Visible to the public Biblio

Filters: Author is Li, Jie  [Clear All Filters]
2023-02-03
Chen, Duanyun, Chen, Zewen, Li, Jie, Liu, Jidong.  2022.  Vulnerability analysis of Cyber-physical power system based on Analytic Hierarchy Process. 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). 10:2024–2028.
In recent years, the blackout accident shows that the cause of power failure is not only in the power network, but also in the cyber network. Aiming at the problem of cyber network fault Cyber-physical power systems, combined with the structure and functional attributes of cyber network, the comprehensive criticality of information node is defined. By evaluating the vulnerability of ieee39 node system, it is found that the fault of high comprehensive criticality information node will cause greater load loss to the system. The simulation results show that the comprehensive criticality index can effectively identify the key nodes of the cyber network.
ISSN: 2693-2865
2022-07-29
Li, Xianxian, Fu, Xuemei, Yu, Feng, Shi, Zhenkui, Li, Jie, Yang, Junhao.  2021.  A Private Statistic Query Scheme for Encrypted Electronic Medical Record System. 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD). :1033—1039.
In this paper, we propose a scheme that supports statistic query and authorized access control on an Encrypted Electronic Medical Records Databases(EMDB). Different from other schemes, it is based on Differential-Privacy(DP), which can protect the privacy of patients. By deploying an improved Multi-Authority Attribute-Based Encryption(MA-ABE) scheme, all authorities can distribute their search capability to clients under different authorities without additional negotiations. To our best knowledge, there are few studies on statistical queries on encrypted data. In this work, we consider that support differentially-private statistical queries. To improve search efficiency, we leverage the Bloom Filter(BF) to judge whether the keywords queried by users exists. Finally, we use experiments to verify and evaluate the feasibility of our proposed scheme.
2022-04-18
Li, Jie, Liu, Hui, Zhang, Yinbao, Su, Guojie, Wang, Zezhong.  2021.  Artificial Intelligence Assistant Decision-Making Method for Main Amp; Distribution Power Grid Integration Based on Deep Deterministic Network. 2021 IEEE 4th International Electrical and Energy Conference (CIEEC). :1–5.
This paper studies the technology of generating DDPG (deep deterministic policy gradient) by using the deep dual network and experience pool network structure, and puts forward the sampling strategy gradient algorithm to randomly select actions according to the learned strategies (action distribution) in the continuous action space, based on the dispatching control system of the power dispatching control center of a super city power grid, According to the actual characteristics and operation needs of urban power grid, The developed refined artificial intelligence on-line security analysis and emergency response plan intelligent generation function realize the emergency response auxiliary decision-making intelligent generation function. According to the hidden danger of overload and overload found in the online safety analysis, the relevant load lines of the equipment are searched automatically. Through the topology automatic analysis, the load transfer mode is searched to eliminate or reduce the overload or overload of the equipment. For a variety of load transfer modes, the evaluation index of the scheme is established, and the optimal load transfer mode is intelligently selected. Based on the D5000 system of Metropolitan power grid, a multi-objective and multi resource coordinated security risk decision-making assistant system is implemented, which provides integrated security early warning and decision support for the main network and distribution network of city power grid. The intelligent level of power grid dispatching management and dispatching operation is improved. The state reality network can analyze the joint state observations from the action reality network, and the state estimation network uses the actor action as the input. In the continuous action space task, DDPG is better than dqn and its convergence speed is faster.