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Unsupervised gas discrimination in uncontrolled environments by exploiting density peaks
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-1662-0960
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0001-5061-5474
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0002-0804-8637
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-0217-9326
2016 (English)In: 2016 IEEE SENSORS, Institute of Electrical and Electronics Engineers (IEEE), 2016Conference paper, Published paper (Refereed)
Abstract [en]

Gas discrimination with Open Sampling Systems based on low-cost electro-chemical sensor arrays is of great interest in several applications, such as exploration of hazardous areas and environmental monitoring. Due to the lack of labeled training data or the high costs of obtaining them, as well as the presence of unknown interferents in the target environments, supervised learning is often not applicable and thus, unsupervised learning is an interesting alternative. In this work, we present a cluster analysis approach that can infer the number of different chemical compounds and label the measurements in a given uncontrolled environment without relying on previously acquired training data. Our approach is validated with data collected in indoor and outdoor environments by a mobile robot equipped with an array of metal oxide sensors. The results show that high classification accuracy can be achieved with a rather low sensitivity to the selection of the only functional parameter of our proposed algorithm. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016.
Series
Proceedings of IEEE Sensors, ISSN 1930-0395
Keyword [en]
gas discrimination, Open Sampling Systems, metal oxide sensors, unsupervised learning
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-54024DOI: 10.1109/ICSENS.2016.7808903ISI: 000399395700485Scopus ID: 2-s2.0-85010987762ISBN: 978-1-4799-8287-5 (print)OAI: oai:DiVA.org:oru-54024DiVA: diva2:1057307
Conference
15th IEEE Sensors Conference (SENSORS 2016), Orlando, USA, October 30 - November 2, 2016
Projects
Mobile Robots with Novel Environmental Sensors for Inspection of Disaster Sites with Low Visibility
Note

Funding Agency:

ICT by the European Commission  645101

Available from: 2016-12-16 Created: 2016-12-16 Last updated: 2017-10-18Bibliographically approved

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Fan, HanHernandez Bennetts, VictorSchaffernicht, ErikLilienthal, Achim J.
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CiteExportLink to record
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