Inside the lab.

About

I am a Ph.D. student in the School of Electrical & Computer Engineering at the University of Georgia, in the Intelligent Power Electronics and Electric Machine Lab (IPEM Lab). My research lies at the intersection of power electronics, electric drives, and machine learning, with a particular focus on cyber-physical security and anomaly detection of networked electric drives.

My work combines physics-guided modeling, contrastive learning, and real-time experiments on hardware testbeds to detect and mitigate cyber-attacks and faults in electric machine drive systems.

News

• 2026: Our paper on physics-guided contrastive learning for cyber-attack detection in networked electric drives is accepted for APEC 2026 (poster presentation).APEC 2026
• 2025: Our work on low-intensity cyber-attack detection in electric drives is accepted by IEEE Transactions on Power Electronics (TPEL).TPEL
• 2025: Demonstration of real-world cyber security for networked electric drives appears in IEEE JESTPE.JESTPE

Selected Publications

• H. Yang and J. Ye, “Detection of Cyber Attacks in Networked Electric Drives via Physics-Guided Contrastive Learning with Few-Shot Labels,” in Proc. IEEE Applied Power Electronics Conference and Exposition (APEC), San Antonio, TX, USA, 2026. (Poster)
• A. Najar, H. Yang, and J. Ye, “A Data-Driven Algorithm for Estimating Battery State of Charge via Smart Plug-Based PCC Measurements,” in Proc. IEEE Applied Power Electronics Conference and Exposition (APEC), San Antonio, TX, USA, 2026.
• S. Coshatt, H. Yang, S. Wu, J. Ye, P. Ma, and W. Song, “SensorAI: A Machine Learning Framework for Sensor Data,” Sensors, vol. 25, no. 19, p. 6223, 2025.
• H. Yang and J. Ye, “Detecting Low-Intensity Cyber Attacks in Electric Drives via Contrastive Few-Shot Learning,” IEEE Transactions on Power Electronics, 2025.
• H. Yang et al., “Real-World Cyber Security Demonstration for Networked Electric Drives,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 13, no. 4, pp. 4659–4668, Aug. 2025.
• Y. Song, H. Xiang, Z. Zeng, J. Chen, Y. Zhang, Z. F. Pitafi, H. Yang, Q. Lu, X. Zhang, B. G. Phillips, F. Dou, and W. Song, “Multi-granularity Supervised Contrastive Learning with Online Adaptation for Contactless In-bed Posture Classification,” Proc. ACM IMWUT, Article 48, June 2025.
• S. Wu, H. Yang, J. Ye, and others, “Online Adaptive Anomaly Detection in Networked Electrical Machines by Adaptive Enveloped Singular Spectrum Transformation,” IEEE Internet of Things Journal, vol. 12, no. 6, pp. 6457–6464, March 2025.
• B. Yang, H. Yang, and J. Ye, “A Four-layer Cyber-physical Security Model for Electric Machine Drives considering Control Information Flow,” IEEE Journal of Emerging and Selected Topics in Power Electronics, 2024.
• K. Hu, H. Yang, and J. Ye, “Dual Model Predictive Torque Control Scheme for Online Torque Ripple Reduction in Switched Reluctance Machines,” in IEEE Energy Conversion Congress and Exposition (ECCE), Nashville, TN, USA, 2023.
• J. Zhang, J. Ye, W. Song, J. Lian, D. Zhao, and H. Yang, “Hybrid Cyber-attack Detection in Photovoltaic Farms,” in IEEE Energy Conversion Congress and Exposition (ECCE), Nashville, TN, USA, 2023.

Projects

• Physics-Guided Contrastive Learning for Cyber-Attack Detection – Contrastive few-shot framework combining simulation and experimental data for detecting subtle cyber-attacks in electric drives.
• Smart Plug–Based PCC Monitoring – Data-driven methods for estimating battery SoC and system anomalies from PCC measurements using low-cost smart-plug sensors.

Contact

For collaboration or questions, feel free to email me at zy1576544643@gmail.com.