He, Haibo

Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning

Wan, Z., Li, H., He, H., & Prokhorov, D. (2019) · IEEE Transactions on Smart Grid

Electric Vehicles and InfrastructureSmart Grid Energy Management

Cited by 548

Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities

Tang, B., Chen, Z., Hefferman, G., Pei, S., Wei, T., He, H., & Yang, Q. (2017) · IEEE Transactions on Industrial Informatics

IoT and Edge/Fog ComputingSmart Cities and Technologies

Cited by 353

Wind Turbine Fault Detection Using a Denoising Autoencoder With Temporal Information

Jiang, G., Xie, P., He, H., & Yan, J. (2018) · IEEE/ASME Transactions on Mechatronics

Machine Fault Diagnosis TechniquesFault Detection and Control Systems

Cited by 288

A hierarchical deep domain adaptation approach for fault diagnosis of power plant thermal system

Wang, X., He, H., & Li, L. (2019) · IEEE Transactions on Industrial Informatics

Fault Detection and Control SystemsMachine Fault Diagnosis Techniques

Cited by 185

Learning Race from Face: A Survey

Fu, S., He, H., & Hou, Z.-G. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Face recognition and analysisFace and Expression Recognition

Cited by 178

Smart Grid Anomaly Detection using a Deep Learning Digital Twin

Danilczyk, W., Sun, Y. L., & He, H. (2021) · 2020 52nd North American Power Symposium (NAPS)

Power System Optimization and StabilityPower Systems Fault Detection

Cited by 89