报告题目Theme： Machine Learning in Engineering Application and Trends
报 告 人Reporter： 莫华东博士 Dr. Huadong Mo
报告语言Language：中文 Chinese
报告时间Time： 2019.4.16（Tuesday） 14:30 pm
报告地点Location： 2003网站太阳集团214教室 Room 214 School of Management,Chang' n Campus
主 持 人Host: 李乘龙副教授 Associate Professor Chenglong Li
报告简介Abstract：
Machine-learning research at system engineering brings a powerful approach to solving problems & making decisions. A child born today will probably ride to middle school in a driverless car or bus, guided safely along its route by machine-learning algorithms. Machine learning is a branch of artificial intelligence in which computers are trained to learn from data to perform tasks on their own, whether detecting anomalies in a secure computer network, accurately predicting customer demand, or navigating an autonomous vehicle through traffic.
For system engineering, machine learning presents both opportunities and challenges, thought machine learning is not new. It is the technology that powers search engines as well as recommendation systems used by Facebook, Amazon, Netflix, eHarmony, and thousands of other sites. What’s new is the rapidly increasing number and scope of applications for machine learning, boosted by the availability of tremendous amounts of data, cheap data storage, advances in high-performance computing, and the development of increasingly sophisticated machine-learning algorithms. Applications run a diverse gamut from supply chains and logistics to computer vision and object recognition; from autonomous vehicles and natural language processing to health data analysis and manufacturing. From an industrial engineer’s perspective, machine learning is a powerful way to automate the optimization process in large, complex systems involving petabytes of data - a scale too large to be handled by traditional computation.
In this presentation, I will present basic ideas about machine learning and its successful applications. The selective research work about the predictive maintenance of wind turbine, short-term load forecast and the resilience of critical infrastructure system based on the SCADA data mining will be briefly introduced, where the results have been surprisingly rich.
报告人简介Introduction of Reporter：
Dr. Huadong Mo is currently a lecturer at School of Engineering and Information Technology, University of New South Wales (UNSW), Australia. Before joining UNSW, he was a postdoc at Department of Mechanical and Process Engineering, EHTZ, Switzerland from 2016 to 2019. He received his B.E. degree in automation from the University of Science and Technology of China, China in 2012; and his Ph.D. degree in systems engineering and engineering management from the City University of Hong Kong, Hong Kong in 2016. During his Ph.D., he has been a visiting research student at some universities and involved in a number of research projects. His research interests include risk modeling, performance prediction, and resilience increase of real-world systems, i.e. the distributed generation systems, networked and degraded control systems and critical infrastructure systems. He has also served as reviewer for several international journals and been awarded the ‘Outstanding Reviewer of Reliability Engineering & System Safety’ in 2015 and 2017, and the ‘European Journal of Operational Research’ in 2018.
