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...
Autores principales: | , , |
---|---|
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 |