Application of sensor dynamic response analysis to improve the accuracy of odour-measuring systems

Sobański, Tomasz; Modrak, Ireneusz; Nitsch, K.; Licznerski, B.W.

doi:10.1088/0957-0233/17/1/001

Cited by 15 publications

(5 citation statements)

References 16 publications

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“…Independent features are expected to be extracted from the measurement data of a gas sensor array. Feature extraction and selection play key roles in the signal processing of an electronic nose [8,9].…”

Section: Introductionmentioning

confidence: 99%

Discriminative power of independent component analysis applied to an electronic nose

Sun

Zheng

2019

Meas. Sci. Technol.

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Electronic noses have been developed to analyze gas composition in many applications. The potential analytical power of an electronic nose needs to be investigated with regard to detection in a complicated gas environment. To date, principal component analysis (PCA) has been a popular analytical tool for use with electronic noses; however, PCA reaches its limits when the measurement data exhibit nonGaussian behavior. Components extracted by independent component analysis (ICA) are expected to have a high discriminative power, conducive to improving the performance of an electronic nose. To verify this hypothesis, the performance of principal components (PCs) and independent components (ICs) in classification and regression tasks is compared. The data were obtained from a chemical gas sensor array for volatile organic compounds (VOCs). In the classification task, silhouette coefficients and Kmeans are employed to measure the performance of ICs and PCs. ICs have evident classification advantages over PCs because of their higher accuracy. In the regression task, ICs and PCs are applied via a neural network to predict the concentration of four VOCs. The errors in the prediction are calculated and analyzed, and it is found that ICs continue to outperform PCs during the regression task. The results of this study demonstrate the potential and ability of ICA to extract features when used with an electronic nose in a complicated gas environment.

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Section: Introductionmentioning

confidence: 99%

Discriminative power of independent component analysis applied to an electronic nose

Sun

Zheng

2019

Meas. Sci. Technol.

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“…The possibilities of shortening the response time by a suitable design of the sensor or applying better materials of the sensing layer are still being examined [ 8 , 9 , 10 ]. In some cases, measurements are based on the specific use of transient characteristics of the sensor [ 11 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Correction of Dynamic Errors of a Gas Sensor Based on a Parametric Method and a Neural Network Technique

Roj

2016

Sensors

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The paper presents two methods of dynamic error correction applied to transducers used for the measurement of gas concentration. One of them is based on a parametric model of the transducer dynamics, and the second one uses the artificial neural network (ANN) technique. This article describes research of the dynamic properties of the gas concentration measuring transducer with a typical sensor based on tin dioxide. Its response time is about 8 min, which may be not acceptable in many applications. On the basis of these studies, a parametric model of the transducer dynamics and an adequate correction algorithm has been developed. The results obtained in the research of the transducer were also used for learning and testing ANN, which were implemented in the dynamic correction task. Despite the simplicity of the used models, both methods allowed a significant reduction of the transducer’s response time. For the algorithm based on the parametric model the response time was shorter by approximately eight-fold (reduced up to 40–80 s, i.e., about 2–4 sample periods), whereas with the use of an ANN the output signal was practically fixed after a time equal to one sampling period, i.e., 20 s. In addition, the use of ANN has allowed reducing the impact of the transducer dynamic non-linearity on the correction effectiveness.

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“…[6][7][8][9][10][11] Dynamic responses as well as static responses of single gas sensor element or using sensor array have been utilized for classification of gases/odors as reported in Refs. [12][13][14][15][16][17][18]. It can therefore be observed that much complex and intricate schemes have been employed for the identification of gases/odors.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Response, Transformed Cluster Analysis and Radial Basis Function Neural Network Based Gases/Odors Identification Approach Using a Thick Film Gas Sensor Array

Kumar¹,

Kumar²,

Mishra³

et al. 2014

j comput theor nanosci

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In the present study, an experiment to identify various gases/odors has been presented. This approach is based on the dynamic responses of a thick film gas sensor array. The dynamic response of the gas sensor array for four gases viz. LPG, CCl 4 , CO and C 3 H 7 OH was first transformed into well separated data using modified transformed cluster analysis (MTCA) technique which is a minor variant of published basic transformed cluster analysis (TCA) data preprocessing method. Subsequently, a radial basis function neural network (RBFNN) was trained using the MTCA transformed data for identification of samples of respective gases/odors. The performance of the best trained radial basis function neural network using MTCA transformed data for 24 unknown test samples had been 100% accurate while the accuracy was 82% only when the same RBFNN was trained with respective raw form of dynamic gas sensor array response. We therefore report that superior identification could be obtained with MTCA cum RBFNN methods applied to the dynamic responses of gas sensor array.

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Application of sensor dynamic response analysis to improve the accuracy of odour-measuring systems

Cited by 15 publications

References 16 publications

Discriminative power of independent component analysis applied to an electronic nose

Discriminative power of independent component analysis applied to an electronic nose

Correction of Dynamic Errors of a Gas Sensor Based on a Parametric Method and a Neural Network Technique

A Dynamic Response, Transformed Cluster Analysis and Radial Basis Function Neural Network Based Gases/Odors Identification Approach Using a Thick Film Gas Sensor Array

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