A quality control method based on physical constraints and data-driven collaborative artificial intelligence for wind observations along high-speed railway lines
Xiong Xiong,Jiajun Chen,Yanchao Zhang,Xin Chen,Yingchao Zhang,and Xiaoling Ye
Xiong Xiong
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Information and Systems Science Institute, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Yanchao Zhang
Information and Systems Science Institute, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Xin Chen
Information and Systems Science Institute, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Yingchao Zhang
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
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Total article views: 3,769 (including HTML, PDF, and XML)
Thereof 3,695 with geography defined
and 74 with unknown origin.
Total article views: 1,328 (including HTML, PDF, and XML)
Thereof 1,268 with geography defined
and 60 with unknown origin.
Total article views: 2,441 (including HTML, PDF, and XML)
Thereof 2,427 with geography defined
and 14 with unknown origin.
This study introduces a novel quality control method, physical constraints and data-driven collaborative artificial intelligence (PD-BX), aimed at reducing wind speed measurement errors caused by the complex environments surrounding high-speed railway lines, thereby enhancing the accuracy and reliability of measurements.
This study introduces a novel quality control method, physical constraints and...
