Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20441
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Dual oxygen and temperature luminescence learning sensor with parallel inference
Authors: Venturini, Francesca
Michelucci, Umberto
Baumgartner, Michael
et. al: No
DOI: 10.3390/s20174886
10.21256/zhaw-20441
Published in: Sensors
Volume(Issue): 20
Issue: 17
Pages: 4886
Issue Date: 2020
Publisher / Ed. Institution: MDPI
ISSN: 1424-8220
Language: English
Subjects: Artificial intelligence; Luminescence; Luminescence quenching; Machine learning; Neural network; Optical sensor; Oxygen sensor; Phase fluorimetry
Subject (DDC): 004: Computer science
Abstract: A 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/20441
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Applied Mathematics and Physics (IAMP)
Appears in collections:Publikationen School of Engineering



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