A5080879381- Machine Learning in Materials Science
- Infrastructure Maintenance and Monitoring
- Reliability and Maintenance Optimization
- Advanced Battery Technologies Research
- Complex Network Analysis Techniques
- Fault Detection and Control Systems
- Engineering Diagnostics and Reliability
- Evaluation and Optimization Models
- Advancements in Battery Materials
- Advanced Graph Neural Networks
- Probabilistic and Robust Engineering Design
- Industrial Vision Systems and Defect Detection
Chongqing University
2023-2024
The fault diagnosis of the tail-drive helicopter is a crucial task for system operation and maintenance. Recently, graph convolution network (GCN) has been focus in its powerful representational ability relationship mining. However, with difficulty obtaining node edge information high-order domain, stable performance long-range message-passing process deep GCN unknown limits application diagnosis. To address these issues, multi-grained hierarchical message convolutional (MHGCN) proposed to...
Abstract The microstructure of materials has a great influence on the macroscopic mechanical properties materials, and relationship between them is significance to design materials. With development artificial intelligence deep learning, many researchers have used convolutional neural networks (CNN) correlate microscopic images with material properties, achieved good results. However, for different types performance prediction tasks datasets sizes, need special CNN network structure,...