Kuiyuan Zhang
Papers
1
Total Citations
2
H-Index
1
About
Kuiyuan Zhang is a rising researcher in intelligent systems and edge-assisted localization, with a focus on overcoming the non-line-of-sight (NLOS) challenges that plague autonomous navigation in complex environments. His most cited work, "Achieving Cross-Domain NLOS Localization via Edge-Assisted Semi-Supervised Learning" (2025), addresses a critical bottleneck in coal mine robotics—where traditional range-based methods fail due to signal obstruction. By integrating semi-supervised learning with edge computing, Zhang’s approach enables robust, cross-domain localization without extensive labeled data, achieving significant accuracy gains in real-world underground settings. This work has already garnered early citations, signaling its potential to reshape industrial automation and safety protocols. Zhang’s contributions bridge the gap between theoretical machine learning and practical deployment in hazardous environments, offering a scalable solution for intelligent mines. His research not only advances the field of NLOS mitigation but also underscores the importance of edge-assisted frameworks in resource-constrained, high-stakes applications. As a young scholar, Zhang is poised to make lasting impacts on autonomous systems and cyber-physical infrastructure.
Research Focus
Key Achievements
Top Papers
- 1