Fujinaka, Toru (Osaka Prefecture Univ.), Omatu, Sigeru (Osaka Prefecture Univ.)

Electric Nose System Using Metal Oxide Gas Sensors

Scheduled for presentation during the Regular Session "Neural Networks II" (ThA12), Thursday, July 27, 2006, 10:50−11:15, Room 104b

17th International Symposium on Mathematical Theory of Networks and Systems, July 24-28, 2006, Kyoto, Japan

This information is tentative and subject to change. Compiled on May 19, 2024

Abstract

In this paper, a reliable electronic nose (EN) system designed from the combination of various metal oxide gas sensors (MOGS) is applied to detect the early stage of fire from various sources. The time series signals of the same source of fire in every repetition data are highly correlated, and each source of fire has a unique pattern of time series data. Therefore, the error back propagation (BP) method can classify the tested smell with 99.6% of correct classification by using only a single training data from each source of fire. The results of the k-means algorithms reach 98.3% of correct classification, which also show the high ability of the EN to detect the early stage of fire from various sources accurately.