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Pattern Recognition Applied to Analysis of Gas Sensors' Array Data

100%
Marczyński P., Szpakowski A., Tyszkiewicz C., Pustelny T.
Acta Physica Polonica A
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2012
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vol. 122
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issue 5
847-849
EN
For an array of eight chemoresistive gas sensors a computer pattern recognition system was built. Multivariate data analysis was performed for measurements of three gases' dilutions: hydrogen (H_2), methane (CH_4), and carbon monoxide (CO). The pattern recognition system included a feature subset selection algorithm involving PCA and objective function. Dimensionality reduction was applied to two kinds of patterns: three aforementioned gases and six different concentrations of hydrogen. For patterns of the three gases, classification tests were performed using k-NN algorithm and N-fold based validation method.
2
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Gas Sensing Supported by Pattern Recognition

100%
Tyszkiewicz C., Szpakowski A., Pustelny T.
Acta Physica Polonica A
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2009
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vol. 116
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issue 3
419-421
EN
The system composed of the array of eight semiconductor, chemoresistive gas sensors was used for the classification of hydrogen, methane and carbon oxide gaseous samples. The classification task was performed by pattern recognition methods applied to the multivariate response of the array. The pattern recognition scheme used for classification uses a feature subset selection algorithm coupled with an objective function designed for clustering and a multilayer perceptron classifier.
3
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Multivariate Analysis in Gas Sensing Applications

100%
Szpakowski A., Tyszkiewicz C., Pustelny T.
Acta Physica Polonica A
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2008
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vol. 114
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issue 6A
A-239-A-242
EN
In gas sensing applications we often use sensors which have poor selectivity. Such sensors are mature, cheap devices with simple application circuits. Unfortunately, their cross sensitivity greatly restricts their usefulness when used in systems which employs univariate analysis. One method which allows to overcome this difficulty is utilization of multivariate analysis methods applied to the response from the group of such non-selective sensors. This work presents the multivariate, pattern recognition system utilized to process the response of an array of non-selective semiconductor sensors to obtain the qualitative information.
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