A Real-Time Multifactor Risk Monitoring Method for the Gas Pipeline Operation Process Based on Mix-Supervised Target Recognition-Reference-Cited by-同舟云学术

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A Real-Time Multifactor Risk Monitoring Method for the Gas Pipeline Operation Process Based on Mix-Supervised Target Recognition

Published:2024-04-16 Issue:3 Volume:31 Page:259-267
ISSN:1871-5532
Container-title:ACS Chemical Health & Safety
language:en
Short-container-title:ACS Chem. Health Saf.

Author:

Ma Denglong¹^ORCID,Mao Weigao¹,Huang Chenlei¹,Zhang Guangsen¹,Han Yi²,Zhang Xiaoming³,Wang Hansheng²,Cen Kang⁴

Affiliation:

1. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

2. Hanzhong Natural Gas Investment Development Co.Ltd., Hanzhong 723011, China

3. Shaanxi Special Equipment Inspection and Testing Institute, Xi’an 710038, China

4. School of Civil Engineering and Architecture, Southwest Petroleum University, Chengdu 610500, China

Funder

China Postdoctoral Science Foundation

Key Research and Development Projects of Shaanxi Province

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chas.4c00012

Reference25 articles.

1. Environmental Issues and Challenges of Chinese Abandoned Oil Pipelines

2. The Piper Alpha Disaster: A Personal Perspective with Transferrable Lessons on the Long-Term Moral Impact of Safety Failures

3. Gas Leakage Recognition Based on Wide-Band Infrared Imaging with the Auxiliary Excitation Method and Machine Learning Model

4. CO2 Leakage Identification Method Based on Complex Time–Frequency Spectrum of Atmospheric CO2 Variation

5. A comprehensive and systematic look up into deep learning based object detection techniques: A review

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