Mixed odor classification for QCM sensor data by neural network

• OMATU, Sigeru ^[1] ; ARAKI, Hideo ^[1] ; FUJINAKA, Toru ^[3] ; YANO, Mitsuaki ^[1] ; YOSHIOKA, Michifumi ^[2] ; NAKAZUMI, Hiroyuki ^[4] ; TANAHASHI, Ichiro ^[1]
  1. [1] Osaka Institute of Technology

     Osaka Institute of Technology

     Kita Ku, Japón

     
  2. [2] Osaka Prefecture University

     Osaka Prefecture University

     Sakai Ku, Japón

     
  3. [3] Hirosima University
  4. [4] Osaka Prefecture Univertisy

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• Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 1, Nº. 2, 2012, págs. 43-48
• Idioma: inglés
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• Resumen

  • 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 analyze 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.