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A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-1662-0960
Örebro University, School of Science and Technology. (AASS Resarch Centre)ORCID iD: 0000-0001-5061-5474
Örebro University, School of Science and Technology. (AASS Resarch Centre)ORCID iD: 0000-0003-4026-7490
Örebro University, School of Science and Technology. (AASS Resarch Centre)ORCID iD: 0000-0003-0217-9326
2018 (English)In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 259, p. 183-203Article in journal (Refereed) Published
Abstract [en]

Gas discrimination in open and uncontrolled environments based on smart low-cost electro-chemical sensor arrays (e-noses) is of great interest in several applications, such as exploration of hazardous areas, environmental monitoring, and industrial surveillance. Gas discrimination for e-noses is usually based on supervised pattern recognition techniques. However, the difficulty and high cost of obtaining extensive and representative labeled training data limits the applicability of supervised learning. Thus, to deal with the lack of information regarding target substances and unknown interferents, unsupervised gas discrimination is an advantageous solution. In this work, we present a cluster-based approach that can infer the number of different chemical compounds, and provide a probabilistic representation of the class labels for the acquired measurements in a given environment. Our approach is validated with the samples collected in indoor and outdoor environments using a mobile robot equipped with an array of commercial metal oxide sensors. Additional validation is carried out using a multi-compound data set collected with stationary sensor arrays inside a wind tunnel under various airflow conditions. The results show that accurate class separation can be achieved with a low sensitivity to the selection of the only free parameter, namely the neighborhood size, which is used for density estimation in the clustering process.

Place, publisher, year, edition, pages
Amsterda, Netherlands: Elsevier, 2018. Vol. 259, p. 183-203
Keywords [en]
Gas discrimination, environmental monitoring, metal oxide sensors, cluster analysis, unsupervised learning
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-63468DOI: 10.1016/j.snb.2017.10.063ISI: 000424877600023Scopus ID: 2-s2.0-85038032167OAI: oai:DiVA.org:oru-63468DiVA, id: diva2:1167983
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Funder
EU, Horizon 2020, 645101Available from: 2017-12-19 Created: 2017-12-19 Last updated: 2018-09-17Bibliographically approved

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Fan, HanHernandez Bennetts, VictorSchaffernicht, ErikLilienthal, Achim

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