Evolutionary Multi-objective Optimization in Engineering and Prognostic Applications
Professor Kay Chen Tan,
Professor with the Department of Electrical and Computer Engineering, National University of Singapore, Singapore.
Abstract:Â Multi-objective optimization is widely found in many fields, such as logistics, economics, engineering, or whenever optimal decisions need to be made in the presence of trade-offs. The problem is challenging because it involves the simultaneous optimization of several conflicting objectives in the Pareto optimal sense and requires researchers to address many issues that are unique to MO problems. This talk will provide an overview of evolutionary computation for multi-objective optimization (EMO). It will then present various applications of EMO for solving engineering problems particularly in the area of robust prognostic. As one of the key enablers of condition based maintenance, prognostic involves the core task of determining the remaining useful life (RUL) of the system. This talk will discuss the use of neural network ensembles to improve the prediction accuracy of RUL estimation as well as the use of EMO to optimize the ensemble hyper-parameters in order to achieve the trade-off between accuracy and diversity of deep neural networks as ensemble members. A case study involving the estimation of RUL for turbofan engines will also be presented in the talk.
Biography:Â Kay Chen Tan (SMâ€™05) (SMâ€™08-Fâ€™14) received the B.Eng. (Hons.) degree in electronics and electrical engineering and the Ph.D. degree from University of Glasgow, Glasgow, U.K., in 1994 and 1997, respectively. He is an Associate Professor with the Department of Electrical and Computer Engineering, National University of Singapore, Singapore. His research interests include computational and artificial intelligence, with applications to multiobjective optimization, scheduling, automation, data mining, and games. He has published over 100 journal papers and over 100 papers in conference proceedings, and also co-authored five books. Dr Tan has been an Invited Keynote/Plenary speaker for over 40 international conferences. He served in the international program committee for over 100 conferences and involved in the organizing committee for over 50 international conferences, including the General Co-Chair for IEEE Congress on Evolutionary Computation 2007 in Singapore. Dr Tan is the General Co-Chair for IEEE World Congress on Computational Intelligence 2016 in Vancouver, Canada. Dr Tan is currently an elected member of AdCom (2014-2016) and is an IEEE Distinguished Lecturer of IEEE Computational Intelligence Society (2011-2013; 2015-2017).
Dr Tan is the Editor-in-Chief of IEEE Transactions on Evolutionary Computation. He was the Editor-in-Chief of IEEE Computational Intelligence Magazine (2010-2013). He currently serves as an Associate Editor / Editorial Board member of over 20 international journals, such as IEEE Transactions on Cybernetics, IEEE Transactions on Computational Intelligence and AI in Games, Evolutionary Computation (MIT Press), European Journal of Operational Research, Neural Computing and Applications, Journal of Scheduling, International Journal of Systems Science, etc.
Dr Tan is a Fellow of IEEE. He is the awardee of the 2012 IEEE Computational Intelligence Society (CIS) Outstanding Early Career Award for his contributions to evolutionary computation in multi-objective optimization. He also received the 2016 IEEE CIS Outstanding TNNLS Paper Award for his paper titled "Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons". He also received the Recognition Award (2008) from the International Network for Engineering Education & Research (iNEER) for his outstanding contributions to engineering education and research. He was felicitated by the International Neural Network Society (INNS) India Regional Chapter (2014) for his outstanding contributions in the field of computational intelligence.
Neurodynamics-based Parallel Data Processing in the Era of Big Data
Professor Jun Wang,
IEEE Fellow, IAPR Fellow,
Professor with Department of Mechanical & Automation Engineering, Chinese University of Hong Kong, Hong Kong.
Abstract:Â 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.
Biography:Â Jun Wang is a Professor at the Chinese University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, and University of North Dakota. He also held various short-term visiting positions at USAF Armstrong Laboratory (1995), RIKEN Brain Science Institute (2001), Universite Catholique de Louvain (2001), Chinese Academy of Sciences (2002), Huazhong University of Science and Technology (2006â€“2007), and Shanghai Jiao Tong University (2008-2011) as a Changjiang Chair Professor. Since 2011, he is a National Thousand-Talent Chair Professor at Dalian University of Technology on a part-time basis. 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 over 180 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 since 2014 and a member of the editorial board of Neural Networks since 2012. 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 â€“ Part C (2002â€“2005), as a member of the editorial advisory board of International Journal of Neural Systems (2006-2013), as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008, 2014), 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.