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Keynote Speakers

 

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Keynote Speakers of ICCCR 2021

 

  • Prof. Chun-Hung Chen (IEEE Fellow)
    Dept. of Systems Engineering & Operations Research, George Mason University, USA

    Biography: Chun-Hung Chen received his Ph.D. degree from Harvard University in 1994. He is currently a Professor at George Mason University. Dr. Chen was an Assistant Professor at the University of Pennsylvania before joining GMU. He was also a professor at National Taiwan University (Electrical Eng. and Industrial Eng.) from 2011-14. Sponsored by NSF, NIH, DOE, NASA, FAA, Missile Defense Agency, and Air Force in US, NSFC in China, MOST in Taiwan, and SMI in Singapore, he has worked on the development of very efficient methodology for simulation-based decision making and its applications. Dr. Chen received several awards such as Best Paper Award from IEEE International Conference on Automation Science and Engineering, “K.D. Tocher Medal” for the best paper in the Journal of Simulation, “National Thousand Talents Award” from China, and Eliahu I. Jury Award from Harvard University. Dr. Chen has served on the editorial boards of IEEE Transactions on Automatic Control, IEEE Transactions on Automation Science and Engineering, IIE Transactions, Asia-Pacific Journal of Operational Research, Journal of Simulation Modeling Practice and Theory, International Journal of Simulation and Process Modeling, and Journal of Traffic and Transportation Engineering. Dr. Chen is an author of two books, including a best seller: “Stochastic Simulation Optimization: An Optimal Computing Budget Allocation”. He is an IEEE Fellow.

    Speech Title: Fast-time Decision and Control of Complex Systems with Digital Twin-Based Look-Ahead Learning
    Abstract: Digital twin is a digital manifestation of physical systems. We will present a new digital twin-based learning framework. It represents a fundamental advance in learning and decision making. Instead of passively learning from observational data limited to historical scenarios and experiential-based actions, the new digital twin-based learning framework proactively learn successful actions under different future scenarios generated using digital twins, and integrate the learned knowledge with online digital twin analysis assimilating dynamic data input to achieve operational efficiency. This new approach will enable fast-time decision and control. We will also present two key components of our methodologies: Optimal Computing Budget Allocation (OCBA) and Ordinal Transformation (OT), initially developed by the speaker. OCBA intends to maximize the overall simulation or sampling efficiency for finding an optimal decision/control. OT intelligently transforms the decision space into a smart space which is smoother and has nice properties. The search for a good decision/control becomes easier and more efficient in the transformed space.

 
Prof. Mo-Yuen Chow (IEEE Fellow)
Director of Advanced Diagnosis, Automation, and Control (ADAC) Laboratory, North Carolina State University, USA

  • Biography: Mo-Yuen Chow earned his degree in Electrical and Computer Engineering from the University of Wisconsin-Madison (B.S., 1982); and Cornell University (M. Eng., 1983; Ph.D., 1987). Upon completion of his Ph.D. degree, Dr. Chow joined the Department of Electrical and Computer Engineering at North Carolina State University as an Assistant Professor. He became an Associate Professor in 1993, and a Professor since 1999. He worked in U.S. Army, TACOM TARDEC Division as a Senior Research Scientist during the summer of 2003. He spent his sabbatical leave as a Visiting Scientist in 1995 in ABB Automated Distribution Division, and as a Distinguished Consultant, SAS Institute, Fall 2016. Dr. Mo-Yuen Chow is the founder and the director of the Advanced Diagnosis, Automation and Control Laboratory at North Carolina State University. His recent research focuses on collaborative distributed control and fault management with applications on smart grids, PHEVs, batteries, and mechatronics/robotics systems. He has served as a Principal Investigator in projects supported by various federal agencies and private companies. He has published one book, seven book chapters, and over three hundred journal and conference articles. Dr. Chow is an IEEE Fellow, the co-Editor-in-Chief of IEEE Transactions on Industrial Informatics 2014-2018, was the Editor-in-Chief of IEEE Transactions on Industrial Electronics 2010-2012, a co-Editor-in-Chief of IEEE Transactions on Industrial Electronics, a past Technical Editor of IEEE Transactions on Mechatronics, a past Associate Editor of the IEEE Transactions on Industrial Electronics and IEEE Transactions on Industrial Informatics. He was the Vice President for Publication of IEEE Industrial Electronics Society in 2006-2007, and the Vice President for Membership of IEEE Industrial Electronics Society in 2000-2001. He was the General Chair of IEEE-IECON05, the General Co-Chair of IEEE-IECON10, IEEE-ISIE12, IECON18, ISIE19. Dr. Chow served as a guest editor for the IEEE Transactions on Mechatronics Focus Section on Mechatronics in Multi Robot Systems (2009), IEEE Transactions on Industrial Electronics special sections on Distributed Network-Based Control Systems and Applications (2003), on Motor Fault Detection and Diagnosis (2000), and on Application of Intelligent Systems to Industrial Electronics (1993). He was a Senior Fellow of Japan Society for the Promotion of Science in 2003. He has received the IEEE Eastern North Carolina Section Outstanding Engineering Educator Award in 2004, the IEEE Region-3 Joseph M. Biedenbach Outstanding Engineering Educator Award in 2005, the IEEE Eastern North Carolina Section Outstanding Service Award in 2007, the IEEE Industrial Electronics Society Anthony J Hornfeck Service Award in 2013. Dr. Chow received the IEEE Industrial Electronics Society Dr.-Ing. Eugene Mittelmann Achievement Award in 2020. He is a Distinguished Lecturer of IEEE IES.

    Speech Title: Mechatronics Education with iSpace Platform
    Abstract: Mechatronics is a popular subject in many universities. In addition to the basic knowledge and principles of sensors, actuators, controllers and their integration to solve mechatronics problems, hands-on experience and projects are invaluable for students to learn about Mechatronics. This presentation will describe a sequence of two Mechatronics courses, the rationales and demonstrations of using Matlab/SIMULINK, Mindstorm Lego ev3, and the project platform iSpace in the courses to allow students to effectively learn the integration of distributed sensors, distributed actuators, and distributed controllers over communication networks to solve large scale problems.

 
Prof. Jangmyung Lee, Pusan National University, Korea
  • Biography: Jangmyung Lee has been a Professor at the Department of Electronics Engineering, Pusan National University since 1992, where he is currently a director for the robotics research center, SPENALO. He received the B.S. and the M.S. degrees in electronics engineering from Seoul National University in 1980 and 1982, respectively and the Ph.D. degree in computer engineering from University of Southern California in 1990. He is currently leading a research laboratory working on intelligent robots (http://robotics.pusan.ac.kr). His current research interests include intelligent robotic systems, integrated manufacturing systems, cooperative control and sensor fusion. Dr. Lee is an IEEE Senior member, and a fellow of ICROS. He served as a president of Korean Robotics Society in 2010 and as a vice-president of ICROS and IEIE several years. He was the general chair for IEEE AIM 2015, IEEE ICIT 2014, ICIRA 2013 and ICT-ROBOT 2017. He has several awards including the presidential award for the contribution to robotics in 2015.

    Title: Autonomous Landing of a Drone onto a Moving Vehicle
    Abstract: 
    A robust landing algorithm has been developed for the recreation drone which can be carried on the roof of the recreation vehicle. The drone is very useful for various applications since it opens a new working space which is not used heavily so far. There are two limitations on the usage of the drone: short flying time and unstable landing. In this research, the charging station is provided on the roof of the recreation vehicle safely. The unstable landing problem has been resolved by using the visual servoing technique and by using the robust control of the landing platform. Specifically, a new urban scene adaptive network has been developed to improve the performance of the semantic segmentation and 2-link structured landing legs are designed/applied for stably landing on an inclined surface or obstacle with a suitable control algorithm. To achieve the stable landing on a slanted surface, a cooperative control algorithm of the quadcopter and the landing platform has been also proposed. With this autonomous landing technique, the applications of the drone become wide.