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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

Response of E-nose to a wound infected with P. aeruginosa.
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sensors-15-15198-f003: Response of E-nose to a wound infected with P. aeruginosa.

Mentions: Each rat is placed in a jar with a volume of 2.8 L equipped with a rubber stopper. Two holes are made in the rubber stopper where two thin glass tubes were nserted, respectively. One glass tube is fixed above the wound as close as possible. The output gases of the tube which contains VOCs of the rat wound flow out of the bottle through the glass tube, and then flow into the test chamber through a Teflon tube. Clean air flows into the bottle through another glass tube. The dynamic headspace method is adopted during all the experiments, and the process is as follows: the first stage is the baseline stage, in which the sensors are exposed to clean air for three minutes. The second stage is the response stage, which the gas stream containing VOCs of the wound passes over the sensors for five minutes. The third stage is the recovery stage: the sensors are exposed to clean air again for fifteen minutes. At the end of each experiment, prior to the next experiment, a five minutes purging of the sensor chamber using clean air is performed. The gas flow is controlled by a gas flow rate control system, which contains a rotor flow meter, a pressure retaining valve, a steady flow valve and a needle valve. The flow rate is kept at 80 mL/min. Twenty experiments for each kind of rats in the same conditions are made, and so 80 samples are collected. The sensor response curves for one wound infected with P. aeruginosa are shown in Figure 3.


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)

Response of E-nose to a wound infected with P. aeruginosa.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15198-f003: Response of E-nose to a wound infected with P. aeruginosa.
Mentions: Each rat is placed in a jar with a volume of 2.8 L equipped with a rubber stopper. Two holes are made in the rubber stopper where two thin glass tubes were nserted, respectively. One glass tube is fixed above the wound as close as possible. The output gases of the tube which contains VOCs of the rat wound flow out of the bottle through the glass tube, and then flow into the test chamber through a Teflon tube. Clean air flows into the bottle through another glass tube. The dynamic headspace method is adopted during all the experiments, and the process is as follows: the first stage is the baseline stage, in which the sensors are exposed to clean air for three minutes. The second stage is the response stage, which the gas stream containing VOCs of the wound passes over the sensors for five minutes. The third stage is the recovery stage: the sensors are exposed to clean air again for fifteen minutes. At the end of each experiment, prior to the next experiment, a five minutes purging of the sensor chamber using clean air is performed. The gas flow is controlled by a gas flow rate control system, which contains a rotor flow meter, a pressure retaining valve, a steady flow valve and a needle valve. The flow rate is kept at 80 mL/min. Twenty experiments for each kind of rats in the same conditions are made, and so 80 samples are collected. The sensor response curves for one wound infected with P. aeruginosa are shown in Figure 3.

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