Improvement of Robustness of Odor Classification in Dynamically Changing Concentration Against Environmental Change

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In this paper, we propose a method for improving the robustness of odor classification against humidity change when the odor concentration changes dynamically. We apply a short-time Fourier transform (STFT) to sensor responses to obtain the frequency characteristics, and then employ a stepwise discriminant analysis to select the frequency components effective for the odor classification. We improve the classification performance by selecting the components robust against humidity change and combining them with humidity data. Using a learning vector quantization (LVQ) method, we successfully achieved high classification rate even if the odor concentration changed dynamically and irregularly at different humidity levels whereas the classification rate was insufficient in the case of using only magnitudes of sensor responses.

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  • 電気学会論文誌. E, センサ・マイクロマシン準部門誌 = The transactions of the Institute of Electrical Engineers of Japan. A publication of Sensors and Micromachines Society  

    電気学会論文誌. E, センサ・マイクロマシン準部門誌 = The transactions of the Institute of Electrical Engineers of Japan. A publication of Sensors and Micromachines Society 128(5), 214-218, 2008-05-01 

    The Institute of Electrical Engineers of Japan

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各種コード

  • NII論文ID(NAID)
    10021135326
  • NII書誌ID(NCID)
    AN1052634X
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    13418939
  • NDL 記事登録ID
    9496188
  • NDL 雑誌分類
    ZN31(科学技術--電気工学・電気機械工業)
  • NDL 請求記号
    Z16-B380
  • データ提供元
    CJP書誌  NDL  J-STAGE 
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