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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-5031
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dc.contributor.authorMichelucci, Umberto-
dc.contributor.authorBaumgartner, Michael-
dc.contributor.authorVenturini, Francesca-
dc.date.accessioned2019-02-20T14:33:36Z-
dc.date.available2019-02-20T14:33:36Z-
dc.date.issued2019-
dc.identifier.issn1424-8220de_CH
dc.identifier.issn1424-8239de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/15524-
dc.description.abstractLuminescence-based sensors for measuring oxygen concentration are widely used in both industry and research due to the practical advantages and sensitivity of this type of sensing. The measuring principle is the luminescence quenching by oxygen molecules, which results in a change of the luminescence decay time and intensity. In the classical approach, this change is related to an oxygen concentration using the Stern-Volmer equation. This equation, which in most cases is non-linear, is parameterized through device-specific constants. Therefore, to determine these parameters, every sensor needs to be precisely calibrated at one or more known concentrations. This study explored an entirely new artificial intelligence approach and demonstrated the feasibility of oxygen sensing through machine learning. The specifically developed neural network learns very efficiently to relate the input quantities to the oxygen concentration. The results show a mean deviation of the predicted from the measured concentration of 0.5% air, comparable to many commercial and low-cost sensors. Since the network was trained using synthetically generated data, the accuracy of the model predictions is limited by the ability of the generated data to describe the measured data, opening up future possibilities for significant improvement by using a large number of experimental measurements for training. The approach described in this work demonstrates the applicability of artificial intelligence to sensing technology and paves the road for the next generation of sensors.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.titleOptical oxygen sensing with artificial intelligencede_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.doi10.21256/zhaw-5031-
dc.identifier.doi10.3390/s19040777de_CH
dc.identifier.pmid30769805de_CH
zhaw.funding.euNode_CH
zhaw.issue4de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start777de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume19de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedPhotonicsde_CH
zhaw.webfeedSensorikde_CH
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Michelucci, U., Baumgartner, M., & Venturini, F. (2019). Optical oxygen sensing with artificial intelligence. Sensors, 19(4), 777. https://doi.org/10.21256/zhaw-5031
Michelucci, U., Baumgartner, M. and Venturini, F. (2019) ‘Optical oxygen sensing with artificial intelligence’, Sensors, 19(4), p. 777. Available at: https://doi.org/10.21256/zhaw-5031.
U. Michelucci, M. Baumgartner, and F. Venturini, “Optical oxygen sensing with artificial intelligence,” Sensors, vol. 19, no. 4, p. 777, 2019, doi: 10.21256/zhaw-5031.
MICHELUCCI, Umberto, Michael BAUMGARTNER und Francesca VENTURINI, 2019. Optical oxygen sensing with artificial intelligence. Sensors. 2019. Bd. 19, Nr. 4, S. 777. DOI 10.21256/zhaw-5031
Michelucci, Umberto, Michael Baumgartner, and Francesca Venturini. 2019. “Optical Oxygen Sensing with Artificial Intelligence.” Sensors 19 (4): 777. https://doi.org/10.21256/zhaw-5031.
Michelucci, Umberto, et al. “Optical Oxygen Sensing with Artificial Intelligence.” Sensors, vol. 19, no. 4, 2019, p. 777, https://doi.org/10.21256/zhaw-5031.


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