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Gas Source Declaration with Tetrahedral Sensing Geometries and Median Value Filtering Extreme Learning Machine
Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-0217-9326
Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 7227-7235, article id 8945323Article in journal (Refereed) Published
Abstract [en]

Gas source localization (including gas source declaration) is critical for environmental monitoring, pollution control and chemical safety. In this paper we approach the gas source declaration problem by constructing a tetrahedron, each vertex of which consists of a gas sensor and a three-dimensional (3D) anemometer. With this setup, the space sampled around a gas source can be divided into two categories, i.e. inside (“source in”) and outside (“source out”) the tetrahedron, posing gas source declaration as a classification problem. For the declaration of the “source in” or “source out” cases, we propose to directly take raw gas concentration and wind measurement data as features, and apply a median value filtering based extreme learning machine (M-ELM) method. Our experimental results show the efficacy of the proposed method, yielding accuracies of 93.2% and 100% for gas source declaration in the regular and irregular tetrahedron experiments, respectively. These results are better than that of the ELM-MFC (mass flux criterion) and other variants of ELM algorithms.

Place, publisher, year, edition, pages
IEEE, 2019. Vol. 8, p. 7227-7235, article id 8945323
Keywords [en]
Gas source declaration, tetrahedron, gas concentration measurement, wind information, extreme learning machine, median value filtering
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-79745DOI: 10.1109/ACCESS.2019.2963059Scopus ID: 2-s2.0-85078246836OAI: oai:DiVA.org:oru-79745DiVA, id: diva2:1391197
Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2020-02-14Bibliographically approved

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Lilienthal, Achim J.

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12345671 of 216
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