Dr. Yan Yang, ProfessorSchool of Information Science and Technology, Southwest Jiaotong University, China
Speech Title: Data-driven Train Condition Recognition
Abstract: Real time monitoring of the running status of train and detecting their hidden failures accurately have great significance. Train bogie is an important part to guarantee the safe operation of High Speed Train (HST) and the comfort of passengers. The main techniques for recognizing conditions of HST are to collect the vibration signals by mounting sensors, analyze data features and build fault diagnosis model. Deep learning, ensemble learning and multi-view learning have attracted considerable attention in recent years. In this talk, I will discuss train condition recognition with Deep Belief Networks (DBNs), Multi-view Clustering Ensemble, Multi-view Classification Ensemble, and etc.
Biography: Dr. Yan Yang is currently Professor and vice dean of Information Science and Technology, Southwest Jiaotong University. She worked as a visiting scholar at the Center of Pattern Analysis and Machine Intelligence (CPAMI) in Waterloo University of Canada for one and half year. She is an Academic and Technical Leader of Sichuan Province. Her research interests include artificial intelligence, big data analysis and mining, ensemble learning and multi-view learning, cloud computing and service. Prof. Yang has participated in more than 10 high-level projects recently. And have taken charge of three programs supported by the National Natural Science Foundation of China (NSFC), and one Project of National Science and Technology Support Program. She has authored and co-authored over 200 papers in journals and international conference proceedings. She also serves as the Vice Chair of ACM Chengdu Chapter, a distinguished member of CCF, a senior member of CAAI, a member of IEEE, ACM, ACM SIGKDD, Vice-Chairman General of Sichuan Computer Society, and Standing Director of Sichuan Artificial Intelligence Alliance.