Xinhao Wei

Control and Systems Engineering · 2022

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 15 citations into it
• Computer Networks and Communications — 7 citations into it

Whom Xinhao Wei trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Pascal Paillier
2. Yanpeng Guan
3. Youbiao He
4. M. I. Ray D. Zimmerman
5. James Jian Qiao Yu
6. Kebina Manandhar
7. Yi Liu
8. Xiaohua Ge
9. Yao Liu
10. Gihan J. Mendis

Papers from Xinhao Wei's perspective

Ordered by how strongly the researchers Xinhao Wei trusts endorse each paper. Same corpus, a different order for every researcher.

1. False data injection attacks against state estimation in electric power grids (Conference on Computer and Communications Security)
2. Smart Grid Technologies: Communication Technologies and Standards (IEEE Transactions on Industrial Informatics)
3. Distributed Attack Detection and Secure Estimation of Networked Cyber-Physical Systems Against False Data Injection Attacks and Jamming Attacks (IEEE Transactions on Signal and Information Processing over Networks)
4. Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter (IEEE Transactions on Control of Network Systems)
5. False Data Injection on State Estimation in Power Systems—Attacks, Impacts, and Defense: A Survey (IEEE Transactions on Industrial Informatics)
6. Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism
7. Power system state estimation : theory and implementation
8. MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education (IEEE Transactions on Power Systems)
9. A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids (IEEE Transactions on Smart Grid)
10. Smart grid communication and information technologies in the perspective of Industry 4.0: Opportunities and challenges (Computer Science Review)

Their papers

• Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach (2022) — 303 citations