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