Prof. Biao Huang (IEEE Fellow, Fellow of the Canadian Academy of Engineering, Fellow of the Chemical Institute of Canada)
University of Alberta, Canada
Biography: Biao Huang obtained his Ph.D. degree in Process Control from the University of Alberta, Canada, in 1997. He had an MSc degree (1986) and a BSc degree (1983) in Automatic Control from the Beijing University of Aeronautics and Astronautics. He joined the University of Alberta in 1997 as an Assistant Professor in the Department of Chemical and Materials Engineering and is currently a Full Professor. He held position of NSERC Senior Industrial Research Chair. He is an IEEE Fellow, Fellow of the Canadian Academy of Engineering, Fellow of the Chemical Institute of Canada, and recipient of many awards, including Germany’s Alexander von Humboldt Research Fellowship, APEGA Summit Award in Research Excellence, ASTech Outstanding Achievement in Science and Engineering Award, R.S. Jane Award and the best paper award from the Journal of Process Control. Biao Huang’s research interests include process data analytics, machine learning, system identification, image processing, fault detection and isolation, and soft sensors. He has published five books and over 500 journal papers. Biao Huang currently serves as the Editor-in-Chief for IFAC Journal Control Engineering Practice, Subject Editor for Journal of the Franklin Institute, Associate Editor for IEEE/CAA Journal of Automatica Sinica, and Associate Editor for Journal of Process Control.
Speech Title: The Role of Data Analytics and Machine Learning in Process Automation and Control
Abstract: Modern industries are awash with a large amount of data. Extraction of information and knowledge discovery from data, especially the data from day-by-day routine operating processes, for process design, control and optimization, is interesting but also very challenging. Data analytics has played an important role in traditional process automation and control. On the other hand, modern machine learning techniques, particularly supervised/unsupervised learning and reinforcement learning techniques, have significantly progressed, attracting great interest from engineering communities. Their roles become even more evident in the autonomous system era. A morden automation system would at least include smart components, autonomous control systems, and fault tolerance capacity while possessing self-learning ability. This presentation will discuss the role of data analytics and machine learning that can play in process automation and control.
Prof. I-Ming Chen (IEEE Fellow, ASME Fellow, Fellow of Singapore Academy of Engineering)
Nanyang Technological University, Singapore
Biography: Professor I-Ming Chen received 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 is currently Full Professor in the School of Mechanical and Aerospace Engineering, and Co-Director of CARTIN (Center for Advanced Robotics Technology and Innovation) in Nanyang Technological University (NTU), and Technical Advisor to National Robotics Program Office in Singapore. He is Editor-in-chief of IEEE/ASME Transactions on Mechatronics (2020-2022) and 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 robotics. His research interests are in logistics and construction robots, wearable devices, human-robot interaction and industrial automation. Professor Chen is Fellow of Singapore Academy of Engineering, Fellow of IEEE and Fellow of ASME, General Chairman of 2017 IEEE International Conference on Robotics and Automation (ICRA 2017) in Singapore.
Speech Title: Perception and Learning in Intelligent Manufacturing and Warehouse Automation Systems
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, intelligent spray painting. We will use our past experiences in Amazon Robotics Challenge and DHL Robotics Challenge as examples to look at the integration of 3D perception, machine learning and robot motion planning in current warehouse automation to handle the item-picking process.
Prof. Zhiwu Li (IEEE Fellow)
Xidian University, China
Biography: Zhiwu Li received the B.S., M.S., and Ph.D. degrees all from Xidian University, Xi’an, China, in 1989, 1992, and 1995, respectively. Dr. Li held visiting professor positions at the University of Toronto, Technion (Israel Institute of Technology), Martin-Luther University at Halle (supported by Alexander von Humboldt Foundation), University of Cagliari, Politecnico di Bari, Conservatoire National des Arts et Métiers (Cnam, supported by the program of Research in Paris), King Saud University, and Meliksah University. He has published three monographs in Springer (2009; 2023) and CRC Press (2013), and 200+ publications in IEEE Transactions and Automaitca. His research was cited by leading business giants including IBM, HP, ABB, Volvo, GE, GM, Mitsubishi, Ford Car, and Huawei. His current research interests include Petri net theory & applications, supervisory control of discrete event systems, data modeling, and production automation. Dr. Li serves (served) as Associate Editor of IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Systems, Man, and Cybernetics: Systems and Human Beings, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Information Sciences (Elsevier), IEEE Access (Senior Editor), IEEE/CAA Journal of Automatica Sinica, and Scientific Reports. He is a Fellow of IEEE (2016) and was selected as Thomson Reuters Highly Cited Researchers in the category of Engineering from 2014—2018.
Speech Title: Optimal Deadlock Control of Automated Manufacturing Systems Using Petri Nets: A Reachability Graph Approach
Abstract: This talk exposes the recent advances of deadlock problems in resource allocation systems using Petri nets. The pertinent methodologies are categorized by structural analysis and reachability graph analysis techniques. The former, without enumerating the reachable states of a system, utilize structural objects to derive a liveness-enforcing supervisor, while its structure can be compact. The latter can usually lead to an optimal supervisor with a minimal control structure subject to a full state enumeration and solution to integer linear programming problems, which is the focus of this talk. Open issues in this area are outlined.
Prof. Carla Seatzu
University of Cagliari, Italy
Biography: Carla Seatzu is currently a Full Professor of Automatic Control with the University of Cagliari, Cagliari, Italy. Her research interests include discrete-event systems, Petri nets, hybrid systems, networked control systems, manufacturing, and transportation systems. She is the author of over 260 publications, including 90 articles in international journals and one textbook. She is the editor of two international books. She has been a Visiting Professor with universities in Spain, (Zaragoza), USA (Atlanta), Mexico (Guadalajara), and China (Xi’an, Hangzhou). Her h-index in Scopus is equal to 37. Prof. Seatzu is a Senior Editor of the IEEE Control Systems Letters and the IEEE Transactions on Automation Science and Engineering. She is Department Editor of Discrete Event Dynamic Systems. She was a Program Chair of the 23rd IEEE International Conference on Emerging Technologies and Factory Automation in 2018, Workshop Chair of the 55th IEEE Conf. on Decision and Control in 2016 and a General Co-Chair of the 18th IEEE International Conference on Emerging Technologies and Factory Automation in 2013.
Speech Title: State Estimation of Partially Observed Discrete Event Systems Under Attack
Abstract: Partially observed discrete event systems are a general formalism dating back to the definition of nondeterministic automata. The assumption is that the sequence of events generated by a plant is observed through a mask, so that an agent observing the plant may have incomplete information concerning its evolution and, correspondingly, the past state trajectory and the current state.
The objective of this talk is to describe the basic principles of partially observable discrete event systems showing how the state estimation problem can be addressed for systems subject to cyber-attacks. An operator receives the sensor readings produced by a plant through a communication channel and uses this information to estimate the current state of the plant. The observation may be corrupted by an attacker which can insert and erase some sensor readings with the aim of altering the state estimation of the operator. Furthermore, the attacker wants to remain stealthy, namely the operator should not realize that its observation has been corrupted.
We will show how to determine an automaton, called joint estimator under attack, that describes for each possible observation produced by the plant and for each possible attack, what is the state estimation computed by the operator. Such a structure is obtained by the concurrent composition of two state observers, called attacker observer and operator observer. The joint estimator can be used to determine if there exists a stealthy harmful attack function such that the set of states consistent with the uncorrupted observation computed by the attacker, and the set of states consistent with the corrupted observation computed by the observer, satisfy a given relation.
A series of open issues in related problems will be illustrated.