Papers
1
Total Citations
9
H-Index
1
About
Xiating Jin is a researcher whose work bridges machine vision and sustainable energy infrastructure, with a particular focus on advancing automation in electric vehicle (EV) charging systems. Jin’s most-cited paper, “A Method for New Energy Electric Vehicle Charging Hole Detection and Location Based on Machine Vision” (2016, 9 citations), tackles a critical bottleneck in EV adoption: the inefficiency and safety risks of manual charging. By designing a machine vision-based method for detecting and locating charging holes, Jin enables robotic automatic charging, eliminating space constraints and leakage hazards. This contribution directly supports the transition to autonomous, user-friendly EV ecosystems, a key pillar of new energy transportation. While Jin’s citation count reflects a niche but impactful early-career focus, the work’s practical implications for robotics and clean energy integration signal its growing relevance. For students and researchers in computer vision or sustainable engineering, Jin’s approach offers a compelling case study in applying algorithmic solutions to real-world infrastructure challenges, demonstrating how targeted technical innovations can accelerate the shift toward smarter, greener mobility.
Research Focus
Key Achievements
Top Papers
- 1