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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

PCA score plot of different features.
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sensors-15-15198-f009: PCA score plot of different features.

Mentions: To visualize the efficacy of the proposed method, PCA is applied for the peak value, WFC and MWFC features and the PCA score plots are shown from Figure 9, respectively. The higher degree of overlaps of four kinds of samples can be observed in Figure 9a and the distribution of four kinds of samples is relatively dispersive in Figure 9b, whereas, in Figure 9c, the cluster of four kind of samples are overlapping little and can be more easily distinguished. In a word, the performance of classification with the MWFC method is better than the others.


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)

PCA score plot of different features.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15198-f009: PCA score plot of different features.
Mentions: To visualize the efficacy of the proposed method, PCA is applied for the peak value, WFC and MWFC features and the PCA score plots are shown from Figure 9, respectively. The higher degree of overlaps of four kinds of samples can be observed in Figure 9a and the distribution of four kinds of samples is relatively dispersive in Figure 9b, whereas, in Figure 9c, the cluster of four kind of samples are overlapping little and can be more easily distinguished. In a word, the performance of classification with the MWFC method is better than the others.

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