C. F., J, H. W., N & Lin, C 1999, 'Techniques and applications of integrated neural-network based fuzzy logic control systems'.
Dingman, SK, Williams, M, Fosbinder, D & Warnick, M 1999, 'Implementing a caring model to improve patient satisfaction.', The Journal of nursing administration, vol. 29, no. 12, pp. 30-37.
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OBJECTIVE: To evaluate the effect of implementing a Caring Model on patient satisfaction. BACKGROUND: Patient satisfaction has become an important indicator of quality care and financial success of healthcare institutions. Acknowledging the importance of nurse caring behaviors and the impact on patient satisfaction has been relatively recent. Based on a synthesis of the literature, five caring behaviors have been formulated into a model; no single study identified the five selected behaviors included in this study. METHODS: In an acute care setting, eight patient satisfaction attributes were incorporated into a Caring Model. Implementation of the model among nursing staff members included an educational in-service, printing of the behaviors on the name badge, reminders in monthly staff meetings and nursing rounds, and inclusion of the caring behaviors in patient care documentation, job descriptions, and performance appraisals. The impact upon patient satisfaction was compared 6 months' preintervention to 6 months' postintervention. RESULTS: Postintervention, the patient satisfaction attributes of Nurses Anticipating Needs and Responds to Requests significantly increased. Attributes that began preintervention as immediate priorities for improvement became major strengths postintervention. CONCLUSIONS/IMPLICATIONS: Results of this study provide evidence that nurse caring behaviors can influence patient satisfaction. For a Caring Model to be effective, it must become an integral part of strategic planning and be implemented throughout the entire organization. To sustain the effects of the model, there must be frequent reminders among staff members. Nurse caring is an important predictor of patient satisfaction. The authors discuss the effect of implementing a caring model on patient satisfaction. In an acute care setting, eight patient satisfaction attributes incorporated into five nurse caring behaviors were evaluated pre- and postintervention. Results of the study...
Hsiao, IL, Kuo, CH, Bai, MS & Lin, CT 1999, 'On-line fault diagnosis of rotor vibration by using signal-based feature generation and neural fuzzy inference', Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao, vol. 20, no. 4, pp. 345-352.
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An on-line fault detection and isolation technique is proposed for the diagnosis of rotor vibration. The architecture of the systems mainly consists of feature generation and fault inference. A signal-based method is used for generating the features required by the subsequent neural fuzzy inference. In the signal-based approach, both lateral and axial vibration data are used for calculating signal features such as the average, the standard deviation, the maximum, and the harmonic multiples. A neural fuzzy network is exploited for intelligent inference of faults based on the extracted features. The proposed systems are implemented on the platform of a digital signal processor. Experiments carried out for a rotor kit, a centrifugal fan, and a centrifugal pump indicate the potential of the proposed techniques in predictive maintenance.
Jay, CB 1999, 'Partial evaluation of shaped programs: experience with FISh', pp. 23 pp-23 pp.
Jay, CB 1999, 'Programming in FISh', International Journal on Software Tools for Technology Transfer, vol. 2.
Juang, CF & Lin, CT 1999, 'A recurrent self-organizing neural fuzzy inference network', IEEE TRANSACTIONS ON NEURAL NETWORKS, vol. 10, no. 4, pp. 828-845.
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Lai, JH & Lin, CT 1999, 'Application of neural fuzzy network to pyrometer correction and temperature control in rapid thermal processing', IEEE Transactions on Fuzzy Systems, vol. 7, no. 2, pp. 160-175.
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Temperature measurement and control are two difficult problems in the rapid thermal processing (RTP) system. For many applications such as rapid thermal processing chemical vapor deposition (RTCVD) and rapid thermal oxidation (RTO), large changes in wafer emissivity can occur during film growing, leading to erroneous temperature measurements with a single wavelength pyrometer. The error in the inferred temperature will affect the temperature control of the RTP system. In order to correct the temperature reading of the pyrometer, a neural fuzzy network is used to predict the emissivity changes for the compensation of measured temperature. As for the temperature control, to overcome ill performance of the temperature tracking system due to the inaccuracy of the identified model, another neural fuzzy network is used in the RTP system for learning inverse control simultaneously. The key advantage of neural fuzzy approach over traditional ones lies on that the approach does not require a mathematical description of the system while performing pyrometer correction and temperature control. Simulation results show that the adopted neural fuzzy networks can not only correct the pyrometer reading accurately, but also be able to track a temperature trajectory very well.
Lin, CT & Chung, IF 1999, 'A reinforcement neuro-fuzzy combiner for multiobjective control.', IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, vol. 29, no. 6, pp. 726-744.
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This paper proposes a neuro-fuzzy combiner (NFC) with reinforcement learning capability for solving multiobjective control problems. The proposed NFC can combine n existing low-level controllers in a hierarchical way to form a multiobjective fuzzy controller. It is assumed that each low-level (fuzzy or nonfuzzy) controller has been well designed to serve a particular objective. The role of the NFC is to fuse the n actions decided by the n low-level controllers and determine a proper action acting on the environment (plant) at each time step. Hence, the NFC can combine low-level controllers and achieve multiple objectives (goals) at once. The NFC acts like a switch that chooses a proper action from the actions of low-level controllers according to the feedback information from the environment. In fact, the NFC is a soft switch; it allows more than one low-level actions to be active with different degrees through fuzzy combination at each time step. An NFC can be designed by the trial-and-error approach if enough a priori knowledge is available, or it can be obtained by supervised learning if precise input/output training data are available. In the more practical cases when there is no instructive teaching information available, the NFC can learn by itself using the proposed reinforcement learning scheme. Adopted with reinforcement learning capability, the NFC can learn to achieve desired multiobjectives simultaneously through the rough reinforcement feedback from the environment, which contains only critic information such as "success (good)" or "failure (bad)" for each desired objective. Computer simulations have been conducted to illustrate the performance and applicability of the proposed architecture and learning scheme.
Lin, CT & Jou, CP 1999, 'Controlling chaos by GA-based reinforcement learning neural network.', IEEE transactions on neural networks, vol. 10, no. 4, pp. 846-859.
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This paper proposes a TD (temporal difference) and GA (genetic algorithm) based reinforcement (TDGAR) neural learning scheme for controlling chaotic dynamical systems based on the technique of small perturbations. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to fulfill the reinforcement learning task. Structurely, the TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network for helping the learning of the other network, the action network, which determines the outputs (actions) of the TDGAR learning system. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. This can usually accelerate the GA learning since an external reinforcement signal may only be available at a time long after a sequence of actions have occurred in the reinforcement learning problems. By defining a simple external reinforcement signal, the TDGAR learning system can learn to produce a series of small perturbations to convert chaotic oscillations of a chaotic system into desired regular ones with a periodic behavior. The proposed method is an adaptive search for the optimum control technique. Computer simulations on controlling two chaotic systems, i.e., the Hénon map and the logistic map, have been conducted to illustrate the performance of the proposed method.
Lin, CT, Juang, CF & Huang, JC 1999, 'Temperature control of rapid thermal processing system using adaptive fuzzy network', FUZZY SETS AND SYSTEMS, vol. 103, no. 1, pp. 49-65.
Lin, CT, Juang, CF & Li, CP 1999, 'Temperature control with a neural fuzzy inference network', IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 29, no. 3, pp. 440-451.
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Although multilayered backpropagation neural networks (BPNN's) have demonstrated high potential in the nonconventional branch of adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained on-line to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, in this paper we propose a neural fuzzy inference network (NFIN) suitable for adaptive control of practical plant systems in general and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule-based model possessing a neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature-control system. As compared to the BPNN under the same training procedure, the simulated results show that not only can the NFIN greatly reduce the training time and avoid the over-tuned phenomenon, but the NFIN also has perfect regulation ability. The performance of the NFIN is also compared to that of the traditional PID controller and fuzzy logic controller (FLC) on the water bath temperature-control system. The three control schemes are compared through experimental studies with respect to set-points regulation, ramp-points tracking, and the influence of unknown impulse noise and large parameter variation in the temperature-control system. It is found that the proposed NFIN control scheme has the best control performance of the three control schemes. © 1999 IEEE.
Lin, CT, Nein, HW & Lin, WC 1999, 'A space-time delay neural network for motion recognition and its application to lipreading.', International journal of neural systems, vol. 9, no. 4, pp. 311-334.
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Motion recognition has received increasing attention in recent years owing to heightened demand for computer vision in many domains, including the surveillance system, multimodal human computer interface, and traffic control system. Most conventional approaches classify the motion recognition task into partial feature extraction and time-domain recognition subtasks. However, the information of motion resides in the space-time domain instead of the time domain or space domain independently, implying that fusing the feature extraction and classification in the space and time domains into a single framework is preferred. Based on this notion, this work presents a novel Space-Time Delay Neural Network (STDNN) capable of handling the space-time dynamic information for motion recognition. The STDNN is unified structure, in which the low-level spatiotemporal feature extraction and high-level space-time-domain recognition are fused. The proposed network possesses the spatiotemporal shift-invariant recognition ability that is inherited from the time delay neural network (TDNN) and space displacement neural network (SDNN), where TDNN and SDNN are good at temporal and spatial shift-invariant recognition, respectively. In contrast to multilayer perceptron (MLP), TDNN, and SDNN, STDNN is constructed by vector-type nodes and matrix-type links such that the spatiotemporal information can be accurately represented in a neural network. Also evaluated herein is the performance of the proposed STDNN via two experiments. The moving Arabic numerals (MAN) experiment simulates the object's free movement in the space-time domain on image sequences. According to these results, STDNN possesses a good generalization ability with respect to the spatiotemporal shift-invariant recognition. In the lipreading experiment, STDNN recognizes the lip motions based on the inputs of real image sequences. This observation confirms that STDNN yields a better performance than the existing TDNN-based syst...
Lin, CT, Nein, HW & Lin, WF 1999, 'Speaker adaptation of fuzzy-perceptron-based speech recognition', International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, vol. 7, no. 1, pp. 1-30.
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In this paper, we propose a speech recognition algorithm which utilizes hidden Markov models (HMM) and Viterbi algorithm for segmenting the input speech sequence, such that the variable-dimensional speech signal is converted into a fixed-dimensional speech signal, called TN vector. We then use the fuzzy perceptron to generate hyperplanes which separate patterns of each class from the others. The proposed speech recognition algorithm is easy for speaker adaptation when the idea of "supporting pattern" is used. The supporting patterns are those patterns closest to the hyperplane. When a recognition error occurs, we include all the TN vectors of the input speech sequence with respect to the segmentations of all HMM models as the supporting patterns. The supporting patterns are then used by the fuzzy perceptron to tune the hyperplane that can cause correct recognition, and also tune the hyperplane that resulted in wrong recognition. Since only two hyperplanes need to be tuned for a recognition error, the proposed adaptation scheme is time-economic and suitable for on-line adaptation. Although the adaptation scheme cannot ensure to correct the wrong recognition right after adaptation, the hyperplanes are tuned in the direction for correct recognition iteratively and the speed of adaptation can be adjusted by a "belief" parameter set by the user. Several examples are used to show the performance of the proposed speech recognition algorithm and the speaker adaptation scheme.
Lin, CT, Wu, GD & Hsiao, SC 1999, 'New techniques on deformed image motion estimation and compensation.', IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, vol. 29, no. 6, pp. 846-859.
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In this paper, new techniques for deformed image motion estimation and compensation using variable-size block-matching are proposed, which can be applied to an image sequence compression system or a moving object recognition system. The motion estimation and compensation techniques have been successfully applied in the area of image sequence coding. Many research papers on improving the performance of these techniques have been published; many directions are proposed, which can all lead to better performance than the conventional techniques. Among them, both generalized block-matching and variable-size block-matching are successfully applied in reducing the data rate of compensation error and motion information, respectively. These two algorithms have their merits, but suffer from their drawbacks. Moreover, reducing the data rate in compensation error is sometimes increasing the data rate in motion information, or vice versa. Based on these two algorithms, we propose and examine several algorithms which are effective in reducing the data rate. We then incorporate these algorithms into a system, in which they work together to overcome the disadvantages to individual and keep their merits at the same time. The proposed system can optimally balance the amount of data rate in two aspects (i.e., compensation error and motion information). Experimental results show that the proposed system outweighs the conventional techniques. Since we propose a recovery operation which tries to recover the incorrect motion vectors from the global motion, this proposed system can also be applied to the moving object recognition in image sequences.
Liu, YK & Zhang, GQ 1999, 'On the completeness of fuzzy measure-space', Fuzzy Sets and Systems, vol. 102, no. 2, pp. 345-351.
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In this paper, we first discuss the completeness of fuzzy measure space. The measurability of analytic sets is also discussed in fuzzy measure space. In the last part of the paper a measurable projection theorem is proved. © 1999 Elsevier Science B.V. All rights reserved.
Benferhat, S, Dubois, D, Prade, H & Williams, MA 1999, 'A practical approach to fusing prioritized knowledge bases', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 222-236.
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© Springer-Verlag Berlin Heidelberg 1999. This paper investigates simple well behaved syntactic methods to fuse prioritized knowledge bases which are semantically meaningful in the frameworks of possibility theory and of Spohn’s ordinal conditional functions. Different types of scales for priorities are discussed: finite vs. infinite, numerical vs. ordinal. Syntactic fusion is envisaged here as a process which combines prioritized knowledge bases into a new prioritized knowledge base, and thus allows for subsequent iteration. Several fusion operations are proposed, according to whether or not the sources are dependent, or conflicting, or sharing the same scale.
Benferhat, S, Dubois, D, Prade, H & Williams, MA 1999, 'Practical approach to revising prioritized knowledge bases', International Conference on Knowledge-Based Intelligent Electronic Systems, Proceedings, KES, pp. 170-173.
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This paper investigates simple syntactic methods to revise prioritized belief bases, that are semantically meaningful in the frameworks of possibility theory and of Spohn's ordinal conditional functions. Here, revising prioritized belief bases amounts to conditioning a distribution function on interpretations. Different types of scales for priorities are discussed: finite vs. infinite, numerical vs. ordinal. Syntactic revision is envisaged here as a process which transforms prioritized belief bases into a new prioritized belief base, and thus allows for the subsequent iteration.
Chung, IF, Chang, HH & Lin, CT 1999, 'Fuzzy control of a six-degree motion platform with stability analysis', Proceedings of the IEEE International Conference on Systems, Man and Cybernetics.
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This paper addresses the control problem of the six-degree-of-freedom parallel manipulator known as the Stewart platform. This manipulating structure has been well-known for its use as a flight simulator. Recently, it has become an important element in virtual reality (VR) applications for providing motion sensation to users. In this research, we first analyze the hydraulic driving system and the kinematics of the Stewart platform. We then develop a fuzzy controller for the Stewart platform. The stability of the proposed fuzzy control systems are analyzed based on the Popov criterion stability theorem. Experimental results have shown that the designed fuzzy controller can drive the six-degree motion platform accurately, smoothly, and stably.
Jaktman, CB, Leaney, J & Liu, M 1999, 'Structural analysis of the software architecture - A maintenance assessment case study', SOFTWARE ARCHITECTURE, 1st Working IFIP Conference on Software Architecture (WICSA1), KLUWER ACADEMIC PUBLISHERS, SAN ANTONIO, TX, pp. 455-470.
Jay, CB 1999, 'Denotational Semantics of Shape: Past, Present and Future', Fifteenth Conference on the Mathematical Foundations of Programming Semantics (MFPS XV) Tulane University New Orleans, LA USA April 28 - May 1, 1999: Proceedings, Elsevier, pp. 197-210.
Jay, CB 1999, 'Denotational semantics of shape: Past, present and future', Electronic Notes in Theoretical Computer Science, pp. 320-333.
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Past work on the semantics of vectors and arrays provides a denotational semantics for the new, higher-order, polymorphic array programming language FISh, that uses static analysis to determine array shapes. This semantics will be combined with that of shape polymorphism to underpin a language that will support both shape analysis and shape polymorphism on both arrays and inductive types. ©1999 Published by Elsevier Science B. V.
Jay, CB 1999, 'Partial evaluation of shaped programs: experience with FISh', ACM SIGPLAN Workshop on Partial Evaluation and Semantics-Based Program Manipulation (PEPM ’99) San Antonio, Texas, January 22-23, 1999: Proceedings, BRICS, pp. 147-158.
Lawson, HW, Leaney, J & O'Neill, T 1999, 'Open complex computer based systems: Only the first step along the way to safe, reliable computing', Proceedings - ECBS 1999, IEEE Conference and Workshop on Engineering of Computer-Based Systems, pp. 294-301.
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© 1999 IEEE. Open systems are being presented as the way forward for the construction of complex computer based systems of all types. They have had some success but as with all such attempts to solve problems in computing, it is being touted as a panacea. Before the success is lost in disgruntled disappointment, this reflection attempts to establish awareness of pitfalls which may save the open systems idea from going the way of all panaceas. This paper further suggests that open systems are only, the first step towards safe and reliable computing platforms for the future, and recommends an evolutionary path which includes such concepts as stable infrastructure architectures, domain and application openness and open systems engineering.
Liu, W & Williams, MA 1999, 'A framework for multi-agent belief revision part I: The role of ontology', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 168-179.
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© Springer-Verlag Berlin Heidelberg 1999. In this paper, we identify that failure to cater for the various forms of heterogeneity is one of the major drawbacks of the previous research on multi-agent belief revision(MABR). Three major categories of heterogeneity, namely social, semantic and syntactic heterogeneity are clarified. Several issues posed by such heterogeneities are addressed in the context of BR. The use of ontology is proposed as a powerful tool to tackle the heterogeneity issues so as to achieve the necessary reliable communication and system interoperability required by MABR. The question of what kind of ontology would be suitable to support MABR in a heterogenous setting is answered in Part I. In its sequel, Part II, a general framework for MABR is presented based on a shared knowledge structure which serves as the theoretical basis for ontology design.
Lu, HY, Zhu, J, Ramsden, VS & Hui, SYR 1999, 'Measurement and modeling of stray capacitances in high frequency transformers', PESC Record - IEEE Annual Power Electronics Specialists Conference, 30th Annual IEEE Annual Power Electronics Specialists Conference, Charleston, SC, USA, pp. 763-768.
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This paper proposes an approach to incorporate stray capacitances into a dynamic circuit model of high frequency transformers. The measuring techniques for stray capacitances in high frequency transformers and inductors are presented. A 500W transformer in a full bridge inverter operating at 25 kHz has been simulated with the new model and the theoretical results are confirmed by experiments.
Moggi, E, Bellè & Jay, CB 1999, 'Monads, Shapely Functors and Traversals', Proceedings of the Eighth Conference on Category Theory and Computer Science (CTCS’99), Elsevier, pp. 265-286.
Peppas, P, Pagnucco, M, Prokopenko, M, Foo, NY & Nayak, A 1999, 'Preferential semantics for causal systems', IJCAI International Joint Conference on Artificial Intelligence, pp. 118-123.
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In the present work we examine the causal theory of actions put forward by McCain and Turner [McCain and Turner, 1995] for determining ramifications. Our principal aim is to provide a characterisation of this causal theory of actions in terms of a Shoham-like preferential semantics [Shoham, 19881. This would have a twofold advantage: it would place McCain and Turner's theory in perspective, allowing a comparison with other logics of action; and, it would allow us to glean further insights into the nature of causality underlying their work. We begin by showing that our aim is not attainable by a preferential mechanism alone. At this point we do not abandon preferential semantics altogether but augment it in order to arrive at the desired result. We draw f he following moral which is at the heart of our paper: two components - minimal change under a preferential structure and causality - are required to provide a concise solution to the frame and ramification problems.
Prokopenko, M, Pagnucco, M, Peppas, P & Nayak, A 1999, 'Causal propagation semantics—a study', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 378-392.
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© Springer-Verlag Berlin Heidelberg 1999. A unifying semantic framework for different reasoning approaches provides an ideal tool to compare these competing alternatives. A historic example is Kripke’s possible world semantics that provided a unifying framework for different systems of modal logic. More recently, Shoham’s work on preferential semantics similarly provided a much needed framework to uniformly represent and compare a variety of nonmonotonic logics (including some logics of action). The present work develops a novel type of semantics for a particuleir causal approach to reasoning about action. The basic idea is to abandon the standard statespace of possible worlds and consider instead a larger set of possibiUties—a hyper-space—tracing the effects of auctions (including indirect effects) with the states in the hyper-space. Intuitively, the purpose of these hyper-states is to supply extra context to record the process of causality.
Zowghi, D. 1999, 'Maintaining and Comparing Requirements', Proceedings of the Fourth Australian Conference on Requirements Engineering, Australian Workshop on Requirements Engineering, Australia, pp. 115-129.