Papers
1
Total Citations
2
H-Index
1
About
Yaxing Jing is a researcher at the forefront of intelligent manufacturing and robotics, with a primary focus on the modeling and active vibration control of flexible manipulators. Their most-cited work, “Modeling and Active Vibration Control of Intelligent Flexible Manipulator Based on Deep Learning” (2022), addresses a critical challenge in modern automation: the precise control of rigid-flexible robotic arms, which are increasingly vital in intelligent production environments. By integrating deep learning techniques, Jing’s research offers novel solutions for suppressing vibrations that compromise accuracy and safety in high-speed, lightweight robotic systems. This contribution bridges theoretical modeling and practical control, enhancing the performance of next-generation manipulators. While still early in their career, Jing’s work has already garnered attention, with citations reflecting growing interest in their approach. Their research stands at the intersection of artificial intelligence and mechanical engineering, promising to advance the capabilities of smart factories and autonomous systems. As the demand for flexible, adaptive robots rises, Jing’s innovations are poised to shape the future of industrial automation and human-robot collaboration.
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Top Papers
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