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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20441
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dc.contributor.authorVenturini, Francesca-
dc.contributor.authorMichelucci, Umberto-
dc.contributor.authorBaumgartner, Michael-
dc.date.accessioned2020-09-10T10:36:04Z-
dc.date.available2020-09-10T10:36:04Z-
dc.date.issued2020-
dc.identifier.issn1424-8220de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20441-
dc.description.abstractA well-known approach to the optical measure of oxygen is based on the quenching of luminescence by molecular oxygen. The main challenge for this measuring method is the determination of an accurate mathematical model for the sensor response. The reason is the dependence of the sensor signal from multiple parameters (like oxygen concentration and temperature), which are cross interfering in a sensor-specific way. The common solution is to measure the different parameters separately, for example, with different sensors. Then, an approximate model is developed where these effects are parametrized ad hoc. In this work, we describe a new approach for the development of a learning sensor with parallel inference that overcomes all these difficulties. With this approach we show how to generate automatically and autonomously a very large dataset of measurements and how to use it for the training of the proposed neural-network-based signal processing. Furthermore, we demonstrate how the sensor exploits the cross-sensitivity of multiple parameters to extract them from a single set of optical measurements without any a priori mathematical model with unprecedented accuracy. Finally, we propose a completely new metric to characterize the performance of neural-network-based sensors, the Error Limited Accuracy. In general, the methods described here are not limited to oxygen and temperature sensing. They can be similarly applied for the sensing with multiple luminophores, whenever the underlying mathematical model is not known or too complex.de_CH
dc.language.isoende_CH
dc.publisherMDPIde_CH
dc.relation.ispartofSensorsde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectArtificial intelligencede_CH
dc.subjectLuminescencede_CH
dc.subjectLuminescence quenchingde_CH
dc.subjectMachine learningde_CH
dc.subjectNeural networkde_CH
dc.subjectOptical sensorde_CH
dc.subjectOxygen sensorde_CH
dc.subjectPhase fluorimetryde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleDual oxygen and temperature luminescence learning sensor with parallel inferencede_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
dc.identifier.doi10.3390/s20174886de_CH
dc.identifier.doi10.21256/zhaw-20441-
dc.identifier.pmid32872357de_CH
zhaw.funding.euNode_CH
zhaw.issue17de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start4886de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume20de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedPhotonicsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Venturini, F., Michelucci, U., & Baumgartner, M. (2020). Dual oxygen and temperature luminescence learning sensor with parallel inference. Sensors, 20(17), 4886. https://doi.org/10.3390/s20174886
Venturini, F., Michelucci, U. and Baumgartner, M. (2020) ‘Dual oxygen and temperature luminescence learning sensor with parallel inference’, Sensors, 20(17), p. 4886. Available at: https://doi.org/10.3390/s20174886.
F. Venturini, U. Michelucci, and M. Baumgartner, “Dual oxygen and temperature luminescence learning sensor with parallel inference,” Sensors, vol. 20, no. 17, p. 4886, 2020, doi: 10.3390/s20174886.
VENTURINI, Francesca, Umberto MICHELUCCI und Michael BAUMGARTNER, 2020. Dual oxygen and temperature luminescence learning sensor with parallel inference. Sensors. 2020. Bd. 20, Nr. 17, S. 4886. DOI 10.3390/s20174886
Venturini, Francesca, Umberto Michelucci, and Michael Baumgartner. 2020. “Dual Oxygen and Temperature Luminescence Learning Sensor with Parallel Inference.” Sensors 20 (17): 4886. https://doi.org/10.3390/s20174886.
Venturini, Francesca, et al. “Dual Oxygen and Temperature Luminescence Learning Sensor with Parallel Inference.” Sensors, vol. 20, no. 17, 2020, p. 4886, https://doi.org/10.3390/s20174886.


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