NDAMA: A Novel Deep Autoencoder and Multivariate Analysis Approach for IoT-Based Methane Gas Leakage Detection-Reference-Cited by-同舟云学术

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NDAMA: A Novel Deep Autoencoder and Multivariate Analysis Approach for IoT-Based Methane Gas Leakage Detection

Published:2023 Issue: Volume:11 Page:140740-140751
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Dashdondov Khongorzul¹^ORCID,Kim Mi-Hye²,Jo Kyuri²^ORCID

Affiliation:

1. Department of Computer Engineering, College of IT Convergence, Gachon University, Seongnam, Republic of Korea

2. Department of Computer Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju, Republic of Korea

Funder

National Research Foundation of Korea (NRF) Grant

Korean Government, Ministry of Science and ICT

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10347215.pdf?arnumber=10347215

Reference61 articles.

1. An open source algorithm to detect natural gas leaks from mobile methane survey data

2. Rapid, Vehicle-Based Identification of Location and Magnitude of Urban Natural Gas Pipeline Leaks

3. Unsupervised Deep Learning for IoT Time Series

4. Enhancing Physical-Layer Security for IoT With Nonorthogonal Multiple Access Assisted Semi-Grant-Free Transmission

5. Situation-Aware Dynamic Service Coordination in an IoT Environment

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