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Keynote Speaker

The following leading international experts will be the conference keynote speakers

Michel Tognini

 

Michel Tognini isa former European CNES and ESA astronaut who served as Head of the European Astronaut Centre of the European Space Agency from Jan. 2005 to Nov. 2011. M. Tognini made his first space flight on July 1992 aboard Soyuz TM-15 to link with Mir station and flew again aboard the Space Shuttle Columbia STS-93 on July 1999. During this second mission his primary task was to assist in the deployment of the Chandra X-Ray Observatory, and to conduct a space walk if needed. M. Tognini also attended the NASA Johnson Space Center and was initially assigned to the Operations Planning Branch of the Astronaut Office working technical issues on the International Space Station, and was subsequently supporting the Mobile Base System and the European Robotic Arm. Mr.Tognini has 4000 flight hours on 80 types of aircraft and has prepared around 200 papers, presentations and conferences about Human Space flights.

Title : Space flights Projects Complexity : ICT and human risk management

Abstract : Michel Tognini will talk about the space projects and the interaction between human and ICT. Space programmes vary significantly in size, duration, and complexity, this necessitates continues research to develop innovative systems, solutions and products for the aerospace, human-machine interaction, robotic systems and so on. This is done at all levels, including operations and astronaut training, end-to-end planning, preparation, implementation and execution of spacecraft and facility operations. Space projects are highly risky, Mr.Tognini will explain how risk management approaches are important and more specifically supporting risk perception and control, and a common vision for the entire organisation. He will also give an overview on the Robotic Arm mission and its ability to 'walk' around the exterior of the station under its own control, moving hand-over-hand between pre-fixed base points, with an ability to perform many tasks automatically or semi-automatically, there by freeing its operators to do other work (Installation and deployment of solar arrays, Inspection of the station, Support of astronauts during space walks).

Flight commander and European Space Agency Astronaut, France
     

Tan Kay Chen

 

Kay Chen TAN is a Researcher in the Department of Electrical and Computer Engineering, National University of Singapore. He is actively pursuing research in computational and artificial intelligence, with applications to multi-objective optimization, scheduling, automation, data mining, and games. Dr Tan has published over 100 journal and over 100 conference papers, co-authored 5 books and co-edited 4 books. Dr Tan is currently a Distinguished Lecturer of IEEE Computational Intelligence Society (CIS). He has been invited to be a keynote/invited speaker for over 30 international conferences, served in the IPC for over 100 conferences and involved in the organizing committee for over 30 international conferences. Dr Tan is currently the Editor-in-Chief of IEEE Computational Intelligence Magazine. He also serves as an Associate Editor / Editorial Board member of over 15 international journals. Dr Tan received the IEEE CIS Outstanding Early Career Award in 2012, and the International Network for Engineering Education & Research (iNEER) Recognition Award in 2008.

Title : Advances in Evolutionary Multi-objective Optimization and Applications

Abstract : Multi-objective optimization is widely found in many fields, such as logistics, economics, engineering, or whenever optimal decisions need to be made in the presence of trade-offs between two or more conflicting objectives. The incorporation of probabilistic graphical approaches in evolutionary mechanism may enhance the iterative search process when interrelationships of the archived data has been learned, modeled, and used in the reproduction for multi-objective optimization. This talk will discuss the implementation of probabilistic graphical approaches in solving multi-objective optimization problems under the evolutionary paradigm. First, the problem of multi-objective optimization and its challenges such as complexities in terms of multimodal, high-dimensional, epistatic, deceptive, constrained, and uncertainties etc., will be studied. It will then show that the optimization problem can be solved using probabilistic graphical models. A binary stochastic neural network, named restricted Boltzmann machine (RBM), will be applied, and its learning, modeling and sampling mechanisms will be highlighted. A few applications for evolutionary multi-objective optimization will also be presented in this talk.

 
  Researcher in computational and artificial intelligence at the National University of Singapore (NUS), Singapore.