CNN-BiLSTM deep learning method for vibration response prediction of the hydropower house-Reference-Cited by-同舟云学术

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CNN-BiLSTM deep learning method for vibration response prediction of the hydropower house

Published:2024-04-19 Issue: Volume:38 Page:249-253
ISSN:
Container-title:Proceedings of the 2024 International Academic Conference on Edge Computing, Parallel and Distributed Computing
language:
Short-container-title:

Author:

Wang Shuo¹^ORCID,Zhang Liaojun²^ORCID,Yin Guojiang³^ORCID

Affiliation:

1. College of Civil and Transportation Engineering, Hohai University, China

2. College of Water Conservancy and Hydropower Engineering, Hohai University, China

3. Hydraulic Metal Structure Safety Monitoring Center of the Ministry of Water Resources, Hohai University, China

Funder

Natural Science Foundation of Jiangsu Province

Jiangsu Funding Program for Excellent Postdoctoral Talents

Transportation Technology Plan Project of Jiangsu Province

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3677404.3677447

Reference15 articles.

1. A Data Augmentation-Based Method for Robust Device-Free Localization in Changing Environments of Passive Radio Frequency Identification System

2. Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN

3. Prediction of significant wave height in hurricane area of the Atlantic Ocean using the Bi-LSTM with attention model

4. Performance evaluation of LSTM and Bi-LSTM using non-convolutional features for blockage detection in centrifugal pump

5. A CNN encoder decoder LSTM model for sustainable wind power predictive analytics;Garg S.;Sust. Comput.,2023

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