Two-stream Convolutional Self-attention Model for Leakage Aperture Equivalent Prediction of High-pressure Gas Pipeline-Reference-Cited by-同舟云学术

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Two-stream Convolutional Self-attention Model for Leakage Aperture Equivalent Prediction of High-pressure Gas Pipeline

Published:2024-05-17 Issue: Volume:161 Page:231-238
ISSN:
Container-title:Proceedings of the 2024 3rd International Symposium on Intelligent Unmanned Systems and Artificial Intelligence
language:
Short-container-title:

Author:

Dong Hongyang¹^ORCID,Liu Tao¹^ORCID,Jiang Dong²^ORCID,Li Huadong²^ORCID,Zhao Dongmei¹^ORCID

Affiliation:

1. School of computer science and technology, Southwest University of Science and Technology, China

2. Sichuan Gas Turbine Establishment, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3669721.3669728

Reference15 articles.

1. A comprehensive review of acoustic based leak localization method in pressurized pipelines [J];Mechanical Systems and Signal Processing,2021

2. A review of leakage detection strategies for pressurised pipeline in steady-state [J];Engineering Failure Analysis,2020

3. Review and analysis of pipeline leak detection methods [J];Journal of Pipeline Science and Engineering,2022

4. Simulation and assessment of underwater gas release and dispersion from subsea gas pipelines leak [J];Process Safety and Environmental Protection,2018

5. Numerical and experimental study on leakage detection for buried gas pipelines based on distributed optical fiber acoustic wave [J];Measurement Science & Technology,2021

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