5 papers
Cross-View Time-Frequency Diffusion for Extreme-Aware Time-Series Generation
Lin Jiang, Rongchao Xu, Guang Wang
Generating realistic time-series requires faithfully reproducing rare but high-impact extreme events. Existing approaches often rely on specialized representations or extreme-focus…
SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation
Lin Jiang, Dahai Yu, Ravikumar Gelli +1
Fine-grained energy consumption data are essential for applications such as demand forecasting, demand response planning, and grid reliability assessment. However, access to such d…
E4GEN: Event-level Explainable Extreme-Enhanced Time-series Generation
Lin Jiang, Dahai Yu, Ximiao Li +1
Generating realistic time series is essential for scientific research and real-world applications. However, existing methods often emphasize overall distributional fidelity while f…
EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction
Dahai Yu, Rongchao Xu, Lin Jiang +1
Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning. Although advanced machine learning methods have…
UrbanHuRo: A Two-Layer Human-Robot Collaboration Framework for the Joint Optimization of Heterogeneous Urban Services
Tonmoy Dey, Lin Jiang, Zheng Dong +1
In the vision of smart cities, technologies are being developed to enhance the efficiency of urban services and improve residents' quality of life. However, most existing research…