Dr. Yiming Fang | Intelligent detection | Best Researcher Award
Dr. Yiming Fang, Beijing University of Technology, China
Dr. Yiming Fang is a doctoral candidate in Mechanical Engineering at Beijing University of Technology, expected to graduate in February 2024. He completed his Masterās in Control Engineering at China Jiliang University and his Bachelorās in Electrical Engineering and Automation from Anhui Polytechnic University. His doctoral research explores image processing, deep learning, intelligent systems, precision measurement, and industrial automation control, building on his master’s work in automation control, sensor technology, and signal processing. Yiming has made significant contributions to publications on gear error determination, complex dark spot detection in plastic gears, and fast measurement methods for fine-pitch spur gears, with his research featured in esteemed journals like Applied Sciences and Optics and Precision Engineering.
Professional Profile:
Orcid
š Education:
Yiming Fang is a doctoral candidate in Mechanical Engineering at Beijing University of Technology, with an anticipated graduation in February 2024. He earned his Masterās in Control Engineering from China Jiliang University and a Bachelorās in Electrical Engineering and Automation from Anhui Polytechnic University.
š¬ Research Area:
Yiming’s doctoral research focuses on image processing, deep learning, intelligent systems, precision measurement, and industrial automation control. His masterās research concentrated on automation control, sensor technology, and signal processing.
š Research Achievements:
Yiming has contributed to several notable publications, including work on gear error determination, complex dark spot detection in plastic gears, and fast measurement methods for fine-pitch spur gears. His research is published in reputable journals like Applied Sciences and Optics and Precision Engineering.
Publication Top Notes:
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Title: Intelligent Inspection Method and System of Plastic Gear Surface Defects Based on Adaptive Sample Weighting Deep Learning Model
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Year: 2024