2019
DOI: 10.3390/mi10120878
|View full text |Cite
|
Sign up to set email alerts
|

Adaptive Nonlinearity Compensation System for Integrated Temperature and Moisture Sensor

Abstract: Measuring temperature and moisture are important in many scenarios. It has been verified that temperature greatly affects the accuracy of moisture sensing. Moisture sensing performance would suffer without temperature calibrations. This paper introduces a nonlinearity compensation technique for temperature-dependent nonlinearity calibration of moisture sensors, which is based on an adaptive nonlinear order regulating model. An adaptive algorithm is designed to automatically find the optimal order number, which… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2020
2020
2025
2025

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 20 publications
0
1
0
Order By: Relevance
“…As previously applied to pressure scanners, traditional software compensation algorithms consist mainly of polynomial fitting, surface fitting with the least-squares method, and curve interpolation [14]. However, these conventional algorithms are limited by the demand for pressure measurement accuracy [15][16][17]. In particular, determining a stable solution for the equation when using the least-squares method for temperature compensation will create a pathological problem if the order of the fitted expression is high.…”
Section: Introductionmentioning
confidence: 99%
“…As previously applied to pressure scanners, traditional software compensation algorithms consist mainly of polynomial fitting, surface fitting with the least-squares method, and curve interpolation [14]. However, these conventional algorithms are limited by the demand for pressure measurement accuracy [15][16][17]. In particular, determining a stable solution for the equation when using the least-squares method for temperature compensation will create a pathological problem if the order of the fitted expression is high.…”
Section: Introductionmentioning
confidence: 99%