On October 25th, Nature Communications published online the latest research findings of Prof. Yuan Ye, Prof. Zhang Haitao from School of Artificial Intelligence and Automation, HUST, and Academician Ding Han from MSE, HUST. The research was mainly about the identification and modeling of interdisciplinary complicated Cyber Physical Systems, and the paper's name was "Data Driven Discovery of Cyber Physical Systems".
Cyber-physical systems (CPSs) embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, intelligent manufacture and medical monitoring. CPSs have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical components and cyber components and the interaction between them. This study proposes a general framework for reverse engineering CPSs directly from data. The method involves the identification of physical systems as well as the inference of transition logic. It has been applied successfully to a number of real-world examples ranging from mechanical and electrical systems to medical applications. The novel framework seeks to enable researchers to make predictions concerning the trajectory of CPSs based on the discovered model. Such information has been proven essential for the assessment of the performance of CPS, the design of failure-proof CPS and the creation of design guidelines for new CPSs.
This paper was the achievement of HUST's creative interdisciplinary key team, with School of Artificial Intelligence and Automation, HUST as the first unit and the State Key Lab of Digital Manufacturing Equipment and Technology as the first corresponding author unit. The research won the subsidiary of some project grants such as the Breeding Program of the Major Research Plan Tri-co Robots of NSFC (91748112) and so on, which reflected the latest research finding of inter-discipline of artificial intelligence, automation and mechanical engineering.
Please see the original article on: http://news.hust.edu.cn/info/1002/36622.htm
