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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-21968
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dc.contributor.authorMichelucci, Umberto-
dc.contributor.authorVenturini, Francesca-
dc.date.accessioned2021-03-12T13:20:49Z-
dc.date.available2021-03-12T13:20:49Z-
dc.date.issued2021-02-
dc.identifier.issn2673-4591de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/21968-
dc.description.abstractThe determination of multiple parameters via luminescence sensing is of great interest for many applications in different fields, like biosensing and biological imaging, medicine, and diagnostics. The typical approach consists in measuring multiple quantities and in applying complex and frequently just approximated mathematical models to characterize the sensor response. The use of machine learning to extract information from measurements in sensors have been tried in several forms before. But one of the problems with the approaches so far, is the difficulty in getting a training dataset that is representative of the measurements done by the sensor. Additionally, extracting multiple parameters from a single measurement has been so far an impossible problem to solve efficiently in luminescence. In this work a new approach is described for building an autonomous intelligent sensor, which is able to produce the training dataset self-sufficiently, use it for training a neural network, and then use the trained model to do inference on measurements done on the same hardware. For the first time the use of machine learning additionally allows to extract two parameters from one single measurement using multitask learning neural network architectures. This is demonstrated here by a dual oxygen concentration and temperature sensor.de_CH
dc.language.isoende_CH
dc.publisherMDPIde_CH
dc.relation.ispartofEngineering Proceedingsde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectOptical sensorde_CH
dc.subjectMachine learningde_CH
dc.subjectArtificial neural networkde_CH
dc.subjectOxygen sensingde_CH
dc.subjectDual sensorde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnikde_CH
dc.titleNew autonomous intelligent sensor design approach for multiple parameter inferencede_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
dc.identifier.doi10.3390/engproc2020002096de_CH
dc.identifier.doi10.21256/zhaw-21968-
zhaw.conference.details7th Electronic Conference on Sensors and Applications, Online, 15-30 November 2020de_CH
zhaw.funding.euNode_CH
zhaw.issue1de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start96de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume2de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedPhotonicsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

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Michelucci, U., & Venturini, F. (2021). New autonomous intelligent sensor design approach for multiple parameter inference [Conference paper]. Engineering Proceedings, 2(1), 96. https://doi.org/10.3390/engproc2020002096
Michelucci, U. and Venturini, F. (2021) ‘New autonomous intelligent sensor design approach for multiple parameter inference’, in Engineering Proceedings. MDPI, p. 96. Available at: https://doi.org/10.3390/engproc2020002096.
U. Michelucci and F. Venturini, “New autonomous intelligent sensor design approach for multiple parameter inference,” in Engineering Proceedings, Feb. 2021, vol. 2, no. 1, p. 96. doi: 10.3390/engproc2020002096.
MICHELUCCI, Umberto und Francesca VENTURINI, 2021. New autonomous intelligent sensor design approach for multiple parameter inference. In: Engineering Proceedings. Conference paper. MDPI. Februar 2021. S. 96
Michelucci, Umberto, and Francesca Venturini. 2021. “New Autonomous Intelligent Sensor Design Approach for Multiple Parameter Inference.” Conference paper. In Engineering Proceedings, 2:96. MDPI. https://doi.org/10.3390/engproc2020002096.
Michelucci, Umberto, and Francesca Venturini. “New Autonomous Intelligent Sensor Design Approach for Multiple Parameter Inference.” Engineering Proceedings, vol. 2, no. 1, MDPI, 2021, p. 96, https://doi.org/10.3390/engproc2020002096.


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