Papers
3
Total Citations
43
H-Index
2
About
Bei Yu is a pioneering researcher at the intersection of robotics and non-destructive evaluation, specializing in ultrasonic imaging for complex industrial and nuclear applications. Her major contributions center on developing robot-assisted inspection systems that overcome fundamental limitations of conventional B-scan imaging, particularly for curved and complexly structured parts. Yu's most cited work, the "Visual Geometry Group-UNet: Deep learning ultrasonic image reconstruction for curved parts" (2021, 38 citations), introduces a deep learning framework that dramatically improves detection coverage and contrast for small defects in challenging geometries—a critical advancement for modern manufacturing quality control. She further advanced this field with her "Robot-Assisted Track-Scan Imaging Approach with Multiple Incident Angles" (2020), which enhances ultrasonic image quality through multi-angle scanning strategies. Demonstrating remarkable versatility, Yu also conducted pioneering gamma-ray irradiation tests on tracked robot control systems for nuclear disaster response (2017), directly addressing challenges highlighted by the Fukushima accident. Her work bridges robotics, deep learning, and safety-critical inspection, with her 2021 paper serving as a foundational reference for researchers developing AI-enhanced ultrasonic imaging solutions.
Research Focus
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