Development of Gas Type Identification Deep-learning Model through Multimodal Method


KIPS Transactions on Software and Data Engineering, Vol. 12, No. 12, pp. 525-534, Dec. 2023
https://doi.org/10.3745/KTSDE.2023.12.12.525,   PDF Download:
Keywords: AI, Deep Learning, Multimodal, Gas Detection, Gas Identification
Abstract

Gas leak detection system is a key to minimize the loss of life due to the explosiveness and toxicity of gas. Most of the leak detection systems detect by gas sensors or thermal imaging cameras. To improve the performance of gas leak detection system using single-modal methods, the paper propose multimodal approach to gas sensor data and thermal camera data in developing a gas type identification model. MultimodalGasData, a multimodal open-dataset, is used to compare the performance of the four models developed through multimodal approach to gas sensors and thermal cameras with existing models. As a result, 1D CNN and GasNet models show the highest performance of 96.3% and 96.4%. The performance of the combined early fusion model of 1D CNN and GasNet reached 99.3%, 3.3% higher than the existing model. We hoped that further damage caused by gas leaks can be minimized through the gas leak detection system proposed in the study.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
S. H. Ahn, G. Y. Kim, D. J. Kim, "Development of Gas Type Identification Deep-learning Model through Multimodal Method," KIPS Transactions on Software and Data Engineering, vol. 12, no. 12, pp. 525-534, 2023. DOI: https://doi.org/10.3745/KTSDE.2023.12.12.525.

[ACM Style]
Seo Hee Ahn, Gyeong Yeong Kim, and Dong Ju Kim. 2023. Development of Gas Type Identification Deep-learning Model through Multimodal Method. KIPS Transactions on Software and Data Engineering, 12, 12, (2023), 525-534. DOI: https://doi.org/10.3745/KTSDE.2023.12.12.525.