Xiaohong Guan

Papers

Total Citations

110

H-Index

About

Xiaohong Guan is a leading researcher in intelligent systems and industrial automation, with a primary focus on fault diagnosis, network science, and mobile robot navigation. His most impactful work, "Knowledge and data dual-driven transfer network for industrial robot fault diagnosis" (2022, 88 citations), introduces a pioneering hybrid approach that combines mechanistic knowledge with data-driven methods to enhance fault detection accuracy in industrial robots—a critical contribution to smart manufacturing and predictive maintenance. Guan also explores the structural dynamics of hierarchical networks, as seen in "The Immense Impact of Reverse Edges on Large Hierarchical Networks" (2023, 11 citations), where he reveals how reverse edges fundamentally alter network behavior in systems ranging from animal groups to smart grids and multi-robot teams. Additionally, his work on the "Bi-directional smooth A-star algorithm for navigation planning of mobile robots" (2021, 11 citations) addresses key limitations in path planning, improving localization precision and reducing path irregularities for real-time robotic navigation. Through these contributions, Guan bridges theoretical network analysis with practical engineering solutions, demonstrating significant impact in both academic and applied contexts.