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A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS MRO Lab)ORCID-id: 0000-0003-1662-0960
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS Resarch Centre)ORCID-id: 0000-0001-5061-5474
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS Resarch Centre)ORCID-id: 0000-0003-4026-7490
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS Resarch Centre)ORCID-id: 0000-0003-0217-9326
2018 (engelsk)Inngår i: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 259, s. 183-203Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Amsterda, Netherlands: Elsevier, 2018. Vol. 259, s. 183-203
Emneord [en]
Gas discrimination, environmental monitoring, metal oxide sensors, cluster analysis, unsupervised learning
HSV kategori
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Datavetenskap
Identifikatorer
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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EU, Horizon 2020, 645101Tilgjengelig fra: 2017-12-19 Laget: 2017-12-19 Sist oppdatert: 2019-02-12bibliografisk kontrollert

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A Cluster Analysis Approach Based on Exploiting Density Peaks for Gas Discrimination with Electronic Noses in Open Environments(31600 kB)154 nedlastinger
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Fan, HanHernandez Bennetts, VictorSchaffernicht, ErikLilienthal, Achim

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