Prof. John Mo, (Fellow of IME and IEA)
Royal Melbourne Institute of Technology, Australia
Biography: John P. T. Mo is Professor of Manufacturing Engineering and former Head of Manufacturing and Materials Engineering at RMIT University, Australia, since 2007. He has been an active researcher in manufacturing and complex systems for over 35 years and worked for educational and scientific institutions in Hong Kong and Australia. From 1996, John was a Project Manager and Research Team Leader with Australia's Commonwealth Scientific and Industrial Research Organisation (CSIRO) for 11 years leading a team of 15 research scientists. John has a broad research interest and has received numerous industrial research grants. A few highlights of the projects include: signal diagnostics for plasma cutting machines, ANZAC ship alliance engineering analysis, optimisation of titanium machining for aerospace industry, critical infrastructure protection modelling and analysis, polycrystalline diamond cutting tools on multi-axes CNC machine, system analysis for support of complex engineering systems John obtained his doctorate from Loughborough University, UK and is a Fellow of Institution of Mechanical Engineers (UK) and Institution of Engineers Australia.
Title: Engineering systems health monitoring and fault trend detection
Abstract: Modern engineering systems have increasing complexity and sophistication. Researches into methods to ensure reliable operation of the system have been continuing for decades. With modern microcontroller technologies, continuous online health monitoring systems are now commonly installed on new engineering systems. The availability of system performance data is not an issue. However, analysis of data and making good sense out of the information is still difficult. Predictive control schemes require continuous assessment of the conditions of the equipment to determine if it will operate properly in the next minute or hour. Statistically based monitoring methods are simple in concept, but accuracy of the algorithm is limited due to applicability of the data. There are always surprises when the performance deviates beyond pre-determined and sometimes broad limits. Hence, this type of analysis could only provide only a rudimentary assessment of the system’s condition based on some ad hoc experience which may not relate to the current situation. Worse still is that the computational process can take a long time. As a consequence, the action taken is often not appropriate and unable to cure the cause. An alternative branch of research investigates system performance on the basis of recognising normal behaviour, thereby providing a means of synthesising their abnormal behaviour, i.e. irregularities. This technique has the advantage that, instead of comparing simple limits, it assesses the system’s condition based on a whole range of performance signal patterns. More importantly, the trend that the system is going into faulty state can be monitored so that while the performance is deteriorating and the system is still working within acceptable conditions, preparation for providing the right repair can be made and the system can be stopped before producing bad outcomes.
Prof. Shane Xie (Chair in Robotics+Autonomous Systems)
University of Leeds, United Kingdom
Biography: Prof Shane (Sheng Q) Xie, Ph.D., FIPENZ, is the Chair of Robotics and Autonomous Systems and Director of the Rehabilitation Robotics Lab at the University of Leeds, and he was the Director of the Rehabilitation and Medical Robotics Centre at the University of Auckland, New Zealand (NZ, 2002-2016). He has >28 years of research experience in healthcare robotics and exoskeletons. He has published > 400 refereed papers and 8 books in rehabilitation exoskeleton design and control, neuromuscular modelling, and advanced human-robot interaction. He has supervised >15 postdocs, 62 PhDs and 80 MEs in his team with funding of >£27M from five countries since 2003. His team has invented three award-winning rehabilitation exoskeletons. He is an expert in control of exoskeletons, i.e. impedance control, adaptive control, sliding mode control, and iterative learning control strategies. He has received many distinguished awards including the New Zealand Science Challenge Award, the David Bensted Fellowship Award, and the AMP Invention Award. He is an elected Fellow of the Institute of Professional Engineers of New Zealand and the Technical Editor for IEEE/ASME Transaction on Mechatronics.
Title: Advanced Robotic Exoskeleton for Stroke Rehabilitation in Residential Settings
Abstract: Stroke and neurological diseases have significant impact on our society, this talk will discuss the key societal challenges, robotic technologies for delivering effective rehabilitation and opportunities for the healthcare industry. The keynote will cover the recent development of robotics for stroke rehabilitation, the research gaps and the need for new technologies in neuroscience, robotics and artificial intelligence. The talk will introduce a EPSRC-funded project on intelligent reconfigurable exoskeletons tailored to meet patients’ needs, deliver effective diagnosis and personalised treatment, and monitored remotely by rehabilitation therapists. The talk will also briefly introduce the Leeds Centre for Assistive/Rehabilitation Robotics and our work on ankle robot, gait exoskeleton, gait upper limb bilaterial robot, neuromuscular and brain computer interfaces. The focus is on the technologies for those whose strength and coordination have been affected by amputation, stroke, spinal cord injury, cerebral palsy and ageing.
Prof. Hesheng Wang
Shanghai Jiao Tong University, China
Biography: Hesheng Wang received the B.Eng. degree in Electrical Engineering from the Harbin Institute of Technology. Harbin, China, in 2002, the M.Phil. and Ph.D. degrees in Automation & Computer-Aided Engineering from the Chinese University of Hong Kong, Hong Kong, in 2004 and 2007, respectively. From 2007 to 2009, he was a Postdoctoral Fellow and Researcher Assistant in the Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong. He joined Shanghai Jiao Tong University as an Associate Professor in 2009. Currently, he is a Professor of Department of Automation, Shanghai Jiao Tong University, China. He worked as a visiting professor at University of Zurich in Switzerland. His research interests include visual servoing, service robot, robot control and computer vision. Prof. Wang has published more than 100 papers in refereed professional journals and international conference proceedings. He has received a number of best paper awards from major international conferences in robotics and automation. Dr. Wang is an Associate Editor of Assembly Automation and the International Journal of Humanoid Robotics, a Technical Editor of the IEEE/ASME TRANSACTIONS ON MECHATRONICS. He served as an Associate Editor of the IEEE TRANSACTIONS ON ROBOTICS from 2015 to 2019. He was a guest editor of Mathematical Problems in Engineering, Journal of Applied Mathematics and International Journal of Advanced Robotic Systems. He serves as The Technical Activities Board Member of IEEE Robotics and Automation Society. He served as associate editor in Conference Editorial Board of IEEE Robotics and Automation Society. Prof. Wang is actively involving in organization of international conferences. He served as organizing committee member for many international conferences such as ICRA and IROS. He was the program chair of the 2014 IEEE International Conference on Robotics and Biomimetics, the general chair of the 2016 IEEE International Conference on Real-time Computing and Robotics. He is the program chair of The 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. He was a recipient of Shanghai Rising Star Award in 2014 and The National Science Fund for Outstanding Young Scholars in 2017. He is a Senior Member of IEEE.