Gas source localization through deep learning method based on gas distribution map database
The incident of harmful gas leakage can cause severe damage to the environment and several casualties to human beings while the gas localization system plays a major role in mitigating those causalities. With the advances in artificial intelligence technology, deep learning is able to enhance the a...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Penerbit UTM
2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28905/2/0026027122024103961484.pdf http://eprints.utem.edu.my/id/eprint/28905/ https://journals.utm.my/jurnalteknologi/article/view/20186/8392 https://doi.org/10.11113/jurnalteknologi.v86.20186 |
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| Summary: | The incident of harmful gas leakage can cause severe damage to the environment and several casualties to human beings while the gas localization system plays a major role in
mitigating those causalities. With the advances in artificial intelligence technology, deep learning is able to enhance the accuracy of the gas localization system to locate the gas
source. This paper proposes a gas localization system that utilizes three different deep learning models namely DNN, 1DCNN, and 2DCNN to locate the gas source within the gas map. The proposed method involves generating the gas distribution map through the large gas sensor array platform in real-world indoor scenarios. Those models are then trained using the collected database which allows for accurate prediction of the gas source location. The performance of each proposed deep learning model was compared to find the best model demonstrating the highest effectiveness in identifying gas leaks. The study has shown that the
1DCNN has the highest effectiveness in predicting the gas source in the range between 0.0m to 0.3 m with 90.3% compared to the DNN and 2DCNN models. |
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