Chemical gas sensor array dataset

Fonollosa, Jordi; Rodríguez-Luján, Irene; Huerta, Ramón

doi:10.1016/j.dib.2015.01.003

Cited by 40 publications

(27 citation statements)

References 5 publications

(13 reference statements)

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“…In the last few years, the research followed two pathways to overcome these limitations. On the one hand, artificial intelligence and new methods of data analysis, e.g., machine learning, were used to improve the MOX sensing performances [ 10 ]. In particular, specifically calibrated arrays of MOX gas sensors equipped with a dedicated algorithm have proven to be suitable for different applications [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Nanostructured SmFeO3 Gas Sensors: Investigation of the Gas Sensing Performance Reproducibility for Colorectal Cancer Screening

Gaiardo

Zonta

Gherardi

et al. 2020

Sensors

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Among the various chemoresistive gas sensing properties studied so far, the sensing response reproducibility, i.e., the capability to reproduce a device with the same sensing performance, has been poorly investigated. However, the reproducibility of the gas sensing performance is of fundamental importance for the employment of these devices in on-field applications, and to demonstrate the reliability of the process development. This sensor property became crucial for the preparation of medical diagnostic tools, in which the use of specific chemoresistive gas sensors along with a dedicated algorithm can be used for screening diseases. In this work, the reproducibility of SmFeO3 perovskite-based gas sensors has been investigated. A set of four SmFeO3 devices, obtained from the same screen-printing deposition, have been tested in laboratory with both controlled concentrations of CO and biological fecal samples. The fecal samples tested were employed in the clinical validation protocol of a prototype for non-invasive colorectal cancer prescreening. Sensors showed a high reproducibility degree, with an error lower than 2% of the response value for the test with CO and lower than 6% for fecal samples. Finally, the reproducibility of the SmFeO3 sensor response and recovery times for fecal samples was also evaluated.

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Section: Introductionmentioning

confidence: 99%

Nanostructured SmFeO3 Gas Sensors: Investigation of the Gas Sensing Performance Reproducibility for Colorectal Cancer Screening

Gaiardo

Zonta

Gherardi

et al. 2020

Sensors

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“…For each gas sensor, the reading contains two steady status and six dynamic measurements. For details of the dataset, see [ 52 ].…”

Section: Methodsmentioning

confidence: 99%

Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

Luo

Qin

et al. 2018

Sensors

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Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy.

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“…Thousands of publications and several extensive datasets [305]- [308] are available for the sequential approach. This number becomes much smaller for gas mixtures.…”

Section: Calibration Profilementioning

confidence: 99%

Improving the Performance of Gas Sensor Systems with Advanced Data Evaluation, Operation, and Calibration Methods

Bastuck

2019

Linköping Studies in Science and Technology. Dissertations

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In order to facilitate the widespread use of gas sensors, some challenges must still be overcome. Many of those are related to the reliable quantification of ultra-low concentrations of specific compounds in a background of other gases. This thesis focuses on three important items in the measurement chain: sensor material and operating modes, evaluation of the resulting data, and test gas generation for efficient sensor calibration. New operating modes and materials for gas-sensitive field-effect transistors have been investigated. Tungsten trioxide as gate oxide can improve the selectivity to hazardous volatile organic compounds like naphthalene even in a strong and variable ethanol background. The influence of gate bias and ultraviolet light has been studied with respect to the transport of oxygen anions on the sensor surface and was used to improve classification and quantification of different gases. DAV 3 E, an internationally recognized MATLAB-based toolbox for the evaluation of cyclic sensor data, has been developed and published as opensource. It provides a user-friendly graphical interface and specially tailored algorithms from multivariate statistics. The laboratory tests conducted during this project have been extended with an interlaboratory study and a field test, both yielding valuable insights for future, more complex sensor calibration. A novel, efficient calibration approach has been proposed and evaluated with ten different gas sensor systems. i styrning och data-utvärdering som tagits fram har både en jämförande undersökning av modellernas/metodernas prestanda av två oberoende laboratorier och fält-mätningar i en av de tilltänkta tillämpningarna genomförts. Bl.a. baserat på resultaten och insikterna från dessa övningar har ett helt nytt angreppssätt avseende robust och effektiv kalibrering och kvalitets-utvärdering av sensor-system utvecklats och utvärderats för tio olika sensor-system. v Preface This dissertation is the result of my binational doctorate supervised by Prof.

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Chemical gas sensor array dataset

Cited by 40 publications

References 5 publications

Nanostructured SmFeO3 Gas Sensors: Investigation of the Gas Sensing Performance Reproducibility for Colorectal Cancer Screening

Nanostructured SmFeO3 Gas Sensors: Investigation of the Gas Sensing Performance Reproducibility for Colorectal Cancer Screening

Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

Improving the Performance of Gas Sensor Systems with Advanced Data Evaluation, Operation, and Calibration Methods

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