New approach for gas identification using supervised learning methods (SVM and LVQ)

This article proposes a new approach for gas identification, this approach relies on applying supervised learning methods to identify a single gas as well as a mixture of two gases. The gas is trapped in a gas discharge tube, it is then ionized at a relatively low pressure using an HV transformer. The images captured after the ionization of each single gas is then captured and transformed into a database after being treated in order to be classified. The obtained results were very satisfying for SVM as well as for LVQ. For the case of identification of a single gas, the learning rate as well as the validation rate for both methods were 100%. However, for the case of mixture of two gases, a Multi-Layer Perceptron neural network was used to identify the gases, the learning rate as well as the validation rate were 98.59% and 98.77% respectively. The program developed on MATLAB takes the captured image as an input and outputs the identified gases for the user. The gases used in the experiments are Argon (Ar), oxygen (O2), Helium (He) and carbon dioxide (CO2)

Design and optimization of a new gas sensor for concentrations of gaseous emissions

Un capteur de gaz est un dispositif dont le rôle est d’obtenir soit des informations sur la concentration exacte de certains gaz dans une atmosphère choisie, soit des informations sur la présence ou l’absence de certains gaz. Dans cette thèse, nous avons discuté de l'optimisation de la conception d'un nouveau capteur optique multi-gaz, du traitement d'image numérique et de nouveaux algorithmes génétiques pour le calcul des concentrations de gaz dans des mélanges contenant jusqu'à cinq gaz. En outre, une nouvelle méthode de mesure de la concentration des gaz en ppm, une validation théorique et une optimisation des paramètres du tube à décharge (la partie essentielle de notre capteur), ainsi qu'une nouvelle approche d'identification des gaz à l'aide de réseaux de neurones ont également été discutées. Les résultats de la recherche ont été validés expérimentalement en effectuant plusieurs tests et essaies en laboratoire sur le banc d'essai expérimental conçu et construit à cet effet. De plus, une validation théorique a également été réalisée à l'aide d’un logiciel de simulation numérique par éléments finis. Notre technologie validée au cours de ce travail de recherche est donc prometteuse et, à l'avenir, notre capteur optique multi-gaz miniature sera un très bon candidat dans de nombreux domaines industriels et scientifiques où le besoin de mesurer la concentration de plusieurs gaz est immense. Certaines collaborations avec l’industrie sont envisagées pour des applications diverses, telles que la mesure des émissions gazeuses des moteurs à combustion interne, la surveillance de la concentration de gaz naturel et la mesure des gaz nocifs dans l’atmosphèreA gas sensor is a device whose role is to obtain either information on the exact concentration of certain gases in a selected atmosphere, or information on the presence or absence of certain gases. In this dissertation, we discussed the optimization of the design of a new optical multi-gas sensor, digital image processing and new genetic algorithms for the calculation of gaseous concentrations in mixtures containing up to five gases. In addition, a new method for measuring the gas concentration in ppm, a theoretical validation and an optimization of the parameters of the gas discharge tube (the essential part of our sensor), as well as a new approach for identifying gases using neural networks were also discussed. The research results have been validated experimentally by performing numerous tests in the laboratory on the experimental test bench designed and built for this purpose. In addition, theoretical validation was also carried out using finite element simulation software. Our technology validated during this research work is promising and, in the future, our miniature multi-gas optical sensor will be a very good candidate in many industrial and scientific fields where the need to measure the concentration of several gases is essential. Certain collaborations with the industry are considered for various applications, such as the measurement of gaseous emissions from internal combustion engines, the monitoring of the concentration of natural gas and the measurement of harmful gases in the atmospher

New approach for gas identification using supervised learning methods (SVM and LVQ)

This article proposes a new approach for gas identification, this approach relies on applying supervised learning methods to identify a single gas as well as a mixture of two gases. The gas is trapped in a gas discharge tube, it is then ionized at a relatively low pressure using an HV transformer. The images captured after the ionization of each single gas is then captured and transformed into a database after being treated in order to be classified. The obtained results were very satisfying for SVM as well as for LVQ. For the case of identification of a single gas, the learning rate as well as the validation rate for both methods were 100%. However, for the case of mixture of two gases, a Multi-Layer Perceptron neural network was used to identify the gases, the learning rate as well as the validation rate were 98.59% and 98.77% respectively. The program developed on MATLAB takes the captured image as an input and outputs the identified gases for the user. The gases used in the experiments are Argon (Ar), oxygen (O2), Helium (He) and carbon dioxide (CO2)