By sharing one common quad-mode DR, two balanced filters or two balun filters are integrated as one single-cavity configuration, featuring compact size and high integration. A cylindrical DR with two short ends is investigated to construct the quad-mode DR. By properly arranging input and output feeding probes, two modes of the DR are only excited by the feeding probes of one channel and the other two modes are excited by that of the other channel. Accordingly, signals cannot be transmitted between the two channels, resulting in high isolation. Moreover, the required out-of-phase characteristics of the balanced and balun filters can be obtained by the inherent electromagnetic field properties of the DR without adding additional circuits, featuring a simple structure. For demonstration, a dual-channel balanced filter and a dual-channel balun filter are designed and fabricated, showing excellent balanced or balun filter performance of each channel as well as high isolation between the two channels. As compared to the other reported DR balanced and balun filters, the proposed designs exhibit a significant size reduction, which are attractive in wireless systems.
Xu, K-D, Li, D & Liu, Y 2019, 'High-Selectivity Wideband Bandpass Filter Using Simple Coupled Lines With Multiple Transmission Poles and Zeros', IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS, vol. 29, no. 2, pp. 107-109.
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Xu, K-D, Xu, H & Liu, Y 2019, 'Low-Profile Filtering End-Fire Antenna Integrated With Compact Bandstop Filtering Element for High Selectivity', IEEE ACCESS, vol. 7, pp. 8398-8403.
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Xu, L, Qiu, X, Zhou, J, Li, F, Zhang, H & Wang, Y 2019, 'A 2D dual-mode composite ultrasonic transducer excited by a single piezoceramic stack', SMART MATERIALS AND STRUCTURES, vol. 28, no. 2.
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Xu, R & Fatahi, B 2019, 'Novel application of geosynthetics to reduce residual drifts of mid-rise buildings after earthquakes', Soil Dynamics and Earthquake Engineering, vol. 116, pp. 331-344.
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© 2018 Elsevier Ltd Geosynthetics have been used in variety of geotechnical engineering projects, such as ground improvement, erosion control, slope stabilisation and foundation strength improvement and they have been proved to be cost and time effective in many cases. In this study, a geosynthetic reinforced composite soil (GRCS) foundation system is proposed for seismic protection of mid-rise buildings supported by a shallow foundation potentially suffering from residual structural drift or permanent foundation settlement. To evaluate the proposed GRCS, a conventional reinforced concrete moment resisting building sitting on this composite ground under the earthquake excitations of 1978 Tabas, 1994 Northridge and 1995 Kobe was numerically simulated using FLAC3D software. The effect of soil-structure interaction (SSI) was captured using direct method of analysis adopting a three-dimensional numerical model. By adopting direct calculation method, the soil deposit, the geosynthetic reinforcement, the foundation and the structure were simulated simultaneously. Inelastic behaviour of the structure was considered, while hysteretic damping algorithm was adopted representing the variation of the shear modulus and corresponding damping ratio of the soil with cyclic shear strain capturing the energy dissipation characteristics of the soil. Both material and geometry nonlinearities were taken into account at the interface between the foundation and ground surface. The results are then presented in terms of mobilised tensile force in geosynthetic layers, the response spectra at bedrock and ground surface level, the shear force developed in the superstructure, the maximum foundation rocking angle, the maximum lateral deflection, the maximum inter-storey drift, and most importantly the residual inter-storey drift and permanent foundation settlement. The results showed that the proposed GRCS could offer design engineers a rational and cost-effective alternative solution to con...
Xu, Y, Gao, Y, Wu, C, Fang, J & Li, Q 2019, 'Robust topology optimization for multiple fiber-reinforced plastic (FRP) composites under loading uncertainties', Structural and Multidisciplinary Optimization.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. This study proposes a non-deterministic robust topology optimization of ply orientation for multiple fiber-reinforced plastic (FRP) materials, such as carbon fiber–reinforced plastic (CFRP) and glass fiber–reinforced plastic (GFRP) composites, under loading uncertainties with both random magnitude and random direction. The robust topology optimization is considered here to minimize the fluctuation of structural performance induced by load uncertainty, in which a joint cost function is formulated to address both the mean and standard deviation of compliance. The sensitivities of the cost function are derived with respect to the design variables in a non-deterministic context. The discrete material optimization (DMO) technique is extended here to accommodate robust topology optimization for FRP composites. To improve the computational efficiency, the DMO approach is revised to reduce the number of design variables by decoupling the selection of FRP materials and fiber orientations. In this study, four material design examples are presented to demonstrate the effectiveness of the proposed methods. The robust topology optimization results exhibit that the composite structures with the proper ply orientations are of more stable performance when the load fluctuates.
Xuan, J, Lu, J & Zhang, G 2019, 'A survey on Bayesian nonparametric learning', ACM Computing Surveys, vol. 52, no. 1.
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© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. Bayesian (machine) learning has been playing a significant role in machine learning for a long time due to its particular ability to embrace uncertainty, encode prior knowledge, and endow interpretability. On the back of Bayesian learning's great success, Bayesian nonparametric learning (BNL) has emerged as a force for further advances in this field due to its greater modelling flexibility and representation power. Instead of playing with the fixed-dimensional probabilistic distributions of Bayesian learning, BNL creates a new “game” with infinite-dimensional stochastic processes. BNL has long been recognised as a research subject in statistics, and, to date, several state-of-the-art pilot studies have demonstrated that BNL has a great deal of potential to solve real-world machine-learning tasks. However, despite these promising results, BNL has not created a huge wave in the machine-learning community. Esotericism may account for this. The books and surveys on BNL written by statisticians are overcomplicated and filled with tedious theories and proofs. Each is certainly meaningful but may scare away new researchers, especially those with computer science backgrounds. Hence, the aim of this article is to provide a plain-spoken, yet comprehensive, theoretical survey of BNL in terms that researchers in the machine-learning community can understand. It is hoped this survey will serve as a starting point for understanding and exploiting the benefits of BNL in our current scholarly endeavours. To achieve this goal, we have collated the extant studies in this field and aligned them with the steps of a standard BNL procedure-from selecting the appropriate stochastic processes through manipulation to executing the model inference algorithms. At each step, past efforts have been thoroughly summarised and discussed. In addition, we have reviewed the common methods for implementing BNL in vari...
Xue, C, Li, W, Li, J, Tam, VWY & Ye, G 2019, 'A review study on encapsulation-based self-healing for cementitious materials', Structural Concrete, vol. 20, no. 1, pp. 198-212.
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© 2018 fib. International Federation for Structural Concrete Encapsulation-based self-healing technology is an effective method for healing the crack-deteriorated cementitious material. Encapsulation-based self-healing initiates by crack occurrence and progresses by chemical reaction of released self-healing agents in the cracks, which are contained in capsules. In this paper, a review has been conducted on various healing agents, encapsulation techniques, as well as experimental approaches, basing on existing substantial studies. Recently, there is no consistent agreement on the effective criteria for evaluating encapsulation-based self-healing and mature solution for increasing the survival ratio of capsules during mixing. However, the polyurethane-based healing agents filled in glass or ceramic tubes are popularly applied for self-healing cementitious materials. Besides, the polymer capsules present promising attractions for engineering application. Mechanical strength and durability are the most widely used self-healing efficiency assessment indexes. On the other hand, nondestructive technique and numerical modeling have also extensively adopted to visualize and evaluate the self-healing behavior of cementitious materials. However, there are still some challenges, which require further investigations, such as behavior of crack propagation, kinetics of healing agent in discrete crack surfaces, effect of inserted capsules on the mechanical properties of self-healed cementitious materials.
Yan, S-P, Zhao, M-H, Ban, Y-L, Lian, J-W & Nie, Z 2019, 'Dual-Layer SIW Multibeam Pillbox Antenna With Reduced Sidelobe Level', IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, vol. 18, no. 3, pp. 541-545.
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Yan, Z, Liu, J & Liu, S 2019, 'DPWeVote: differentially private weighted voting protocol for cloud-based decision-making', Enterprise Information Systems, vol. 13, no. 2, pp. 236-256.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. With the advent of Industry 4.0, cloud computing techniques have been increasingly adopted by industry practitioners to achieve better workflows. One important application is cloud-based decision-making, in which multiple enterprise partners need to arrive an agreed decision. Such cooperative decision-making problem is sometimes formed as a weighted voting game, in which enterprise partners express ‘YES/NO’ opinions. Nevertheless, existing cryptographic approaches to Cloud-Based Weighted Voting Game have restricted collusion tolerance and heavily rely on trusted servers, which are not always available. In this work, we consider the more realistic scenarios of having semi-honest cloud server/partners and assuming maximal collusion tolerance. To resolve the privacy issues in such scenarios, the DPWeVote protocol is proposed which incorporates Randomized Response technique and consists the following three phases: the Randomized Weights Collection phase, the Randomized Opinions Collection phase, and the Voting Results Release phase. Experiments on synthetic data have demonstrated that the proposed DPWeVote protocol managed to retain an acceptable utility for decision-making while preserving privacy in semi-honest environment.
Yang, CT, Chen, ST, Liu, JC, Su, YW, Puthal, D & Ranjan, R 2019, 'A predictive load balancing technique for software defined networked cloud services', Computing, vol. 101, no. 3, pp. 211-235.
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© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. With the advent of OpenFlow, the concept of Software-Defined Networking (SDN) becomes much popular. In the past, SDN had often been used for network virtualization; however, with the rise of OpenFlow, which speeds up network performance by separating the control layer from the data layer, SDN can be further used to manage physical network facilities. Currently, some OpenFlow controller providers have already provided users with load balancer packages in their controllers for virtual networks, such as the Neutron package in OpenStack; nevertheless, the existing load balancer packages work in the old fashion that causes extra delay since they poll controllers for every new coming connection. In this paper, we use the wildcard mask to implement the load balance method directly on switches or routers and add a user prediction mechanism to change the range of the wildcard mask dynamically. In this way, the load balance mechanism can be applied conforming to real service situations. In our experiment, we test the accuracies of flow prediction for different predicted algorithms and compare the delay times and balance situations of the proposed method with other load balancers. With the popularity of cloud computing, the demand for cloud infrastructure also increases. As a result, we also apply our load balance mechanism on cloud services and prove that the proposed method can be implemented to varieties of service platforms.
Yang, J, Liu, X, Wang, D, Xu, Q, Yang, Q, Zeng, G, Li, X, Liu, Y, Gong, J, Ye, J & Li, H 2019, 'Mechanisms of peroxymonosulfate pretreatment enhancing production of short-chain fatty acids from waste activated sludge.', Water research, vol. 148, pp. 239-249.
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Peroxymonosulfate (PMS) has been recently used as an additive to pretreat waste activated sludge (WAS) to enhance short-chain fatty acids (SCFAs) production. However, the mechanisms of how PMS enhances SCFAs production remain largely unknown. This work therefore aims to explore the mechanisms through deeply understanding its impact on the disintegration of sludge cells, the biodegradability of organics released and the bioprocesses involved in anaerobic fermentation, and differentiating the contributions of its degradation intermediates to SCFAs production. This was demonstrated by a series of batch fermentation tests using either real sludge or model organic compounds as fermentation substrates. Experimental results showed that the maximal SCFAs yield increased from 29.69 to 311.67 mg COD/g VSS with PMS level increasing from 0 to 0.09 g/g TSS. No obvious increase in SCFAs yield was observed when PMS further increased. The mechanism explorations revealed that PMS pretreatment not only enhanced the disintegration of sludge cells but also promoted the biodegradability of organics released, thereby providing more biodegradable substrates for subsequent SCFAs production. PMS pretreatment decreased the percentages of fulvic acid-like and humic acid-like substances in the released organics. Moreover, the species and total detection frequency of other recalcitrant organics such as cyclopentasiloxane, heptasiloxane, and ethylene glycol, which were hardly degraded in ordinary anaerobic condition, also decreased remarkably. Although PMS caused harms to some extents to all the microbes in the anaerobic fermentation, its inhibitions to SCFAs consumers were much severer than that to SCFAs producers, probably due to the less tolerance of methanogens. Further analyses exhibited that 1O2, SO4•- and •OH were the major contributors to the increased SCFAs production, and their contributions were in the order of 1O2 > SO4•- > •OH. The findings obtained in this work provide insights ...
Yang, S, Gao, B, Zhao, P, Wang, C, Shen, X, Yue, Q & Shon, HK 2019, 'The application of forward osmosis for simulated surface water treatment by using trisodium citrate as draw solute', Environmental Science and Pollution Research.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. In this study, trisodium citrate was used as draw solute in forward osmosis (FO) due to its biodegradability and easy reuse after FO dilution. The effect of operating conditions on FO performance was investigated. The study focused on the long-term flux performance and membrane fouling when surface water was used as feed solution. A water flux of 9.8 LMH was observed using 0.5 M trisodium citrate as draw solution in PRO mode. In the long-term FO process, trisodium citrate showed a slight decrease in total flux loss (13.06%) after 20 h of operation. The membrane fouling was significantly reduced after a two-step physical cleaning. A considerable flux recovery (> 95%) of the fouled membrane was finally obtained. Therefore, this study proves the superiority of trisodium citrate as draw solution and paves a new way in applying FO directly for surface water reclamation.
Yang, T, Ding, C & Guo, Y 2019, 'A Highly Birefringent and Nonlinear AsSe2-As2S5 Photonic Crystal Fiber with Two Zero-Dispersion Wavelengths', IEEE Photonics Journal.
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OAPA A hybrid AsSe$_{2}$-As$_{2}$S$_{5}$ photonic crystal fiber (PCF) with a solid elliptical core is proposed and studied theoretically by full-vector finite element method (FEM). The core and cladding of the PCF are made of AsSe$_{2}$ and As$_{2}$S$_{5}$ glasses, respectively. Simulation results demonstrates that, at the operating wavelength of 1.55 μm, the proposed PCF not only exhibits a very high birefringence of 0.091 but also has large nonlinear coefficients of 147.8 and 78.2 W$^{-1}$m$^{-1}$ for the X- and Y-polarized (X- and Y-pol) modes, respectively. Moreover, it is able to achieve two zero-dispersion wavelengths (ZDWs) for both the X-pol (1.52 and 2.19 μm) and Y-pol (1.43 and 2.12 μm) modes. The proposed hybrid PCF exhibits excellent polarization maintaining and nonlinearity performance, thus suitable to be used in supercontinuum spectrum generation and polarization maintaining nonlinear signal processing.
Yang, W, Liang, J, Yang, J & Zhang, N 2019, 'Optimal control of a novel uninterrupted multi-speed transmission for hybrid electricmining trucks', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering.
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© IMechE 2019. Considering the energy consumption and specific performance requirements of mining trucks, a novel uninterrupted multi-speed transmission is proposed in this paper, which is composed of a power-split device, and a three-speed lay-shaft transmission with a traction motor. The power-split device is adapted to enhance the efficiency of the engine by adjusting the gear ratio continuously. The three-speed lay-shaft transmission is designed based on the efficiency map of traction motor to guarantee the drivability. The combination of the power-split device and three-speed lay-shaft transmission can realize uninterrupted gear shifting with the proposed shift strategy, which benefits from the proposed adjunct function by adequately compensating the torque hole. The detailed dynamic models of the system are built to verify the effectiveness of the proposed shift strategy. To evaluate the maximum fuel efficiency that the proposed uninterrupted multi-speed transmission could achieve, dynamic programming is implemented as the baseline. Due to the “dimension curse” of dynamic programming, a real-time control strategy is designed, which can significantly improve the computing efficiency. The simulation results demonstrate that the proposed uninterrupted multi-speed transmission with dynamic programming and real-time control strategy can improve fuel efficiency by 11.63% and 8.51% compared with conventional automated manual transmission system, respectively.
Yang, W, Yang, J & Zhang, N 2019, 'A novel coaxial multi-mode hybrid transmission system for mining trucks', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering.
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© IMechE 2018. Given that hybrid electric vehicles could significantly reduce fuel consumption, and mining trucks are much more sensitive to energy conversion efficiency, it is essential to develop a hybrid system, which could meet both the specific requirements of the structure of mining trucks and the needs for overall efficiency improvements. In this paper, a novel multi-mode transmission system, which is composed of an engine, two motors, one planetary gear set, and a parallel transmission structure, is proposed. The planetary gear set serves as a power-split device, and the parallel transmission structure can make the engine and motor work in the high-efficiency area both in low-speed and high-speed conditions. Due to the flexibility of the proposed system, five power-flow modes can be implemented, and they are illustrated with a specially designed rule-based energy management strategy. To validate the effectiveness of the multi-mode transmission system, dynamic programming is used to assess the fuel consumption during the given driving cycle without considering drivability. The simulation results reveal that the proposed multi-mode transmission system owns better fuel efficiency for mining trucks compared with highly regarded Toyota Hybrid System and conventional automated manual transmission system.
Yang, W, Yang, J, Liang, J & Zhang, N 2019, 'Implementation of velocity optimisation strategy based on preview road information to trade off transport time and fuel consumption for hybrid mining trucks', IET Intelligent Transport Systems, vol. 13, no. 1, pp. 194-200.
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© The Institution of Engineering and Technology 2018 Unmanned mining is one of the essential fields of mining technology at present. Therefore, optimisation with known driving speed cycle for fuel economy, emissions, driveability, and more is no longer exclusive, the optimal velocity trajectory based on road information has been studied. Given that both fuel consumption and transport time influence transport costs of mining trucks, an energy management strategy (EMS) based on velocity optimisation is proposed and illustrated on a series hybrid electric mining truck in this study. The vehicle speed and SOC are adopted as state variables. Then two-scale dynamic programming is applied to calculate optimum velocity trajectory and power distribution. Simulation results reveal that the weighting coefficients of transport time and fuel economy can be optimally distributed for the different design requirements. Compared to the results under the known driving speed cycle, the proposed EMS can enhance fuel economy by 26.59% under the guarantee of same transport time, or transport time can be reduced by 42.4% without sacrificing the fuel consumption. Therefore, the proposed velocity optimisation strategy can reduce transport costs for mining enterprises significantly.
Yao, M, Woo, YC, Ren, J, Tijing, LD, Choi, J-S, Kim, S-H & Shon, HK 2019, 'Volatile fatty acids and biogas recovery using thermophilic anaerobic membrane distillation bioreactor for wastewater reclamation.', Journal of environmental management, vol. 231, pp. 833-842.
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The effects of bioreactor temperatures and salinities of an anaerobic membrane distillation bioreactor (anMDBR) on the permeation performance and their potential recovery of bioresources were fully examined in this study. To the best of our knowledge, this is the first study of a lab-scale anMDBR process utilizing sub-merged hollow fiber membranes. The hybrid system utilizing both membrane distillation (MD) and anaerobic bioreactors achieved 99.99% inorganic salt rejection regardless the operation temperatures and high initial flux from (2-4 L m-2 h-1) at 45-65 °C. However, after 7-day operation, the flux dropped by 16-50% proportional to the bioreactor temperatures. It was found that the effects of bioreactor temperatures had strong impacts on both the permeation performance and fouling behavior while salinity had insignificant effect. A compact non-porous fouling layer was observed on the membrane surface from the bioreactor operated at 65 °C while only a few depositions was found on the membrane from 45 °C bioreactor. In the present study, the optimal anMDBR temperature was found to be 45 °C, showing a balanced biogas production and membrane permeation performance including less fouling formation. At this bioreactor temperature (45 °C), the biogas yield was 0.14 L/g CODremoval, while maintaining a methane recovery of 42% in the biogas, similar recovery to those at bioreactor temperatures of 55 and 65 °C. The potential recovery of volatile fatty acids made anMDBR a more economically efficient system, in addition to its lower operation cost and smaller footprint compared with most other technologies for on-site wastewater treatment.
Yao, Y, Shen, F, Zhang, J, Liu, L, Tang, Z & Shao, L 2019, 'Extracting Multiple Visual Senses for Web Learning', IEEE Transactions on Multimedia, vol. 21, no. 1, pp. 184-196.
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© 1999-2012 IEEE. Labeled image datasets have played a critical role in high-level image understanding. However, the process of manual labeling is both time consuming and labor intensive. To reduce the dependence on manually labeled data, there have been increasing research efforts on learning visual classifiers by directly exploiting web images. One issue that limits their performance is the problem of polysemy. Existing unsupervised approaches attempt to reduce the influence of visual polysemy by filtering out irrelevant images, but do not directly address polysemy. To this end, in this paper, we present a multimodal framework that solves the problem of polysemy by allowing sense-specific diversity in search results. Specifically, we first discover a list of possible semantic senses from untagged corpora to retrieve sense-specific images. Then, we merge visual similar semantic senses and prune noise by using the retrieved images. Finally, we train one visual classifier for each selected semantic sense and use the learned sense-specific classifiers to distinguish multiple visual senses. Extensive experiments on classifying images into sense-specific categories and reranking search results demonstrate the superiority of our proposed approach.
Yao, Y, Shen, F, Zhang, J, Liu, L, Tang, Z & Shao, L 2019, 'Extracting Privileged Information for Enhancing Classifier Learning', IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 436-450.
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© 1992-2012 IEEE. The accuracy of data-driven learning approaches is often unsatisfactory when the training data is inadequate either in quantity or quality. Manually labeled privileged information (PI), e.g., attributes, tags or properties, is usually incorporated to improve classifier learning. However, the process of manually labeling is time-consuming and labor-intensive. Moreover, due to the limitations of personal knowledge, manually labeled PI may not be rich enough. To address these issues, we propose to enhance classifier learning by exploring PI from untagged corpora, which can effectively eliminate the dependency on manually labeled data and obtain much richer PI. In detail, we treat each selected PI as a subcategory and learn one classifier for each subcategory independently. The classifiers for all subcategories are integrated together to form a more powerful category classifier. Particularly, we propose a novel instance-level multi-instance learning model to simultaneously select a subset of training images from each subcategory and learn the optimal SVM classifiers based on the selected images. Extensive experiments on four benchmark data sets demonstrate the superiority of our proposed approach.
Ye, K & Ji, J 2019, 'Current, wave, wind and interaction induced dynamic response of a 5 MW spar-type offshore direct-drive wind turbine', Engineering Structures, vol. 178, pp. 395-409.
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© 2018 Elsevier Ltd This paper studies the dynamic response of a spar-type direct-drive wind turbine subjected to external and internal excitations. A free-free end model is developed for the wind turbine structure with a spar-type floating platform under deep sea condition. Firstly, the spar supported platform with tower structure is modelled as a rigid body while the nacelle is considered as a point mass attached on the top of the tower. Then the dynamic interaction between the drive-train system and the tower is considered by incorporating the modelling of a direct-drive drive-train system. The hydrodynamic and aerodynamic excitations applied include current, wave, and wind excitations as well as buoyant forces. The misalignments of the wind, wave and current are also considered to examine the induced response. With the help of the time history and FFT spectrum, the effects of both hydrodynamic and aerodynamic excitations along with the dynamic interaction between the drive-train system and tower structure on the dynamic behaviour of the spar-type floating platform are investigated under different sea conditions.
Ye, X, Yang, Z, Zhu, J & Guo, Y 2019, 'Modeling and operation of a bearingless fixed-pole rotor induction motor', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2002-2011 IEEE. The rotor currents are induced by both suspension force winding magnetic field and torque winding magnetic field in the traditional bearingless induction motors (BIMs). Due to the currents induced by the suspension force winding, there are errors in the generation of radial suspension forces. To address such problems, a novel BIM with fixed-pole rotor, called bearingless fixed-pole rotor induction (BFPRI) motor is proposed. The structure of BFPRI motor is first analyzed and the mathematical models of radial suspension forces are deduced. Based on the finite element analysis, the induced currents and radial suspension forces are also investigated and compared with the traditional BIM. Finally, the prototype motor is built and experimental research is carried out. In this novel motor, only the torque winding magnetic field induces currents in the rotor, which makes the precision of radial suspension forces higher and thereby reduces the complexity of BIM control system. The effectiveness of the proposed BFPRI motor is validated by both simulation and experiments.
Ye, Y, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Y, Nghiem, LD, Zhang, X & Wang, J 2019, 'Effect of organic loading rate on the recovery of nutrients and energy in a dual-chamber microbial fuel cell.', Bioresource technology, vol. 281, pp. 367-373.
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This study aimed to assess the impacts of organic loading rate (OLR) (435-870 mgCOD/L·d) on nutrients recovery via a double-chamber microbial fuel cell (MFC) for treating domestic wastewater. Electricity generation was also explored at different OLRs, including power density and coulombic efficiency. Experimental results suggested the MFC could successfully treat municipal wastewater with over 90% of organics being removed at a wider range of OLR from 435 to 725 mgCOD/L·d. Besides, the maximum power density achieved in the MFC was 253.84 mW/m2 at the OLR of 435 mgCOD/L·d. Higher OLR may disrupt the recovery of PO43--P and NH4+-N via the MFC. The same pattern was observed for the coulombic efficiency of the MFC and its highest value was 25.01% at the OLR of 435 mgCOD/L·d. It can be concluded that nutrients and electrical power can be simultaneously recovered from municipal wastewater via the dual-chamber MFC.
Ye, Y, Ngo, HH, Guo, W, Liu, Y, Chang, SW, Nguyen, DD, Ren, J, Liu, Y & Zhang, X 2019, 'Feasibility study on a double chamber microbial fuel cell for nutrient recovery from municipal wastewater', Chemical Engineering Journal, vol. 358, pp. 236-242.
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© 2018 Elsevier B.V. Microbial fuel cell (MFC) is currently considered a promising technology for wastewater treatment. This study aims to evaluate the feasibility of a double-chamber MFC in terms of: (i) operating mode (batch mode, self-circulation mode, single-continuous mode) of anolyte on the nutrient accumulation in the catholyte, (ii) aeration conditions (anode effluent with aeration supplied in catholyte; anode effluent without aeration supplied in catholyte; cathode effluent with aeration supplied in catholyte and cathode effluent without aeration supplied in catholyte) on the nutrient recovery and (iii) types of separators (cation exchange membrane (CEM), forward osmosis (FO), and nonwoven (NW)) to remove nutrients toward their recovery from municipal wastewater. Results showed that there was no negligible increase in the phosphate concentration of the catholyte at the three different modes but accumulation of ammonium. At different aeration conditions, nutrients can be recovered by chemical precipitation at high pH generated by the MFC itself. Basically, phosphate was removed by microbial absorption and recovered by chemical precipitation while ammonium was accumulated by current generation and recovered as precipitates. It was found that double-chamber MFC with the CEM as the separator reported the best nutrients removal with >97.58% of NH 4+ -N and >94.9% of PO 43− -P removed/recovered, followed by the MFC with the nonwoven and FO membrane, respectively. Thus, the double-chamber MFC is feasible for recovering nutrients in a comprehensive bioelectrochemical system.
Yin, S, Ji, J, Wen, G & Wu, X 2019, 'Use of degeneration to stabilize near grazing periodic motion in impact oscillators', Communications in Nonlinear Science and Numerical Simulation, vol. 66, pp. 20-30.
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© 2018 In controlling the discontinuous grazing bifurcations in impact oscillators, a discrete-in-time linear feedback control strategy in the existing literature was used to change the conditions at the grazing point based on the grazing stability criterion. Though this strategy is effective for its linear inequality constraint of the control parameter domain, a smooth and predictable bifurcating response cannot be obtained for the controlled system, but the grazing induced chaos or period-adding phenomena. To improve this control strategy and stabilize the elementary near-grazing impact periodic motion in impact oscillators, one feasible control criterion is established in this paper by performing the perturbation analysis of the eigenvalues of the Jacobian matrix. It is found that the degeneration of both eigenvalues and grazing bifurcation can stabilize the elementary near-grazing impact periodic motion and eliminate the discontinuous jump phenomenon at grazing.
Ying, M 2019, 'Toward automatic verification of quantum programs', Formal Aspects of Computing, vol. 31, no. 1, pp. 3-25.
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© 2018, British Computer Society. This paper summarises the results obtained by the author and his collaborators in a program logic approach to the verification of quantum programs, including quantum Hoare logic, invariant generation and termination analysis for quantum programs. It also introduces the notion of proof outline and several auxiliary rules for more conveniently reasoning about quantum programs. Some problems for future research are proposed at the end of the paper.
Yong, B, Li, F, Lv, Q, Shen, J & Zhou, Q 2019, 'Derivative-based acceleration of general vector machine', Soft Computing, vol. 23, no. 3, pp. 987-995.
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© 2017, Springer-Verlag GmbH Germany, part of Springer Nature. General vector machine (GVM) is one of supervised learning machine, which is based on three-layer neural network. It is capable of constructing a learning model with limited amount of data. Generally, it employs Monte Carlo algorithm (MC) to adjust weights of the underlying network. However, GVM is time-consuming at training and is not efficient when compared with other learning algorithm based on gradient descent learning. In this paper, we present a derivative-based Monte Carlo algorithm (DMC) to accelerate the training of GVM. Our experimental results indicate that DMC algorithm is faster than the original MC method. Specifically, the training time of our DMC algorithm in GVM for function fitting is also less than some gradient descent-based methods, in which we compare DMC with back-propagation neural network. Experimental results indicate that our algorithm is promising for training GVM.
Yu, J, Ji, J, Miao, Z & Zhou, J 2019, 'Neural network-based region reaching formation control for multi-robot systems in obstacle environment', Neurocomputing, vol. 333, pp. 11-21.
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© 2018 Elsevier B.V. This study is concerned with the region reaching formation control problem with collision and obstacle avoidance for multi-robot systems in the presence of model uncertainties and external disturbances. A novel neural network based robust control scheme combining with the adaptive compensator and the adaptive control gain is proposed to achieve the region reaching formation control with collision and obstacle avoidance. It is shown that under the proposed control method, all the robots can always reach into the objective region, maintain their formation, and guarantee collision and obstacle avoidance. Illustrative examples are presented to show the effectiveness of the proposed control scheme.
Yu, Y, Dackermann, U, Li, J & Niederleithinger, E 2019, 'Wavelet packet energy–based damage identification of wood utility poles using support vector machine multi-classifier and evidence theory', Structural Health Monitoring, vol. 18, no. 1, pp. 123-142.
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© The Author(s) 2018. This article presents a novel assessment framework to identify the health condition of wood utility poles. The innovative approach is based on the integration of data mining and machine learning methods and combines advanced signal processing, multi-sensor data fusion and decision ensembles to classify different damage condition types of wood poles. In the proposed framework, wavelet packet analysis is employed to transform captured multi-channel stress wave signals into energy information, which is consequently compressed by principal component analysis to extract a feature vector. Furthermore, support vector machine multi-classifier, optimized by genetic algorithm, is designed to identify the pole condition type. Finally, evidence theory is applied to fuse different assessment results from different sensors for a final decision. For validation of the proposed approach, the wood pole specimens with three common damage condition types are tested using a novel multi-sensor narrow-band frequency-excitation non-destructive testing system in the laboratory. The final experimental analysis results confirm that the proposed approach is capable of making full use of multi-sensor information and providing an effective and accurate identification on types of conditions in wood poles.
Yu, Y, Wang, C, Gu, X & Li, J 2019, 'A novel deep learning-based method for damage identification of smart building structures', Structural Health Monitoring, vol. 18, no. 1, pp. 143-163.
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© The Author(s) 2018. In the past few years, intelligent structural damage identification algorithms based on machine learning techniques have been developed and obtained considerable attentions worldwide, due to the advantages of reliable analysis and high efficiency. However, the performances of existing machine learning–based damage identification methods are heavily dependent on the selected signatures from raw signals. This will cause the fact that the damage identification method, which is the optimal solution for a specific application, may fail to provide the similar performance on other cases. Besides, the feature extraction is a time-consuming task, which may affect the real-time performance in practical applications. To address these problems, this article proposes a novel method based on deep convolutional neural networks to identify and localise damages of building structures equipped with smart control devices. The proposed deep convolutional neural network is capable of automatically extracting high-level features from raw signals or low-level features and optimally selecting the combination of extracted features via a multi-layer fusion to satisfy any damage identification objective. To evaluate the performance of the proposed deep convolutional neural network method, a five-level benchmark building equipped with adaptive smart isolators subjected to the seismic loading is investigated. The result shows that the proposed method has outstanding generalisation capacity and higher identification accuracy than other commonly used machine learning methods. Accordingly, it is deemed as an ideal and effective method for damage identification of smart structures.
Yu, Z, Hu, Y, Dzakpasu, M, Wang, XC & Ngo, HH 2019, 'Dynamic membrane bioreactor performance enhancement by powdered activated carbon addition: Evaluation of sludge morphological, aggregative and microbial properties.', Journal of environmental sciences (China), vol. 75, pp. 73-83.
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The effects of powdered activated carbon (PAC) addition on sludge morphological, aggregative and microbial properties in a dynamic membrane bioreactor (DMBR) were investigated to explore the enhancement mechanism of pollutants removal and filtration performance. Sludge properties were analyzed through various analytical measurements. The results showed that the improved sludge aggregation ability and the evolution of microbial communities affected sludge morphology in PAC-DMBR, as evidenced by the formation of large, regularly shaped and strengthened sludge flocs. The modifications of sludge characteristics promoted the formation process and filtration flux of the dynamic membrane (DM) layer. Additionally, PAC addition did not exert very significant influence on the propagation of eukaryotes (protists and metazoans) and microbial metabolic activity. High-throughput pyrosequencing results indicated that adding PAC improved the bacterial diversity in activated sludge, as PAC addition brought about additional microenvironment in the form of biological PAC (BPAC), which promoted the enrichment of Acinetobacter (13.9%), Comamonas (2.9%), Flavobacterium (0.31%) and Pseudomonas (0.62%), all contributing to sludge flocs formation and several (such as Acinetobacter) capable of biodegrading relatively complex organics. Therefore, PAC addition could favorably modify sludge properties from various aspects and thus enhance the DMBR performance.
Yuan, Z, Dong, L, Gao, Q, Huang, Z, Wang, L, Wang, G & Yu, X 2019, 'SnSb alloy nanoparticles embedded in N-doped porous carbon nanofibers as a high-capacity anode material for lithium-ion batteries', Journal of Alloys and Compounds, pp. 775-783.
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© 2018 SnSb alloy is a promising anode material for lithium-ion batteries due to its high specific capacity. However, the large volume change in the process of charge/discharge causes significant pulverizing of SnSb alloy particles, which leads to a rapid capacity fading. This paper reports the synthesis of homogenous SnSb nanoparticles that are embedded in N-doped porous carbon nanofibers through electrospinning technique with LiN 3 serving as poregen agent. This distinctive structure prevents the direct contact of SnSb nanoparticles with the electrolyte and provides enough space for the volume change of SnSb alloy during the Li + insertion/extraction process, enabling this material to deliver a high reversible capacity of 892 mA h g −1 after 100th cycle at 100 mA g −1 , and a stable capacity of 487 mA h g −1 after 1000 cycles at 2000 mA g −1 . These results highlight the importance of the synergistic effect of SnSb alloy nanoparticles and N-doped porous carbon nanofibers for the high performance of lithium-ion batteries.
Zhan, K, Chang, X, Guan, J, Chen, L, Ma, Z & Yang, Y 2019, 'Adaptive Structure Discovery for Multimedia Analysis Using Multiple Features.', IEEE transactions on cybernetics.
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Multifeature learning has been a fundamental research problem in multimedia analysis. Most existing multifeature learning methods exploit graph, which must be computed beforehand, as input to uncover data distribution. These methods have two major problems confronted. First, graph construction requires calculating similarity based on nearby data pairs by a fixed function, e.g., the RBF kernel, but the intrinsic correlation among different data pairs varies constantly. Therefore, feature learning based on such predefined graphs may degrade, especially when there is dramatic correlation variation between nearby data pairs. Second, in most existing algorithms, each single-feature graph is computed independently and then combine them for learning, which ignores the correlation between multiple features. In this paper, a new unsupervised multifeature learning method is proposed to make the best utilization of the correlation among different features by jointly optimizing data correlation from multiple features in an adaptive way. As opposed to computing the affinity weight of data pairs by a fixed function, the weight of affinity graph is learned by a well-designed optimization problem. Additionally, the affinity graph of data pairs from different features is optimized in a global level to better leverage the correlation among different channels. In this way, the adaptive approach correlates the features of all features for a better learning process. Experimental results on real-world datasets demonstrate that our approach outperforms the state-of-the-art algorithms on leveraging multiple features for multimedia analysis.
Zhan, K, Nie, F, Wang, J & Yang, Y 2019, 'Multiview Consensus Graph Clustering.', IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol. 28, no. 3, pp. 1261-1270.
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A graph is usually formed to reveal the relationship between data points and graph structure is encoded by the affinity matrix. Most graph-based multiview clustering methods use predefined affinity matrices and the clustering performance highly depends on the quality of graph. We learn a consensus graph with minimizing disagreement between different views and constraining the rank of the Laplacian matrix. Since diverse views admit the same underlying cluster structure across multiple views, we use a new disagreement cost function for regularizing graphs from different views toward a common consensus. Simultaneously, we impose a rank constraint on the Laplacian matrix to learn the consensus graph with exactly connected components where is the number of clusters, which is different from using fixed affinity matrices in most existing graph-based methods. With the learned consensus graph, we can directly obtain the cluster labels without performing any post-processing, such as -means clustering algorithm in spectral clustering-based methods. A multiview consensus clustering method is proposed to learn such a graph. An efficient iterative updating algorithm is derived to optimize the proposed challenging optimization problem. Experiments on several benchmark datasets have demonstrated the effectiveness of the proposed method in terms of seven metrics.
Zhang, G, Tao, J, Qiu, X & Burnett, I 2019, 'Decentralized Two-Channel Active Noise Control for Single Frequency by Shaping Matrix Eigenvalues', IEEE/ACM Transactions on Audio Speech and Language Processing, vol. 27, no. 1, pp. 44-52.
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© 2014 IEEE. In an active noise control (ANC) system, computational complexity is one major concern when designing practical control algorithms. For an ANC system with multiple secondary sources and error microphones, one approach to reducing computational complexity is to apply a decentralized control scheme rather than centralized approaches. A decentralized scheme attempts to control a number of small-size ANC subsystems independently. In this paper, we consider the decentralized control of a two-channel ANC system tackling a noise disturbance in the frequency domain, where each channel consists of one secondary source and one error microphone. We propose a decentralized control method that is able to achieve the same noise reduction performance as the centralized controller with guaranteed convergence. The key step in designing the control method is to properly shape the eigenvalues of a matrix that models the two-channel secondary paths for each frequency index.
Zhang, H, Dong, Y, Chiclana, F & Yu, S 2019, 'Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design', European Journal of Operational Research, vol. 275, no. 2, pp. 580-598.
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© 2018 Elsevier B.V. Consensus reaching processes (CRPs) aim to help decision-makers achieve agreement regarding the solution to a common decision problem, and consequently play an increasingly important role in the resolution of group decision making (GDM) problems. To date, a large number of CRPs have been reported. However, there is a lack of a general framework and criteria to evaluate the efficiency of the different CRPs. This paper aims to fill this gap in the research literature on CRPs. To achieve this goal, firstly, a comprehensive review regarding the different approaches to CRP is reported, and a series of CRPs as the comparison objects are presented. Secondly, the following comparison criteria for measuring the efficiency of CPRs are proposed: the number of adjusted decision-makers, the number of adjusted alternatives, the number of adjusted preference values, the distance between the original and the adjusted preference information (adjustment cost), and the number of negotiation rounds required to reach consensus. Following this, a detailed simulation experiment is designed to analyze the efficiency of different CRPs under the mentioned different comparison criteria. Furthermore, new multi-stage optimization-based CRPs are also developed, which the simulation experiment shows to have better comprehensive consensus efficiency in different GDM settings.
Zhang, J, Ding, F, Zhang, N, Chen, S & Zhang, B 2019, 'A New SSUKF Observer for Sliding Mode Force Tracking H∞ Control of Electrohydraulic Active Suspension', Asian Journal of Control.
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© 2018 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd Compared to passive and semi-active suspensions, active suspensions have the capacity to overcome the tradeoff design of vehicle ride comfort and handling performance. In this paper, a new control system with modified spherical simplex UKF (SSUKF) observer and sliding mode force tracker is proposed for electrohydraulic active suspensions. The estimated states obtained from the SSUKF observer are used to derive the mode energy of body motion and design the target demanded forces for suspension actuators. A sliding mode controller is presented to drive electrohydraulic actuators to track the demanded forces. Based on a robust H∞ control method, a new control strategy depending on body mode energy is proposed to restrict body motion. The performance comparisons between passive and active suspensions under three typical excitations are presented, and the obtained results indicate that the proposed control system can significantly depress body motion and decrease the mode energy to improve ride comfort and also effectively enhance the road hold abilities.
Zhang, J, Norambuena, M, Li, L, Dorrell, D & Rodriguez, J 2019, 'Sequential model predictive control of three-phase direct matrix converter', Energies, vol. 12, no. 2.
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© 2019 by the authors. The matrix converter (MC) is a promising converter that performs the direct AC-to-AC conversion. Model predictive control (MPC) is a simple and powerful tool for power electronic converters, including the MC. However, weighting factor design and heavy computational burden impose significant challenges for this control strategy. This paper investigates the generalized sequential MPC (SMPC) for a three-phase direct MC. In this control strategy, each control objective has an individual cost function and these cost functions are evaluated sequentially based on priority. The complex weighting factor design process is not required. Compared with the standard MPC, the computation burden is reduced because only the pre-selected switch states are evaluated in the second and subsequent sequential cost functions. In addition, the prediction model computation for the following cost functions is also reduced. Specifying the priority for control objectives can be achieved. A comparative study with traditional MPC is carried out both in simulation and an experiment. Comparable control performance to the traditional MPC is achieved. This controller is suitable for the MC because of the reduced computational burden. Simulation and experimental results verify the effectiveness of the proposed strategy.
Zhang, K, Qu, Z, Dong, Y, Lu, H, Leng, W, Wang, J & Zhang, W 2019, 'Research on a combined model based on linear and nonlinear features - A case study of wind speed forecasting', Renewable Energy, vol. 130, pp. 814-830.
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© 2018 Elsevier Ltd As one of the most promising sustainable energy sources, wind energy is being paid more attention by the researchers. Because of the volatility and instability of wind speed series, wind power integration faces a severe challenge; thus, an accurate wind energy forecasting plays a key role in smart grid planning and management. However, many traditional forecasting models do not consider the necessity and importance of data preprocessing and neglect the limitation of using a single forecasting model, which leads to poor forecasting accuracy. To solve these problems, a novel combined model based on two linear and four nonlinear forecasting algorithms is proposed to adapt both the linear and nonlinear characteristics of the wind energy time series. In addition, a modified Artificial Fish Swarm Algorithm and Ant Colony Optimization (AFSA-ACO) algorithm is proposed and employed to determine the optimal weight coefficients of the combined models. To verify the forecasting performance of the developed combined model, several experiments were implemented by using 10-min interval wind speed data in Shandong, China. Then, one-step (10-min), three-step (30-min) and five-step (50-min) predictions were conducted. The experimental results indicate that the developed combined model is remarkably superior to all benchmark models for the high precision and stability of wind-speed predictions.
Zhang, Q, Shi, C, Niu, Z & Cao, L 2019, 'HCBC: A Hierarchical Case-Based Classifier Integrated with Conceptual Clustering', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 1, pp. 152-165.
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© 1989-2012 IEEE. The structured case representation improves case-based reasoning (CBR) by exploring structures in the case base and the relevance of case structures. Recent CBR classifiers have mostly been built upon the attribute-value case representation rather than structured case representation, in which the structural relations embodied in their representation structure are accordingly overlooked in improving the similarity measure. This results in retrieval inefficiency and limitations on the performance of CBR classifiers. This paper proposes a hierarchical case-based classifier, HCBC, which introduces a concept lattice to hierarchically organize cases. By exploiting structural case relations in the concept lattice, a novel dynamic weighting model is proposed to enhance the concept similarity measure. Based on this similarity measure, HCBC retrieves the top-K concepts that are most similar to a new case by using a bottom-up pruning-based recursive retrieval (PRR) algorithm. The concepts extracted in this way are applied to suggest a class label for the case by a weighted majority voting. Experimental results show that HCBC outperforms other classifiers in terms of classification performance and robustness on categorical data, and also works confidently well on numeric datasets. In addition, PRR effectively reduces the search space and greatly improves the retrieval efficiency of HCBC.
Zhang, T, Bao, JF, Zeng, RZ, Yang, Y, Bao, LL, Bao, FH, Zhang, Y & Qin, F 2019, 'Long lifecycle MEMS double-clamped beam based on low stress graphene compound film', Sensors and Actuators, A: Physical, vol. 288, pp. 39-46.
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© 2019 Elsevier B.V. Graphene based three-layer compound film on the silicon substrate is formed by gold deposition of electron beam evaporation (EBE) and graphene transfer. Processed with different high temperature annealing in nitrogen, the film with residual tensile stress of 52.58 MPa at 500 ℃ can be achieved by using an X-ray diffraction (XRD) method. Based on this low stress film, a series of long lifecycle MEMS double-clamped beams (DCBs) are fabricated by the standard MEMS manufacturing technology. The achieved beam can be turned on/off for up to 100 million times at the pull-in voltage of 30 V, which is compatible with the conventional, complementary metal-oxide-semiconductor (CMOS) circuit requirements.
Zhang, W, Liu, T, Ye, L, Ueland, M, Forbes, SL & Su, SW 2019, 'A novel data pre-processing method for odour detection and identification system', Sensors and Actuators, A: Physical, vol. 287, pp. 113-120.
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© 2018 Elsevier B.V. This paper presents a novel electronic nose (E-nose) data pre-processing method, based on a recently developed non-parametric kernel-based modelling (KBM) approach. The proposed method is tested by an automated odour detection and classification system, named “NOS.E” developed by the NOS.E team in University of Technology Sydney. Experimental results show that when extracting the derivative-related features from signals collected by the NOS.E, the proposed non-parametric KBM odour data pre-processing method achieves more reliable and stable pre-processing results comparing with other pre-processing methods such as wavelet package correlation filter (WPCF), mean filter (MF), polynomial curve fitting (PCF) and locally weighted regression (LWR). Based on these derivative-related features, the NOS.E can achieve a 96.23% accuracy of classification with the popular Support Vector Machine (SVM) classifier.
Zhang, X, Zhao, Z, Zheng, Y & Li, J 2019, 'Prediction of Taxi Destinations Using a Novel Data Embedding Method and Ensemble Learning', IEEE Transactions on Intelligent Transportation Systems, pp. 1-11.
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Zhang, Y, Huang, Y, Porter, AL, Zhang, G & Lu, J 2019, 'Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study', Technological Forecasting and Social Change.
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© 2018 As one of the most impactful emerging technologies, big data analytics and its related applications are powering the development of information technologies and are significantly shaping thinking and behavior in today's interconnected world. Exploring the technological evolution of big data research is an effective way to enhance technology management and create value for research and development strategies for both government and industry. This paper uses a learning-enhanced bibliometric study to discover interactions in big data research by detecting and visualizing its evolutionary pathways. Concentrating on a set of 5840 articles derived from Web of Science covering the period between 2000 and 2015, text mining and bibliometric techniques are combined to profile the hotspots in big data research and its core constituents. A learning process is used to enhance the ability to identify the interactive relationships between topics in sequential time slices, revealing technological evolution and death. The outputs include a landscape of interactions within big data research from 2000 to 2015 with a detailed map of the evolutionary pathways of specific technologies. Empirical insights for related studies in science policy, innovation management, and entrepreneurship are also provided.
Zhang, Y, Liu, Q, He, Z, Zong, Z & Fang, J 2019, 'Dynamic impact response of aluminum honeycombs filled with Expanded Polypropylene foam', Composites Part B: Engineering, vol. 156, pp. 17-27.
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© 2018 The paper investigated the dynamic impact response and characteristics of aluminum honeycomb filled with EPP foam (Expanded polypropylene) experimentally and numerically. It was found that the initial peak strength and mean strength of the filled honeycomb were improved significantly attributable to the interaction effect between the aluminum honeycomb and the foam, but the specific energy absorption (SEA) decreased. For the filled specimens with the same foam density, the initial peak strength, mean strength and SEA increased with the increase in impact velocity. Compared with the characteristics in the static compression test, the initial peak strength in the dynamic impact test increased, whereas the mean strength and SEA decreased. The study showed that EPP foam filling was effective to improve the impact characteristics of the bare aluminum honeycomb. Numerical simulation for the dynamic impact of filled honeycombs was also explored. It accurately reproduced the deformation process and addressed the interaction between the wall and EPP foam. By comparison of the properties in different filling types, it showed the single-cell filling was a good choice to improve the load resistance while using the least filling material.
Zhang, Y, Lu, W, Ou, W, Zhang, G, Zhang, X, Cheng, J & Zhang, W 2019, 'Chinese medical question answer selection via hybrid models based on CNN and GRU', Multimedia Tools and Applications.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Question answer selection in the Chinese medical field is very challenging since it requires effective text representations to capture the complex semantic relationships between Chinese questions and answers. Recent approaches on deep learning, e.g., CNN and RNN, have shown their potential in improving the selection quality. However, these existing methods can only capture a part or one-side of semantic relationships while ignoring the other rich and sophisticated ones, leading to limited performance improvement. In this paper, a series of neural network models are proposed to address Chinese medical question answer selection issue. In order to model the complex relationships between questions and answers, we develop both single and hybrid models with CNN and GRU to combine the merits of different neural network architectures. This is different from existing works that can onpy capture partial relationships by utilizing a single network structure. Extensive experimental results on cMedQA dataset demonstrate that the proposed hybrid models, especially BiGRU-CNN, significantly outperform the state-of-the-art methods. The source codes of our models are available in the GitHub (https://github.com/zhangyuteng/MedicalQA-CNN-BiGRU).
Zhang, Y, Porter, A, Chiavetta, D, Newman, NC & Guo, Y 2019, 'Forecasting technical emergence: An introduction', Technological Forecasting and Social Change.
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Zhang, Z, Oberst, S & Lai, J 2019, 'A non-linear friction work formulation for the analysis of self-excited vibrations', Journal of Sound and Vibration, vol. 443, pp. 328-340.
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Even though much research has been devoted to understand friction-induced vibrations, its root cause is not yet fully understood. Reliable prediction of friction-induced unstable vibrations such as in brake squeal or hip squeak remains a challenge because of nonlinearities involved and because the complex eigenvalue analysis (CEA) widely used in industry is linear. The energy fed back into the system by friction has been shown to be useful for analysis of measurements and numerical simulations. In numerical simulations, the linearised method of feed-in energy, calculated purely based on friction work has provided some insights into the physical mechanism for instabilities. However, the dynamics due to friction-induced instabilities is highly nonlinear and damping may offset some or all of the excess friction energy provided to the system. By using a nonlinear 2-DOF dry friction oscillator, a nonlinear friction work formulation is proposed to demonstrate that in combination with viscous damping the energy budget provides an improved analysis capability over linearised friction work. The results highlight the potential of nonlinear friction work as a reliable tool to study friction-induced instabilities to gain deeper physical insights into squeal triggering mechanisms and to better understand the over- and under-predictive character inherent to linear methods.
Zhao, L, Huang, S & Dissanayake, G 2019, 'Linear SLAM: Linearising the SLAM problems using submap joining', Automatica, vol. 100, pp. 231-246.
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© 2018 Elsevier Ltd The main contribution of this paper is a new submap joining based approach for solving large-scale Simultaneous Localization and Mapping (SLAM) problems. Each local submap is independently built using the local information through solving a small-scale SLAM; the joining of submaps mainly involves solving linear least squares and performing nonlinear coordinate transformations. Through approximating the local submap information as the state estimate and its corresponding information matrix, judiciously selecting the submap coordinate frames, and approximating the joining of a large number of submaps by joining only two maps at a time, either sequentially or in a more efficient Divide and Conquer manner, the nonlinear optimization process involved in most of the existing submap joining approaches is avoided. Thus the proposed submap joining algorithm does not require initial guess or iterations since linear least squares problems have closed-form solutions. The proposed Linear SLAM technique is applicable to feature-based SLAM, pose graph SLAM and D-SLAM, in both two and three dimensions, and does not require any assumption on the character of the covariance matrices. Simulations and experiments are performed to evaluate the proposed Linear SLAM algorithm. Results using publicly available datasets in 2D and 3D show that Linear SLAM produces results that are very close to the best solutions that can be obtained using full nonlinear optimization algorithm started from an accurate initial guess. The C/C++ and MATLAB source codes of Linear SLAM are available on OpenSLAM.
Zhao, LS, Zhou, WH, Geng, X, Yuen, KV & Fatahi, B 2019, 'A closed-form solution for column-supported embankments with geosynthetic reinforcement', Geotextiles and Geomembranes.
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© 2019 Elsevier Ltd Soil arching effect results from the non-uniform stiffness in a geosynthetic-reinforced and column-supported embankment system. However, most theoretical models ignore the impact of modulus difference on the calculation of load transfer. In this study, a generalized mathematical model is presented to investigate the soil arching effect, with consideration given to the modulus ratio between columns and the surrounding soil. For simplification, a cylindrical unit cell is drawn to study the deformation compatibility among embankment fills, geosynthetics, columns, and subsoils. A deformed shape function is introduced to describe the relationship between the column and the adjacent soil. The measured data gained from a full-scale test are applied to demonstrate the application of this model. In the parametric study, certain influencing factors, such as column spacing, column length, embankment height, modulus ratio, and tensile strength of geosynthetic reinforcement, are analyzed to investigate the performance of the embankment system. This demonstrates that the inclusion of a geosynthetic reinforcement or enlargement of the modulus ratio can increase the load transfer efficiency. When enhancing the embankment height or applying an additional loading, the height of the load transfer platform tends to be reduced. However, a relatively long column has little impact on the load transfer platform.
Zhao, S, Qiu, X, Lacey, J & Maisch, S 2019, 'Configuring fixed-coefficient active control systems for traffic noise reduction', Building and Environment, vol. 149, pp. 415-427.
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© 2018 Elsevier Ltd Practical implementation of active noise control (ANC) systems for outdoor traffic noise reduction remains rare. One challenge is the difficulty of configuring an ANC controller due to moving noise sources, which are typically located far from ANC systems. In this paper, a pseudo noise source method is proposed for configuring fixed-coefficient feedforward ANC systems for traffic noise control. First, a minimum of one pseudo noise source is placed near an ANC system to determine the control coefficients in the tuning stage. Second, the ANC systems are run to reduce the noise from far-field traffic noise sources using the optimal control coefficients in the cancelling stage. The feasibility and limitations of the proposed method are investigated by illustrating the effect of the pseudo noise source position on the noise reduction performance of the ANC system. The simulation results show that the performance of the ANC system increases with distance when the pseudo noise sources move farther from the system but approaches a constant when the pseudo noise sources are in the far field. The indoor experimental results are consistent with the simulation results. The outdoor experimental results of a six-channel coupled system show a noise reduction of 3 dB below 500 Hz at the position of a dummy head.
Zheng, D, Zhang, H, Andrew Zhang, J & Li, Y 2019, 'Consensus of the Second-order Multi-agent Systems under Asynchronous Switching with a Controller Fault', International Journal of Control, Automation and Systems, vol. 17, no. 1, pp. 136-144.
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© 2019, Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature. Asynchronous switching differing from asynchronous consensus may hinder the system to reach a consensus. This receives very limited attention, especially when the multi-agent systems have a controller fault. In order to analyze the consensus in this situation, this paper studies the consensus of the second-order multi-agent systems under asynchronous switching with a controller fault. We convert the consensus problems under asynchronous switching into stability problems and obtain important results for consensus with the aid of linear matrix inequalities. An example is given to illustrate the effect of asynchronous switching on the consensus, and to validate the analytical results in this paper.
Zheng, J, Luo, Z, Jiang, C & Gao, J 2019, 'Robust topology optimization for concurrent design of dynamic structures under hybrid uncertainties', Mechanical Systems and Signal Processing, vol. 120, pp. 540-559.
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Zheng, J, Luo, Z, Jiang, C & Wu, J 2019, 'Level-set topology optimization for robust design of structures under hybrid uncertainties', International Journal for Numerical Methods in Engineering, pp. 523-542.
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© 2018 John Wiley & Sons, Ltd. This paper will develop a new robust topology optimization (RTO) method based on level sets for structures subject to hybrid uncertainties, with a more efficient Karhunen-Loève hyperbolic Polynomial Chaos–Chebyshev Interval method to conduct the hybrid uncertain analysis. The loadings and material properties are considered hybrid uncertainties in structures. The parameters with sufficient information are regarded as random fields, while the parameters without sufficient information are treated as intervals. The Karhunen-Loève expansion is applied to discretize random fields into a finite number of random variables, and then, the original hybrid uncertainty analysis is transformed into a new process with random and interval parameters, to which the hyperbolic Polynomial Chaos–Chebyshev Interval is employed for the uncertainty analysis. RTO is formulated to minimize a weighted sum of the mean and standard variance of the structural objective function under the worst-case scenario. Several numerical examples are employed to demonstrate the effectiveness of the proposed RTO, and Monte Carlo simulation is used to validate the numerical accuracy of our proposed method.
Zheng, W-L, Gao, K, Li, G, Liu, W, Liu, C, Liu, J-Q, Wang, G & Lu, B-L 2019, 'Vigilance Estimation Using a Wearable EOG Device in Real Driving Environment', IEEE Transactions on Intelligent Transportation Systems, pp. 1-15.
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Zheng, Y, Peng, H, Ghosh, S, Lan, C & Li, J 2019, 'Inverse similarity and reliable negative samples for drug side-effect prediction.', BMC bioinformatics, vol. 19, no. Suppl 13, p. 554.
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BACKGROUND:In silico prediction of potential drug side-effects is of crucial importance for drug development, since wet experimental identification of drug side-effects is expensive and time-consuming. Existing computational methods mainly focus on leveraging validated drug side-effect relations for the prediction. The performance is severely impeded by the lack of reliable negative training data. Thus, a method to select reliable negative samples becomes vital in the performance improvement. METHODS:Most of the existing computational prediction methods are essentially based on the assumption that similar drugs are inclined to share the same side-effects, which has given rise to remarkable performance. It is also rational to assume an inverse proposition that dissimilar drugs are less likely to share the same side-effects. Based on this inverse similarity hypothesis, we proposed a novel method to select highly-reliable negative samples for side-effect prediction. The first step of our method is to build a drug similarity integration framework to measure the similarity between drugs from different perspectives. This step integrates drug chemical structures, drug target proteins, drug substituents, and drug therapeutic information as features into a unified framework. Then, a similarity score between each candidate negative drug and validated positive drugs is calculated using the similarity integration framework. Those candidate negative drugs with lower similarity scores are preferentially selected as negative samples. Finally, both the validated positive drugs and the selected highly-reliable negative samples are used for predictions. RESULTS:The performance of the proposed method was evaluated on simulative side-effect prediction of 917 DrugBank drugs, comparing with four machine-learning algorithms. Extensive experiments show that the drug similarity integration framework has superior capability in capturing drug features, achieving much better performance tha...
Zhong, Z, Zheng, L, Zheng, Z, Li, S & Yang, Y 2019, 'CamStyle: A Novel Data Augmentation Method for Person Re-Identification.', IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol. 28, no. 3, pp. 1176-1190.
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Person re-identification (re-ID) is a cross-camera retrieval task that suffers from image style variations caused by different cameras. The art implicitly addresses this problem by learning a camera-invariant descriptor subspace. In this paper, we explicitly consider this challenge by introducing camera style (CamStyle). CamStyle can serve as a data augmentation approach that reduces the risk of deep network overfitting and that smooths the CamStyle disparities. Specifically, with a style transfer model, labeled training images can be style transferred to each camera, and along with the original training samples, form the augmented training set. This method, while increasing data diversity against overfitting, also incurs a considerable level of noise. In the effort to alleviate the impact of noise, the label smooth regularization (LSR) is adopted. The vanilla version of our method (without LSR) performs reasonably well on few camera systems in which overfitting often occurs. With LSR, we demonstrate consistent improvement in all systems regardless of the extent of overfitting. We also report competitive accuracy compared with the state of the art on Market-1501 and DukeMTMC-re-ID. Importantly, CamStyle can be employed to the challenging problems of one view learning and unsupervised domain adaptation (UDA) in person re-identification (re-ID), both of which have critical research and application significance. The former only has labeled data in one camera view and the latter only has labeled data in the source domain. Experimental results show that CamStyle significantly improves the performance of the baseline in the two problems. Specially, for UDA, CamStyle achieves state-of-the-art accuracy based on a baseline deep re-ID model on Market-1501 and DukeMTMC-reID. Our code is available at: https://github.com/zhunzhong07/CamStyle .
Zhou, X, Jin, W, Tu, R, Guo, Q, Han, SF, Chen, C, Wang, Q, Liu, W, Jensen, PD & Wang, Q 2019, 'Optimization of microwave assisted lipid extraction from microalga Scenedesmus obliquus grown on municipal wastewater', Journal of Cleaner Production, vol. 221, pp. 502-508.
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© 2019 Elsevier Ltd Efficient cost effective lipid extraction from microalgae is a challenging topic for large scale production of microalgae-based biodiesel. In this study, a microwave-assisted lipid extraction process was applied to the oil-rich green microalga Scenedesmus obliquus grown on municipal wastewater. N-hexane/isopropanol solvent was used as alternative solvent. Optimal extraction parameters were determined as: operational temperature of 130 °C, extraction time of 0.25 h, solvent ratio of N-hexane/isopropanol of 3:2 (V:V), phase ratio of co-solvent/biomass was 50:1 (mL:g). Relative extraction rates of lipid and fatty acid methyl esters (FAMEs) achieved using microwave-assisted extraction (MAE) were 88.25% and 95.58%, respectively, which is higher than traditional water bath heating extraction process (WHE). In addition, compared with WHE, the apparent first order rate constant of MAE was enhanced by 18 times compared to traditional methods. Analysis using scanning electron microscopy indicated that disruption of the cell wall of Scenedesmus obliquus by microwave led to the enhancement of solvents’ penetration and lipid extraction.
Zhu, G, Liu, X, Li, L, Chen, H, Tong, W & Zhu, J 2019, 'Cooling System Design of a High-Speed PMSM Based on a Coupled Fluidic-Thermal Model', IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY, vol. 29, no. 2.
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Zhu, H & Guo, YJ 2019, 'Wideband Filtering Phase Shifter Using Transversal Signal-Interference Techniques', IEEE Microwave and Wireless Components Letters, pp. 1-3.
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Zhu, H, Cheng, Z & Guo, YJ 2019, 'Design of Wideband In-Phase and Out-of-Phase Power Dividers Using Microstrip-to-Slotline Transitions and Slotline Resonators', IEEE Transactions on Microwave Theory and Techniques, pp. 1-13.
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Zhu, LF, Ke, LL, Zhu, XQ, Xiang, Y & Wang, YS 2019, 'Crack identification of functionally graded beams using continuous wavelet transform', Composite Structures, vol. 210, pp. 473-485.
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© 2018 Elsevier Ltd This paper proposes a new damage index for the crack identification of beams made of functionally graded materials (FGMs) by using the wavelet analysis. The damage index is defined based on the position of the wavelet coefficient modulus maxima in the scale space. The crack is assumed to be an open edge crack and is modeled by a massless rotational spring. It is assumed that the material properties follow exponential distributions along the beam thickness direction. The Timoshenko beam theory is employed to derive the governing equations which are solved analytically to obtain the frequency and mode shape of cracked FGM beams. Then, we apply the continuous wavelet transform (CWT) to the mode shapes of the cracked FGM beams. The locations of the cracks are determined from the sudden changes in the spatial variation of the damage index. An intensity factor, which relates to the size of the crack and the coefficient of the wavelet transform, is employed to estimate the crack depth. The effects of the crack size, the crack location and the Young's modulus ratio on the crack depth detection are investigated.
Zhu, Q, Coleman, P, Qiu, X, Wu, M, Yang, J & Burnett, I 2019, 'Robust Personal Audio Geometry Optimization in the SVD-Based Modal Domain', IEEE/ACM Transactions on Audio Speech and Language Processing, vol. 27, no. 3, pp. 610-620.
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© 2014 IEEE. Personal audio generates sound zones in a shared space to provide private and personalized listening experiences with minimized interference between consumers. Regularization has been commonly used to increase the robustness of such systems against potential perturbations in the sound reproduction. However, the performance is limited by the system geometry such as the number and location of the loudspeakers and controlled zones. This paper proposes a geometry optimization method to find the most geometrically robust approach for personal audio amongst all available candidate system placements. The proposed method aims to approach the most 'natural' sound reproduction so that the solo control of the listening zone coincidently accompanies the preferred quiet zone. Being formulated in the SVD-based modal domain, the method is demonstrated by applications in three typical personal audio optimizations, i.e., the acoustic contrast control, the pressure matching, and the planarity control. Simulation results show that the proposed method can obtain the system geometry with better avoidance of 'occlusion,' improved robustness to regularization, and improved broadband equalization.
Zijlema, AF, Van den hoven, E & Eggen, B 2019, 'A qualitative exploration of memory cuing by personal items in the home', Memory Studies.
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We are surrounded by personal items that can trigger memories, such as photos, souvenirs, and heirlooms. Also during holidays, we collect items to remind us of the events, but not all bring back memories to the same extent. Therefore, we explored peoples’ responses to personal items related to a holiday, using the home tour interviewing method. Sixty-three accounts of cuing responses from nine home tours were analysed using thematic analysis. This resulted in four types of cuing responses: a) ‘no-memory’ responses, b) ‘know’ responses, c) ‘memory evoked think or feel’ responses, and d) ‘remember’ responses. For each of these cuing response categories, we looked into the types of items and their characteristics. Further, we found that some items can evoke multiple memories. The majority of the memories’ content refer to events close to the moment of acquiring the item.
Zuo, H, Lu, J, Zhang, G & Liu, F 2019, 'Fuzzy Transfer Learning Using an Infinite Gaussian Mixture Model and Active Learning', IEEE Transactions on Fuzzy Systems, vol. 27, no. 2, pp. 291-303.
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© 2018 IEEE. Transfer learning is gaining considerable attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a related domain. Fuzzy transfer learning, which is based on fuzzy system (especially fuzzy rule-based models), has been developed because of its capability to deal with the uncertainty in transfer learning. However, two issues with fuzzy transfer learning have not yet been resolved: choosing an appropriate source domain and efficiently selecting labeled data for the target domain. This paper proposes an innovative method based on fuzzy rules that combines an infinite Gaussian mixture model (IGMM) with active learning to enhance the performance and generalizability of the constructed model. An IGMM is used to identify the data structures in the source and target domains providing a promising solution to the domain selection dilemma. Further, we exploit the interactive query strategy in active learning to correct imbalances in the knowledge to improve the generalizability of fuzzy learning models. Through experiments on synthetic datasets, we demonstrate the rationality of employing an IGMM and the effectiveness of applying an active learning technique. Additional experiments on real-world datasets further support the capabilities of the proposed method in practical situations.
Zuo, H, Lu, J, Zhang, G & Pedrycz, W 2019, 'Fuzzy Rule-Based Domain Adaptation in Homogeneous and Heterogeneous Spaces', IEEE Transactions on Fuzzy Systems, vol. 27, no. 2, pp. 348-361.
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© 2018 IEEE. Domain adaptation aims to leverage knowledge acquired from a related domain (called a source domain) to improve the efficiency of completing a prediction task (classification or regression) in the current domain (called the target domain), which has a different probability distribution from the source domain. Although domain adaptation has been widely studied, most existing research has focused on homogeneous domain adaptation, where both domains have identical feature spaces. Recently, a new challenge proposed in this area is heterogeneous domain adaptation where both the probability distributions and the feature spaces are different. Moreover, in both homogeneous and heterogeneous domain adaptation, the greatest efforts and major achievements have been made with classification tasks, while successful solutions for tackling regression problems are limited. This paper proposes two innovative fuzzy rule-based methods to deal with regression problems. The first method, called fuzzy homogeneous domain adaptation, handles homogeneous spaces while the second method, called fuzzy heterogeneous domain adaptation, handles heterogeneous spaces. Fuzzy rules are first generated from the source domain through a learning process; these rules, also known as knowledge, are then transferred to the target domain by establishing a latent feature space to minimize the gap between the feature spaces of the two domains. Through experiments on synthetic datasets, we demonstrate the effectiveness of both methods and discuss the impact of some of the significant parameters that affect performance. Experiments on real-world datasets also show that the proposed methods improve the performance of the target model over an existing source model or a model built using a small amount of target data.
Zuo, Y, Wu, Q, Fang, Y, An, P, Huang, L & Chen, Z 2019, 'Multi-scale Frequency Reconstruction for Guided Depth Map Super-resolution via Deep Residual Network', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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