| Summary: | 碩士 === 國立清華大學 === 電機工程學系 === 103 === In current research of artificial olfaction, effective analysis and recognition of mixed odors is still a challenging issue. Considering the specific case of a gas sensor array exposed to multiple target gases and various background gases simultaneously, and the sensor data are subject to interference from the environment such as temperature and humidity. In this research, we intend to find out some signal processing methods to get meaningful responses as multivariate features for odor identification (classification) and concentration estimation (regression). First, the thesis compares the performance with conductance difference changes and resistance fractional changes, the results show that using of conductance can achieve better accuracy rates. Secondly, we use transient features together with steady-state features. The underlying idea is that the transient phase may include additional information concerning to the constituting gases that the steady state does not provide. For the odor identification tasks, we perform multi-label classification using so-called the Individual Constituent Decision Method (ICDM) instead of conventional multi-class classification. The results show that multi-label classification reduces the computational complexity and improves the recognition accuracy. For the concentration estimation tasks, Multiple Linear Regression (MLR) is the common approach to estimate the concentration of individual constituents. In addition, in order to avoid the collinearity problem, this thesis uses the Partial Least Squares (PLS) method to estimate the concentration, and this method can also be applied to the classification problem. The results show that we can reasonably estimate the concentration of the individual constituent with conductance responses when two analyte gases existed simultaneously.
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