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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 |
| Page(s): | 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): | 006: Special computer methods |
| 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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| File | Description | Size | Format | |
|---|---|---|---|---|
| 2020_Venturini-etal_Dual-oxygen-temperature-luminescence-learning-sensor-parallel-inference.pdf | 1.23 MB | Adobe PDF |  View/Open |
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Venturini, F., Michelucci, U., & Baumgartner, M. (2020). Dual oxygen and temperature luminescence learning sensor with parallel inference. Sensors, 20(17), 4886. https://doi.org/10.3390/s20174886
Venturini, F., Michelucci, U. and Baumgartner, M. (2020) ‘Dual oxygen and temperature luminescence learning sensor with parallel inference’, Sensors, 20(17), p. 4886. Available at: https://doi.org/10.3390/s20174886.
F. Venturini, U. Michelucci, and M. Baumgartner, “Dual oxygen and temperature luminescence learning sensor with parallel inference,” Sensors, vol. 20, no. 17, p. 4886, 2020, doi: 10.3390/s20174886.
VENTURINI, Francesca, Umberto MICHELUCCI und Michael BAUMGARTNER, 2020. Dual oxygen and temperature luminescence learning sensor with parallel inference. Sensors. 2020. Bd. 20, Nr. 17, S. 4886. DOI 10.3390/s20174886
Venturini, Francesca, Umberto Michelucci, and Michael Baumgartner. 2020. “Dual Oxygen and Temperature Luminescence Learning Sensor with Parallel Inference.” Sensors 20 (17): 4886. https://doi.org/10.3390/s20174886.
Venturini, Francesca, et al. “Dual Oxygen and Temperature Luminescence Learning Sensor with Parallel Inference.” Sensors, vol. 20, no. 17, 2020, p. 4886, https://doi.org/10.3390/s20174886.
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