The generation of rule-bases in conventional fuzzy logic controllers can be a difficult and time consuming problem for implementation by process operators thus affecting their wider applicability. A Self-Learning Fuzz...
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The generation of rule-bases in conventional fuzzy logic controllers can be a difficult and time consuming problem for implementation by process operators thus affecting their wider applicability. A Self-Learning Fuzzy Logic control (SLFLC) offers a possible solution. A performance study is therefore presented to evaluate the performance of a proposed SLFLC by analysing its transient performance for a variety of on-line tests and examining its ability to generate a consistent set of rules, based on a predetermined criteria. The results presented show that even with a limited knowledge of the process, the self-learning procedure is able to develop a suitable set of rules and produce a satisfactory process performance with some degree of robustness and repeatability when applied to a non-linear single-input single-output (SISO) or multi-input multi-output (MIMO) laboratory liquid-level processes.
作者：
FUJIWARA, ETANAKA, TMemberFaculty of Engineering
Tokyo Institute of Technology Tokyo Japan 152 Eui Fujiwara:received his B.S. and M.S. degrees in Electronics Engineering in 1968 and 1970
respectively and his Dr. of Eng. degree in 1981 all from Tokyo Institute of Technology. In 1970 he joined the NTT Musashino Electrical Communication Laboratories and engaged in developing PIPS-1 and PIPS-11 computer systems. In 1988 he joined the Department of Computer Science Tokyo Institute of Technology as an Associate Professor. In 1990 he became a full Professor. He was a Visiting Professor at the Center for Advanced Computer Studies the University of Southwestern Louisiana from June 1985 to July 1986. His current research interests include coding theory for computers fault-tolerant memories VLSI defect-toleranceand WSI systems. He is a co-author ofError Control Coding for Computer Systems(Prentice-Hall1989) EssentiaLF of Error-Correcting Coding Techniques (Academic Press 1990) and other books. Dr. Fujiwara received the Young Engineer Award from the I.E.I.C.E. in 1978 and the Teshima Memorial Research Award in 1991. He is a senior member of the IEEE and a member of the Information Processing Society Japan. Associate Member
Because of its capability of high-speed search, the associative memory (CAM) is expected to be used in a variety of information processing systems. In this paper, novel fault-tolerant techniques which are effective fo...
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Because of its capability of high-speed search, the associative memory (CAM) is expected to be used in a variety of information processing systems. In this paper, novel fault-tolerant techniques which are effective for on-line use are proposed for TLB which is an example of the application of CAM. First, fault and error models of the TLB consisting of the CAM part and the SRAM part are clarified. Then, the fault-tolerant techniques for these faults and errors, such as distance separable technique, cod-ing technique, simplified 1-out-of-n check and graceful degradation, are proposed. The distance separable technique which encodes the data stored in the CAM part is the one which masks the faulty CAM part and prevents errors from propagating to the subsequent circuits. The coding technique checks the one-to-one correspondence between the data in the CAM and those in SRAM by using the SEC-DED code with byte error detection capability, i.e., SEC-DED-SbED code, and at the same time it detects and corrects errors in the data stored in SRAM. The simplified 1-out-of-n check processes association errors. The graceful degradation gives a flag in the faulty memory section and prevents it from being used. The methods proposed in this paper are evaluated from area augmentation and error detection capability perspectives. The results show that the fault-tolerant TLB with 32 virtual address bits, 32 physical address bits and 128 entries gives single fault detection probability of nearly 99 percent with 28 percent area increase.
In this article a multimedia computer-assisted learning (MCAL) system is presented. The major objective of this work was to investigate the potential of using such systems as tools for transferring instructional cours...
In this article a multimedia computer-assisted learning (MCAL) system is presented. The major objective of this work was to investigate the potential of using such systems as tools for transferring instructional course information through various types of computer media as opposed to the classic CAL systems. The philosophy and techniques employed to design the system are investigated. Usage of the implemented system and its merits have been illustrated through its application to teach engineering students and technicians the theory and concepts of marine radar. System design, implementation, test, and revision phases are presented and discussed.
作者：
Andria, G.Salvatore, L.Savino, M.Trotta, A.Dr. Gregorio Andria (1956)
AEI received the M. S. degree in Electrical Engineering from the State University of Bari/Italy in 1981 and the Ph. D. degree in Electrical Engineering in 1987 from the same University. From 1981 to 1983 he was working in the Electrotechnics and Electronics Department of the University of Bari as a member of the research team on electrical measurements. From 1984 to 1986 he was a Doctoral Fellow and currently he is a researcher in the same department. His research interests are in the fields of electrical and electronic measurements on components and systems including digital measurements for the analysis of electrical quantities in non-sinusoidal systems and the design of integrated optical sensors for measurement and control of non-electrical physical quantities. (Department of Electrotechnics and Electronics Faculty of Engineering polytechnic of Bovia E. Orabona 4 1-70125 Bari. Italy T +3980/242266 Fax + 3980/242410) Prof. Luigi Salvatore (1945) AEI
received the degree in electrical engineering from the University of Bari/Italy in 1970. Since 1976 he has worked in the Electrotechnical and Electronic Department of the same University as a member of the research team on electrical machines. From 1983 to 1987 he was a researcher of electrical machines in the same department. Since 1987 he has been an Associate Professor of electrical machines at the University of Bari. At the present time his research interests include the control monitoring and diagnostics of AC drives and the areas of signal processing anddigital measurements on power electronics systems. (Department of Electrotechnics and Electronics Faculty of Engineering Polytechnic of Bari via E. Orabona 4 I-70125 Bari Italy T + 3980/242258 Fax + 3980/242410) Prof. Mario Savino (1947)
AEI received the degree in Electrical Engineering from the University of Barif Italy in 1971. Since then he has been working in the Electrotechnical Institute of Bari until 1973 as researcher from 1973 to 1982 as Assistant Profe
The paper deals with the instantaneous power theory in three‐phase circuits by using the instantaneous time phasors of voltage and current. Particularly it is shown that the instantaneous components of the current t...
作者：
Theocharis, J.Petridis, V.Dr.-Ing. John Theocharis (1956) graduated as an Electrical Engineer from Aristotelian University of Thessaloniki.Greece
in 1980. From 1980 to 1985 he has been with the scientific staff of the Department of Electrical Engineering at the Aristotelian University where he received the Ph.D. degree in 1985. Since 1986 he is working as a lecturer and in 1990 he became assistant professor at the Department of Electronics and Computer Engineering in the m e university. His research activities include control power electronics and electrical motor drives. Recently he is working with the Neural Network Systems with applications to field oriented control problems. Aristotelian University of Thessaloniki School of Engineering Faculty of Electrical Engineering Dept. of Electronics & Computer Engineering P.O. Box 438 GR-Thessalonikil/Greece.T+3131/219784Fax + 3031/274868) Prof. Dr.-Ing. Vasilis Petridis (1946) graduated from National Technical University of Athens
Greece in 1969.He obtained the M.Sc. and the Ph.D. in electronics and systems from the University of London in 1970 and 1974. respectively. H i s interests include applied automatic control neural networks drives dynamic systems robotics etc. He is currently professor in the Department of Electronics and Computer Engineering of the University of Thessaloniki. (Aristotelian University of Thessaloniki. School of EngineeringFaculty of Electrical Engineering Dept. of Electronics & Computer Engineering P.O. Box 438. GR-ThessaloniW Greece T+3031/219784.Fax+3031/274868)
The procedure of harmonic insertion is generalized in this paper. Analytical expressions of the voltage spectra are derived. The insertion of the 3rd harmonic to the modulating signal, which is of particular interest,...
Smart grids have become an emerging topic due to net-zero emissions and the rapid development of artificial intelligence (AI) technology focused on achieving targeted energy distribution and maintaining operating rese...
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Smart grids have become an emerging topic due to net-zero emissions and the rapid development of artificial intelligence (AI) technology focused on achieving targeted energy distribution and maintaining operating reserves. In order to prevent cyber-physical attacks, issues related to the security and privacy of grid systems are receiving much attention from researchers. In this paper, privacy-aware energy grid management systems with anomaly detection networks and distributed learning mechanisms are proposed. The anomaly detection network consists of a server and a client learning network, which collaboratively learn patterns without sharing data, and periodically train and exchange knowledge. We also develop learning mechanisms with federated, distributed, and split learning to improve privacy and use Q-learning for decision-making to facilitate interpretability. To demonstrate the effectiveness and robustness of the proposed schemes, extensive simulations are conducted in different energy grid environments with different target distributions, ORRs, and attack scenarios. The experimental results show that the proposed schemes not only improve management performance but also enhance privacy and security levels. We also compare the management performance and privacy level of the different learning machines and provide usage recommendations.
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