175 citations · 225 across the 16 of their papers we have counts for
16 papers
Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Framework
Xiaolu Chen, Chenghao Huang, Yanru Zhang +1
The wide adoption of residential photovoltaic (PV) systems introduces new challenges for generation fraud detection (FD). Unlike traditional electricity theft detection, which focu…
Smart Energy Guardian: A Hybrid Deep Learning Model for Detecting Fraudulent PV Generation
Xiaolu Chen, Chenghao Huang, Yanru Zhang +1
With the proliferation of smart grids, smart cities face growing challenges due to cyber-attacks and sophisticated electricity theft behaviors, particularly in residential photovol…
Season-Independent PV Disaggregation Using Multi-Scale Net Load Temporal Feature Extraction and Weather Factor Fusion
Xiaolu Chen, Chenghao Huang, Yanru Zhang +1
With the advancement of energy Internet and energy system integration, the increasing adoption of distributed photovoltaic (PV) systems presents new challenges on smart monitoring…
Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity
Xiaolu Chen, Chenghao Huang, Yanru Zhang +1
The rapid expansion of distributed photovoltaic (PV) installations worldwide, many being behind-the-meter systems, has significantly challenged energy management and grid operation…
Semantic Communication based on Large Language Model for Underwater Image Transmission
Weilong Chen, Wenxuan Xu, Haoran Chen +4
Underwater communication is essential for environmental monitoring, marine biology research, and underwater exploration. Traditional underwater communication faces limitations like…
FedCoSR: Personalized Federated Learning with Contrastive Shareable Representations for Label Heterogeneity in Non-IID Data
Chenghao Huang, Xiaolu Chen, Yanru Zhang +1
Heterogeneity arising from label distribution skew and data scarcity can cause inaccuracy and unfairness in intelligent communication applications that heavily rely on distributed…
Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets
Jinhao Li, Changlong Wang, Yanru Zhang +1
The battery energy storage system (BESS) has immense potential for enhancing grid reliability and security through its participation in the electricity market. BESS often seeks var…
Deep Reinforcement Learning for Voltage Control and Renewable Accommodation Using Spatial-Temporal Graph Information
Jinhao Li, Ruichang Zhang, Hao Wang +3
Renewable energy resources (RERs) have been increasingly integrated into distribution networks (DNs) for decarbonization. However, the variable nature of RERs introduces uncertaint…
PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Model
Chenghao Huang, Yanbo Cao, Yinlong Wen +2
Poker, also known as Texas Hold'em, has always been a typical research target within imperfect information games (IIGs). IIGs have long served as a measure of artificial intelligen…
FedTADBench: Federated Time-Series Anomaly Detection Benchmark
Fanxing Liu, Cheng Zeng, Le Zhang +5
Time series anomaly detection strives to uncover potential abnormal behaviors and patterns from temporal data, and has fundamental significance in diverse application scenarios. Co…
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets
Chenghao Huang, Weilong Chen, Shengrong Bu +1
Short-term load forecasting (STLF) plays a significant role in the operation of electricity trading markets. Considering the growing concern of data privacy, federated learning (FL…
DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning
Chenghao Huang, Weilong Chen, Yuxi Chen +2
In federated learning (FL), model aggregation has been widely adopted for data privacy. In recent years, assigning different weights to local models has been used to alleviate the…
Protum: A New Method For Prompt Tuning Based on "[MASK]"
Pan He, Yuxi Chen, Yan Wang +1
Recently, prompt tuning \cite{lester2021power} has gradually become a new paradigm for NLP, which only depends on the representation of the words by freezing the parameters of pre-…
Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information
Jinhao Li, Ruichang Zhang, Hao Wang +3
Renewable energy resources (RERs) have been increasingly integrated into large-scale distributed power systems. Considering uncertainties and voltage fluctuation issues introduced…
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection
Yingjie Zhou, Xucheng Song, Yanru Zhang +3
Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data. Recent works build deep neural networks…
Deep Anomaly Detection in Packet Payload
Jiaxin Liu, Xucheng Song, Yingjie Zhou +4
With the widespread adoption of cloud services, especially the extensive deployment of plenty of Web applications, it is important and challenging to detect anomalies from the pack…