Dr. Paul P. Lin
Professor and Associate Dean of Engineering
Cleveland State University
Topic: The Practical Aspects of Fault Detection, Fault Isolation and System Reconfiguration
In many engineering applications, such as chemical plants, nuclear power plants, aircrafts and vehicles, the ability to accurately detect and identify faults, followed by system reconfiguration is critical. Generally speaking, there are three types of faults, namely sensor faults, actuator faults and process faults. Most studies in fault diagnosis have been on the process faults, assuming that sensors and actuators are not faulty. Nevertheless, combination of multiple types of faults can lead to false detection and isolation of faults.
Although many techniques for fault detection and isolation (better known as FDI) have been developed and published, there are, in general, two approaches in FDI, model-based and model-free. This study uses some MIMO (Multiple Inputs and Multiple Outputs) systems to illustrate the difference between using the approaches. The possibility of false detection or complete misdetection due to simultaneous multiple faults presents a big concern in FDI. Finally, a control strategy to automatically reconfigure a faulty system is proposed.
Biographical Sketch :
Dr. Paul P. Lin is a lifetime Fellow of American Society of Mechanical Engineering (ASME) who received his Ph.D. in Mechanical Engineering from University of Rhode Island, USA in 1985. He has been with Cleveland State University as faculty since 1985, where he became the ME Department Chair in 2002, and Associate Dean of Engineering since 2007 until present. His research areas of interest include Robotics, Design Optimization, Optical Inspection, Intelligent System Monitoring, and Fault Diagnosis. He has conducted several key research projects with NASA and US Air Force. With the NASA Glenn Research Center, he developed fast multidisciplinary design optimization technique using Taguchi methods and soft computing to optimize the engine cycle design of next generation aircraft. In addition, he also developed an intelligent system for NASA to monitor the microgravity environment quality onboard the International Space Station. With the US Air Force, he developed an optical technique to quantify the F- 16 aircraft tire deformation subject to dynamic loading, such as during take-off and landing. His recent research interest has been in the area of fault detection, fault isolation and system reconfiguration using model-based and model-free techniques. He has published over 50 refereed journal and conference papers, served as editor and reviewer for journals and conferences, as well as the keynote speaker for seven conferences since 2010. Furthermore, his administrative accomplishments and industrial expereince as well as interaction with local insdustry won him the Leadership Award in 2009 from Cleveland Engineering Society.
Professor Tong Heng Lee
Prosessor, Dep. ECE, National University of Singapore
Ph.D., Yale University
Topic: Developments in Intelligent Systems for Autonomous & Automated Engineering Systems
In this talk, presentation will be made of recent newer developments on innovations in the tools/methodologies of Intelligent Systems, Platforms & Technologies --- particularly those of autonomous systems (such as autonomous vehicles), discrete-event systems, hybrid systems, the concept of bisimulation/bi-similar systems and communicating sequential processes --- which now all play important roles in the development of newer types of Intelligent Autonomous & Automated Engineering Systems. The presentation will firstly, consider pertinent general recent developments in intelligent automation, mechatronics and systems; and then more specifically, consider the development of an intelligent and hybrid control structure for Autonomous Vehicles, which covers the control sub-module interactions, and captures the discrete nature of the decision making unit and continuous evolution of the system collectively. The various newer innovations needed of newer classes of Intelligent Systems and Technologies will be discussed. In all of the above, which is an instance of a newer class of an Intelligent Automated Engineering System, the presentation will also carefully describe how the computer science/computer engineering elements of discrete-event systems, hybrid systems, the concept of bi-simulation/bi-similar systems and communicating sequential processes play crucial roles in ensuring the successful deployment of the system.
Biographical Sketch :
Tong Heng Lee received the B.A. degree with First Class Honours in the Engineering Tripos from Cambridge University, England, in 1980; the M.Engrg. degree from NUS in 1985; and the Ph.D. degree from Yale University in 1987. He is a Professor in the Department of Electrical and Computer Engineering at the National University of Singapore (NUS); and also a Professor in the NUS Graduate School, NUS NGS. He was a Past Vice-President (Research) of NUS. Dr. Lee’s research interests are in the areas of adaptive systems, knowledge-based control, intelligent mechatronics and computational intelligence. He currently holds Associate Editor appointments in the IEEE Transactions in Systems, Man and Cybernetics; Control Engineering Practice (an IFAC journal); and the International Journal of Systems Science (Taylor and Francis, London). In addition, he is the Deputy Editor-in-Chief of IFAC Mechatronics journal. Dr. Lee was a recipient of the Cambridge University Charles Baker Prize in Engineering; the 2004 ASCC (Melbourne) Best Industrial Control Application Paper Prize; the 2009 IEEE ICMA Best Paper in Automation Prize; and the 2009 ASCC Best Application Paper Prize. He has also co-authored five research monographs (books), and holds four patents (two of which are in the technology area of adaptive systems, and the other two are in the area of intelligent mechatronics). Dr. Lee was an Invited Panelist at the World Automation Congress, WAC2000 Maui U.S.A.; an Invited Keynote Speaker for IEEE International Symposium on Intelligent Control, IEEE ISIC 2003 Houston U.S.A.; an Invited Keynote Speaker for LSMS 2007, Shanghai China; an Invited Expert Panelist for IEEE AIM2009; an Invited Plenary Speaker for IASTED RTA 2009, Beijing China; an Invited Keynote Speaker for LSMS 2010, Shanghai China; an Invited Keynote Speaker for IASTED CA 2010, Banff Canada; an Invited Keynote Speaker for IFTOMM ICDMA 2010, Changsha China; an Invited Keynote Speaker for ICUAS 2011, Denver USA; an Invited Keynote for IEEE CISRAM 2011 Qingdao; an Invited Keynote for IASTED EAS 2012 Colombo; and also an Invited Keynote for IEEE ICCSE 2014 Vancouver.
Professor Janusz Kacprzyk
Professor, Ph.D, D.Sc
Fellow of IEEE, IFSA
Systems Research Institute
Polish Academy of Sciences
Topic:Distributed Human/Social Inspired Computation Systems Based on Information and Knowledge Sharing
We are concerned with broadly perceived distributed information systems, networks, etc. the very essence of which is reflected in, for instance, many modern technological and social phenomena like social networks, the internet of things, etc. We assume that such systems can involve technical devices (e.g. robots, computer systems), human beings (individuals, groups and maybe even organizations), software agents, etc. They constitute a (possibly) synergistic combination of technology, people and organization aimed at facilitating the communication, cooperation, collaboration, coordination, etc. They should possibly contribute to a more effectively and efficiently functioning to attain some common/shared goal, with mutual benefits for the participating parties. Meanwhile, biologically inspired computing paradigms, exemplified by broadly perceived evolutionary computation, immunological, swarm intelligence, ant colony, etc. models have recently attracted much attention in the research community and have shown a considerable potential for solving many complicated real world problems. One can however view them as based on inspirations from primitive living organisms or their colonies. An immediate question is whether it would make sense to devise models based on inspirations from more sophisticated human behavior inspirations, both at the level of individuals and groups. Human individuals or human groups exhibit some sophisticated types of rationality, emotions, and other feelings, and there is usually a leader(s) in social groups. To be more specific, we deal with some new computation paradigms in the context of decision making type models. We consider both the decision analytic type and game theoretic types of models, and present some examples of experiments, mainly obtained in the area of behavioral economics and neuroeconomiccs, which clearly suggest a discrepancy between solutions adopted by humans and obtained by using directly the traditional decision analytic and game theoretic models that are in principle based on a greedy utility maximization. In our study, we emphasize what has been experimentally shown in many cases of real world human decision making that a human being is in general not a deliberate, hence slow, decision maker driven by a greedy and selfish utility maximization, which is a point of departure to traditional formal models, but is rather an emotional, fast decision maker who is often willing to faster arrive at a decision, even if it is “worse”, and – what is maybe more important – whose behavior is often motivated by a willingness to be fair to others, expecting a reciprocal reaction. Consequently, we finish with a message that what has been so far considered as a collaborative or cooperative system approach, sometimes depending on the author’s preference. That should be meant in fact as a coordinative-collaborative-cooperative systems approach to be able to grasp more aspects and issues related to a crucial role of human beings in the system, who is however working with inanimate beings like robots, software agents, etc., in a complex setting of common, shared, competitive, conflicting, etc. preferences, goals, etc., and a variety of selfish, and fair, or even altruistic, types of attitudes and behaviors.
Biographical Sketch :
Professor Janusz Kacprzyk graduated from Warsaw University of Technology, Poland, with M.Sc. in automatic control and computer science, obtained in 1977 Ph.D. in systems analysis and in 1991 D.Sc. in computer science. He is Professor of Computer Science at the Systems Research Institute, Polish Academy of Sciences, and at WIT – Warsaw School of Information Technology, and Professor of Automatic Control at PIAP – Industrial Institute of Automation and Measurements, and Department of Electrical and Computer Engineering, Cracow University of Technology. He is Honorary Foreign Professor at the Department of Mathematics, Yli Normal University, Xinjiang, China, and Visiting Scientist at RIKEN Brain Research Institute, Tokyo, Japan. He is Full Member of the Polish Academy of Sciences, Member of Academia Eueopaea (Informatics), Member of European Academy of Sciences and arts (Technical Sciences), Foreign Member of the Spanish Royal Academy of Economic and Financial Sciences (RACEF), and Foreign Member of the Bulgarian Academy of Sciences. He is Fellow of IEEE, IFSA, ECCAI and MICAI. He has been a frequent visiting professor in the USA, Italy, UK, Mexico, China. His main research interests include the use of modern computation computational and artificial intelligence tools, notably fuzzy logic, in decisions, optimization, control, data analysis and data mining, with applications in databases, ICT, mobile robotics, etc. He is the editor in chief of 6 book series at Springer, and of 2 journals, and is on the editorial boards of ca. 40 journals. He is a member of the Adcom of IEEE CIS, and was a Distinguished Lecturer of IEEE CIS. He received many awards: 2006 IEEE CIS Pioneer Award in Fuzzy Systems, 2006 Sixth Kaufmann Prize and Gold Medal for pioneering works on soft computing in economics and management, 2007 Pioneer Award of the Silicon Valley Section of IEEE CIS for contribution in granular computing and computing in words, 2010 Award of the Polish Neural Network Society for exceptional contributions to the Polish computational intelligence community, IFSA 2013 Award for his lifetime achievements in fuzzy systems and service to the fuzzy community, and the 2014 World Automation Congress Lifetime Award for contributions in soft computing. He is President of the Polish Operational and Systems Research Society and Past President of International Fuzzy Systems Association. In September 2015, he was awarded the title Fellow of the Mexican Society of Artificial Intelligence, and Permanent Honorary Member of the Society.
Professor Jun Wang
Topic:Neurodynamics-based Optimization Processing in the Big Data Era
In the present information era, huge amount of data to be processed daily. In contrast of conventional sequential data processing techniques, parallel data processing approaches can expedite the processes and more efficiently deal with big data. In the last few decades, neural computation emerged as a popular area for parallel and distributed data processing. The data processing applications of neural computation included, but not limited to, data sorting, data selection, data mining, data fusion, and data reconciliation. In this talk, neurodynamic approaches to parallel data processing will be introduced, reviewed, and compared. In particular, my talk will compare several mathematical problem formulations of well-known multiple winners-take-all problem and present several recurrent neural networks with reducing model complexity. Finally, the best one with the simplest model complexity and maximum computational efficiency will be highlighted. Analytical and Monte Carlo simulation results will be shown to demonstrate the computing characteristics and performance of the continuous-time and discrete-time models. The applications to parallel sorting, rank-order filtering, and data retrieval will be also discussed. In this talk, novel neurodynamic optimization approaches to robust pole assignment will be presented for synthesizing linear control systems via state and output feedback. The problem is formulated as a pseudoconvex optimization problem with the spectral condition number as the objective function (robustness measure) and linear matrix equality constraints for exact pole assignment. Two coupled recurrent neural networks are applied for solving the formulated problem in real time. In contrast to existing approaches, the exponential convergence of proposed neurodynamcs to global optimal solutions can be guaranteed even with lower model complexity in terms of the number of variables. Simulation results of the proposed neurodynamic approaches for eleven benchmark problems will be reported to demonstrate their superiority. In addition, the application of the proposed approach to piecewise linear systems will be delineated. The extensions of the present results based on convex reformulations will be also discussed.
Biographical Sketch :
Jun Wang is a Chair Professor of Computational Intelligence in the Department of Computer Science at City University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, University of North Dakota, and Chinese University of Hong Kong. He also held various part-time visiting positions at US Air Force Armstrong Laboratory, RIKEN Brain Science Institute, Huazhong University of Science and Technology, Dalian University of Technology, and Shanghai Jiao Tong University as a Changjiang Chair Professor. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. He published about 200 journal papers, 15 book chapters, 11 edited books, and numerous conference papers in these areas. He is the Editor-in-Chief of the IEEE Transactions on Cybernetics. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009), IEEE Transactions on Cybernetics and its predecessor (2003-2013), and IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005), as a member of the editorial advisory board of International Journal of Neural Systems (2006-2013), and a member of the editorial board of Neural Networks (2012-2014) as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008, 2014, 2016), and International Journal of Fuzzy Systems (2010, 2011). He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence, and a Program Chair of the IEEE International Conference on Systems, Man, and Cybernetics (2012). He has been an IEEE Computational Intelligence Society Distinguished Lecturer (2010-2012, 2014-2016). In addition, he served as President of Asia Pacific Neural Network Assembly (APNNA) in 2006 and many organizations such as IEEE Fellow Committee (2011-2012); IEEE Computational Intelligence Society Awards Committee (2008, 2012, 2014), IEEE Systems, Man, and Cybernetics Society Board of Directors (2013-2015), He is an IEEE Fellow, IAPR Fellow, and a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award and APNNA Outstanding Achievement Award in 2011, Natural Science Awards from Shanghai Municipal Government (2009) and Ministry of Education of China (2011), and Neural Networks Pioneer Award from IEEE Computational Intelligence Society (2014), among others.