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A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance.

Guo X, Peng C, Zhang S, Yan J, Duan S, Wang L, Jia P, Tian F - Sensors (Basel) (2015)

Bottom Line: The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques.Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows.By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced.

View Article: PubMed Central - PubMed

Affiliation: College of Electronics and Information Engineering, Southwest University, Chongqing 400715, China. swugxz@163.com.

ABSTRACT
In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC) has been proposed to analyze signals of an electronic nose (E-nose) used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO) algorithm is implemented in conjunction with support vector machine (SVM) for realizing a synchronization optimization of the sensor array and SVM model parameters. The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques. Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows. By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced.

No MeSH data available.


Related in: MedlinePlus

Sensor array.
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sensors-15-15198-f001: Sensor array.

Mentions: The metabolites in the reproduction process of the three pathogens are shown in Table 1. According to the pathogen metabolites in Table 1 and the sensitive characteristics of gas sensors, fourteen metal oxide sensors and one electrochemical sensor are selected to construct the sensor array (shown in Figure 1). They are nine TGS sensors (TGS2600, TGS2602, TGS2620, TGS800, TGS822, TGS825, TGS826, TGS813, TGS816) from Figaro Engineering Inc. (Tianjin, China), one WSP-2111 XSC sensor from New Creators Electronic Technology Co. Ltd. (Shenzhen, China), two MQ sensors (MQ135, MQ138) from Winsen Electronics Technology Co. Ltd. (Zhengzhou, China), one QS-01 sensor from Bluemoon Technology Co. Ltd. (Shenzhen, China), one SP3S-AQ2 FIS sensor from FIS Inc. (Itami, Japan), and one AQ electrochemical sensor from Dart Sensors Ltd. (Exeter, UK).


A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance.

Guo X, Peng C, Zhang S, Yan J, Duan S, Wang L, Jia P, Tian F - Sensors (Basel) (2015)

Sensor array.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541827&req=5

sensors-15-15198-f001: Sensor array.
Mentions: The metabolites in the reproduction process of the three pathogens are shown in Table 1. According to the pathogen metabolites in Table 1 and the sensitive characteristics of gas sensors, fourteen metal oxide sensors and one electrochemical sensor are selected to construct the sensor array (shown in Figure 1). They are nine TGS sensors (TGS2600, TGS2602, TGS2620, TGS800, TGS822, TGS825, TGS826, TGS813, TGS816) from Figaro Engineering Inc. (Tianjin, China), one WSP-2111 XSC sensor from New Creators Electronic Technology Co. Ltd. (Shenzhen, China), two MQ sensors (MQ135, MQ138) from Winsen Electronics Technology Co. Ltd. (Zhengzhou, China), one QS-01 sensor from Bluemoon Technology Co. Ltd. (Shenzhen, China), one SP3S-AQ2 FIS sensor from FIS Inc. (Itami, Japan), and one AQ electrochemical sensor from Dart Sensors Ltd. (Exeter, UK).

Bottom Line: The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques.Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows.By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced.

View Article: PubMed Central - PubMed

Affiliation: College of Electronics and Information Engineering, Southwest University, Chongqing 400715, China. swugxz@163.com.

ABSTRACT
In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC) has been proposed to analyze signals of an electronic nose (E-nose) used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO) algorithm is implemented in conjunction with support vector machine (SVM) for realizing a synchronization optimization of the sensor array and SVM model parameters. The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques. Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows. By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced.

No MeSH data available.


Related in: MedlinePlus