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Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers
Örebro universitet, Institutionen för naturvetenskap och teknik. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID-id: 0000-0003-1662-0960
Örebro universitet, Institutionen för naturvetenskap och teknik. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID-id: 0000-0001-5061-5474
Örebro universitet, Institutionen för naturvetenskap och teknik. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID-id: 0000-0003-4026-7490
Örebro universitet, Institutionen för naturvetenskap och teknik. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID-id: 0000-0003-0217-9326
2019 (engelsk)Inngår i: 18th ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), IEEE, 2019, artikkel-id 151773Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE, 2019. artikkel-id 151773
Emneord [en]
Metal oxide semiconductor sensor, electronic nose, gas detection, gas sensing, open sampling systems
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
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
Konferanse
2019 IEEE 18th International Symposium on Olfaction and Electronic Nose (ISOEN), Fukoka, Japan, May 26-29, 2019
Prosjekter
SmokeBotTilgjengelig fra: 2019-10-13 Laget: 2019-10-13 Sist oppdatert: 2020-02-06bibliografisk kontrollert

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Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers(1134 kB)44 nedlastinger
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Fan, HanHernandez Bennetts, VictorSchaffernicht, ErikLilienthal, Achim J.

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