The 25th International Conference on Automation and Computing (ICAC2019)

Lancaster University, UK, 5-7 Sep. 2019

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ICAC 2019

The 2019 International Conference on Automation and Computing (ICAC 2019) was successfully held at Lancaster University, UK on 5-7 September 2019. 

PROGRAM

Keynote Speakers

Prof Wenhua Chen
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Prof Malcolm J Joyce
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ABOUT

Workshop

Prof Dongling Xu
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Prof Shan Lou
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Prof Chenguan Yang
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Prof Jing Wang
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ABOUT

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Calls

>Call for Regular Papers

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> Join Instruction for the Delegates of ICAC 2019

SPECIAL THANKS TO

Organiser and sponsors

ABOUT

Organiser

Viewing the Organising Committee
Prof Xiandong Ma

General Chair

Prof Hui Yu

Program Chair

Prof Xiangjun Zeng

Program Co-Chair

Prof Guohai Liu

Program Co-Chair

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.

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:

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.

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: 

Hybrid Data-Driven and Knowledge-Based Fraud Detection via Evidential Reasoning

Abstract:

In this presentation, a transparent machine learning process is discussed which can help make decisions by acquiring evidence and evaluating the reliability of evidence from both experience and training data. The Evidential Reasoning (ER) rule is used to combine multiple pieces of evidence gathered for multiple feature variables. Each piece of evidence is weighted and combined conjunctively with the weights trained for extended probabilistic inference. Supported by a multinational law firm and UK government, the system is trained by real-world datasets on insurance claims and implemented in to the law firm’s IT system to help monitoring and detecting car insurance fraud for its clients. Results from the application are presented, which reveal that the proposed method has high interpretability and outperforms a number of widely used machine learning models.

Bio: 

Dong-Ling Xu is Chair Professor of Decision Science and Systems at Decision and Cognitive Sciences Research Centre, Alliance Manchester Business School, The University of Manchester, UK. Over the past 30 years, she has been conducting research in data analysis, statistical inference, machine learning, decision support under uncertainty, system development and their applications. She has developed several interactive web and Windows based decision support systems. Those systems are used by organizations such as General Motors, Tesco, NHS, Ford, Shell, BP and CNOOC in a wide range of applications. The ER approach and its software implementation, the IDS software, is used by practitioners and researchers from over 50 countries. She has published over 100 peer reviewed journal papers, book chapters and books.

Title:

Surface Metrology for Additive Manufacturing

Abstract: 

Additive manufacturing (AM) is paving its way toward the next industrial revolution with wide applications in key industrial sectors, e.g. aerospace, automotive, healthcare, defence. However, many technical barriers still hinder its full commercialisation today. One major issue is that AM processes are not robust enough and AM needs measurement methods for its process control and product verification. This presentation addresses the challenges of measuring and characterising AM surface texture. Various measurement techniques, including tactile, optical and X-ray computed tomography (XCT), are used to measure surfaces produced by selective laser melting (SLM) and electron beam melting (EBM). Their capabilities in measuring AM surface textures are evaluated and compared. Bespoke surface characterisation methods, including feature extraction and parameterisation, are developed in order to reflect the characteristics of AM processes. A case study is given to illustrate how to use XCT to measure an SLM built acetabular implant and how to use the advanced characterisation methods to evaluate the surface quality of the implant in terms of Osseo integration.

Bio: 

Shan Lou is a Senior Research Fellow at the Future Metrology Hub, with the University of Huddersfield.His expertise is surface metrology for additive manufacturing and X-ray computed tomography metrology. He was recently awarded two EPSRC grants to work on surface metrology for additive manufacturing, RCUK Catapult Researchers in Residence fellowship and EPSRC New Investigator grant. Previously, from 2006-2012, he was a R&D engineer with the Hexagon Metrology, a leading global company in the metrology industry. Dr Lou has co-authored 26 refereed journal and 27 conference publications. He is an active participating member of ASTM Committee F42 Additive Manufacturing Technologies, a committee member of the British Standard Institution CT working group, and an editorial board member of the journal ‘Bio-design and manufacturing’. He is a Chartered Engineer of the Institution of Mechanical Engineers and a member of the Institute of Mathematics and its Applications.

Title: 

Human-Like Robot Control Design and Human Robot Skill Transfer

Abstract: 

In the near future, robots are expected to co-habit with our human beings and work closely with us in various fields and even our daily lives. Unfortunately, most of the current robot control technologies are designed for conventional industrial robots which operate behind safeguarding and for predefined tasks, and thus are not able to cope with the varying tasks in unknown dynamic environments. I have therefore developed human-like adaptive control techniques as well as highly effective human robot skill transfer techniques. My work follows the “from human and for human” principle, i.e., study human motor control skills, in order to develop better robot controllers to support human collaborators. My design not only enable versatile and dexterous robot manipulation but also make robot providing personalized assistance to human factors. My investigations not only create a new cross-disciplinary application area where physiologists are able to employ their knowledge and experiences together with roboticists, but will also have a huge impact on the robotics community, through in-depth investigations on the relation between humans and robots. 

Bio: 

Chenguang (Charlie) Yang is Professor with Bristol Robotics Laboratory, University of the West of England, UK. He is a Theme Leader of Immersive Teleoperation. He received PhD degree from the National University of Singapore (2010) and performed postdoctoral research at Imperial College London. Prof. Yang has published over 300 international journal and conference papers. He has been awarded EU Marie Curie International Incoming Fellowship, UK EPSRC UKRI Innovation Fellowship, and the Best Paper Award of the IEEE Transactions on Robotics as well as over ten conference Best Paper Awards. His research interest lies in human robot interaction and intelligent system design.

Title:

Machine Learning Tools for Engineering Problems

Abstract: 

The fast-developing machine learning techniques have been widely used as tools to solve many computer science and engineering problems. This workshop presentation is for our students who wish to start the journey of using the machine learning methods in their researches. I will use a “Hello World” style case study as an example to explain how to develop an end-to-end machine learning system. I hope this topic can let you have an impression of the machine learning system pipeline and have a taste of how to develop a machine learning system from scratch.

Bio: 

Jing Wang gained his BEng degree in Machine and Electronic Technology from the Xidian University, China, in 2006. After graduation, he was appointed as a software engineer and carried out development work on Computer Vision (CV)-based quality control systems. These included assembly line monitoring and industrial robotic controls. In 2008, he became a postgraduate student in the University of Huddersfield and gained his PhD degree at 2012. He then became a research fellow and carried out independent research on image processing, analysing and understanding. Dr Wang joined Sheffield Hallam University in 2017. His interests are focused on the real-world applications of computer vision systems, and he have published more than 20 journal and conference papers in fields related to this. Dr Wang is a member of British Machine Vision Association (BMVA) and British Computer Society (BCS).I also served as chair and editor for International Conference on Automation and Computing.

ICAC2019 Organising Committee

General Chair

  • Xiandong Ma, Lancaster University, UK

Program Chair

  • Hui Yu, University of Portsmouth, UK

Conference Co-Program Chair:

  • Xiangjun Zeng, Changsha University of Science & Technology, China
  • Guohai Liu, Jiangsu University, China

Awards Chair

  • Jie Zhang, Lancaster University, UK
  • Marco Ramirez Sosa Moran, University of Westminster, UK

Publicity Chair

  • Hui Yu, University of Portsmouth, UK

Financial Chair

  • Hong Yue, University of Strathclyde, UK

Publication Chair

  • Jing Wang, Sheffield Halam University, UK

Local Organisation Committee members

  • Qiang Ni
  • Zheng Wang
  • Xiaonan Hou