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The 11th International Conference on Computer Modeling and Simulation
ICCMS 2019   Melbourne, Australia   January 16-19, 2019

Keynote Speakers

Prof. Ghassan Beydoun
University of Technology Sydney, Australia

Professor Ghassan Beydoun is currently based at the Faculty of Engineering and Information Technology in University of Technology Sydney, where he is also deputy Head of School (Research) Systems, Management and Leadership at the University of Technology Sydney. He is also an adjunct senior research fellow at the School of Information Systems, Management and Technology at the University of New South Wales, an associate editor of the International Journal of Intelligent Information Technologies (IJIIT) and an Editorial member of the Journal of Software. He received a degree in computer science and a PhD degree in knowledge systems from the University of New South Wales in 2000. His research interests include multi agent systems applications, ontologies and their applications, and knowledge acquisition. He is currently working on a project sponsored by an Australian Research Council Discovery Grant to investigate the best uses of ontologies in developing methodologies for complex systems and another project with SES on exploring the use of ontologies for flood management decision support. He has authored more than 100 journal and conference papers in these areas over the past 15 years. His most recent publication appeared in IEEE Transactions of Software Engineering, Information Systems journal, Information and Management, International Journal of Human Computer Studies, Information Processing and management and others.

Keynote Speech

Agent Oriented Analysis in Support of Knowledge Based Interfaces

The use of Agent Oriented Analysis to facilitate knowledge analysis and transfer in complex settings is a non-traditional way of using the agent based paradigm. The final outcome in this context is not an agent based software system rather a knowledge rich interface between two software components or between a human user and a software system. I will show the effectiveness of agent analysis from this point view in two domains of applications illustrating each case. The first is in modelling of complex Disaster Management scenarios and the second is in developing an IoT based interface.

Prof. William Guo
Central Queensland University, Australia

Professor William Guo teaches and researches in computation and applied mathematics at Central Queensland University Australia (CQU). He was the Dean of the School of Engineering and Technology at CQU from Jan 2014-Jan 2015, and the Deputy Dean of the School from Feb 2013-Jan 2014. He has significant experience in academic governance through his services in various committees and boards since 2009, including CQU Education Committee (2011-2012), CQU Academic Board (2013-2014), and Australian Council of Deans of ICT (2013-), and as an Executive Member of Australian Council of Professors and Heads of IS (2012-). His teaching over the past 13 years has covered data structures and algorithms analysis, computational intelligence, systems analysis and architecture, IT/IS project management, e-Business, digital forensics, information security, research methods, and engineering mathematics. He was the recipient of CQU Vice-Chancellor’s Award for Good Practice in Learning and Teaching (2012) and Commendation in Student Voice Awards (2014). His research interests include computational intelligence, image processing, bioinformatics, big data modelling and simulation. He has published more than seventy papers in international journals and conference proceedings, and a new text (published by Pearson) in advanced engineering mathematics in 2014. He has supervised research higher degree students to completion. He is a member of IEEE, ACM, ACS, and Australian Mathematics Society (AUSTMS).

Keynote Speech

Title-Computational Intelligence for Solving Traveling Salesman Problems: Common Issues on the Research Output

Abstract-The traveling salesman problem (TSP) is a classic problem of combinatorial optimization called the NP-complete problem, which is hardly solvable efficiently by any analytical algorithm. Hence, a variety of heuristic algorithms using techniques of computational intelligence have been devised to produce approximate solutions that may be deemed good enough to applications involving TSPs. Some common issues are often encountered during the evaluation of research outputs of using computational intelligence to solve various TSPs. These issues can be raised from either some improper statements wrongly or overly claimed by the researcher who conducted the research or the comments on the research output made by some ‘so-called’ expert reviewers. In this talk, some of these common issues or questions encountered from the both ends are discussed.

Plenary Speakers



Assoc. Prof. Kai Qin
Swinburne University of Technology, Australia

Dr. Kai Qin received the B.Eng. degree from the School of Automation at Southeast University (Nanjing, Jiangsu, China) in 2001, and the Ph.D. degree from the School of Electrical and Electronic Engineering at Nanyang Technological University (Singapore) in 2007. In 2005, he had visited the Illinois Genetic Algorithms laboratory (led by Prof. David E. Goldberg) at the University of Illinois at Urbana- Champaign (Urbana, IL, USA) for three months. From 2007 to 2009, he had worked as a postdoctoral fellow in the Vision and Image Processing Lab in the Department of Systems Design Engineering at the University of Waterloo (Waterloo, Ontario, Canada). From 2010 to 2012, he had worked in the MISTIS Team at INRIA Grenoble Rhone-Alpes (Grenoble, France), firstly as a postdoctoral researcher and then as an expert engineer. From 2012 to 2017, he had worked in the School of Science at RMIT University (Melbourne, Australia) as a Vice-Chancellor's Research Fellow, Lecturer and Senior Lecturer. Now, he is an Associate Professor in the Department of Computer Science and Software Engineering and the Data Science Research Institute at Swinburne University of Technology. His major research interests include: Machine Learning, Evolutionary Optimization, Image Processing, GPU Computing, Services Computing, and Mobile and Pervasive Computing.

Plenary Speech

Collaborative Learning and Optimisation

Abstract: Learning and optimisation are two essential tasks that computational intelligence aims at addressing, where numerous techniques have been developed for these two purposes separately. In fact, learning and optimisation are closely related. On the one hand, learning can be formulated as a model-centric or data-centric optimisation problem, and accordingly solved by optimisation techniques. On the other hand, optimisation can be regarded as an adaptive learning process, and thus tackled via learning approaches. This talk will discuss collaborations between learning and optimisation from the aspects of optimisation for learning, learning for optimisation and learning plus optimisation, and describe some recent works as case studies in each of these three aspects. 

Prof. Wernhuar Tarng
National Tsing Hua University, Taiwan

Wernhuar Tarng is currently a professor at the Institute of Learning Science and Technology, National Tsing Hua University, Hsinchu, Taiwan. He was the director of Computing and Networking Center, National Hsinchu University of Education, Taiwan from 1993 to 2004 and the chairman of the Graduate Institute of Computer Science from 2008 to 2012. From 1980, Prof. Tarng conducted his undergraduate study at National Chiao Tung University, Hsinchu, Taiwan and he was graduated from the Department of Control Engineering in 1984. He received his M.S. degree (1987) and Ph.D. degree (1992) from the Department of Electrical and Computer Engineering, State University of New York at Buffalo, USA. Prof. Tarng has received more than 20 grant projects funded by Ministry of Science and Technology (MOST), Taiwan and published over 100 research papers in the field of computer science, engineering, networking, and learning technologies. Prof. Tarng was a visiting professor of Distant and Online Learning Center, Oxford University, UK in 2002 and a visiting scholar at Hear and Say Centre, Brisbane, Australia from 2014 to 2015. His current research interests include: e-Learning technologies, virtual reality, augmented reality, game-based learning, image processing, pattern recognition, computer architecture, and computer networking.

Plenary Speech

Applications of Virtual Reality and Augmented Reality in Science Education

Abstract: Virtual reality (VR) is a human-computer interface to create a 3D virtual world for simulating the real environments through one's sense organs. Based on the sensor data such as the current orientation and position, a VR system can detect the user’s reaction and provide interactive feedback in real time. Augmented reality (AR) emphasizes the combination of contexts in the real world with virtual or real situations to intensify its interaction with the user. AR can integrate a real environment with virtual objects to enhance the comprehension of environmental context and the sense of reality in a more interactive way. Recently, more and more VR and AR applications have been developed on mobile and head-mounted devices for entertainment and educational applications. VR and AR can simulate realistic situations by 3D animation, so they are useful for presenting abstract concepts, theories and experimental processes to enhance cognition and immersion in learning activities. A virtual yet close-to-real environment can avoid the danger and reduce the cost in the real world while satisfying major requirements. Because VR and AR have high interactivity and the sense of reality, they can attract the interest of users and are thus suitable for applications in science education.

Invited Speaker



Assoc. Prof. Guolei Tang
Dalian University of Technology, China

Dr. Guolei Tang is currently working at the Research Institute of Port, Coastal and Offshore Engineering at Dalian University of Technology (Dalian, China). In 2009, he received the Ph.D. degree from the School of Hydraulic Engineering at Dalian University of Technology. From 2009 to 2011, he had worked as a postdoctoral fellow in the Faculty of Infrastructure Engineering at Dalian University of Technology and then worked as lecturer from 2011 to 2015. In 2015, he visited Delft University of Technology (Delft, Netherlands) for one year. Now, he is an Associate Professor in the Research Institute of Port, Coastal and Offshore Engineering at Dalian University of Technology. His research focuses on Smart Port: Simulation, Planning & Scheduling and Decision Support System. In the past few years, his research has been supported by National and Provincial Natural Science Foundation, such as “Adaptive container terminal operation coupling multisource information fusion with the big data challenges” and “Ship behavior analysis based on AIS data using Hadoop/Spark”.

Invited Speech

Evaluating the Impact of LNG Carriers’ Arrivals and Departures on Port Performance Based on Agent-based Simulation

Abstract: China’s imports of Liquefied Natural Gas (LNG) have grown to meet increasing domestic natural gas consumption, which has been primarily driven by environmental policies to transition away from coal-fired electricity generation. As well known, ports play a pivotal role in the growth of LNG, holding the responsibility for building the infrastructure required to handle cryogenic fuel. Therefore, there are many ports in China planning to build up LNG terminals in recognition of the new opportunities and a growing LNG market will generate.
During the planning and development phase of an LNG terminal, in addition to the marine facilities needed to safely berth they need to consider load and discharge, suitable storage tanks, maintaining a safety perimeter and the provision of other equipment which may be needed. For example, no passing shall take place between an LNG vessel and any vessel other than controlled craft/s during the transit through the Channel area; separation between LNG vessels and other vessels in the Channel in the same direction shall be minimum one hour for all type of vessels throughout the transit; and the entry of the vessel into the channel on her arrival and the departure from the berth will only commence during daylight hours when it is estimated that the vessel transit will also be completed during daylight hours. These safety standards and regulations for operating LNG carries during the transit through the Channel area, may prevent development of other vessels especially the container vessels and Ro-Pax vessels within the port. And this would lead to more waiting time for other vessels, and increase their operational costs because of the limited capacity of entrance channels, especially in the case of very long one-way traffic entrance channels.
Therefore, considering stochastic characteristics of the port system, an evaluation framework is necessary to explore the impact of LNG carriers’ arrivals and departures on port performance, locate the bottleneck and propose improvement plans. To evaluate the performance of the stochastic port system, this study implemented a agent-based simulation model, which simulates the traffic flows of ships with different priorities in channels including LNG vessels, container vessels, Ro-Pax vessels, bulk carriers, and oil carriers. Finally, the proposed framework is applied to Dandong Port with 100 berths and 12-nautical-mile channel, and to explore the feasibility of new built LNG terminals, and evaluate the LNG carriers’ arrivals and departures on port performance, and identify possible improvement strategies to support the building of LNG terminals.