Recognition of O&lt;inf&gt;3&lt;/inf&gt; concentration using WO&lt;inf&gt;3&lt;/inf&gt; gas sensor and principal component analysis

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

doi:10.1109/atsip.2014.6834629

Cited by 4 publications

(6 citation statements)

References 16 publications

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“…For one to obtain a PCA graph with great discriminating ability among different classes, a careful selection and gradation of important features is highly recommended using a variance analysis test (ANOVA F-test) and heat maps in order to avoid the data overlapping. For example, Faleh et al [131] studied the recognition of ozone (O 3 ) using an array of four WO 3 sensors and PCA calculations. They reported that the static parameter R gas /R air is not sufficient to distinguish among various concentrations of the target gas.…”

Section: Chemiresistive Type Smart Gas Sensors Using Machine Learningmentioning

confidence: 99%

“…Therefore, for better discrimination among various concentrations, they used the area under the response time curve from the dynamic (transient) response. It was concluded that using the response time parameter, the class separation among different concentrations was much better than the resistance ratio [131]. Later, Nallon et al [123] used unmodified graphene as a single sensor for discrimination among different chemicals/compounds.…”

Section: Chemiresistive Type Smart Gas Sensors Using Machine Learningmentioning

confidence: 99%

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Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning—A Review

Yaqoob

Younis

2021

Sensors

127

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Nowadays, there is increasing interest in fast, accurate, and highly sensitive smart gas sensors with excellent selectivity boosted by the high demand for environmental safety and healthcare applications. Significant research has been conducted to develop sensors based on novel highly sensitive and selective materials. Computational and experimental studies have been explored in order to identify the key factors in providing the maximum active location for gas molecule adsorption including bandgap tuning through nanostructures, metal/metal oxide catalytic reactions, and nano junction formations. However, there are still great challenges, specifically in terms of selectivity, which raises the need for combining interdisciplinary fields to build smarter and high-performance gas/chemical sensing devices. This review discusses current major gas sensing performance-enhancing methods, their advantages, and limitations, especially in terms of selectivity and long-term stability. The discussion then establishes a case for the use of smart machine learning techniques, which offer effective data processing approaches, for the development of highly selective smart gas sensors. We highlight the effectiveness of static, dynamic, and frequency domain feature extraction techniques. Additionally, cross-validation methods are also covered; in particular, the manipulation of the k-fold cross-validation is discussed to accurately train a model according to the available datasets. We summarize different chemresistive and FET gas sensors and highlight their shortcomings, and then propose the potential of machine learning as a possible and feasible option. The review concludes that machine learning can be very promising in terms of building the future generation of smart, sensitive, and selective sensors.

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Section: Chemiresistive Type Smart Gas Sensors Using Machine Learningmentioning

confidence: 99%

Section: Chemiresistive Type Smart Gas Sensors Using Machine Learningmentioning

confidence: 99%

Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning—A Review

Yaqoob

Younis

2021

Sensors

127

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“…Konstantynovski et al [94] use PCA to process signal change rates from physical sensors and corrected resistance value from metal oxide gas sensors. R. Faleh et al [95] use a sensor array based on WO3 gas sensors to detect O3. In their research, PCA is used to evaluate the contribution to the classification in order to enhance the sensors’ selectivity based on a database with four gas sensors.…”

Section: Gas Sensors Array and Signal Preprocessingmentioning

confidence: 99%

“…The advantage of PCA is to eliminate the correlation between evaluation indicators, reduce the workload of indicator selection, and keep most of the information while saving data space. As shown in Figure 6 [95], fact 1 (the response time parameter) and fact 2 (the separation concentration) use only 75% of the information to represent 99% of the information of gas database.…”

Section: Gas Sensors Array and Signal Preprocessingmentioning

confidence: 99%

Review on Smart Gas Sensing Technology

Feng

Farha

et al. 2019

Sensors

225

122

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With the development of the Internet-of-Things (IoT) technology, the applications of gas sensors in the fields of smart homes, wearable devices, and smart mobile terminals have developed by leaps and bounds. In such complex sensing scenarios, the gas sensor shows the defects of cross sensitivity and low selectivity. Therefore, smart gas sensing methods have been proposed to address these issues by adding sensor arrays, signal processing, and machine learning techniques to traditional gas sensing technologies. This review introduces the reader to the overall framework of smart gas sensing technology, including three key points; gas sensor arrays made of different materials, signal processing for drift compensation and feature extraction, and gas pattern recognition including Support Vector Machine (SVM), Artificial Neural Network (ANN), and other techniques. The implementation, evaluation, and comparison of the proposed solutions in each step have been summarized covering most of the relevant recently published studies. This review also highlights the challenges facing smart gas sensing technology represented by repeatability and reusability, circuit integration and miniaturization, and real-time sensing. Besides, the proposed solutions, which show the future directions of smart gas sensing, are explored. Finally, the recommendations for smart gas sensing based on brain-like sensing are provided in this paper.

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“…E-nose is an intelligent artificial system that mimics the human olfactory system, it consists of a gas sensor matrix coupled to a data acquisition system. The obtained data will be processed through a classification program in order to identify and quantify the examined gas [17], [18], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Acetone discriminator and concentration estimator for diabetes monitoring in human breath

Boumali¹,

Benhabiles²,

Bouziane³

et al. 2021

Semicond. Sci. Technol.

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Recognition of O<inf>3</inf> concentration using WO<inf>3</inf> gas sensor and principal component analysis

Cited by 4 publications

References 16 publications

Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning—A Review

Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning—A Review

Review on Smart Gas Sensing Technology

Acetone discriminator and concentration estimator for diabetes monitoring in human breath

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