Zuoxue Wang
Papers
6
Total Citations
158
H-Index
5
About
Zuoxue Wang is a leading researcher in the field of industrial robotics, with a primary focus on energy consumption modeling, prediction, and optimization. His work addresses the critical challenge of improving energy efficiency in widely deployed industrial robots, which have significant energy-saving potential. Wang’s major contributions include pioneering the use of LSTM-based neural networks for energy consumption prediction, as demonstrated in his highly cited 2023 paper (95 citations), and developing a novel hybrid approach combining LSTM with masked multi-head attention mechanisms (2025, 17 citations). He has also advanced the field through a coupling model-driven and data-driven paradigm for energy consumption model identification (2024, 23 citations), and an operation-mechanism-based modeling approach (2025, 12 citations). Beyond energy optimization, Wang has contributed to active vibration control using PID based on the receptance method (2020, 8 citations). His recent work on time-scaling optimization for industrial robots (2025) further demonstrates his ongoing commitment to practical, implementable solutions. With a growing body of highly cited work, Zuoxue Wang is establishing himself as a key figure in sustainable and intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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- 5Active Vibration Control of PID Based on Receptance Method8 citations · 2020
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