Plenary Speech I
Time: 08:30-9:10, May 8, 2019
Venue: Howard Hall, B2, Howard Plaza Hotel
Chair: Prof. Antonio Luque
Prof. Xinghuo Yu
RMIT University, Melbourne, Australia
Industrial Cyber-Physical Systems and Smart Grids: Interplay and Interaction
Industrial Cyber-Physical Systems (ICPS) represent a broad range of complex,multidisciplinary, physically aware next-generation engineered systems that integrate embedded computing technologies (cyber part) into the physical world. Smart Grids (SG) as a typical ICPS are electric networks that can intelligently integrate the actions of all users (e.g. generators and prosumers) in order to efficiently deliver sustainable, economic and secure electricity supplies. The recent fast advances in ICPS have provided a powerful methodology for SG to deal with demand for deeper control, increased cross connectivity, embedded generation, smart metering and using wires as carriers for information transmission. On the other hand, SG presents technical challenges that ICPS need to address.
In this talk, we will first discuss some recent developments in both SG and ICPS and then examine potential issues associated with interplay and interaction between them to bring out the best of both fields. We will also touch on potential new thinking paradigms such as Artificial Intelligence to deal with complexity arising from these systems, speculating potential methodologies inspired by the Nature as future smart technologies. A number of real-world cases, including some of our own research projects, will be used as case studies. Finally, we will lay out the potential issues and challenges for future developments.
Professor Xinghuo Yu is an Associate Deputy Vice-Chancellor and Distinguished Professor at RMIT University (Royal Melbourne Institute of Technology), Melbourne, Australia. He chairs RMIT Professorial Academy.
He is the President of IEEE Industrial Electronics Society, and a Non-Executive Director of Oceania Cyber Security Centre Limited.
He received BEng and MEng degrees from the University of Science and Technology of China, Hefei, China, in 1982 and 1984, and PhD degree from Southeast University, Nanjing, China in 1988, respectively. He started his career in 1989 as a Postdoctoral Fellow with the University of Adelaide, Adelaide, Australia. In 1991, he joined Central Queensland University, Rockhampton, Australia, where, before he left in 2002, he was Chair Professor of Intelligent Systems and Associate Dean (Research) of Faculty of Informatics & Communication. Since 2002, he has been with RMIT University, where he held various positions such as Associate Dean and Research Institute Director.
His main research areas include control systems engineering, intelligent and complex systems, and smart grids and energy systems. He received many awards and honours for his contributions, including the prestigious 2018 M. A. Sargent Medal from Engineers Australia, the 2018 Australasian AI Distinguished Research Contribution Award from the Australian Computer Society, and the 2013 Dr.-Ing. Eugene Mittelmann Achievement Award from IEEE Industrial Electronics Society. He was named a Highly Cited Researcher by Clarivate Analytics (formerly Thomson Reuters) in 2015-2018. He is a Fellow of the IEEE, Engineers Australia, Australian Computer Society, and Australian Institute of Company Directors.
Plenary Speech II
Time: 09:10-09:50, May 8, 2019
Venue: Howard Hall, B2, Howard Plaza Hotel
Chair: Prof. Kim Man
Prof. Uwe D. Hanebeck
Karlsruhe Institute of Technology (KIT), Germany
Progressive Stochastic Estimation for Multisensor Fusion
I will focus on stochastic estimation in nonlinear and high-dimensional systems with the following important sub-problems: (1) data fusion for combining several pieces of information, (2) estimating the hidden state of a system given a measurement model and observations, and (3) filtering, i.e., recursive state estimation for dynamic systems. A wealth of stochastic estimation methods are available, ranging from the Kalman filter or the Unscented Kalman filter to Gaussian mixture filters and particle filters. However, coping with nonlinear and high-dimensional systems remains a challenge. I promise to surprise you with a radically different class of efficient estimation methods for optimally approximating the true posterior probability density functions. The key idea is to generate a continuous flow from the prior density to the true posterior and morph the desired approximate density accordingly. Different variants of these progressive estimation methods exist that can be customized for specific problems. I will demonstrate their usefulness by several examples.
Professor Uwe D. Hanebeck (IEEE, StM'89-M'91-SM'13-F'17) is a chaired professor of Computer Science at the Karlsruhe Institute of Technology (KIT) in Germany and director of the Intelligent Sensor-Actuator-Systems Laboratory (ISAS). From 2005 to 2015, he was the chairman of the Research Training Group RTG 1194 "Self-Organizing Sensor-Actuator-Networks" financed by the German Research Foundation. Prof. Hanebeck obtained his Ph.D. degree in 1997 and his habilitation degree in 2003, both in Electrical Engineering from the Technical University in Munich, Germany. His research interests are in the areas of information fusion, nonlinear state estimation, stochastic modeling, system identification, and control with a strong emphasis on theory-driven approaches based on stochastic system theory and uncertainty models. Research results are applied to various application topics like localization, human-robot interaction, assistive systems, sensor-actuator-networks, medical engineering, distributed measuring systems, and extended range telepresence. He is author and coauthor of more than 450 publications in various high-ranking journals and conferences and an IEEE Fellow.