Xingang Yang

Xi'an University of Technology, Shanghai Electric (China)

Papers

Total Citations

H-Index

About

Xingang Yang is a leading researcher in intelligent robotics and sensor-based automation, with a primary focus on mobile robot localization and deep learning-accelerated inspection systems. His pioneering work on omni-directional scanning localization using ultrasonic sensors, published in 2016, introduced a novel ranging algorithm that simultaneously accounts for divergence and incidence angles, significantly improving accuracy over conventional methods. This foundational contribution has garnered 12 citations and remains influential in the field of autonomous navigation. More recently, Yang has advanced the practical deployment of artificial intelligence in industrial settings. His 2021 paper on substation intelligent inspection robots, which integrates the Nvidia Jetson TX2 module to enable real-time deep learning on low-computation platforms, has earned 7 citations and demonstrates his commitment to bridging the gap between high-performance AI and resource-constrained robotics. By developing robots capable of supporting deep learning acceleration, Yang has directly enhanced the efficiency and reliability of critical infrastructure monitoring. His work exemplifies a rare combination of theoretical innovation and applied engineering, making him a key figure in the evolution of intelligent inspection systems.