arXiv subjects

Zhenguo Wang

Publications and source records attributed to Zhenguo Wang.

5 recordsLinked to original sources

arXiv2026

From AI Weather Prediction to Infrastructure Resilience: A Real-Time Correction-Downscaling Framework for Tropical Cyclone Impact Forecasting

This paper addresses a missing capability in infrastructure resilience: turning fast, global AI weather forecasts into asset-scale, actionable risk intelligence. We introduce the AI-based Correction-Downscaling Framework (ACDF), which combines real-time bias correction, terrain-informed downscaling, and fragility-based power transmission system risk assessment for tropical cyclone impacts. ACDF separates storm-scale bias correction from terrain-aware refinement, mitigating error propagation while restoring the sub-kilometer wind variability that governs structural loading. Tested on 11 typhoons affecting Zhejiang, China under leave-one-storm-out evaluation, ACDF produces 500 m wind fields over a province-scale domain, reduces station-scale wind-speed MAE by 38.8% relative to Pangu-Weather, and runs in approximately 25 s per 12-h cycle on a single GPU. In the Typhoon Hagupit case, ACDF reproduced observed high-wind tails, identified a coastal high-risk corridor, and flagged the transmission line that subsequently failed, demonstrating actionable guidance at tower and line scales. ACDF provides an end-to-end pathway from global AI weather forecasts to operational, impact-based early warning for critical infrastructure.

eess.SY↗

arXiv2026

From Forecast to Action: A Deep Learning Model for Predicting Power Outages During Tropical Cyclones

Power outages caused by tropical cyclones (TCs) pose serious risks to electric power systems and the communities they serve. Accurate, high-resolution outage forecasting is essential for enabling both proactive mitigation planning and real-time emergency response. This study introduces the SpatioTemporal Outage ForeCAST (STO-CAST) model, a deep learning framework developed for real-time, regional-scale outage prediction during TC events with high-resolution outputs in both space and time. STO-CAST integrates static environmental and infrastructure attributes with dynamic meteorological and outage sequences using gated recurrent units (GRUs) and fully connected layers, and is trained via a Leave-One-Storm-Out (LOSO) cross-validation strategy along with holdout grid experiments to demonstrate its preliminary generalization capability to unseen storms and grids. The model produces hourly outage forecasts at a 4 km * 4 km resolution and supports dual forecasting modes: short-term nowcasting with a 6-hour lead time via assimilation of real-time observations, and long-term forecasting with a 60-hour lead time based on evolving meteorological projections. A case study on Typhoon Muifa (2022) demonstrates STO-CAST's operational effectiveness, including error decomposition across model design, meteorological uncertainty, and observation gaps, while highlighting the value of real-time data assimilation and the model's capacity to identify evolving outage hotspots. STO-CAST offers a scalable, data-driven solution to support risk-informed emergency response and enhance power system resilience under intensifying TC threats.

eess.SY↗

arXiv2026

DST-Net: A Dual-Stream Transformer with Illumination-Independent Feature Guidance and Multi-Scale Spatial Convolution for Low-Light Image Enhancement

Low-light image enhancement aims to restore the visibility of images captured by visual sensors in dim environments by addressing their inherent signal degradations, such as luminance attenuation and structural corruption. Although numerous algorithms attempt to improve image quality, existing methods often cause a severe loss of intrinsic signal priors. To overcome these challenges, we propose a Dual-Stream Transformer Network (DST-Net) based on illumination-agnostic signal prior guidance and multi-scale spatial convolutions. First, to address the loss of critical signal features under low-light conditions, we design a feature extraction module. This module integrates Difference of Gaussians (DoG), LAB color space transformations, and VGG-16 for texture extraction, utilizing decoupled illumination-agnostic features as signal priors to continuously guide the enhancement process. Second, we construct a dual-stream interaction architecture. By employing a cross-modal attention mechanism, the network leverages the extracted priors to dynamically rectify the deteriorated signal representation of the enhanced image, ultimately achieving iterative enhancement through differentiable curve estimation. Furthermore, to overcome the inability of existing methods to preserve fine structures and textures, we propose a Multi-Scale Spatial Fusion Block (MSFB) featuring pseudo-3D and 3D gradient operator convolutions. This module integrates explicit gradient operators to recover high-frequency edges while capturing inter-channel spatial correlations via multi-scale spatial convolutions. Extensive evaluations and ablation studies demonstrate that DST-Net achieves superior performance in subjective visual quality and objective metrics. Specifically, our method achieves a PSNR of 25.64 dB on the LOL dataset. Subsequent validation on the LSRW dataset further confirms its robust cross-scene generalization.

cs.CV↗

arXiv2026

Quest2ROS2: A ROS 2 Framework for Bi-manual VR Teleoperation

Quest2ROS2 is an open-source ROS2 framework for bi-manual teleoperation designed to scale robot data collection. Extending Quest2ROS, it overcomes workspace limitations via relative motion-based control, calculating robot movement from VR controller pose changes to enable intuitive, pose-independent operation. The framework integrates essential usability and safety features, including real-time RViz visualization, streamlined gripper control, and a pause-and-reset function for smooth transitions. We detail a modular architecture that supports "Side-by-Side" and "Mirror" control modes to optimize operator experience across diverse platforms. Code is available at: https://github.com/Taokt/Quest2ROS2.

cs.RO↗

arXiv2025

Dependence of the critical temperature and disorder in holographic superconductors on superfluid density

Recent experiments strongly indicate deep connections between transports of strange metal and high $T_c$ superconductors. For instance, it is known that the dependence of the zero-temperature phase stiffness on the critical superconducting temperature becomes linear in underdoped materials. In this paper, we investigate relation meticulously between the phase stiffness and the critical superconducting temperature for the Gubser-Rocha holographic superconductor model in the probe limit. The superfluid density (or phase stiffness) can be extracted from the low-frequency dependence of the AC conductivity. More importantly, we find linear dependence of zero-temperature superfluid density on the critical superconducting temperature, which has a similarity to a recent experiment in a film cuprate. In addition, we also provide some approximate formulas for the critical temperatures and the AC conductivity.

hep-th↗