In:
Journal of Physics: Conference Series, IOP Publishing, Vol. 2724, No. 1 ( 2024-03-01), p. 012041-
Kurzfassung:
With the escalating demand for precision in piezoresistive pressure sensors to cater to a wide spectrum of applications, a significant challenge emerges due to the material properties of these sensors, which induce a substantial temperature coefficient. This limitation restricts the operational temperature range and is further exacerbated by ambient temperature variations, resulting in pronounced nonlinearity in sensor responses. To address these challenges, A new method using Graph Neural Networks (GNNs) is introduced to address nonlinearity and temperature drift in piezoresistive pressure sensors. GNNs improve the sensors’ accuracy and reliability across different temperatures, expanding their applicability. Test results show a notable accuracy enhancement, with a maximum full-scale error below 0.05% and even lower in specific ranges (under 0.04%). This precision makes the method ideal for industrial pressure sensor applications and production, offering significant benefits.
Materialart:
Online-Ressource
ISSN:
1742-6588
,
1742-6596
DOI:
10.1088/1742-6596/2724/1/012041
Sprache:
Unbekannt
Verlag:
IOP Publishing
Publikationsdatum:
2024
ZDB Id:
2166409-2
