Cargando…

Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications

Detecting chemical compounds using electronic noses is important in many gas sensing related applications. A gas detection system is supposed to indicate a significant event, such as the presence of new chemical compounds or a noteworthy change of concentration levels. Existing gas detection methods...

Descripción completa

Detalles Bibliográficos
Autores principales: Fan, Han, Schaffernicht, Erik, Lilienthal, Achim J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9096169/
https://www.ncbi.nlm.nih.gov/pubmed/35572118
http://dx.doi.org/10.3389/fchem.2022.863838
_version_ 1784705916795355136
author Fan, Han
Schaffernicht, Erik
Lilienthal, Achim J.
author_facet Fan, Han
Schaffernicht, Erik
Lilienthal, Achim J.
author_sort Fan, Han
collection PubMed
description Detecting chemical compounds using electronic noses is important in many gas sensing related applications. A gas detection system is supposed to indicate a significant event, such as the presence of new chemical compounds or a noteworthy change of concentration levels. Existing gas detection methods typically rely on prior knowledge of target analytes to prepare a dedicated, supervised learning model. However, in some scenarios, such as emergency response, not all the analytes of concern are a priori known and their presence are unlikely to be controlled. In this paper, we take a step towards addressing this issue by proposing an ensemble learning based approach (ELBA) that integrates several one-class classifiers and learns online. The proposed approach is initialized by training several one-class models using clean air only. During the sampling process, the initialized system detects the presence of chemicals, allowing to learn another one-class model and update existing models with self-labelled data. We validated the proposed approach with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an uncontrolled environment. We demonstrated that the ELBA algorithm not only can detect gas exposures but also recognize baseline responses under a suspect short-term sensor drift condition. Depending on the problem setups in practical applications, the present work can be easily hybridized to integrate other supervised learning models when the prior knowledge of target analytes is partially available.
format Online
Article
Text
id pubmed-9096169
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-90961692022-05-13 Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications Fan, Han Schaffernicht, Erik Lilienthal, Achim J. Front Chem Chemistry Detecting chemical compounds using electronic noses is important in many gas sensing related applications. A gas detection system is supposed to indicate a significant event, such as the presence of new chemical compounds or a noteworthy change of concentration levels. Existing gas detection methods typically rely on prior knowledge of target analytes to prepare a dedicated, supervised learning model. However, in some scenarios, such as emergency response, not all the analytes of concern are a priori known and their presence are unlikely to be controlled. In this paper, we take a step towards addressing this issue by proposing an ensemble learning based approach (ELBA) that integrates several one-class classifiers and learns online. The proposed approach is initialized by training several one-class models using clean air only. During the sampling process, the initialized system detects the presence of chemicals, allowing to learn another one-class model and update existing models with self-labelled data. We validated the proposed approach with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an uncontrolled environment. We demonstrated that the ELBA algorithm not only can detect gas exposures but also recognize baseline responses under a suspect short-term sensor drift condition. Depending on the problem setups in practical applications, the present work can be easily hybridized to integrate other supervised learning models when the prior knowledge of target analytes is partially available. Frontiers Media S.A. 2022-04-28 /pmc/articles/PMC9096169/ /pubmed/35572118 http://dx.doi.org/10.3389/fchem.2022.863838 Text en Copyright © 2022 Fan, Schaffernicht and Lilienthal. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Chemistry
Fan, Han
Schaffernicht, Erik
Lilienthal, Achim J.
Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications
title Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications
title_full Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications
title_fullStr Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications
title_full_unstemmed Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications
title_short Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications
title_sort ensemble learning-based approach for gas detection using an electronic nose in robotic applications
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9096169/
https://www.ncbi.nlm.nih.gov/pubmed/35572118
http://dx.doi.org/10.3389/fchem.2022.863838
work_keys_str_mv AT fanhan ensemblelearningbasedapproachforgasdetectionusinganelectronicnoseinroboticapplications
AT schaffernichterik ensemblelearningbasedapproachforgasdetectionusinganelectronicnoseinroboticapplications
AT lilienthalachimj ensemblelearningbasedapproachforgasdetectionusinganelectronicnoseinroboticapplications