Research Output per year
Fingerprint Dive into the research topics where Minghui Zhu is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
Stochastic systems
Engineering & Materials Science
State estimation
Engineering & Materials Science
Unknown Inputs
Mathematics
Motion planning
Engineering & Materials Science
State Estimation
Mathematics
Reinforcement learning
Engineering & Materials Science
Attack
Mathematics
Discrete-time Systems
Mathematics
Network
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Research Output 2006 2020
Simultaneous input and state estimation for stochastic nonlinear systems with additive unknown inputs
Kim, H., Guo, P., Zhu, M. & Liu, P., Jan 1 2020, In : Automatica. 111, 108588.Research output: Contribution to journal › Article
State estimation
Error analysis
Nonlinear systems
Stochastic systems
Covariance matrix
A control-theoretic perspective on cyber-physical privacy: Where data privacy meets dynamic systems
Lu, Y. & Zhu, M., Jan 1 2019, In : Annual Reviews in Control. 47, p. 423-440 18 p.Research output: Contribution to journal › Review article
Data privacy
Dynamical systems
Cyber Physical System
MTD techniques for memory protection against zero-day attacks
chen, P., Hu, Z., Xu, J., Zhu, M., Erbacher, R., Jajodia, S. & Liu, P., Jan 1 2019, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, p. 129-155 27 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11830 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Chapter
Attack
Data storage equipment
Zero
Methodology
Review
1
Citation
(Scopus)
On convergence rates of game theoretic reinforcement learning algorithms
Hu, Z., Zhu, M., Chen, P. & Liu, P., Jun 2019, In : Automatica. 104, p. 90-101 12 p.Research output: Contribution to journal › Article
Reinforcement learning
Learning algorithms
Reinforcement learning for adaptive cyber defense against zero-day attacks
Hu, Z., Chen, P., Zhu, M. & Liu, P., Jan 1 2019, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, p. 54-93 40 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11830 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Chapter
Reinforcement learning
Reinforcement Learning
Attack
Zero
Control theory