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.
Original language | English |
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Title of host publication | Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018) |
Pages | 158–166 |
Number of pages | 9 |
ISBN (Electronic) | 978-3-030-17065-3 |
DOIs | |
Publication status | Published (in print/issue) - 10 Apr 2019 |
Event | International Conference on Soft Computing and Pattern Recognition - Porto, Portugal Duration: 13 Dec 2018 → 15 Dec 2018 Conference number: 2018 https://link.springer.com/conference/socpar |
Publication series
Name | Advances in Intelligent systems and Computing |
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Volume | 942 |
ISSN (Print) | 2194-5357 |
ISSN (Electronic) | 2194-5365 |
Conference
Conference | International Conference on Soft Computing and Pattern Recognition |
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Abbreviated title | SoCPaR |
Country/Territory | Portugal |
City | Porto |
Period | 13/12/18 → 15/12/18 |
Internet address |
Keywords
- Feature extraction
- Gas sensor array
- Gas classification
- Support vector machine
- Chemical plants