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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

Optimal importance factors with QPSO for 15 sensors.
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sensors-15-15198-f008: Optimal importance factors with QPSO for 15 sensors.

Mentions: The importance of the 15 sensors is shown in Figure 8, where the corresponding optimal normalizing importance factors, that is the weighting coefficients of sensors, are [0.7445, 0.1032, 0.1144, 0.0180, 0.0816, 0.2771, 0.0668, 0.0224, 0.9095, 0.0299, 0.0539, 1.0000, 0.0279, 0.5109, 0.0646].


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)

Optimal importance factors with QPSO for 15 sensors.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15198-f008: Optimal importance factors with QPSO for 15 sensors.
Mentions: The importance of the 15 sensors is shown in Figure 8, where the corresponding optimal normalizing importance factors, that is the weighting coefficients of sensors, are [0.7445, 0.1032, 0.1144, 0.0180, 0.0816, 0.2771, 0.0668, 0.0224, 0.9095, 0.0299, 0.0539, 1.0000, 0.0279, 0.5109, 0.0646].

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