A Transient Signal Extraction Method of WO<sub>3</sub>Gas Sensors Array to Identify Polluant Gases

Faleh, Rabeb; Othman, Mehdi; Gomri, Sami; Aguir, Khalifa; Kachouri, Abdennaceur

doi:10.1109/jsen.2016.2521578

Cited by 34 publications

(25 citation statements)

References 36 publications

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“…To circumvent this drawback, the PCA algorithm is applied to reduce the number of variables prior application of the CVA [22]. PCA is amongst the most widely applied unsupervised feature extraction methods [25], [36], [37]. PCA is aimed to condense the statistically significant information explained by the original variables into a reduced set of orthogonal latent variables called principal components or PCs [35].…”

Section: Feature Extraction and Classification Methodsmentioning

confidence: 99%

Identification of NR and EPDM Samples by Means of Thermogravimetric Analysis and Multivariate Methods

2016

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Section: Feature Extraction and Classification Methodsmentioning

confidence: 99%

Identification of NR and EPDM Samples by Means of Thermogravimetric Analysis and Multivariate Methods

2016

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“…Tungsten oxide (WO3) is very suitable for gas controlling applications [51,52]. Take this latter material, for example, which is especially sensitive to ozone [53], A large number of researchers developed and tested a WO3 gas sensor to detect various gases, such as NO2, where it was shown that the optimum working temperature for detecting this gas was approximately 225 ° C [54] and H2 where the best annealing temperature was just about 500 °C [55].…”

Section: Related Workmentioning

confidence: 99%

“…SVM presents a considerable attention in gas classification in real time [77,78]. R Faleh et al [52] showed in their research that successful classifications have been reached in the discrimination of three kinds of oxidizing and reducing gas using support vector machine.…”

Section: Related Workmentioning

confidence: 99%

Review on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring

Faleh¹,

Bedoui²,

Kachouri³

2020

Adv. sci. technol. eng. syst. j.

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With the advent of the Internet of Things Technologies (IOT), smart homes, and smart city applications, E-Nose was created. Almost of gas sensors consisting the electronic nose system suffer from cross sensitivity and lack of selectivity. Coupling smart gas sensors with artificial intelligence algorithms can thus empower conventional gas sensing technologies and increase accuracy in gas detection. This study describes the overall types of smart gas sensors used in air quality control, signal pre-processing and extraction features. Also it presents pattern recognition methods used in E-nose applications including linear methods such as Linear Discriminant Analysis LDA, K-Nearest Neighbors KNN and Non-linear such as algorithms Support vector Machine SVM and Artificial Neural Network ANN and their impact on improving accuracy rate for gas detection. Finally, this paper summarizes by providing directions for about how to leverage the benefits of combining these classifiers which is known as Data fusion approach and ensemble classifiers.

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“…Feature extraction techniques are intended to cope with the redundancy problem by selecting a subset of features that can facilitate data interpretation while reducing data storage requirements and improving prediction performance [63,64,65,66,67]. …”

Section: Electronic Nose (E-nose)mentioning

confidence: 99%

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges

Zhou

Luo

GholamHosseini

et al. 2017

Sensors

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This paper provides a review of the most recent works in machine olfaction as applied to the identification of Chinese Herbal Medicines (CHMs). Due to the wide variety of CHMs, the complexity of growing sources and the diverse specifications of herb components, the quality control of CHMs is a challenging issue. Much research has demonstrated that an electronic nose (E-nose) as an advanced machine olfaction system, can overcome this challenge through identification of the complex odors of CHMs. E-nose technology, with better usability, high sensitivity, real-time detection and non-destructive features has shown better performance in comparison with other analytical techniques such as gas chromatography-mass spectrometry (GC-MS). Although there has been immense development of E-nose techniques in other applications, there are limited reports on the application of E-noses for the quality control of CHMs. The aim of current study is to review practical implementation and advantages of E-noses for robust and effective odor identification of CHMs. It covers the use of E-nose technology to study the effects of growing regions, identification methods, production procedures and storage time on CHMs. Moreover, the challenges and applications of E-nose for CHM identification are investigated. Based on the advancement in E-nose technology, odor may become a new quantitative index for quality control of CHMs and drug discovery. It was also found that more research could be done in the area of odor standardization and odor reproduction for remote sensing.

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A Transient Signal Extraction Method of WO₃Gas Sensors Array to Identify Polluant Gases

Cited by 34 publications

References 36 publications

Identification of NR and EPDM Samples by Means of Thermogravimetric Analysis and Multivariate Methods

Identification of NR and EPDM Samples by Means of Thermogravimetric Analysis and Multivariate Methods

Review on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges

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A Transient Signal Extraction Method of WO3Gas Sensors Array to Identify Polluant Gases

Cited by 34 publications

References 36 publications

Identification of NR and EPDM Samples by Means of Thermogravimetric Analysis and Multivariate Methods

Identification of NR and EPDM Samples by Means of Thermogravimetric Analysis and Multivariate Methods

Review on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges

Contact Info

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A Transient Signal Extraction Method of WO₃Gas Sensors Array to Identify Polluant Gases