Papers
2
Total Citations
4
H-Index
1
About
Lijie Zhao’s research bridges the gap between classical control theory and modern computer vision, with a focus on intelligent systems for industrial automation. His early work in adaptive fuzzy control, particularly the 2005 paper "Friction compensating modeling and control based on adaptive fuzzy system" (3 citations), established a rigorous mathematical framework for compensating nonlinear friction in mechanical systems, deriving tracking error bounds that remain foundational for precision motion control. More recently, Zhao has pioneered deep learning approaches for manufacturing, exemplified by his 2025 work "SANet: A Pure Vision Strip-Aware Network with PSSCA and Multistage Fusion for Weld Seam Detection" (1 citation). This paper introduces a novel strip-aware architecture that directly addresses the long-standing challenge of detecting elongated, low-contrast weld seams against cluttered industrial backgrounds—a critical bottleneck in robotic welding automation. By combining parallel spatial-channel attention with multistage feature fusion, SANet achieves robust detection under severe environmental interference. Zhao’s trajectory from adaptive control to vision-based automation demonstrates a sustained commitment to solving real-world industrial problems through mathematically principled innovations, with his work laying groundwork for both theoretical advances in fuzzy systems and practical deployments in smart manufacturing.
Research Focus
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Top Papers
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