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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

The schematic diagram of WFC technique.
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sensors-15-15198-f004: The schematic diagram of WFC technique.

Mentions: Then we can choose the value of the area surrounded by two curves as extracted features and refer to this method as window function capturing (WFC). The schematic diagram of the feature extraction approach using WFC is shown in Figure 4. The advantage of WFC is that it can be employed as a filter to capture information from the time domain rather than spectral representations. There are several kinds of common window functions, as shown in Table 3, and the performance of the E-nose will be changed by changing the width, position, shape of the window. In addition, we make the window move along with the time axis and simultaneously choose the area values of two curves during the moving process as features, which is referred to as moving window function capturing (MWFC). We place a 64 points window around the peak value and then make the window move 64 points to the left and right along with the time axis, respectively. Thus three area values surrounded by two curves can be obtained during the moving process and we can choose the three area values as features simultaneously. The schematic diagram of this method referred as MWFC is shown in Figure 5.


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)

The schematic diagram of WFC technique.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15198-f004: The schematic diagram of WFC technique.
Mentions: Then we can choose the value of the area surrounded by two curves as extracted features and refer to this method as window function capturing (WFC). The schematic diagram of the feature extraction approach using WFC is shown in Figure 4. The advantage of WFC is that it can be employed as a filter to capture information from the time domain rather than spectral representations. There are several kinds of common window functions, as shown in Table 3, and the performance of the E-nose will be changed by changing the width, position, shape of the window. In addition, we make the window move along with the time axis and simultaneously choose the area values of two curves during the moving process as features, which is referred to as moving window function capturing (MWFC). We place a 64 points window around the peak value and then make the window move 64 points to the left and right along with the time axis, respectively. Thus three area values surrounded by two curves can be obtained during the moving process and we can choose the three area values as features simultaneously. The schematic diagram of this method referred as MWFC is shown in Figure 5.

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