XueFei Li
Papers
1
Total Citations
24
H-Index
1
About
XueFei Li is a leading researcher in the application of deep learning to industrial automation and material handling systems. His work focuses on the intelligent monitoring and optimization of bulk material transport, particularly in mining and port logistics. Li’s most notable contribution is his pioneering study on deep learning-based prediction of piled-up status and payload distribution of bulk material (2020, 24 citations), which addresses critical challenges in real-time load monitoring and safety. This work has been instrumental in advancing smart conveyor belt systems, enabling more efficient and safer operations in heavy industries. By integrating computer vision and neural networks, Li has provided a robust framework for automating the assessment of material flow, reducing human error and operational downtime. His research bridges the gap between theoretical AI models and practical engineering applications, earning recognition for its direct impact on industrial productivity. With a growing citation record, Li continues to influence the fields of intelligent manufacturing and cyber-physical systems, making his work essential reading for engineers and researchers developing next-generation automation solutions.
Research Focus
Key Achievements
Top Papers
- 1