1. ZHAW digitalcollection
  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
Full metadata record
DC FieldValueLanguage
dc.contributor.authorVenturini, Francesca-
dc.contributor.authorMichelucci, Umberto-
dc.contributor.authorBaumgartner, Michael-
dc.date.accessioned2020-03-19T13:53:30Z-
dc.date.available2020-03-19T13:53:30Z-
dc.date.issued2020-01-27-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/19822-
dc.description.abstractLuminescence-based sensors for measuring oxygen concentration are widely used both in 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 complicated models. These models, which in most of the cases are non-linear, are parametrized through device-specific constants that are different for each sensor. This work explores an entirely new artificial intelligence approach and demonstrates the feasibility of oxygen sensing through machine learning. For this purpose, a multi-task learning neural network was specifically developed and trained on a large amount of data. The results show that with this approach it is possible to reach an accuracy comparable to that with the conventional approach. However, a significant advantage over the latter is that the network also learns the interdependencies of influencing parameters that no longer have to be measured separately and used to correct the results. The approach described in this work demonstrates the applicability of artificial intelligence and multi-task learning to sensing technology and paves the road for the next generation of sensors.de_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectOxygen sensorde_CH
dc.subjectLuminescencede_CH
dc.subjectLuminescence quenchingde_CH
dc.subjectArtificial intelligencede_CH
dc.subjectMachine learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleMulti-task learning approach for optical luminescence sensingde_CH
dc.typeKonferenz: Posterde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
zhaw.conference.detailsApplied Machine Learning Days (AMLD), Lausanne, 25-29 January 2020de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.webfeedPhotonicsde_CH
zhaw.author.additionalNode_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
There are no files associated with this item.
Show simple item record
Venturini, F., Michelucci, U., & Baumgartner, M. (2020, January 27). Multi-task learning approach for optical luminescence sensing. Applied Machine Learning Days (AMLD), Lausanne, 25-29 January 2020.
Venturini, F., Michelucci, U. and Baumgartner, M. (2020) ‘Multi-task learning approach for optical luminescence sensing’, in Applied Machine Learning Days (AMLD), Lausanne, 25-29 January 2020.
F. Venturini, U. Michelucci, and M. Baumgartner, “Multi-task learning approach for optical luminescence sensing,” in Applied Machine Learning Days (AMLD), Lausanne, 25-29 January 2020, Jan. 2020.
VENTURINI, Francesca, Umberto MICHELUCCI und Michael BAUMGARTNER, 2020. Multi-task learning approach for optical luminescence sensing. In: Applied Machine Learning Days (AMLD), Lausanne, 25-29 January 2020. Conference poster. 27 Januar 2020
Venturini, Francesca, Umberto Michelucci, and Michael Baumgartner. 2020. “Multi-Task Learning Approach for Optical Luminescence Sensing.” Conference poster. In Applied Machine Learning Days (AMLD), Lausanne, 25-29 January 2020.
Venturini, Francesca, et al. “Multi-Task Learning Approach for Optical Luminescence Sensing.” Applied Machine Learning Days (AMLD), Lausanne, 25-29 January 2020, 2020.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.