| Publikationstyp: | Konferenz: Poster |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Implementation of multi-task learning neural network architectures for robust industrial optical sensing |
| Autor/-in: | Venturini, Francesca Michelucci, Umberto Baumgartner, Michael |
| et. al: | No |
| DOI: | 10.1117/12.2593469 |
| Tagungsband: | Optical Measurement Systems for Industrial Inspection XII |
| Herausgeber/-in des übergeordneten Werkes: | Lehmann, Peter Osten, Wolfgang Albertazzi Gonçalves Jr., Armandi |
| Angaben zur Konferenz: | SPIE Optical Metrology, online, 21-25 June 2021 |
| Erscheinungsdatum: | Jun-2021 |
| Reihe: | Proceedings of SPIE |
| Reihenzählung: | 11782 |
| Verlag / Hrsg. Institution: | Society of Photo-Optical Instrumentation Engineers |
| Verlag / Hrsg. Institution: | Bellingham |
| ISBN: | 9781510643987 9781510643994 |
| ISSN: | 0277-786X 1996-756X |
| Sprache: | Englisch |
| Schlagwörter: | Oxygen monitoring; Luminescence quenching; Artificial neural networks |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 620: Ingenieurwesen |
| Zusammenfassung: | The simultaneous determination of multiple physical or chemical parameters can be very advantageous in many sensor applications. In some cases, it is unavoidable because the parameters of interest display cross sensitivities or depend on multiple quantities varying simultaneously. One notable example is the determination of oxygen partial pressure via luminescence quenching. The measuring principle is based on the measurement of the luminescence of a specific molecule, whose intensity and decay time are reduced due to collisions with oxygen molecules. Since both the luminescence and the quenching phenomena are strongly temperature-dependent, this type of sensor needs continuous monitoring of the temperature. This is typically achieved by adding temperature sensors and employing a multi-parametric model (Stern{Volmer equation), whose parameters are all temperature-dependent. As a result, the incorrect measurement of the temperature of the indicator is a major source of error. In this work a new approach based on multi-task learning (MTL) artificial neural networks (ANN) was successfully implemented to achieve robust sensing for industrial applications. These were integrated in a sensor that not only does not need the separate detection of temperature but even exploits the intrinsic cross-interferences of the sensing principle to predict simultaneously oxygen partial pressure and temperature. A detailed analysis of the robustness of the method was performed to demonstrate its potential for industrial applications. This type of sensor could in the future signi ficantly simplify the design of the sensor and at the same time increase its performance. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/23062 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Angewandte Mathematik und Physik (IAMP) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
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Venturini, F., Michelucci, U., & Baumgartner, M. (2021). Implementation of multi-task learning neural network architectures for robust industrial optical sensing [Conference poster]. In P. Lehmann, W. Osten, & A. Albertazzi Gonçalves Jr. (Eds.), Optical Measurement Systems for Industrial Inspection XII. Society of Photo-Optical Instrumentation Engineers. https://doi.org/10.1117/12.2593469
Venturini, F., Michelucci, U. and Baumgartner, M. (2021) ‘Implementation of multi-task learning neural network architectures for robust industrial optical sensing’, in P. Lehmann, W. Osten, and A. Albertazzi Gonçalves Jr. (eds) Optical Measurement Systems for Industrial Inspection XII. Bellingham: Society of Photo-Optical Instrumentation Engineers. Available at: https://doi.org/10.1117/12.2593469.
F. Venturini, U. Michelucci, and M. Baumgartner, “Implementation of multi-task learning neural network architectures for robust industrial optical sensing,” in Optical Measurement Systems for Industrial Inspection XII, Jun. 2021. doi: 10.1117/12.2593469.
VENTURINI, Francesca, Umberto MICHELUCCI und Michael BAUMGARTNER, 2021. Implementation of multi-task learning neural network architectures for robust industrial optical sensing. In: Peter LEHMANN, Wolfgang OSTEN und Armandi ALBERTAZZI GONÇALVES JR. (Hrsg.), Optical Measurement Systems for Industrial Inspection XII. Conference poster. Bellingham: Society of Photo-Optical Instrumentation Engineers. Juni 2021. ISBN 9781510643987
Venturini, Francesca, Umberto Michelucci, and Michael Baumgartner. 2021. “Implementation of Multi-Task Learning Neural Network Architectures for Robust Industrial Optical Sensing.” Conference poster. In Optical Measurement Systems for Industrial Inspection XII, edited by Peter Lehmann, Wolfgang Osten, and Armandi Albertazzi Gonçalves Jr. Bellingham: Society of Photo-Optical Instrumentation Engineers. https://doi.org/10.1117/12.2593469.
Venturini, Francesca, et al. “Implementation of Multi-Task Learning Neural Network Architectures for Robust Industrial Optical Sensing.” Optical Measurement Systems for Industrial Inspection XII, edited by Peter Lehmann et al., Society of Photo-Optical Instrumentation Engineers, 2021, https://doi.org/10.1117/12.2593469.
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