Jia Yijing
Papers
1
Total Citations
8
H-Index
1
About
Jia Yijing is a researcher focused on the intersection of artificial intelligence and power systems, with a particular emphasis on deep learning applications for equipment monitoring and safety. Her most cited work, "Research on the state detection of the secondary panel of the switchgear based on the YOLOv5 network model" (2021), has garnered 8 citations, reflecting its practical relevance in the field. In this study, she pioneered the use of the YOLOv5 deep learning algorithm for real-time target detection on switchgear secondary panels—a critical component in power systems where manual inspection is labor-intensive and error-prone. By automating state detection, her research directly addresses the need for improved operational efficiency and safety in electrical infrastructure. Jia Yijing’s contributions are notable for bridging cutting-edge AI techniques with traditional power engineering challenges, offering scalable solutions for the vast number of switchgear units in service. Her work stands as a valuable resource for researchers and engineers seeking to enhance predictive maintenance and reduce human oversight in critical energy systems.
Research Focus
Key Achievements
Top Papers
- 1