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An Improved Gas Classification Technique Using New Features and Support Vector Machines

机译：使用新功能和支持向量机的改进的气体分类技术

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摘要

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.
机译：在本文中，我们提出了一种基于在化工厂提取新特征和支持向量机（SVM）的气体分类技术。首先，使用半导体气体调火收集各种气体，然后计算这些气体的组成比，其定义为特征。这些提取的特征是高度辨别的并且量化气体的存在。此外，这些特征用作用于分类气体类型的SVM输入。此外，我们在SVM中应用网格搜索技术，用于调整超级参数，如错误分类率，C和内核带宽，σ，以提高分类性能。为了验证所提出的技术，我们使用经济高效的自动测试钻机收集各种气体组合物。实验结果表明，该方法高度能够以良好的准确性对各种危险气体进行分类。

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