Xiaoming Yang
Papers
2
Total Citations
9
H-Index
2
About
Xiaoming Yang is a researcher at the intersection of artificial intelligence, robotics, and sports science, with a focus on applying machine learning to real-world interactive systems. Their work explores how convolutional neural networks and neurorobotics can enhance autonomous perception, particularly in underwater environments for educational applications. Yang’s most-cited paper, “Application of Target Detection Method Based on Convolutional Neural Network in Sustainable Outdoor Education” (2023, 5 citations), pioneers a submersible vision system that uses CNN-based target detection to improve underwater robot intelligence, offering new possibilities for environmental monitoring and outdoor learning. In a complementary study, “Return Strategy and Machine Learning Optimization of Tennis Sports Robot for Human Motion Recognition” (2022, 4 citations), Yang addresses the challenge of autonomous ball return in tennis, optimizing human motion recognition to reduce the need for human-controlled training equipment. Though their citation counts are modest, Yang’s contributions are notable for bridging computer vision, robotics, and sustainable education, demonstrating how AI can enable more autonomous, resource-efficient systems in both natural and athletic settings. Their work highlights a growing trend toward intelligent, human-robot interaction in specialized domains.
Research Focus
Key Achievements
Top Papers
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- 2