Authors Ze Dong , Xiaohu Zhang , Shiyi Cai , Yuhang Yang , Wei Jiang , Yaqi Song , Wenqing Zhao , Dongyang Zhang Affiliations Abstract: Non-intrusive load monitoring (NILM) is a great significance for energy conservation, cost reduction, and environmental protection. It helps with better energy management and planning […]
Real-time NILM: A lightweight and low power approach Find more»
Authors Ya Shen Affiliations Abstract: Graph node anomaly detection aims to identify anomalous nodes that deviate from regular patterns in complex networks, and the technique has important application value in the fields of cyber security, biomedicine, and social network analysis. Compared with traditional tabular data, the irregular topology of graph
Research on graph anomaly detection method based on hyperbolic space Find more»
Authors Lei Hua Affiliations Abstract: Fault detection in power grids is a core function of smart grid systems. The increasing frequency of extreme environmental events and the continuous evolution of power consumption patterns pose long-term and complex challenges to grid stability, consequently affecting industrial operations and daily life. Developing efficient
Research on traveling wave fault detection of heterogeneous computing platform Find more»
Authors Ren Guan Affiliations Abstract: This project is designed and implemented based on the field programmable gate array (FPGA) and an embedded data acquisition system for traveling wave fault detection. The entire sampling system is divided into hardware and software parts. The hardware part is based on the Xilinx XC7Z020CLG400
Authors Dongyang Zhang1,2, Xiaohu Zhang1 ,Lei Hua1 ,Jian Di1 , Wenqing Zhao1,3 , Yumei Ma1 Affiliations Abstract: With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning
Dataset: Transient dataset of household appliances with Intensive switching events Find more»
Abstract: Aiming at the existing blind image quality evaluation algorithms with low performance in the face of real distorted images, this paper proposes the multi-level feature fusion and semantic information enhancement for NR (MFFSE-NR), which combines multi-layer feature fusion and semantic enhancement for NR. The local and global distortion features
Blind Image Quality Evaluation with Multi-Layer Feature Fusion and Semantic Enhancement Find more»
Abstract: In order to reduce the impact and improve the detection efficiency of transformer oil leakage inspection images, a transformer oil leakage detection model based on depth-separable void convolution pyramid is proposed. Firstly, the ordinary convolutional blocks in the cavity pyramid are modified to depth-separable convolutional blocks, so as to
Transformer Oil Leakage Detection Based on Depth Separable Void Convolutional Pyramid Find more»
Abstract: Electric vehicle charging is an important constituent of electric vehicles to provide power, and giving electric vehicles the most convenient charging path planning service is an indispensable part of the development of electric vehicles. Considering the conditions of transportation network, distribution network and electric vehicle itself as the hindering clay
Electric Vehicle Charging Path Planning Study Find more»