Shun-Feng Su received the B.S. degree in electrical engineering from National Taiwan University, Taiwan, R.O.C., in 1983, and the M.S. and Ph.D. degrees in electrical engineering from Purdue University, West Lafayette, Indiana, in 1989 and 1991, respectively. He is now a Chair Professor in the Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. He is an IEEE Fellow and also a CACS Fellow. He has published more than 160 refereed journals and conference papers in the areas of robotics, intelligent control, fuzzy systems, neural networks, and non-derivative optimization. His current research interests include neural networks, fuzzy modeling, machine learning, virtual reality simulation, intelligent transportation systems.Ning Wang received his B. Eng. degree in Marine Engineering and the Ph.D. degree in control theory and engineering from the Dalian Maritime University, Dalian, China in 2004 and 2009, respectively. From September 2008 to September 2009, he was financially supported by China Scholarship Council to work as a joint-training PhD student at the Nanyang Technological University (NTU), Singapore. In view of his significant research at NTU, he received the Excellent Government-funded Scholars and Students Award in 2009. From August 2014 to August 2015, he worked as a Visiting Scholar at the University of Texas at San Antonio. His research interests include self-learning modeling and control, unmanned (marine) vehicles, machine learning and autonomous systems. He has authored 3 books, and more than 100 SCI-indexed journal papers. He currently serves as members of IEEE TC on Industrial Informatics, the American Society of Mechanical Engineers (ASME), the Society of Naval Architects and Marine Engineers (SNAME), the Chinese Association of Automation (CAA), and the China's Society of Naval Architecture and Marine Engineering (CSNAME).He is an editorialboard member of several academic journals, such as Neurocomputing, International Journal of Fuzzy Systems, Transactions of the Institute of Measurement and Control, Journal of Electrical Engineering and Technology, and Electronics.

• Marine robotics scientific and technological discussion.- Autonomous marine vehicle design, test and launch.- Autonomous marine vehicle modeling, identification and simulation.- Marine guidance, navigation and control.- Marine robot visual servo, intelligence and autonomy.- Situation awareness, collision avoidance and decision making.- Multiple marine vehicles swarm intelligence, cooperation and optimization.- Autonomous power management system.- Safety in offshore robotics.- Information systems in offshore robotics.- AI in marine robotics.- Education in offshore robotics.

Shun-Feng Su received the B.S. degree in electrical engineering from National Taiwan University, Taiwan, R.O.C., in 1983, and the M.S. and Ph.D. degrees in electrical engineering from Purdue University, West Lafayette, Indiana, in 1989 and 1991, respectively. He is now a Chair Professor in the Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. He is an IEEE Fellow and also a CACS Fellow. He has published more than 160 refereed journals and conference papers in the areas of robotics, intelligent control, fuzzy systems, neural networks, and non-derivative optimization. His current research interests include neural networks, fuzzy modeling, machine learning, virtual reality simulation, intelligent transportation systems.Ning Wang received his B. Eng. degree in Marine Engineering and the Ph.D. degree in control theory and engineering from the Dalian Maritime University, Dalian, China in 2004 and 2009, respectively. From September 2008 to September 2009, he was financially supported by China Scholarship Council to work as a joint-training PhD student at the Nanyang Technological University (NTU), Singapore. In view of his significant research at NTU, he received the Excellent Government-funded Scholars and Students Award in 2009. From August 2014 to August 2015, he worked as a Visiting Scholar at the University of Texas at San Antonio. His research interests include self-learning modeling and control, unmanned (marine) vehicles, machine learning and autonomous systems. He has authored 3 books, and more than 100 SCI-indexed journal papers. He currently serves as members of IEEE TC on Industrial Informatics, the American Society of Mechanical Engineers (ASME), the Society of Naval Architects and Marine Engineers (SNAME), the Chinese Association of Automation (CAA), and the China's Society of Naval Architecture and Marine Engineering (CSNAME).He is an editorialboard member of several academic journals, such as Neurocomputing, International Journal of Fuzzy Systems, Transactions of the Institute of Measurement and Control, Journal of Electrical Engineering and Technology, and Electronics.

• Marine robotics scientific and technological discussion.- Autonomous marine vehicle design, test and launch.- Autonomous marine vehicle modeling, identification and simulation.- Marine guidance, navigation and control.- Marine robot visual servo, intelligence and autonomy.- Situation awareness, collision avoidance and decision making.- Multiple marine vehicles swarm intelligence, cooperation and optimization.- Autonomous power management system.- Safety in offshore robotics.- Information systems in offshore robotics.- AI in marine robotics.- Education in offshore robotics.

Shun-Feng Su received the B.S. degree in electrical engineering from National Taiwan University, Taiwan, R.O.C., in 1983, and the M.S. and Ph.D. degrees in electrical engineering from Purdue University, West Lafayette, Indiana, in 1989 and 1991, respectively. He is now a Chair Professor in the Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. He is an IEEE Fellow and also a CACS Fellow. He has published more than 160 refereed journals and conference papers in the areas of robotics, intelligent control, fuzzy systems, neural networks, and non-derivative optimization. His current research interests include neural networks, fuzzy modeling, machine learning, virtual reality simulation, intelligent transportation systems.Ning Wang received his B. Eng. degree in Marine Engineering and the Ph.D. degree in control theory and engineering from the Dalian Maritime University, Dalian, China in 2004 and 2009, respectively. From September 2008 to September 2009, he was financially supported by China Scholarship Council to work as a joint-training PhD student at the Nanyang Technological University (NTU), Singapore. In view of his significant research at NTU, he received the Excellent Government-funded Scholars and Students Award in 2009. From August 2014 to August 2015, he worked as a Visiting Scholar at the University of Texas at San Antonio. His research interests include self-learning modeling and control, unmanned (marine) vehicles, machine learning and autonomous systems. He has authored 3 books, and more than 100 SCI-indexed journal papers. He currently serves as members of IEEE TC on Industrial Informatics, the American Society of Mechanical Engineers (ASME), the Society of Naval Architects and Marine Engineers (SNAME), the Chinese Association of Automation (CAA), and the China's Society of Naval Architecture and Marine Engineering (CSNAME).He is an editorialboard member of several academic journals, such as Neurocomputing, International Journal of Fuzzy Systems, Transactions of the Institute of Measurement and Control, Journal of Electrical Engineering and Technology, and Electronics.

• Marine robotics scientific and technological discussion.- Autonomous marine vehicle design, test and launch.- Autonomous marine vehicle modeling, identification and simulation.- Marine guidance, navigation and control.- Marine robot visual servo, intelligence and autonomy.- Situation awareness, collision avoidance and decision making.- Multiple marine vehicles swarm intelligence, cooperation and optimization.- Autonomous power management system.- Safety in offshore robotics.- Information systems in offshore robotics.- AI in marine robotics.- Education in offshore robotics.