4 papers
Asymmetric Adaptation-based Real-time Fault Diagnosis Under Transitional Operating Conditions
Hongshuo Zhao, Zeyi Liu, Xiao He
Data streams in real-world industrial scenarios often contain transitional operating conditions that are uncovered during offline training, leading to significant distribution shif…
Replay-guided Test-time Adaptation for Fault Diagnosis Under Unseen Operating Conditions
Yakun Wang, Pengyu Han, Zeyi Liu +3
In modern industrial systems, machinery frequently operates under dynamic environments with continuously varying loads and speeds. Consequently, deep learning-based fault diagnosis…
Dual-Head Physics-Informed Graph Decision Transformer for Distribution System Restoration
Hong Zhao, Jin Wei-Kocsis, Adel Heidari Akhijahani +1
Driven by recent advances in sensing and computing, deep reinforcement learning (DRL) technologies have shown great potential for addressing distribution system restoration (DSR) u…
Unsupervised Machine Learning for Detecting and Locating Human-Made Objects in 3D Point Cloud
Hong Zhao, Huyunting Huang, Tonglin Zhang +3
A 3D point cloud is an unstructured, sparse, and irregular dataset, typically collected by airborne LiDAR systems over a geological region. Laser pulses emitted from these systems…