Guzman-Chavez Ana Dinora, Vargas-Rodriguez Everardo
Departamento de Estudios Multidisciplinarios, Universidad de Guanajuato, Yuriria 38940, Mexico.
Sensors (Basel). 2025 Feb 20;25(5):1292. doi: 10.3390/s25051292.
Sensors based on interferometric systems have been studied due to their wide range of advantages, such as high sensitivity. For these types of sensors, traditional methods, which generally depend on the linear sensitivity of one variable, have been used to determine the measurand parameter. Usually, these methods are only effective for short measurement ranges, which is one of the main limiting factors of these sensors. In this work, it is shown that Kernel Ridge Regression (KRR), which is a machine learning method, can be applied to improve the range of measurement of multilayer interferometric sensors. This method estimates the value of a response variable (temperature) based on a set of spectral features, which are transformed by means of kernel functions. Here, these features were the wavelength positions and maximum amplitudes of some peaks of the interference spectrum of the sensing system. To sustain the application of the method, four kernel functions were used to estimate the values of the response variable. Finally, the results show that by implementing KRR with a Gaussian kernel, the temperature could be estimated with a root-mean-square error of 0.094 °C for the measurement range from 4.5 to 50 °C, which indicates that it was widened by a factor of eight compared with traditional methods.
基于干涉系统的传感器因其具有诸如高灵敏度等广泛优势而得到了研究。对于这类传感器，通常依赖一个变量线性灵敏度的传统方法已被用于确定被测量参数。通常，这些方法仅在短测量范围内有效，这是这些传感器的主要限制因素之一。在这项工作中，结果表明，作为一种机器学习方法的核岭回归（KRR）可用于扩大多层干涉传感器的测量范围。该方法基于一组通过核函数变换的光谱特征来估计响应变量（温度）的值。在此，这些特征是传感系统干涉光谱某些峰值的波长位置和最大幅度。为了支持该方法的应用，使用了四种核函数来估计响应变量的值。最后，结果表明，通过使用高斯核实现KRR，在4.5至50°C的测量范围内，温度估计的均方根误差为0.094°C，这表明与传统方法相比，测量范围扩大了八倍。
