Tingyu Liu

Nantong University, Southeast University

Papers

Total Citations

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About

Tingyu Liu is a researcher at the forefront of intelligent manufacturing and human-robot collaboration, with a focus on digital twin modeling and human activity recognition. Their work bridges the gap between physical production systems and their virtual counterparts, advancing how factories simulate, monitor, and optimize assembly processes. Liu’s most cited paper introduces ATD-GCN, an adaptive skeleton tree-decomposition graph convolutional network for human activity recognition in collaborative robotics—a method that enhances how machines understand and respond to human movements in shared workspaces. Complementing this, Liu proposed a dynamic assembly and fusion approach for digital twin logic models using Petri nets, offering a three-level modeling framework—from individual components to full production lines—that significantly improves modeling accuracy and efficiency for complex systems like radar assembly lines. With citations already accruing for these recent contributions, Liu is establishing a reputation for solving practical industrial challenges through elegant computational models. Their work not only advances the theoretical underpinnings of digital twins and human-robot interaction but also delivers actionable tools for smarter, more responsive manufacturing environments.