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

  • Se-Jong Kang
  • Jae-Young Kim
  • In-Kyu Jeong
  • M. M. Manjurul Islam
  • Kichang Im
  • Jong-Myon KimEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 942)

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.

Keywords

Feature extraction Gas sensor array Gas classification Support vector machine Chemical plants 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Se-Jong Kang
    • 1
  • Jae-Young Kim
    • 1
  • In-Kyu Jeong
    • 1
  • M. M. Manjurul Islam
    • 1
  • Kichang Im
    • 2
  • Jong-Myon Kim
    • 1
    Email author
  1. 1.Department of Electrical and Computer EngineeringUniversity of UlsanUlsanSouth Korea
  2. 2.ICT Safety Convergence CenterUniversity of UlsanUlsanSouth Korea

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