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Odor Classification Based on Weakly Responding Sensors

  • Sigeru OmatuEmail author
  • Mitsuaki Yano
  • Toru Fujinaka
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 217)

Abstract

We consider an array sensing system of odors and adopt a layered neural network for classification. Measurement data obtained from fourteen metal oxide semiconductor gas (MOG) sensors are used, where some sensors exhibit relatively weak responses.We propose two methods for enhancing such weak signals to obtain better classification results. One method is to apply scaling to magnify the weak signals as to increase their significance in the classification criteria. The other method also involves magnifying the weak signals. However, predetermined values are assigned in the order of the magnitude of the actual signals. In both methods the group of weak signals is first determined. Then their values are negated prior to scaling, in order to be distinguished from stronger signals. An experiment shows that the accuracy of classifying five kinds of odors is improved from 74% to 85%.

Keywords

feature vector scaling weak odor signals odor classification neural networks 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Faculty of EngineeringOsaka Institute of TechnologyOsakaJapan
  2. 2.Faculty of EducationHiroshima UniversityHigashi-HiroshimaJapan

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