Papers
3
Total Citations
21
H-Index
2
About
Dr. Yuanjun Guo is a researcher whose work bridges optimization algorithms, intelligent robotics, and industrial inspection. Her key research areas include metaheuristic optimization, reinforcement learning for motion planning, and deep learning for defect detection. Her most notable contribution is the development of a "New Compact Teaching-Learning-Based Optimization Method" (2014), which has garnered 17 citations, offering a memory-efficient variant of the popular TLBO algorithm for engineering problems. More recently, Dr. Guo has applied reinforcement learning to enhance the motion planning of robotic arms for welding robots (2024), and proposed YOLOv8_CB, an improved YOLOv8 model integrating CBAM and BiFPN for pipeline girth weld defect detection (2024). These works demonstrate her commitment to advancing automation and quality control in manufacturing. With a focus on practical, real-world applications, Dr. Guo’s research continues to impact both algorithmic theory and industrial deployment, making her a valuable contributor to the fields of computational intelligence and robotic vision.
Research Focus
Key Achievements
Top Papers
- 1A New Compact Teaching-Learning-Based Optimization Method17 citations · 2014
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