Odor Classification Based on Weakly Responding Sensors
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%.
Keywordsfeature vector scaling weak odor signals odor classification neural networks
Unable to display preview. Download preview PDF.
- 1.Milke, J.A.: Application of Neural Networks for discriminating Fire Detectors. In: 10th International Conference on Automatic Fire Detection, AUBE 1995, Duisburg, Germany, pp. 213–222 (1995)Google Scholar
- 4.Omatu, S., Araki, H., Fujinaka, T., Yano, M.: Intelligent Classification of Odor Data Using Neural Networks. In: ADVCOMP 2012, Barcelona, Spain, pp. 1–7 (2012)Google Scholar
- 5.Omatu, S.: Pattern Analysis for Odor Sensing System, pp. 20–34. IGI Global (2012)Google Scholar
- 6.Fujinaka, T., Yoshioka, M., Omatu, S., Kosaka, T.: Intelligent Electronic Nose Systems for Fire 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
- 7.General Information for TGS sensors, Figaro Engineering (2012), http://www.figarosensor.com/products/general.pdf