Stochastic Resonance Enhancement for Leak Detection in Pipelines Using Fluid Transients and Convolutional Neural Networks-Reference-Cited by-同舟云学术

Stochastic Resonance Enhancement for Leak Detection in Pipelines Using Fluid Transients and Convolutional Neural Networks

Published:2022-03 Issue:3 Volume:148 Page:
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Bohorquez Jessica¹^ORCID,Lambert Martin F.²^ORCID,Alexander Bradley³,Simpson Angus R.⁴,Abbott Derek⁵

Affiliation:

1. Postdoctoral Researcher, School of Civil, Environmental and Mining Engineering, Univ. of Adelaide, Adelaide, SA 5005, Australia (corresponding author). ORCID: .

2. Professor, School of Civil, Environmental and Mining Engineering, Univ. of Adelaide, Adelaide, SA 5005, Australia. ORCID: .

3. Senior Lecturer, School of Computer Science, Univ. of Adelaide, Adelaide, SA 5005, Australia.

4. Professor Emeritus, School of Civil, Environmental and Mining Engineering, Univ. of Adelaide, Adelaide, SA 5005, Australia; Honorary Professorial Fellow, Dept. of Infrastructure Engineering, Univ. of Melbourne, Parkville, VIC 3010, Australia.

5. Professor, School of Electrical and Electronic Engineering, Univ. of Adelaide, Adelaide, SA 5005, Australia.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Management, Monitoring, Policy and Law,Water Science and Technology,Geography, Planning and Development,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29WR.1943-5452.0001504

Reference45 articles.

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5. Bishop, C. M. 1995. Neural networks for pattern recognition. Oxford, UK: Oxford University Press.

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