Analysis of co-authorship network and the correlation between academic performance and social network measures

This project conducted link analysis and graph cluster analysis to analyze the co-authorship network of 166 researchers, mainly from three top universities in Shanghai, China. The publication data of researchers in the area of social science between 2014 and 2016 were collected from Scopus, and the g index was calculated as their performance indicator. For this project, the centrality measures, the efficiency of the egocentric network were calculated as well as authorities and hubs were identified in the link analysis. In addition, clustering algorithms based on betweenness centrality were used to conduct the graph cluster analysis. Finally, in order to identify productive researchers, this project employed the Spearman correlation test to analyze the correlation between a researcher's performance and social network measures. Results from this test indicate that except for closeness centrality and degree centrality, the correlation between g-index and betweenness centrality, eigenvector centrality and efficiency is significant

Safety-Enhanced Self-Learning for Optimal Power Converter Control

Data-driven learning-based control methods such as reinforcement learning (RL) have become increasingly popular with recent proliferation of the machine learning paradigm. These methods address the parameter sensitiveness and unmodeled dynamics in model-based controllers, such as finite control-set model predictive control. RL agents are typically utilized in simulation environments, where they are allowed to explore multiple "unsafe" actions during the learning process. However, this type of learning is not applicable to online self-learning of controllers in physical power converters, because unsafe actions would damage them. To address this, this letter proposes a safe online RL-based control framework to autonomously find the optimal switching strategy for the power converters, while ensuring system safety during the entire self-learning process. The proposed safe online RL-based control is validated in a practical testbed on a two-level voltage source converter system, and the results confirm the effectiveness of the proposed method