Changle Gu
Papers
1
Total Citations
2
H-Index
1
About
Changle Gu is a researcher advancing the integration of intelligent robotics and machine vision in critical infrastructure monitoring. His primary research areas include intelligent inspection systems, deep learning for defect detection, and automation in hydropower and energy facilities. Gu’s most notable contribution is the design of a machine vision–based inspection robot system that addresses longstanding challenges in pumped storage stations—specifically, the high costs and accuracy limitations of traditional manual crack and seepage detection. By constructing a convolutional neural network that integrates cross-entropy loss, his work enables automated, precise identification of structural anomalies, significantly improving safety and operational efficiency. Although his 2024 paper on intelligent inspection robots in hydropower stations has garnered 2 citations early in its lifecycle, it represents a practical innovation with strong potential for real-world deployment. Gu’s research bridges the gap between theoretical deep learning and applied industrial robotics, offering scalable solutions for aging infrastructure monitoring. His work is particularly relevant for students and engineers interested in smart grid technologies, predictive maintenance, and the role of AI in civil and energy systems.
Research Focus
Key Achievements
Top Papers
- 1