Identify Smells Using Time Series Data from Metal Oxide Gas Sensors

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概要

• 論文の詳細を見る

• Nowadays, various metal oxide gas sensors (MOGSs) are widely combined and used as an electronic nose (EN). Most of the ENs always use only single feature (e.g. peak signal, average signal of saturation stage) from each MOGS without considering the signals before reaching the saturation points. In this letter, we increase the ability of an EN to identify smells that yield nearly the same saturation points without increasing the number of MOGS by using the information from the time series signals of MOGSs during absorbing the tested odors. The results from multivariate analysis show perfectly classification in all tested odors.

• 社団法人 電気学会の論文
• 2003-10-01

著者

• Omatu S

  Division Of Computer And Systems Sciences Graduate School Of Engineering Osaka Prefecture University

• CHARUMPORN Bancha

  Division of Computer and Systems Sciences, Graduate School of Engineering, Osaka Prefecture Universi

• YOSHIOKA Michifumi

  Division of Computer and Systems Sciences, Graduate School of Engineering, Osaka Prefecture Universi

• OMATU Sigeru

  Division of Computer and Systems Sciences, Graduate School of Engineering, Osaka Prefecture Universi

• Yoshioka M

  Division Of Computer And Systems Sciences Graduate School Of Engineering Osaka Prefecture University

• Yoshioka Michifumi

  Division Of Computer And Systems Sciences Graduate School Of Engineering Osaka Prefecture University

• Charumporn Bancha

  Division Of Computer And Systems Sciences Graduate School Of Engineering Osaka Prefecture University

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