Prof. Carlos Guedes Soares
Universidade de Lisboa, Portugal
Biodata: Carlos Guedes Soares (Coordinator of PLENOSE CENTEC unit) is Professor and the President of the Centre for Marine Technology and Engineering at IST (CENTEC). He had his post-graduate education at the Massachusetts Institute of Technology and the Norwegian Institute of Technology. He has been involved in about 60 European projects, coordinating 6 of them, and has also co-ordinated about 15 national projects. He has published about 400 papers in international journals, and 600 papers in books and conferences, and is a member of several international organisations and of the Editorial Board of several scientific journals. He has coordinated projects such as WAVEMOD, HIPOCAS, FREAK WAVES, SHIPREL, MARSTRUCT and SAFERELNET dealing with the design and safety of floating structures in extreme conditions. It was also technical coordinator of projects like REBASDO, SAFEOFFLOAD, WAVELOADS, HANDLING WAVES and EXTREME SEAS.
Prof. Yunfei En
The Fifth Electronics Research Institute of the Ministry of Industry and Information Technology (CEPREI), China
Title: Reliability and failure mechanism of SiC power MOSFETs
Abstract: Silicon carbide (SiC) power metal-oxide semiconductor field-effect transistors (MOSFETs) are the centre of attention for wide bandgap (WBG) device development in recent years because they can enable power systems of high frequency, high efficiency, and high density due to the superior properties of SiC material. Therefore, SiC MOSFETs are attractive alternatives to Si devices for a wide spectrum of power electronics systems, including photovoltaic systems, electric vehicles, charging piles, UPS power supply and so on. Although SiC MOSFETs have superior performances to Si devices, obvious differences between the properties of SiC and Si materials and devices provide challenges to the ruggedness and reliability of SiC MOSFETs. There are a number of potential reliability issues associated with SiC power MOSFETs, including threshold voltage stability, gate-oxide reliability, body-diode robustness, short-circuit current robustness, and radiation effects. Extensive characterization studies of SiC MOSFETs’ endurance capability under harsh conditions including unclamped inductive-switching (UIS), short-circuit (SC), transmission line pulse (TLP), power cycling (PC) high-temperature gate bias stress (HTRB) and radiation effects have been reported by our team. The reliability issues and the degradation mechanism of SiC MOSFETs under different conditions have been discussed here.
Biodata: Yunfei En is a researcher professor and the chief executive engineer of the Fifth Electronics Research Institute of the Ministry of Industry and Information Technology. She received her Ph.D in semiconductor devices and microelectronics from Xidian University. She is a fellow of the Chinese Institute of Electronics and a senior member of IEEE. She is the Principal Investigator (PI) of more than 20 government projects including the project in National Key R&D Program of China. Her research areas include reliability evaluation of electronic components, failure analysis, reliability design verification, prognostics and health management of electronic systems, with a particular focus on integrated circuit reliability and electromagnetic compatibility. She is author or coauthor of more than 30 publications in international journals, such as IEEE Transactions on Electron Devices, Applied Physics Letters and so on. And she has won a number of national and provincial science and technology awards.
Prof. Yu Liu
University of Electronic Science and Technology of China, China
Title: Selective Maintenance Strategy under Uncertainty
Abstract: Due to limited maintenance resources, such as budget, time, manpower, etc., selective maintenance has widespread applications in both industry and military environments. By selective maintenance strategy, a subset of feasible maintenance actions for a repairable system can be chosen to be performed so as to ensure the success of the subsequent mission. However, in reality, various uncertainties are inevitable in selective maintenance optimziation. In addition to a comprehensive review on the existing selective maintenance models under uncertainties, this talk will introduce new selective maintenance models by taking account of the uncertainty of the durations of breaks and maintenance actions and the uncertainty caused by imperfect observations. Illustrative examples are presented to demonstrate the effectiveness of the proposed models.
Biodata: Yu Liu is a full Professor with the Department of Industrial Engineering in the School of Mechanical and Electrical Engineering at the University of Electronic Science and Technology of China. He received his PhD degree in Mechatronics Engineering from the University of Electronic Science and Technology of China. He was a Visiting Pre-doctoral Fellow in the Department of Mechanical Engineering at Northwestern University, Evanston, U.S.A. from 2008 to 2010, and a Postdoctoral Research Fellow in the Department of Mechanical Engineering, at the University of Alberta, Edmonton, Canada from 2012 to 2013. His research interests include system reliability modeling and analysis, maintenance decisions, prognostics and health management, and design under uncertainty. He has published over 70 peer-reviewed papers in international journals, such as IEEE Transactions on Reliability, IISE Transactions, European Journal of Operational Research, ASME Journal of Mechanical Design, Mechanical Systems and Signal Processing. He has been recognized as one of the Most Cited Chinese Researchers by Elsevier since 2016. He was a recipient of the National Science Fund for Excellent Young Scholars and the HIWIN Doctoral Dissertation Award sponsored by HIWIN Technologies Corporation and Chinese Society of Mechanical Engineers. He serves as the Vice President of the Reliability Committee of Operations Research Society of China and the Associate Editor of IISE Transactions and IEEE Transactions on Reliability. He is a Fellow of ISEAM.
Prof. Yanfu Li
Tsinghua University, China
Biodata: Yan-Fu Li is currently the Deputy-Director of the Institute for Quality & Reliability and a full professor at the Department of Industrial Engineering (IE), Tsinghua University. He received his Ph.D in Industrial Engineering from National University of Singapore in 2010. He was a faculty member at Laboratory of Industrial Engineering at CentraleSupélec, France, from 2011 to 2016. His research areas include RAMS (reliability, availability, maintainability, safety) assessment and optimization with the applications onto energy systems, transportation systems, computing systems, etc.
Dr. Li has published more than 100 research papers, including more than 50 peer-reviewed international journal papers with H-index 26. He is currently an Associate Editor of IEEE Transactions on Reliability, a member of IEEE Reliability Society AdCom, a senior member of IEEE and IISE and a member of INFORMS. He is a vice president of the System Reliability Chapter of System Engineering Society of China. He is an expert examiner of funding agencies include NSFC, Dutch Research Council (NWO).
He is the Principal Investigator (PI) of several government projects including the key project funded by National Natural Science Foundation of China, the project in National Key R&D Program of China, and participated in several projects supported by EU or French funding bodies. He is also experienced in industrial research, the partners include Huawei, China Railway, Volkswagen, Mitsubishi Heavy Industries, EDF, ALSTOM, etc. The total amount of funding exceeds 10 million CNY.
Prof. Xiao Liu
University of Arkansas, USA
Title: Regression Trees on Grassmann Manifold for Adapting Reduced-Order Models
Abstract: Low-dimensional and computationally less-expensive reduced-order models (ROMs) have been widely used to capture the dominant behaviors of high-4dimensional systems. An ROM can be obtained, using the well-known proper orthogonal decomposition (POD), by projecting the full-order model to a subspace spanned by modal basis modes that are learned from experimental, simulated, or observational data, i.e., training data. However, the optimal basis can change with the parameter settings. When an ROM, constructed using the POD basis obtained from training data, is applied to new parameter settings, the model often lacks robustness against the change of parameters in design, control, and other real-time operation problems. This work proposes to use regression trees on Grassmann manifold to learn the mapping between parameters and POD bases that span the low-dimensional subspaces onto which full-order models are projected. Motivated by the observation that a subspace spanned by a POD basis can be viewed as a point in the Grassmann manifold, we propose to grow a tree by repeatedly splitting the tree node to maximize the Riemannian distance between the two subspaces spanned by the predicted POD bases on the left and right daughter nodes. Numerical examples are presented to comprehensively demonstrate the performance of the proposed method, and compare the proposed tree-based method to the existing interpolation method for POD basis and the use of global POD basis. The results show that the proposed tree-based method is capable of establishing the mapping between parameters and POD bases, and thus adapt ROMs for new parameters.
Biodata: Xiao Liu is an Assistant Professor at the Department of Industrial Engineering, University of Arkansas. Before that, he was a Research Staff Member (RSM) at IBM Thomas J. Watson Research Center, New York (2015~2017), and IBM Smarter Cities Research Collaboratory Singapore (2012~2015). From 2013 to 2016, he served as an Adjunct Assistant Professor at the Department of Industrial and Systems Engineering, National University of Singapore. Dr. Liu's research focuses on Physics-Informed and Domain-Aware Data-Driven Methodologies for various engineering applications, and has published on premium journals in the fields of Industrial Engineering and Applied Statistics, such as Journal of the American Statistical Association (JASA), Technometrics, IISE Transactions, Annals of Applied Statistics (AOAS), Journal of Quality Technology (JQT), RESS, etc. Dr. Liu's research is currently supported by the U.S. National Science Foundation (including NSF CAREER) and industry.
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