Xiaojing Yin
Papers
5
Total Citations
27
H-Index
3
About
Xiaojing Yin is a leading researcher in intelligent fault diagnosis, health assessment, and rehabilitation evaluation, with a focus on belief rule base (BRB) and evidential reasoning methodologies. Her most impactful work addresses critical challenges in both industrial robotics and medical rehabilitation. Notably, her 2023 study on "Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule Base" (10 citations) pioneers a quantitative, intellectualized approach to stroke rehabilitation assessment, moving beyond traditional scales to enhance clinical decision-making. In industrial applications, Yin has made significant contributions to robot health monitoring and fault diagnosis. Her 2024 work on "Health state assessment based on the Parallel–Serial Belief Rule Base for industrial robot systems" (7 citations) and her 2023 paper on "Fault Diagnosis for Body-in-White Welding Robot Based on Multi-Layer Belief Rule Base" (5 citations) provide novel frameworks for ensuring the safety and efficiency of automated production lines. Additionally, her research on singularity analysis and avoidance trajectory planning (2021) addresses fundamental challenges in industrial robot control. With a cumulative citation count exceeding 27 across her most-cited works, Yin’s interdisciplinary approach—bridging artificial intelligence, robotics, and healthcare—positions her as a key innovator in developing intelligent, data-driven solutions for real-world systems.
Research Focus
Key Achievements
Top Papers
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