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Mixed Odor Classification for QCM Sensor Data by Neural Networks

  • Sigeru OmatuEmail author
  • Hideo Araki
  • Toru Fujinaka
  • Michifumi Yoshioka
  • Hiroyuki Nakazumi
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 151)

Abstract

Compared with metal oxide semiconductor gas sensors, quarts crystal microbalance (QCM) sensors are sensitive for odors. Using an array of QCM sensors, we measure mixed odors and classify them into an original odor class before mixing based on neural networks. For simplicity we consider the case that two kinds of odor are mixed since more than two becomes too complex to analize the classification results. We have used eight sensors and four kinds of odor are used as the original odors. The neural network used here is a conventional layered neural network. The classification is acceptable although the perfect classification could not been achieved.

Keywords

odor feature vector neural networks separation of mixed gasses odor classification 

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References

  1. 1.
    Carlson, W.L., Thorne, B.: Applied Statistical Methods. Prentice Hall International (1997)Google Scholar
  2. 2.
    General Information for TGS sensors, Figaro Engineering, http://www.figarosensor.com/products/general.pdf
  3. 3.
    Fujinaka, T., Yoshioka, M., Omatu, S., Kosaka, T.: Intelligent Electronic Nose Systems for Fiore Detection Systems Based on Neural Networks. In: The second International Conference on Advanced Engineering Computing and Applications in Sciences, Valencia, Spain, pp. 73–76 (2008)Google Scholar
  4. 4.
    Milke, J.A.: Application of Neural Networks for discriminating Fire Detectors. In: 10th International Conference on Automatic Fire Detection, AUBE 1995, pp. 213–222 (1995)Google Scholar
  5. 5.
    Omatu, S., Yano, M.: Intelligent Electronic Nose System Independent on Odor Concentration. In: International Symposium on Distributed Cimputing and Artificial Intelligence, Salamanca, Spain, pp. 1–9 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sigeru Omatu
    • 1
    Email author
  • Hideo Araki
    • 2
  • Toru Fujinaka
    • 3
  • Michifumi Yoshioka
    • 4
  • Hiroyuki Nakazumi
    • 5
  1. 1.Faculty of EngineeringOsaka Institute of TechnologyOsakaJapan
  2. 2.Faculty of Information Science and TechnologyOsaka Institute of TechnologyHirakataJapan
  3. 3.Faculty of EducationHiroshima UniversityHigashi-HiroshimaJapan
  4. 4.Faculty of EngineeringOsaka Prefecture UniversitySakaiJapan
  5. 5.Faculty of EngineeringOsaka Prefecture UniversitySakaiJapan

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