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Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID iD: 0000-0003-1662-0960
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID iD: 0000-0001-5061-5474
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID iD: 0000-0003-4026-7490
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID iD: 0000-0003-0217-9326
2019 (English)In: 18th ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), IEEE, 2019, article id 151773Conference paper, Published paper (Refereed)
Abstract [en]

Detecting chemical compounds using electronic noses is important in many gas sensing related applications. Existing gas detection methods typically use prior knowledge of the target analytes. However, in some scenarios, the analytes to be detected are not fully known in advance, and preparing a dedicated model is not possible. To address this issue, we propose a gas detection approach using an ensemble of one-class classifiers. The proposed approach is initialized by learning a Mahalanobis-based and a Gaussian based model using clean air only. During the sampling process, the presence of chemicals is detected by the initialized system, which allows to learn a one-class nearest neighbourhood model without supervision. From then on the gas detection considers the predictions of the three one-class models. The proposed approach is validated 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 open environment.

Place, publisher, year, edition, pages
IEEE, 2019. article id 151773
Keywords [en]
Metal oxide semiconductor sensor, electronic nose, gas detection, gas sensing, open sampling systems
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-77210DOI: 10.1109/ISOEN.2019.8823148Scopus ID: 2-s2.0-85072989108OAI: oai:DiVA.org:oru-77210DiVA, id: diva2:1360454
Conference
2019 IEEE 18th International Symposium on Olfaction and Electronic Nose (ISOEN), Fukoka, Japan, May 26-29, 2019
Projects
SmokeBotAvailable from: 2019-10-13 Created: 2019-10-13 Last updated: 2020-02-06Bibliographically approved

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Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers(1134 kB)4 downloads
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Fan, HanHernandez Bennetts, VictorSchaffernicht, ErikLilienthal, Achim J.

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