Lizhe Xie
Papers
1
Total Citations
4
H-Index
1
About
Dr. Lizhe Xie is a researcher whose work sits at the intersection of materials science and artificial intelligence, with a particular focus on the non-destructive evaluation of advanced power transmission components. Her most cited work, "Defect detection for aluminium conductor composite core X-ray image with deep convolution network" (2020, 4 citations), tackles a critical challenge in the energy sector: ensuring the reliability of Aluminum Conductor Composite Core (ACCC) lines. These lines are vital for meeting China's surging electricity demands, prized for their light weight, high strength, and superior current-carrying capacity. However, their widespread adoption is hampered by susceptibility to damage. Dr. Xie’s major contribution lies in pioneering a deep convolutional network approach to automatically detect defects in ACCC X-ray images, offering a faster, more accurate alternative to manual inspection. This work directly supports the safe and efficient expansion of power grids. While her citation count reflects the emerging nature of this specialized field, her research is foundational for integrating AI into industrial quality control, promising to enhance the durability and performance of critical energy infrastructure.
Research Focus
Key Achievements
Top Papers
- 1