A5083417541- High voltage insulation and dielectric phenomena
- Infrastructure Maintenance and Monitoring
- Power Transformer Diagnostics and Insulation
- Machine Fault Diagnosis Techniques
- Elevator Systems and Control
- Groundwater and Isotope Geochemistry
- Power System Reliability and Maintenance
- Network Security and Intrusion Detection
- Geoscience and Mining Technology
- Advanced Computational Techniques and Applications
- Hydrocarbon exploration and reservoir analysis
- Karst Systems and Hydrogeology
- Recycling and utilization of industrial and municipal waste in materials production
- Concrete and Cement Materials Research
- Anomaly Detection Techniques and Applications
- Geochemistry and Geologic Mapping
- Groundwater and Watershed Analysis
- Simulation and Modeling Applications
- Industrial Technology and Control Systems
- Electrical Fault Detection and Protection
- Methane Hydrates and Related Phenomena
- Magnesium Oxide Properties and Applications
- Neuroscience and Neural Engineering
- Remote Sensing and Land Use
- Geophysical Methods and Applications
State Key Laboratory of Electrical Insulation and Power Equipment
2020-2025
Xi'an Jiaotong University
2019-2025
Tianjin University of Science and Technology
2024-2025
Shanghai Jiao Tong University
2024
Beihang University
2024
National University of Singapore
2024
China University of Geosciences (Beijing)
2006-2024
China Automotive Technology and Research Center
2024
Yangtze University
2024
Ministry of Natural Resources
2024
Data-driven artificial intelligence methods, especially convolutional neural networks (CNNs), have achieved excellent performance in high-voltage circuit breaker (HVCB) fault diagnosis. However, CNN relies heavily on massive data. When the amount of data decreases, diagnosis drops severely. To settle these problems, a few-shot transfer learning (FSTL) with attention mechanism (AM) to realize mechanical HVCBs is proposed. First, one-dimensional AM used extract features HVCBs. The introduction...
Kernel methods are widely used in statistical learning for many fields, such as protein classification and image processing. We recently extend kernel to intrusion detection domain by introducing a new family of kernels suitable detection. These kernels, combined with an unsupervised method - one-class support vector machine, anomaly Our experiments show that the able achieve better accuracy rates than conventional detectors.
With the construction and promotion of Ubiquitous Power Internet Things (UPIoT), it is an increasingly urgent challenge to comprehensively improve recognition accuracy gasinsulated switchgear (GIS) partial discharge (PD), incorporate model into UPIoT intelligent terminals supported by edge computing in embedded systems.Therefore, this paper proposes a novel MobileNets convolutional neural network (MCNN) identify GIS PD patterns.We first construct pattern classification datasets means...
Brain-machine interface (BMI) can be used to control the robotic arm assist paralysis people for performing activities of daily living. However, it is still a complex task BMI users process objects grasping and lifting with arm. It hard achieve high efficiency accuracy even after extensive trainings. One important reason lacking sufficient feedback information user perform closed-loop control. In this study, we proposed method augmented reality (AR) guiding assistance provide enhanced visual...
Intelligent fault diagnosis methods, especially convolutional neural network (CNN), have made significant progress in gas-insulated switchgear (GIS) partial discharge (PD) diagnosis, which are attributable to two reasons: 1) the training and testing samples come from identical distribution; 2) there massive labeled data with PD information. However, owing specific operating conditions of GIS, collecting same distribution is difficult field conditions. With purpose resolving dilemma...
The advancement of Industry 4.0 and Industrial Internet Things (IIoT) has laid more emphasis on reducing the parameter amount storage space model in addition to automatic accurate fault diagnosis. In this case, paper proposes a lightweight convolutional neural network (LCNN) method for intelligent diagnosis bearing, which can largely satisfy need less as well high accuracy. First, depthiwise separable convolution is adopted, LCNN structure constructed through an inverse residual linear...
Agricultural vehicles are adopted to undertake farming tasks by traversing along crop rows in the field. Working quality depends significantly on driving skills of operator. Automatic guidance has been introduced into agriculture achieve high-accuracy path tracking during last decades, which contributes considerably straight-line navigation. The objective this research was develop an autonomous navigation controller that allowed movement autonomy for various agricultural vehicles. Three...
Abstract Deep-learning-driven methods have made great progress in the condition assessment of partial discharge (PD) which including diagnosis and location gas-insulated switchgear (GIS). However, these perform as two separate tasks ignore coupling relationship. In addition, all require obtaining sufficient samples to develop models, model becomes ineffective when there is a significant difference sample distribution. Therefore, we propose novel domain-alignment multitask learning network...