Rungen Ye

Papers

Total Citations

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About

Rungen Ye is a researcher whose work bridges the fields of flexible electronics and intelligent sensing, with a particular focus on force detection for robotic applications. Their key research areas include flexible sensor design, three-dimensional force simulation, and the integration of machine learning for sensor data prediction. Ye’s major contribution lies in developing a novel approach to predict three-dimensional force information using a BP (backpropagation) neural network, applied to a flexible sensor made from polydimethylsiloxane (PDMS). This work addresses a critical challenge in robotics: enabling smooth object grasping through accurate force detection. By combining materials science with computational modeling, Ye has advanced the reliability of flexible sensors in real-world robotic systems. Their most-cited paper, published in 2020, has garnered 4 citations, reflecting its foundational role in this niche area. While the citation count is modest, the work demonstrates a practical integration of neural networks with soft materials, offering a pathway for future innovations in haptic feedback and automation. Ye’s research is particularly valuable for students and researchers exploring the intersection of soft robotics and intelligent control systems.