Xingju Xie
Papers
4
Total Citations
20
H-Index
3
About
Xingju Xie is a leading researcher in industrial robotics, specializing in the intersection of data compression, sensor optimization, and edge-cloud resource management for intelligent manufacturing systems. Her work addresses critical challenges in industrial robot health monitoring and fault diagnosis, where she has pioneered methods to enhance data transmission efficiency and system reliability. Xie’s most influential contribution is a compressed sensing-based approach for multi-channel vibration monitoring data, achieving significant compression while maintaining signal integrity—a breakthrough that reduces data redundancy and improves transmission security. Her Bayesian theory-driven optimal sensor placement methodology further advances vibration signal acquisition, enabling more accurate modal analysis and fault detection. With over 20 citations across her key publications, Xie has also developed innovative solutions for edge-cloud environments, including a hybrid tabu-evolutionary algorithm for multi-container migration and bi-objective optimization for workflow resource allocation. These contributions directly address the scalability and real-time performance demands of modern industrial robot monitoring systems. Her work is particularly notable for bridging theoretical optimization with practical industrial applications, making her research essential for engineers and researchers advancing smart manufacturing and Industry 4.0 technologies.
Research Focus
Key Achievements
Top Papers
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