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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

Schematic diagram of the experimental system.
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sensors-15-15198-f002: Schematic diagram of the experimental system.

Mentions: The sensitive characteristics of the sensors used are listed in Table 2. All sensors are placed in a 240 mL stainless steel chamber which is coated with Teflon to avoid the attachment of VOCs. The schematic diagram of the experimental system is shown in Figure 2. A three-way valve is used to change the gas circuit to let the desired gas flow into the chamber. The flow velocity of gas is controlled by a flow meter and its value is set as 80 mL/min. A data acquisition system (DAS) is employed for the sensor signal sampling and its sample frequency is set as 10 Hz. The response of sensors is firstly processed by the conditioning circuit and then sampled and saved in a computer via the DAS.


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)

Schematic diagram of the experimental system.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15198-f002: Schematic diagram of the experimental system.
Mentions: The sensitive characteristics of the sensors used are listed in Table 2. All sensors are placed in a 240 mL stainless steel chamber which is coated with Teflon to avoid the attachment of VOCs. The schematic diagram of the experimental system is shown in Figure 2. A three-way valve is used to change the gas circuit to let the desired gas flow into the chamber. The flow velocity of gas is controlled by a flow meter and its value is set as 80 mL/min. A data acquisition system (DAS) is employed for the sensor signal sampling and its sample frequency is set as 10 Hz. The response of sensors is firstly processed by the conditioning circuit and then sampled and saved in a computer via the DAS.

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