Keynote Speakers

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Prof. Wen-Hua Chen, Department of Aeronautical and Automotive Engineering, Loughborough University, UK

Bio: Wen-Hua Chen holds professor in autonomous vehicles in the Department of Aeronautical and Automotive Engineering at Loughborough University, UK, where he is also heading the Controls and Reliability Research Group. Before joining Loughborough in 2000 as Lecturer in Flight Control Systems, Dr. Chen was a Research Fellow and then a Lecturer in Control Engineering in the Centre for Systems and Control at the University of Glasgow, Scotland. Dr Chen has a considerable experience in advanced control, signal processing and computational intelligence and their applications in aerospace and automotive engineering. In the last 15 years, he has been spending most of his effort in developing autonomous system technologies and their applications in agriculture, environment and defence. Prof Chen is a Chartered Engineer, and a Fellow of IEEE, the Institution of Engineering and Technology and the Institution of Mechanical Engineers, UK. He has published about 250 papers with about a total of 9,000 citations.

Title: Towards High Level Automation through Integrating Computational Intelligence and Control: A Case Study

Abstract: Automation is generally realised by automatic control systems with clearly specified references. To further increase the level of automation where only a high-level goal is specified, autonomous control with reasoning is required. This talk presents a case study of this type of new control systems – control a mobile sensor platform (e.g. a ground robot or an unmanned aerial vehicle) to approach unknown sources of airborne chemical and biological substance release. Hazard substance release in atmosphere is of major concerns in environment monitoring, anti-terrorist, and disaster and emergence management. The whole system consists of chemical sensors, mobile sensor platforms, reasoning and planning algorithms. By utilising the current and previous chemical sensor readings, reasoning algorithms developed in a Bayesian framework estimate key parameters associated with the release and environment conditions. Based on that, at each step, the decision for the next move of the sensor platform is optimised in order to maximise the chance of finding the source and reduce uncertainty in location estimation. Driven by the inference algorithm and informative based planning and control, the sensor platform is able to approach unknown sources under an unknown environment condition without a specified goal location and driving path. The Bayesian inference algorithms are implemented through the particle filtering technique. Experimental tests of the complete system were successfully conducted, which overcome the challenges of intermittent sensor readings due to air turbulent conditions, unknown release including location and release rate, unknown environment conditions (e.g. wind direction and speed) and a high level of noise in chemical sensors. The developed autonomous search systems could be widely used for environment protection and monitoring, oil and gas industry, and disaster or emergency management and keep the first responders out of harm.


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Prof. Malcolm J Joyce, Department of Engineering, Lancaster University, United Kingdom

Bio: Malcolm Joyce is Professor of Nuclear Engineering and Associate Dean for Research for the Faculty of Science & Technology at Lancaster University (UK).His research is focused on applied radiation detection, and particularly on nuclear material characterisation with organic scintillation detectors. He is author on > 160 journal papers and > 100 conference papers. He serves on the UK Government's Nuclear Industry Research Advisory Board (NIRAB) and was awarded a high doctorate (DEng) in 2012, the James Watt medal (Institution of Civil Engineers) in 2014 and a Royal Society Wolfson Research Merit Award in 2016. He wrote 'Nuclear Engineering: A Conceptual Guide to Nuclear Power', published in 2017.

Title: Fast neutron detection and measurement: improving application performance through automation and computing

Abstract: Fast neutrons have long been a useful medium for diagnostic applications associated with nuclear reactor operations, nuclear safeguards and materials assay (particularly radiography and tomography).However, they are difficult to detect, relative to their thermalized counterparts, and the principal measurable properties associated with them on which these applications are based (i.e., time, energy and their origin) are dispersed by traditional detection methods which require that they are slowed down in order to increase detection efficiency. Approximately 10 years ago, significant improvements in real-time processing firmware enabled a number of important developments that have effectively commoditised fast neutron detection for real-time detection, imaging and tomography applications. These developments will be described in this paper.