Papers
9
Total Citations
113
H-Index
5
About
Keji Yang is an innovative robotics and medical imaging researcher whose work bridges advanced mechanical design with clinical and industrial applications. His research spans robotic ultrasound systems, minimally invasive surgical robotics, and non-destructive testing, with a particular focus on developing intelligent systems that enhance precision and accessibility in complex environments. Yang's most impactful contribution applies deep learning to ultrasonic imaging, with his VGG-UNet framework for curved-part defect detection accumulating 38 citations and demonstrating how neural networks can dramatically improve industrial inspection quality. Complementing this, his robot-assisted ultrasound scanning systems for spinal surgery navigation — garnering over 21 citations — represent a meaningful step toward replacing radiation-dependent X-ray fluoroscopy in minimally invasive procedures. Beyond imaging, Yang has pioneered novel mechanical architectures, including a bioinspired time-share driven handheld robot (25 citations) that creatively overcomes actuator space constraints, and hyper-redundant manipulators using zigzag mechanism doublets designed for confined surgical and inspection environments. His endoscope-holder and compliant joint robotic systems further reflect his commitment to reducing surgeon fatigue and improving procedural consistency. Collectively, Yang's body of work demonstrates a productive integration of biomechanics, robotics, and clinical need, positioning him as an emerging voice in surgical and industrial robotics research.
Research Focus
Key Achievements
Top Papers
- 1
- 2Bioinspired handheld time-share driven robot with expandable DoFs25 citations · 2024
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- 6A Novel Articulated Manipulator Design With Mechanism Doublet3 citations · 2024
- 7Design of a Hyper-Redundant Manipulator With Zigzag Mechanism Doublet3 citations · 2025
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