Abstract
In this paper, we propose a gas classification technique based on extracting new features and support vector machines (SVM) in a chemical plant. First, various gases are collected using semiconductor gas seniors, and then we calculate the composition ratio of these gasses, which are defined as features. These extracted features are highly discriminative and quantify the presence of gas. Moreover, these features are used as the SVM input for classifying gas types. In addition, we apply a grid search technique in SVM for tuning hyper-parameters such as misclassification rate, C, and kernel bandwidth, σ, to improve the classification performance. To verify the proposed technique, we collect various gases composition using a cost-effective self-designed test rig. The experimental results indicate that the proposed method is highly capable of classifying various hazardous gases with good accuracy.
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Acknowledgment
This research was supported by the Ministry of Science and Technology, Ministry of Information and Communication, and the Korea Information and Telecommunication Industry Promotion Agency (No. S0702-18-1045).
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Kang, SJ., Kim, JY., Jeong, IK., Islam, M.M.M., Im, K., Kim, JM. (2020). An Improved Gas Classification Technique Using New Features and Support Vector Machines. In: Madureira, A., Abraham, A., Gandhi, N., Silva, C., Antunes, M. (eds) Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018). SoCPaR 2018. Advances in Intelligent Systems and Computing, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-030-17065-3_16
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DOI: https://doi.org/10.1007/978-3-030-17065-3_16
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