Publication type: Conference paper
Type of review: Peer review (abstract)
Title: Dual oxygen and temperature sensing with single indicator using multi-task-learning neural networks
Authors: Venturini, Francesca
Michelucci, Umberto
Baumgartner, Michael
et. al: No
DOI: 10.1117/12.2554941
Proceedings: Proceedings Volume 11354 : Optical Sensing and Detection VI
Issue: 113541C
Conference details: SPIE Photonics Europe, Digital Forum, France, 6 - 10 April 2020
Issue Date: 2020
Publisher / Ed. Institution: Society of Photo-Optical Instrumentation Engineers (SPIE)
Publisher / Ed. Institution: Bellingham
ISBN: 9781510634800
ISSN: 0277-786X
Language: English
Subjects: Optical sensor; Luminescence; Multi-task learning; Oxygen sensing; Dual sensing
Subject (DDC): 
Abstract: The optical determination of oxygen partial pressure is of great interest in numerous areas, like medicine, biotechnology, and chemistry. A well-known optical measuring approach is based on the quenching of luminescence by the oxygen molecules. The conventional approach consists in measuring the intensity decay time and relate it to the oxygen concentration through a multi-parametric model (Stern–Volmer equation). The parameters of this equation are, however, all temperature-dependent. Therefore the temperature needs to be known to determine the oxygen concentration and is measured separately, either optically or with a completely different sensor. This work proposes a new approach based on a multi-task learning (MTL) neural network. Using the luminescence data of one single indicator, which is sensitive to both oxygen and temperature, the neural network achieves predictions of both parameters which are comparable to the accuracy of commercial senors. The impact of the new proposed approach is however not limited to dual oxygen and temperature sensing, but can be applied to all those cases in which the sensor response is too complex, to be comfortably described by a mathematical model.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Applied Mathematics and Physics (IAMP)
Appears in collections:Publikationen School of Engineering

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