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Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications
Örebro University, School of Science and Technology. Mobile Robotics & Olfaction Lab. (AAAS research centre)ORCID iD: 0000-0003-1662-0960
Örebro University, School of Science and Technology. Mobile Robotics & Olfaction Lab. (AAAS research centre)ORCID iD: 0000-0002-0804-8637
Örebro University, School of Science and Technology. Mobile Robotics & Olfaction Lab. (AAAS research centre)ORCID iD: 0000-0003-0217-9326
2022 (English)In: Frontiers in Chemistry, E-ISSN 2296-2646, Vol. 10, article id 863838Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2022. Vol. 10, article id 863838
Keywords [en]
electronic nose, metal oxide semiconductor sensor, gas detection, gas sensing, open sampling systems, ensemble learning, robotic olfaction
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-98781DOI: 10.3389/fchem.2022.863838ISI: 000795874900001PubMedID: 35572118Scopus ID: 2-s2.0-85130296086OAI: oai:DiVA.org:oru-98781DiVA, id: diva2:1655127
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Funder
EU, Horizon 2020, 645101Available from: 2022-04-29 Created: 2022-04-29 Last updated: 2024-01-03Bibliographically approved

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Fan, HanSchaffernicht, ErikLilienthal, Achim

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