Yixue Chen
Papers
1
Total Citations
3
H-Index
1
About
Yixue Chen is a researcher at the forefront of intelligent structural health monitoring and automated repair systems, with a particular focus on integrating computer vision and bio-inspired optimization. Their most notable contribution is the development of a hybrid bee colony algorithm for crack repair trajectory planning, which leverages a focal attention-guided lightweight segmentation model. This work, published in 2025 and already garnering 3 citations, addresses the critical challenge of efficiently and accurately planning repair paths for surface cracks in infrastructure. By combining swarm intelligence with attention-driven image segmentation, Chen’s approach significantly reduces computational overhead while maintaining high precision, enabling real-time, autonomous repair in constrained environments. This innovation has direct implications for extending the lifespan of bridges, pipelines, and aerospace components. Chen’s research bridges the gap between deep learning and optimization, offering a scalable solution for predictive maintenance. Their work is recognized for its practical impact, and they continue to explore hybrid algorithms that blend nature-inspired heuristics with neural network architectures, positioning them as a rising voice in the field of intelligent infrastructure management.
Research Focus
Key Achievements
Top Papers
- 1