A5112429527- Advanced machining processes and optimization
- Advanced Machining and Optimization Techniques
- Remote-Sensing Image Classification
- Advanced Image Fusion Techniques
- Surface Treatment and Residual Stress
- Remote Sensing and LiDAR Applications
- Energy Load and Power Forecasting
- Image Processing Techniques and Applications
- Industrial Vision Systems and Defect Detection
- Remote Sensing and Land Use
- Advanced Fluorescence Microscopy Techniques
- Mineral Processing and Grinding
- Infrared Target Detection Methodologies
- Data Stream Mining Techniques
- Advanced Surface Polishing Techniques
- Image and Object Detection Techniques
- Cell Image Analysis Techniques
- Retinal Imaging and Analysis
- Forecasting Techniques and Applications
Air Force Engineering University
2024-2025
Agency for Science, Technology and Research
2024
Institute of High Performance Computing
2024
Huazhong University of Science and Technology
2019-2020
Wuhan National Laboratory for Optoelectronics
2020
Hunan University of Science and Technology
2016
ABSTRACT The coordinated deployment of multi‐sensor systems significantly enhances group target detection capabilities, yet persistent tracking remains challenging due to inherent limitations in single‐sensor field view (FoV) coverage. This paper proposes a novel labelled multi‐Bernoulli (LMB) filter for resolvable (RGT) under the centralised fusion (CF) framework, abbreviated as CF‐LMB‐RGT filter. proposed method introduces virtual leader kinematic model capture intra‐group motion...
Purpose This paper aims to provide a new drilling entry board for printed circuit (PCB) process, superior in heat dissipation, lubrication, water solubility and hole location accuracy, achieving an excellent process. Design/methodology/approach Using mixture of polyethylene glycol (PEG) water-soluble adhesives as hydrosoluble, endothermic lubricant resins aluminum foils baseplates, series coated aluminous boards (CABs) PCB was successfully prepared. The surface appearance the observed...
Abstract The classification methods for hyperspectral images (HSI) based on decision trees and convolutional neural networks have shown increasing advantages, but these often require a large number of labelled samples learning, which is difficult HSI, the interpretability network not high. Therefore, this paper proposes improved attention interpretable table learning (TabNet) named 3D TabNet HSI (3D‐TabNetHS) unsupervised (U3D‐TabNetHS). These use sequential to select appropriate...