Yutang Ma

Papers

Total Citations

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About

Yutang Ma is a leading researcher in intelligent power systems, specializing in the fusion of artificial intelligence and robotics for substation automation. His work centers on two critical areas: knowledge graph-driven multi-source data integration and deep learning for real-time equipment monitoring. Ma’s most cited paper, “Multi-source fusion of substation intelligent inspection robot based on knowledge graph: A overview and roadmap” (2022, 10 citations), provides a foundational framework for integrating heterogeneous sensor data—such as thermal, visual, and acoustic inputs—into a unified knowledge graph. This enables inspection robots to make context-aware decisions, significantly improving fault detection accuracy in complex substation environments. In his second key contribution, “State identification of transfer learning based Yolov4 network for isolation switches used in substations” (2022, 3 citations), Ma pioneers the application of transfer learning to adapt YOLOv4 object detection models for recognizing switch states under varying lighting and weather conditions. This reduces the need for extensive labeled training data, making 24/7 substation monitoring more practical and cost-effective. With a growing citation footprint, Ma’s work directly addresses the industry’s push toward fully autonomous, intelligent power grids, offering scalable solutions that enhance both safety and reliability.