科研项目
(1) 国家自然科学基金委员会, 面上项目, 62173147, 数据驱动的复杂社会网络传播动力学分析与分布式 控制研究, 2022-01-01 至 2025-12-31, 58万元, 在研, 参与
(2) 国家自然科学基金委员会, 面上项目, 62173142, 智能攻击下的信息物理系统的检测机制与信息安全 估计, 2022-01-01 至 2025-12-31, 58万元, 在研, 参与
(3) 国家自然科学基金委员会, 面上项目, 62073137, 真核生物细胞及其受病毒感染后基因表达的随机动态建模研究, 2021-01-01 至 2024-12-31, 59万元, 资助期满, 参与
(4) 国家科技部，重点研发计划项目课题，2018YFC0809302，基于多领域数据融合的危险化学品安全知识图谱构建技术，2018-01-01 至 2020-12-31，443万元，在研, 参与
(5) 国家自然科学基金委员会，人工智能基础研究应急管理项目，61751305，基于人机合作互学习的炼油生产风险预警与智能决策，2018-01-01 至 2020-12-31，262万元，在研, 参与
科研成果
主要论文：
1.Huang J, Ho D W C, Li F, et al. Secure remote state estimation against linear man-in-the-middle attacks using watermarking[J]. Automatica, 2020, 121: 109182.
2.Huang J, Yang W, Ho D W C, et al. Security analysis of distributed consensus filtering under replay attacks[J]. IEEE Transactions on Cybernetics, 2023, 54(6): 3526-3539.
3.Huang J, Tang Y, Yang W, et al. Resilient consensus-based distributed filtering: Convergence analysis under stealthy attacks[J]. IEEE Transactions on Industrial Informatics, 2019, 16(7): 4878-4888.
4.Huang J, Tang Y. A Coding-based Differentiation Approach for False Data Injection Attacks and Sensor Faults in Networked Control Systems[C]//2024 43rd Chinese Control Conference (CCC). IEEE, 2024: 5172-5177.
5.黄家豪,王冰,唐漾.化工安全知识图谱的本体设计与基于规则推理的知识补全[J/OL].控制工程,1-9[2025-03-13].https://doi.org/10.14107/j.cnki.kzgc.20240077.
6.Wang D, Huang J, Tang Y, et al. A watermarking strategy against linear deception attacks on remote state estimation under K–L divergence[J]. IEEE Transactions on Industrial Informatics, 2020, 17(5): 3273-3281.
7.Chen K, Xue M, Huang J, et al. Leader selection in impulsive multi-agent systems with switching topologies[J]. IEEE Transactions on Cybernetics, 2024.
8.Zhang W, Mao S, Huang J, et al. Data-driven resilient control for linear discrete-time multi-agent networks under unconfined cyber-attacks[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2020, 68(2): 776-785.
9.Wang J, Huang J, Tang Y. Swarm intelligence capture-the-flag game with imperfect information based on deep reinforcement learning[J]. Sci. Sin. Technol, 2021.
10.Zhou Z, Huang J, Xu J, et al. Two-phase jointly optimal strategies and winning regions of the capture-the-flag game[C]//IECON 2021–47th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2021: 1-6.
