Vapour Recognition Based on Deep-Convolutional Neural Network: Portable Impedance Analyzer

Vercoutere, E.; Kenne, S.; Morchain, C.; Pecqueur, Sebastien; Hafsi, Bilel

Type de document :

Communication dans un congrès avec actes

DOI :

10.1109/SENSORS60989.2024.10785096

URL permanente :

http://hdl.handle.net/20.500.12210/120017

Titre :

Vapour Recognition Based on Deep-Convolutional Neural Network: Portable Impedance Analyzer

Auteur(s) :

Vercoutere, E. [Auteur]
Institut Catholique d'Arts et Métiers [ICAM]
Kenne, S. [Auteur]
Institut Catholique d'Arts et Métiers [ICAM]
Morchain, C. [Auteur]
Institut Catholique d'Arts et Métiers [ICAM]
Pecqueur, Sebastien [Auteur]
Nanostructures, nanoComponents & Molecules - IEMN [NCM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Hafsi, Bilel [Auteur]
Institut Catholique d'Arts et Métiers [ICAM]
Nanostructures, nanoComponents & Molecules - IEMN [NCM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]

Titre de la manifestation scientifique :

2024 IEEE SENSORS

Ville :

Kobe

Pays :

Japon

Date de début de la manifestation scientifique :

2024-10-20

Éditeur :

IEEE

Mot(s)-clé(s) en anglais :

Polymer Sensors
Impedance Measurements
Environmental Monitoring
E-Nose
Deep Neural Network

Discipline(s) HAL :

Physique [physics]
Sciences de l'ingénieur [physics]

Résumé en anglais : [en]

Gas detection technologies are essential tools in maintaining safety and environmental standards across various applications. Through advanced sensors and analytical techniques, these systems aim to quickly detect and ...
Lire la suite >Gas detection technologies are essential tools in maintaining safety and environmental standards across various applications. Through advanced sensors and analytical techniques, these systems aim to quickly detect and classify molecular content in an environment, providing valuable insights for early warning and effective response to incidents. In this work, we present the development of miniaturized, multiplexed, and connected electronic nose (e-nose) based on impedance spectroscopy technology. Our platform has been tested and optimized to process electrical responses of 15 conductimetric cells, each cell is tuned using a drop-casted conducting polymer poly(3-hexylthiophene) and 14 different triflate salts. The recognition of various solvents vapor (acetone, methanol, isopropanol, water, ethanol and blends of the last two at various concentrations) relies on a Deep-Convolutional Neural Network based on a back propagation algorithm with two hidden layers of 64 and 32 neurons respectively. The achieved experimental results show an effective classification for the e-nose data to discriminate the alcoholic blends by type and composition, with high classification accuracy (∼96%).Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL

Date de dépôt :

2025-01-23T09:17:24Z

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