Biography: Michael Yu Wang is a Professor and the Head of Department of Mechanical and Aerospace Engineering of Monash University. Before joining Monash University in 2022, he was the Founding Director of the Cheng Kar-Chun Robotics Institute. He also served on the engineering faculty at University of Maryland, Chinese University of Hong Kong, and National University of Singapore. He has numerous professional honors - Kayamori Best Paper Award of 2001 IEEE International Conference on Robotics and Automation, the Compliant Mechanisms Award-Theory of ASME 31st Mechanisms and Robotics Conference in 2007, Research Excellence Award (2008) of CUHK, and ASME Design Automation Award (2013). He is the current Editor-in-Chief of IEEE Trans. on Automation Science and Engineering, and served as an Associate Editor of IEEE Trans. on Robotics and Automation and ASME Journal of Manufacturing Science and Engineering. He is a Fellow of ASME, HKIE and IEEE. He received his Ph.D. degree from Carnegie Mellon University.
Title: Robotic Manipulation: Sense, Touch, and Learn
Abstract: This presentation focuses on our research work on developing tactile sensors and dry adhesion skins for robotic hands with dexterous and versatile capability for grasping and adaptive manipulation. It also presents an overview of exploratory solutions to modeling of hyper-elastic soft robots, distributed control of soft actuators (polymers or fluids), strategies for soft manipulation, and rapid prototyping and fabrication of the sensors and elastic robots. I will showcase the ability to adjust fingertip pose for better contact using sensor feedback, especially for top-side gripping onto a nearly flat surface (smooth or rough) of an object with firm attachment. I will show practical applications in industrial automation and discuss the recent developments throughout the robotics community advancing in this promising direction.
Dr. Birgit Vogel-Heuser
Biography: Birgit Vogel-Heuser received her Dipl. Ing. degree in electrical engineering in 1987 and her Dr.-Ing. degree in mechanical engineering in 1990 from the RWTH Aachen, Germany. She acquired over ten years industrial experience in industrial automation. After different professorship positions, she was appointed to the Chair of Automation and Information Systems at the Technical University Munich in 2009. Her research is focusing on evolvable field-level automation and appropriate architectures for manufacturing and logistics systems. She is a Senior Member of the IEEE; IEEE RAS Distinguished Lecturer, Co-Chair of IEEE RAS TC Digital Manufacturing and Human-Centered Automation and a member of the National Academy of Science and Engineering in Germany (acatech).
Title: Evolvable field-level automation architectures to leverage AI for physical manufacturing and logistics systems
Abstract: Manufacturing and logistics systems operate for decades and need to evolve to manufacture new products, increase quality, energy, or overall efficiency. Consequently, automation hardware and software adaptation in the operation phase is crucial. Means to design such automation architectures compliant to Industry 4.0 are of high economic interest. The talk will introduce means to analyze existing automation architectures as a first step to refactoring. In the second step, the integration of AI into such architectures will be discussed. Finally, automation architectures that ease the adaptation of physical manufacturing and logistics systems will be presented.
Dr. Luis Enrique Sucar
Biography: Dr. L. Enrique Sucar is Senior Reseacrh Scientist at the National Institute for Astrpophysics, Optics and Electronics, Puebla, Mexico. He received a Master degree in Computer Systems from the Stanford University and a PhD in Computing from Imperial College. He has been an invited professor at Imperial College, UK; the University of British Columbia, Canada; INRIA, France and CREATE-NET, Italy. Dr. Sucar received the National Science Prize from the Mexican President in 2016. He is Member of the National Research System, the Mexican Science Academy, a Senior Member of the IEEE, and Ex-President of the Mexican Academy of Computing. He has more than 400 publications in journals and conference proceedings. He has served as president of the Mexican AI Society, has been member of the Advisory Board of IJCAI, and is associate editor of the journals Pattern Recognition and Computational Intelligence. He is interested in understanding and building intelligent systems that can interact with the real world, taking the best decisions under uncertainty, based on probabilistic and causal graphical models.
Title: Incorporating causal knowledge in robot learning
Abstract: Reinforcement learning has been applied to solve several complex problems in robotics and automation; however, learning optimal policies in continuous state and action spaces takes a very long time. Incorporating causal knowledge helps to focus exploration and avoid unnecessary actions, thus significantly reducing the number of episodes to obtain an optimal solution. Additionally, the causal models can be easily transferred to similar tasks. In this talk I introduce causal graphical models, including causal reasoning and discovery. I will then explain how to incorporate a causal model into a traditional reinforcement algorithm, and apply it to solve different problems, including robotic manipulation. Finally, I will present our recent work on learning and using a causal model simultaneously.
Dr. Xiaohong Guan
Biography: Xiaohong Guan received his B.S. and M.S. degrees in Control Engineering from Tsinghua University, Beijing, China, in 1982 and 1985, respectively, and his Ph.D. degree in Electrical and Systems Engineering from the University of Connecticut in 1993. He was a senior consulting engineer with Pacific Gas and Electric from 1993 to 1995. He visited the Division of Engineering and Applied Science, Harvard University from 1999 to 2000. From 1985 to 1988 and since 1995 he has been with Xian Jiaotong University, Xian, China, and has been as the Cheung Kong Professor of Systems Engineering and Director of Systems Engineering Institute since 1999, was the director of the State Key Lab for Manufacturing Systems 1999-2009, Dean of School of Electronic and Information Engineering 2008-2018, and Dean of Faculty of Electronic and Information Engineering since 2019. From 2001 he has also been with the Center for Intelligent and Networked Systems, Tsinghua University, Beijing, China, and severed the Head of Department of Automation, Tsinghua University, 2003-2008.
Professor Guan is the member of Chinese Academy of Science and the Fellow of IEEE. His research interests include optimization of electrical power and energy systems, manufacturing systems, etc., and cyber-physical systems.
Title: Zero-Carbon Intelligent Energy Systems and Energy Revolution
Abstract: This speech will discuss the new structure of green energy systems and how zero carbon emission energy system can be realized. In fact, economic energy storage technology is the key for fully utilizing new renewable energy sources. Production, storage and transportation, and utilization of hydrogen as a main energy source are introduced in the speech, and it is shown that hydrogen can become a major secondary energy source as important as electricity. The hydrogen based intelligent energy system will provide a new solution for energy supply and consumption with nearly zero-carbon emission and may lead to the energy revolution in the near future.
Dr. Lixin Tang
Biography: Professor Lixin Tang is the Vice President of Northeastern University, China, a member of Chinese Academy of Engineering, the Director of Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, China, the Director of Center for Artificial Intelligence and Data Science, and the Director and Chair Professor of the National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University. He is also a member of the discipline review group of the State Council for Control Science and Engineering, the Deputy Director of Artificial Intelligence Special Committee in Science and Technology Commission, Ministry of Education, China, the Vice President of Operations Research Society of China (ORSC), and the Founding Director of Data Analytics and Optimization Society for Smart Industry of ORSC.
His research interests cover industrial intelligence and systems optimization theories and methods, covering industrial big data, data analytics and machine learning, deep learning and evolutionary learning, reinforcement learning and dynamic optimization, convex and sparse optimization, integer and combinatorial optimization, and computational intelligence-based optimization. For technologies, he mainly investigates on systems optimization technology for plant-wide production and inventory planning, production and logistics batching and scheduling, process optimization and optimal control; quality analytics technology such as process monitoring, equipment diagnosis, and product quality perception; industrial intelligence technology such as image and speech understanding and visualization. Meanwhile, he applies the above theories and technologies to engineering applications in manufacturing, logistics and energy systems.
He has published more than 137 papers in international journals such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Transactions on Control Systems Technology, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Power Systems, Operations Research, Manufacturing & Service Operations Management, INFORMS Journal on Computing, IISE Transactions and Naval Research Logistics. His paper published on IISE Transactions received the Best Applications Paper Award of 2017.
He currently serves as an Associate Editor of IISE Transactions, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Journal of Scheduling, International Journal of Production Research, and Journal of the Operational Research Society. Meanwhile, he is on the Editorial Board of Annals of Operations Research, and serves as an Area Editor of the Asia-Pacific Journal of Operational Research.
Title: Data analytics and optimization for smart industry
Abstract: Data analytics is the frontier basic research direction of industrial intelligence and one of the driving forces to promote scientific development. Systems optimization is the core basic theory of decision-making in smart industry, as well as the heart and engine of data analytics. This talk will discuss some systems modeling methods and optimization solution methods we have been working on. The systems modeling methods are to quantitatively describe different practical problems with proper formulations, including set-packing model, space-time network model, and continuous-time based model. The optimization solution methods include integer optimization, convex optimization, intelligent optimization, and dynamic optimization. This talk will also introduce systems optimization and data analytics of production, logistics, and energy in the steel industry, including: 1) production batching and scheduling in steelmaking/continuous casting, and hot/cold rolling operations; 2) logistics scheduling in loading operations, shuffling/reshuffling, and stowage; 3) data analytics-based energy optimization, including dynamic energy allocation and scheduling, energy analytics covering energy description, diagnosis and prediction; 4) data analytics, including temperature prediction of blast furnace, dynamic analytics of BOF steelmaking process based on multi-stage modeling, temperature prediction of reheat furnace based on mechanism and machine learning, and strip quality analytics of continuous annealing based on multi-objective ensemble learning.
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