5 papers
Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling
Haoyu Yan, Shutong Ding, Jiebao Zhang +5
The rapid proliferation of Electric Vehicles (EVs) introduces significant spatio-temporal uncertainties into power grids, while Vehicle-to-Grid (V2G) technology offers critical fle…
A Predict-then-Schedule framework for Power Distribution Networks with AI Data Centers
Siqi Yan, Jiebao Zhang, Xi Yao +2
The surge of GPU-intensive workloads in artificial intelligence (AI) data centers drives massive energy demands, leading to soaring costs and significant stress on local power dist…
Unsupervised Learning for AC Optimal Power Flow with Fast Physics-Aware Layer
Jiebao Zhang, Haoyu Yan, Zhichao Sheng +4
Learning to solve the Alternating Current Optimal Power Flow (AC-OPF) problem by neural networks (NNs) is a promising approach in real-time applications. Existing methods to ensure…
LP-BFGS attack: An adversarial attack based on the Hessian with limited pixels
Jiebao Zhang, Wenhua Qian, Rencan Nie +2
Deep neural networks are vulnerable to adversarial attacks. Most -norm based white-box attacks craft perturbations by the gradient of models to the input. Since the computat…
Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual Information
Jiebao Zhang, Wenhua Qian, Rencan Nie +2
A counter-intuitive property of convolutional neural networks (CNNs) is their inherent susceptibility to adversarial examples, which severely hinders the application of CNNs in sec…