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A Novel Parameter-Optimized Recurrent Attention Network for Pipeline Leakage Detection

Tong Sun Chuang Wang Hongli Dong Yina Zhou Chuang Guan

自动化学报（英文版）2023，Vol.10Issue(4)：1064-1076,13.

自动化学报（英文版）2023，Vol.10Issue(4)：1064-1076,13.DOI:10.1109/JAS.2023.123180

A Novel Parameter-Optimized Recurrent Attention Network for Pipeline Leakage Detection

Tong Sun ¹Chuang Wang ¹Hongli Dong ¹Yina Zhou ¹Chuang Guan¹

作者信息

1. Sanya Offshore Oil&Gas Research Institute,Northeast Petroleum University,Sanya 572024,Artificial Intelligence Energy Research Institute,Northeast Petroleum University,and also with the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control,Northeast Petroleum University,Daqing 163318,China
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摘要

关键词

Anomaly-attention mechanism (AM)/long short-term memory (LSTM)/parameter-optimized recurrent attention net-work (PRAN)/particle swarm optimization (PSO)/pipeline leakage detection (PLD)

Key words

Anomaly-attention mechanism (AM)/long short-term memory (LSTM)/parameter-optimized recurrent attention net-work (PRAN)/particle swarm optimization (PSO)/pipeline leakage detection (PLD)

引用本文复制引用

Tong Sun,Chuang Wang,Hongli Dong,Yina Zhou,Chuang Guan..A Novel Parameter-Optimized Recurrent Attention Network for Pipeline Leakage Detection[J].自动化学报（英文版）,2023,10(4):1064-1076,13.

基金项目

This work was supported in part by the National Natural Science Foundation of China(U21A2019,61873058),Hainan Province Science and Technology Special Fund of China(ZDYF2022SHFZ105),and the Alexander von Humboldt Foundation of Germany. （U21A2019,61873058）

自动化学报（英文版）

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ISSN：2329-9266

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