arXiv subjects

Chen Chao

Publications and source records attributed to Chen Chao.

4 recordsLinked to original sources

arXiv2026

Dynamic Resilience Assessment of Power Systems With Data Center Load Events Using Physics-Informed Neural Networks

Large data center loads introduce new resilience challenges to power systems because their disconnection and staged reconnection can induce fast voltage and frequency dynamics that are not captured by static service-status or energy-based metrics. This paper proposes a utility-side, physics-informed resilience assessment framework that evaluates these events using only grid-side dynamic models and observable post-disturbance trajectories, without requiring detailed internal data center models. An unsupervised differential algebraic equation-physics informed neural network (DAE-PINN) based on an implicit backward Euler residual is developed to jointly predict dynamic and algebraic states, enabling repeated post-disturbance trajectory evaluation while enforcing network algebraic consistency. Normalized multi-phase resilience metrics are then used to quantify disturbance, degraded-state, and restoration-period impacts and to screen data center reconnection timing and load-ramping strategies under security constraints. Case studies on a modified IEEE 33-bus feeder show that the proposed DAE-PINN accurately tracks numerical DAE solutions and substantially reduces computation time in repeated restoration screening. The proposed metrics distinguish the effects of disturbance size, data center location, and reconnection strategy, revealing the trade-off between restoration speed and transient resilience loss.

eess.SY↗

arXiv2025

Cyber Resilience Assessment of Unbalanced Distribution System Restoration under Sparse Load Forecasting Attacks

System restoration is critical for power-system resilience, but its growing reliance on artificial intelligence (AI)-based load forecasting creates a cyber-physical vulnerability in the restoration decision loop. Manipulated forecasts can cause infeasible restoration schedules, insufficient inverter-based-resource ramping margins, and unsuccessful recovery of de-energized segments, yet the resilience of restoration processes to such attacks remains largely unexplored. This paper evaluates restoration vulnerability at the system level rather than only measuring forecasting error. A gradient-based sparse perturbation method is developed as a stress-testing tool to identify influential forecasting inputs. We further create a restoration-aware validation framework that embeds these compromised forecasts into a sequential restoration model and evaluates operational feasibility using an unbalanced three-phase optimal power flow formulation. Case studies on a modified IEEE 123-bus feeder show that sparse input perturbations can substantially increase forecasting error and make selected microgrid restoration stages infeasible. The results reveal system-level failures caused by active-power-balance infeasibility and power ramping violations, which can prevent the restoration of critical loads. These findings provide actionable insights for designing cybersecurity-aware restoration planning frameworks.

eess.SY↗

arXiv2024

CTSM: Combining Trait and State Emotions for Empathetic Response Model

Empathetic response generation endeavors to empower dialogue systems to perceive speakers' emotions and generate empathetic responses accordingly. Psychological research demonstrates that emotion, as an essential factor in empathy, encompasses trait emotions, which are static and context-independent, and state emotions, which are dynamic and context-dependent. However, previous studies treat them in isolation, leading to insufficient emotional perception of the context, and subsequently, less effective empathetic expression. To address this problem, we propose Combining Trait and State emotions for Empathetic Response Model (CTSM). Specifically, to sufficiently perceive emotions in dialogue, we first construct and encode trait and state emotion embeddings, and then we further enhance emotional perception capability through an emotion guidance module that guides emotion representation. In addition, we propose a cross-contrastive learning decoder to enhance the model's empathetic expression capability by aligning trait and state emotions between generated responses and contexts. Both automatic and manual evaluation results demonstrate that CTSM outperforms state-of-the-art baselines and can generate more empathetic responses. Our code is available at https://github.com/wangyufeng-empty/CTSM

cs.CL↗

arXiv2021

Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projection Matching

Learning to generate 3D point clouds without 3D supervision is an important but challenging problem. Current solutions leverage various differentiable renderers to project the generated 3D point clouds onto a 2D image plane, and train deep neural networks using the per-pixel difference with 2D ground truth images. However, these solutions are still struggling to fully recover fine structures of 3D shapes, such as thin tubes or planes. To resolve this issue, we propose an unsupervised approach for 3D point cloud generation with fine structures. Specifically, we cast 3D point cloud learning as a 2D projection matching problem. Rather than using entire 2D silhouette images as a regular pixel supervision, we introduce structure adaptive sampling to randomly sample 2D points within the silhouettes as an irregular point supervision, which alleviates the consistency issue of sampling from different view angles. Our method pushes the neural network to generate a 3D point cloud whose 2D projections match the irregular point supervision from different view angles. Our 2D projection matching approach enables the neural network to learn more accurate structure information than using the per-pixel difference, especially for fine and thin 3D structures. Our method can recover fine 3D structures from 2D silhouette images at different resolutions, and is robust to different sampling methods and point number in irregular point supervision. Our method outperforms others under widely used benchmarks. Our code, data and models are available at https://github.com/chenchao15/2D\_projection\_matching.

cs.CV↗