Jiacun Wang
Papers
13
Total Citations
268
H-Index
5
About
Jiacun Wang is a researcher whose work spans two compelling and increasingly relevant domains: computer vision-based human-robot interaction and intelligent disassembly line optimization. His most recognized contribution lies in dynamic hand gesture recognition, where he has pioneered deep learning approaches to enable more intuitive human-robot communication. His 2021 paper introducing short-term sampling neural networks for gesture recognition has garnered 179 citations, establishing him as a leading voice in the field, while his earlier 2019 work on 3D convolutional neural network models further demonstrates his sustained commitment to advancing vision-based interfaces using accessible hardware such as standard laptop cameras. Beyond gesture recognition, Wang has made significant strides in sustainable manufacturing, specifically addressing human-robot collaborative disassembly line balancing problems. His research applies sophisticated metaheuristic algorithms — including shuffled frog leading, multi-verse optimization, and tabu search — to optimize the recycling of end-of-life electronic products, tackling both efficiency and environmental concerns. This dual research agenda reflects a broader mission: designing smarter, more collaborative robotic systems that benefit both industrial productivity and environmental sustainability. Wang's work bridges artificial intelligence, robotics, and green manufacturing, making him a valuable contributor to the future of intelligent automation.
Research Focus
Key Achievements
Top Papers
- 1Dynamic hand gesture recognition based on short-term sampling neural networks179 citations · 2021
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- 5Soft Computing and Signal Processing5 citations · 2021
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