Skip to content
RDL
Network
Ekosistem
Uygulama değiştir
EN
Hakkımızda
SSS
Giriş yap
Başla
Secondary decomposition with temporal convolutional gated recurrent unit network for strong wind prediction in high-speed railway system — Wei Gu (2025) | RDL Network
Back
Cite
Save
Save for later
Share
Home
Publications
Secondary decomposition with temporal convolutional gated recurrent unit network for strong wind prediction in high-speed railway system
Shared by
Hongyan Xing
Nanjing University of Information Science and Technology
Secondary decomposition with temporal convolutional gated recurrent unit network for strong wind prediction in high-speed railway system
Article
2025
en
Authors
+2 more
WG
Wei Gu
Hongyan Xing
Nanjing University of Information Science and Technology
GY
Guoyuan Yang
Discussion
(0)
Sign in
to like and join the discussion.
No comments yet. Be the first to comment.
Related publications
Article
2025
Wind speed forecasting in high-speed railway system based on secondary decomposition and gated recurrent unit network
Wei Gu
,
Chunjie Xu
,
Guoyuan Yang
,
Hongyan Xing
,
Tongyuan Liu
Article
2025
Temporal Convolutional Network with Attention Mechanisms for Strong Wind Early Warning in High-Speed Railway Systems
Wei Gu
,
Guoyuan Yang
,
Hongyan Xing
,
Yajing Shi
,
Tongyuan Liu
Article
2025
Improved Landslide Deformation Prediction Using Convolutional Neural Network–Gated Recurrent Unit and Spatial–Temporal Data
Honglei Yang
,
Youfeng Liu
,
Qinglong Qinglong Han
,
Linlin Xu
,
Tianyu Zhang
,
Zeping Wang
,
Ao Yan
,
Songxue Zhao
,
Jianfeng Han
,
Yuedong Wang
Remote Sensing
Article
2024
Artificial-Intelligence-Based Model for Early Strong Wind Warnings for High-Speed Railway System
Wei Gu
,
Hongyan Xing
,
Guoyuan Yang
,
Yajing Shi
,
Tongyuan Liu
Article
2024
Temporal Modeling for Power Converters With Physics-in-Architecture Recurrent Neural Network
Discussion(0)
No comments yet. Be the first to comment.