Advances in Distributed Computing and Artificial Intelligence Journal
Mixed Odor Classification for QCM Sensor Data by Neural Network
Authors:
Sigeru OMATU, Hideo ARAKI, Toru FUJINAKA, Mitsuaki YANO, Michifumi YOSHIOKA, Hiroyuki NAKAZUMI, Ichiro TANAHASHI
DOI:
10.14201/ADCAIJ2012124348
Volume:
Regular Issue 1 (2), 2012
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.
MILKE , John. Application of Neural Networks for discriminating Fire Detectors, International Conference on Automatic Fire Detection, AUBE’95, 10th, Duisburg, 1995. Germany
CHARUMPORN, Bancha. An Electronic Nose System Using Back Propagation Neural Networks with a Centroid Training Data Set, Proc.
Eighth International Symposium on Artificial Life and Robotics, 2003. Japan
FUJINAKA, Toru, Intelligent Electronic Nose Systems for Fire Detection Systems Based on Neural Networks, The second International Conference on Advanced Engineering Computing and Applications in Sciences, 2008. Spain
OMATU, Sigeru, Intelligent Electronic Nose System Independent on Odor
Concentration, International Symposium on Distributed Computing and Artificial Intelligence, 2011. Spain