Articles | Volume 18, issue 3
https://doi.org/10.5194/amt-18-737-2025
https://doi.org/10.5194/amt-18-737-2025
Research article
 | 
10 Feb 2025
Research article |  | 10 Feb 2025

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

Viewed

Total article views: 922 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
549 182 191 922 30 33
  • HTML: 549
  • PDF: 182
  • XML: 191
  • Total: 922
  • BibTeX: 30
  • EndNote: 33
Views and downloads (calculated since 03 Jun 2024)
Cumulative views and downloads (calculated since 03 Jun 2024)

Viewed (geographical distribution)

Total article views: 922 (including HTML, PDF, and XML) Thereof 876 with geography defined and 46 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 May 2025
Download
Short summary

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.

Share