The Application of Kernel Ridge Regression for the Improvement of a Sensing Interferometric System
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View abstract on PubMed
Summary
This summary is machine-generated.Kernel Ridge Regression (KRR) significantly enhances multilayer interferometric sensor measurement ranges. This machine learning approach extends temperature sensing capabilities eightfold compared to traditional methods.
Area Of Science
- Optoelectronics
- Machine Learning
- Sensor Technology
Background
- Interferometric sensors offer high sensitivity but are limited by short measurement ranges using traditional linear methods.
- Existing techniques for determining measurand parameters in interferometric systems often rely on linear sensitivity, restricting their effective operational span.
- Multilayer interferometric sensors are valuable but face challenges in achieving broad measurement ranges with conventional analysis.
Purpose Of The Study
- To investigate the application of Kernel Ridge Regression (KRR), a machine learning technique, for improving the measurement range of multilayer interferometric sensors.
- To demonstrate how spectral features from interferometric systems can be leveraged using KRR for enhanced parameter estimation.
- To evaluate the effectiveness of different kernel functions within KRR for temperature sensing applications.
Main Methods
- Utilized spectral features, specifically wavelength positions and peak amplitudes from interference spectra, as input for the machine learning model.
- Applied Kernel Ridge Regression (KRR) with four distinct kernel functions to estimate temperature based on spectral features.
- Focused on transforming spectral features using kernel functions to enable non-linear regression for improved sensor performance.
Main Results
- Kernel Ridge Regression (KRR) successfully estimated temperature using spectral features from multilayer interferometric sensors.
- Implementation of KRR with a Gaussian kernel achieved a root-mean-square error of 0.094 °C for temperature measurements.
- The measurement range for temperature was extended by a factor of eight, from a limited range to 4.5–50 °C, compared to traditional methods.
Conclusions
- Kernel Ridge Regression (KRR) is an effective machine learning method for expanding the measurement range of interferometric sensors.
- The use of spectral features and kernel functions in KRR provides a robust approach for accurate temperature estimation in sensing systems.
- This study demonstrates a significant advancement in interferometric sensor technology by overcoming traditional range limitations through advanced machine learning.
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