A Gas Leakage Source Positioning and Estimation Algorithm Based on Deep Neural Network (DNN)

Rongxue Yi; Jie Li; Xiang Guo; Bo Wang

doi:10.1109/CAC59555.2023.10451389

A Gas Leakage Source Positioning and Estimation Algorithm Based on Deep Neural Network (DNN)

Rongxue Yi, Jie Li, Xiang Guo, Bo Wang

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Gas leakage source positioning and estimation is a crucial approach to ensuring urban safety. This paper presents a gas leakage source estimation method based on deep neural network. The gas leakage diffusion model and machine learning model are applied to the estimation method of leakage gas source. Taking into account the characteristics of the leakage gas, we proposed the usage of both the Gaussian plume model and the DNN model. Then corresponding observation models are compiled based on these different models. The Particle Swarm Optimization algorithm is applied to analyze and predict the observation data. Simulation results show that compared with traditional modeling methods, the improved DNN algorithm has better performance, further reducing the source estimation and positioning error.

Original language	English
Title of host publication	Proceedings - 2023 China Automation Congress, CAC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5414-5418
Number of pages	5
ISBN (Electronic)	9798350303759
DOIs	https://doi.org/10.1109/CAC59555.2023.10451389
Publication status	Published - 2023
Event	2023 China Automation Congress, CAC 2023 - Chongqing, China Duration: 17 Nov 2023 → 19 Nov 2023

Publication series

Name	Proceedings - 2023 China Automation Congress, CAC 2023

Conference

Conference	2023 China Automation Congress, CAC 2023
Country/Territory	China
City	Chongqing
Period	17/11/23 → 19/11/23

Keywords

Deep Neural Network
Gas leakage Source Estimation
Gaussian Plume Model
Particle Swarm Optimization

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10.1109/CAC59555.2023.10451389

Cite this

Yi, R., Li, J., Guo, X., & Wang, B. (2023). A Gas Leakage Source Positioning and Estimation Algorithm Based on Deep Neural Network (DNN). In Proceedings - 2023 China Automation Congress, CAC 2023 (pp. 5414-5418). (Proceedings - 2023 China Automation Congress, CAC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAC59555.2023.10451389

@inproceedings{76906509b7c64028a333a0bba43b0d86,

title = "A Gas Leakage Source Positioning and Estimation Algorithm Based on Deep Neural Network (DNN)",

abstract = "Gas leakage source positioning and estimation is a crucial approach to ensuring urban safety. This paper presents a gas leakage source estimation method based on deep neural network. The gas leakage diffusion model and machine learning model are applied to the estimation method of leakage gas source. Taking into account the characteristics of the leakage gas, we proposed the usage of both the Gaussian plume model and the DNN model. Then corresponding observation models are compiled based on these different models. The Particle Swarm Optimization algorithm is applied to analyze and predict the observation data. Simulation results show that compared with traditional modeling methods, the improved DNN algorithm has better performance, further reducing the source estimation and positioning error.",

keywords = "Deep Neural Network, Gas leakage Source Estimation, Gaussian Plume Model, Particle Swarm Optimization",

author = "Rongxue Yi and Jie Li and Xiang Guo and Bo Wang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 China Automation Congress, CAC 2023 ; Conference date: 17-11-2023 Through 19-11-2023",

year = "2023",

doi = "10.1109/CAC59555.2023.10451389",

language = "English",

series = "Proceedings - 2023 China Automation Congress, CAC 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5414--5418",

booktitle = "Proceedings - 2023 China Automation Congress, CAC 2023",

address = "United States",

}

Yi, R, Li, J, Guo, X & Wang, B 2023, A Gas Leakage Source Positioning and Estimation Algorithm Based on Deep Neural Network (DNN). in Proceedings - 2023 China Automation Congress, CAC 2023. Proceedings - 2023 China Automation Congress, CAC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 5414-5418, 2023 China Automation Congress, CAC 2023, Chongqing, China, 17/11/23. https://doi.org/10.1109/CAC59555.2023.10451389

A Gas Leakage Source Positioning and Estimation Algorithm Based on Deep Neural Network (DNN). / Yi, Rongxue; Li, Jie; Guo, Xiang et al.
Proceedings - 2023 China Automation Congress, CAC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 5414-5418 (Proceedings - 2023 China Automation Congress, CAC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Gas Leakage Source Positioning and Estimation Algorithm Based on Deep Neural Network (DNN)

AU - Yi, Rongxue

AU - Li, Jie

AU - Guo, Xiang

AU - Wang, Bo

PY - 2023

Y1 - 2023

N2 - Gas leakage source positioning and estimation is a crucial approach to ensuring urban safety. This paper presents a gas leakage source estimation method based on deep neural network. The gas leakage diffusion model and machine learning model are applied to the estimation method of leakage gas source. Taking into account the characteristics of the leakage gas, we proposed the usage of both the Gaussian plume model and the DNN model. Then corresponding observation models are compiled based on these different models. The Particle Swarm Optimization algorithm is applied to analyze and predict the observation data. Simulation results show that compared with traditional modeling methods, the improved DNN algorithm has better performance, further reducing the source estimation and positioning error.

AB - Gas leakage source positioning and estimation is a crucial approach to ensuring urban safety. This paper presents a gas leakage source estimation method based on deep neural network. The gas leakage diffusion model and machine learning model are applied to the estimation method of leakage gas source. Taking into account the characteristics of the leakage gas, we proposed the usage of both the Gaussian plume model and the DNN model. Then corresponding observation models are compiled based on these different models. The Particle Swarm Optimization algorithm is applied to analyze and predict the observation data. Simulation results show that compared with traditional modeling methods, the improved DNN algorithm has better performance, further reducing the source estimation and positioning error.

KW - Deep Neural Network

KW - Gas leakage Source Estimation

KW - Gaussian Plume Model

KW - Particle Swarm Optimization

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DO - 10.1109/CAC59555.2023.10451389

M3 - Conference contribution

AN - SCOPUS:85189354251

T3 - Proceedings - 2023 China Automation Congress, CAC 2023

SP - 5414

EP - 5418

BT - Proceedings - 2023 China Automation Congress, CAC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 China Automation Congress, CAC 2023

Y2 - 17 November 2023 through 19 November 2023

ER -

A Gas Leakage Source Positioning and Estimation Algorithm Based on Deep Neural Network (DNN)

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Keywords

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