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Plenary Speakers

Tianyou Chai, Professor, Northeastern University, China

Biography: Prof. Chai received the Ph.D. degree in control theory and engineering from Northeastern University, Shenyang, China, in 1985. Since 1985, he has been with the Research Center of Automation, Northeastern University, where he became a Professor in 1988, and a Chair Professor in 2004. He is the founder and Director of the Center of Automation, which became a National Engineering and Technology Research Center in 1997. He has made a number of important contributions in control technologies and applications. He has authored and coauthored two monographs, 84 peer reviewed international journal papers, and around 219 international conference papers. He has been invited to deliver more than 20 plenary speeches in international conferences of IFAC and IEEE. His research interests include adaptive control, intelligent decoupling control, integrated plant control and systems, and the development of control technologies with applications to various industrial processes. Prof. Chai is a member of the Chinese Academy of Engineering, an Academician of International Eurasian Academy of Sciences, an IEEE Fellow and an IFAC Fellow. He is a Distinguished Visiting Fellow of The Royal Academy of Engineering (U.K.) and an Invitation Fellow of Japan Society for the Promotion of Science. For his contributions, he has won three prestigious awards of National Science and Technology Progress, the 2002 Technological Science Progress Award from the Ho Leung Ho Lee Foundation, the 2007 Industry Award for Excellence in Transitional Control Research from the IEEE Control Systems Society, and the 2010 Yang Jia-Chi Science and Technology Award from the Chinese Association of Automation.

Title: CPS Driven Control System

Abstract: China has abundance of mineral resources such as magnesite, hematite and bauxite, which constitute a key component of its economy. The relatively low grade, and the widely varying and complex compositions of the raw extracts, however, pose difficult processing challenges including specialized equipment with excessive energy demands. The energy intensive furnaces together with widely uncertain features of the extracts form hybrid complexities of the system, where the existing modeling, optimization and control methods have met only limited success. The recently developed Cyber-Physical System (CPS) provides a new key for us to address these challenges. The idea is to make the control system of energy intensive equipment into a CPS, which will lead to a CPS driven control system.This talk presents the syntheses and implementation of a CPS driven control system for energy-intensive equipment under the framework of CPS. The proposed CPS driven control system consists of four main functions: (I) setpoint control; (II) tracking control; (III) self-optimized tuning; and (IV) remote and mobile monitoring for operating condition. The key in realizing the above functions is the integrated optimal operational control methods to implement setpoint control, tracking control and self-optimized tuning together seamlessly.  This talk introduces the integrated optimal operational control methods we proposed.

 


Chun Lung Philip Chen, Professor, South China University of Technology, China

Biography: C. L. Philip Chen is the Chair Professor and Dean of the College of Computer Science and Engineering, South China University of Technology. Being a Program Evaluator of the Accreditation Board of Engineering and Technology Education (ABET) in the U.S., for computer engineering, electrical engineering, and software engineering programs, he successfully architects the University of Macau’s Engineering and Computer Science programs receiving accreditationsfrom Washington/Seoul Accord through Hong Kong Institute of Engineers (HKIE), of which is considered as his utmost contribution in engineering/computer science education for Macau as the former Dean of the Faculty of Science and Technology.He is a Fellow of IEEE,AAAS, IAPR, CAA, and HKIE; a member of Academia Europaea (AE), European Academy of Sciences and Arts (EASA), and International Academy of Systems and Cybernetics Science (IASCYS). He received IEEE Norbert Wiener Award in 2018 for his contribution in systems and cybernetics, and machine learnings. He is also a 2018 highly cited researcher in Computer Science by Clarivate Analytics. His current research interests include systems, cybernetics, and computational intelligence. Dr. Chen was a recipient of the 2016 Outstanding Electrical and Computer Engineers Award from his alma mater, Purdue University, after he graduated from the University of Michigan at Ann Arbor, Ann Arbor, MI, USA in 1985. He was the IEEE Systems, Man, and Cybernetics Society President from 2012 to 2013, and currently, he is the Editor-in-Chief of the IEEE Transactions on Systems, Man, and Cybernetics: Systems, and an Associate Editor of the IEEE Transactions on Fuzzy Systems, and IEEE Transactions on Cybernetics. Currently, he is a vice president of Chinese Association of Automation (CAA).

Title: Unmanned Systems Planning and Control based on Bionic Swarm Movement

Abstract: This talk presents swarm control for self-organized system with fixed and switching topologies. The generation strategy, motion control law of a novel leader-follower relation-invariable persistent formation (RIPF), which is a kind of distance-based directed formation for multi-agent systems (MASs), will be discussed. An efficient algorithm is designed to find out if a persistent formation can be generated from a rigid graph. Derived from the properties of a rigid graph, an algorithm to generate a RIPF from any initial location is presented. The communication topology is automatically generated based on RIPF. With the selected minimum agent-movement RIPF, lastly, a control law is designed to drive this initial RIPF to the desired RIPF with given distances among agents.Simulation results show the proposed generative method, control law, and downward-tree are effective to realize the desired formation.

 


Lei Xu, Emeritus Professor, Chinese University of Hong Kong, China

Biography: Prof. Xu is the Emeritus Professor of Chinese University of Hong Kong, Zhiyuan Chair Professor of Shanghai Jiao Tong University (SJTU), Chief Scientist of SJTU AI Research Institute and Director of Neural Computation Research Centre in Brain and Intelligence Science-Technology Institute, Shanghai ZhangJiang National Lab. Prof. Xu received several national and international academic awards, including 1993 National Nature Science Award, 1995 Leadership Award from International Neural Networks Society (INNS) and 2006 APNNA Outstanding Achievement Award. Prof. Xu was elected to Fellow of IEEE in 2001, Fellow of intl. Association for Pattern Recognition in 2002 and of European Academy of Sciences (EURASC) in 2003. Prof. Xu has given over dozens keynote/invited lectures at various international conferences. Prof. Xu served as EIC and associate editors of several academic journals, including Neural Networks (1995-2016), IEEE Trans. Neural Networks (1994-98). Prof. Xu has taken various roles in academic societies, e.g., INNS Governing Board (2001-03), the INNS award committee (2002-03), and the Fellow committee of IEEE Computational Intelligence society (2006-07) and the EURASC scientific committee (2014-17).

Title: Deep bidirectional learning and deep bidirectional intelligence

Abstract: Insights on learning and intelligence are provided from a deep bidirectional perspective, featured by inward encoding/cognition and outward reconstruction/implementation. First, we make an overview on bidirectional learning from those studied in the later eighties and the early nineties, such as autoencoder, Lmser reconstruction, and BYY harmony learning, to ones developed in recent years, such as variational autoencoders, deep generative models, GAN, U-net and DenseNet. Then, we proceed to bidirectional intelligence, driven by long term dynamics for parameter learning and short term dynamics for image thinking and rational thinking. Image thinking deals with information flow as if thinking was displayed in the real world, exemplified by typical tasks of bidirectional deep learning, while rational thinking handles symbolic strings, performing uncertainty reasoning and problem solving, exemplified by AlphaGoZero like searching, IBM Watson system, and causal computation.

 


I-Ming Chen, Professor, Nanyang Technological University, Singapore

Biography: Prof. I-Ming Chen is an internationally renowned robotics researcher. He received the B.S. degree from National Taiwan University in 1986, and M.S. and Ph.D. degrees from California Institute of Technology, Pasadena, CA in 1989 and 1994 respectively. He has been with the School of Mechanical and Aerospace Engineering of Nanyang Technological University (NTU) in Singapore since 1995. He is Director of Robotics Research Centre in NTU from 2013 to 2017.  He is a member of the Robotics Task Force 2014 under the National Research Foundation which is responsible for Singapore’s strategic R&D plan in future robotics. His research interests are in logistics and construction robots, wearable devices, human-robot interaction and industrial automation. Professor Chen is Fellow of IEEE and Fellow of ASME, General Chairman of 2017 IEEE International Conference on Robotics and Automation (ICRA 2017) in Singapore. He is also CEO of Transforma Robotics Pte Ltd developing robots for construction industry and CTO of Hand Plus Robotics Pte Ltd developing robotics and AI solutions for logistics and manufacturing industry. He will be the Editor-in-chief for the tier 1 journal: IEEE/ASME Transactions on Mechatronics starting from 2020.

Title: Robotic Perception and Learning for Intelligent Manufacturing and Warehouse Automation

Abstract: Industry robot manipulators have been invented for nearly 50 years. In the past, such robot manipulators are used in mass manufacturing lines and programmed manually by engineers. However, as modern manufacturing moves into low volume high mix products in a very tight schedule, it becomes very challenge to program the robots to handle large variety of products and parts and also to make changes to the manufacturing lines in a very short time. With advancement in 3D machine vision, machine learning methods and fast computing power, there is an emerging trend to put 3D perception device, machine learning technique into industry robots to make them “smart” enough to handle a variety of products in a changing environment. In this speech, we will discuss how 3D perception systems and machine learning techniques are used in manufacturing scenarios like intelligent masking/taping for component maintenance, bi-manual manipulation for parts assembly and handling, intelligent spray painting. We will use also Amazon Robotics Challenge and DHL Robotics Challenge as examples to look at the integration of 3D perception, machine learning and robot motion planning in warehouse automation to handle the item-picking process.

 


Kenzo Nonami, Professor, Chiba University, Japan

Biography: Prof. Kenzo Nonami received his MS degree and Ph.D. degree in Mechanical Engineering in 1976 and 1979 respectively, from Tokyo Metropolitan University. He joined Chiba University in 1979 as a Research Associate, Associate Professor from 1988 to 1994. Since 1994, he has been a full professor in Department of Mechanical Engineering and Department of Electronics and Mechanical Engineering at Chiba University. In 2004, Dr. Kenzo Nonami was a vice dean of faculty of Engineering. Also, he has carried out a research in NASA in USA during two years from 1985 to 1988. Now, Dr. Nonami is a vice president from April, 2008. His recent research interests are fully autonomous unmanned small-scale helicopter, micro air vehicle, quad tilt wing unmanned aerial vehicle, land mine detection robots with multi-functional arm, walking machines, master slave manipulator and dual manipulator hand system, unmanned autonomous boats, flywheel energy storage system with active magnetic bearing powered electric vehicle, robust and nonlinear control, control applications.

Title: Current Status of world Industrial Drones and Urgent Technical Issues

Abstract: In this presentation, I first overlook the current state of utilization of world industrial drones and the forefront including world UAS aviation reguration. Next, I would like to consider the technical issues in each field and discuss how to overcome the problem.

 


Kwang-Joon Yoon, Professor, Konkuk University, Korea

Biography: Kwang-Joon Yoon is a professor of Aerospace Engineering, director of Smart Drone Center. He received his doctoral degree in Aeronautics & Astronautics Engineering from Purdue University, USA, master and bachelor degree in Aerospace Engineering from Seoul National University, Korea. Professor Yoon leads a number of research projects with funding from Korea government agencies and industries. Dr. Yoon received several awards including the world’s smallest Micro Aerial Vehicle Award in 1995 from International MAV competition. His current research interests are development of small UAV with fixed wing, rotary wing and flapping wing, smart structure and material. He was the general chair of “International Conference on Emerging System Technology,” 2005, Seoul, Korea. He was the president of ISIUS (Int. Society of Unmanned Intelligence System) during 2016-7 and is a senior member of National Academy of Engineering of Korea. He published more than 140 peer-reviewed journal, conference proceedings papers and patents.

Title: Development of small winged drone for missions in amphibious environment

Abstract: In recent years, many countries are developing military and civil unmanned aerial vehicle (UAV) for various utilization in many fields. Multi-copter system has been specifically developed for UAV because it has a lot of advantages due to its simple automatic propeller control system. This lecture introduces the design/ manufacturing/ flight test of winged drone with 4 propeller and 2 tilt propeller system for missions in amphibious environment. Conceptual system design has performed to have VTOL (vertical take-off and landing) function and tilt propeller system to fly with cruse speed more than 100km/h. MTOW (maximum take-off weight). The total weight is less than 10kg and maximum size is less than 2.0m. A drone with wings was chosen to increase energy efficiency and to withstand strong wind environment. Automatic flight control system and landing system on ground and water surface also designed and verified through flight test. Dual floater system with small wheel was designed for missions in amphibious environment. The developed winged drone can be used for various missions, such as military application, fireman, policeman, monitoring of disaster, delivery service.