Quantitative Analysis of Structural Parameters Importance of Helical Temperature Microfiber Sensor by Artificial Neural Network

Juan Liu,Minghui Chen,Jinjin Han,Xining Zhang,Zhili Lin,Hongyi Jia,Zhijun Wu,Jixiong Pu,Hao Dai,Hang Yu

doi:10.1109/access.2021.3124665

Juan Liu, Minghui Chen + Show 8 more

Open Access

https://doi.org/10.1109/access.2021.3124665

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Journal: IEEE Access	Publication Date: Jan 1, 2021
Citations: 6	License type: CC BY 4.0

Affiliation: Huaqiao University, Xiamen University

Abstract

With the assistance of the evaluation algorithms based on the well-performed backpropagation neural network (BPNN), we quantitatively analyze the importance of the structural parameters of the supported helical microfiber (HMF) temperature sensor. The relative output intensities of HMF sensor at different temperatures are predicted by the BPNN with the HMF’s structural parameters as the input variables. The best-forecasted performance is obtained by the BPNN with one hidden layer of ten neurons. Compared with the actual values, the root-mean-square error (RMSE) and the correlation coefficient of the predicted values are 9.7 $\times \,\,10^{-3}$ dB and 99.84%, respectively. Based on the BPNN with precise prediction, the backward stepwise elimination and the holdback input randomization methods are used to quantitatively discuss the influence of the structural parameters on the output intensity of the HMF. The relative importance from high to low is the helical length (~38%), microfiber diameter (~27%), helical angle (~25%), and cone angle (~10%). The importance of four geometric parameters obtained by the two methods is ranked the same. Quantitative analysis of structural parameters relying on the well-predicted BPNN can give basic information on the structural characteristics of the HMF sensor, which helps to optimize the structure design of the optical sensors based on micro/nanofiber and provides a powerful guarantee for its practical application.

Highlights

Optical sensors based on micro/nanofibers (MNFs), which have flexible and various sensing units, could provide high sensitivity and have become one of the most important applications of MNFs
backpropagation neural network (BPNN) PREDICTION PERFORMENT To quantitatively analyze the relative importance of helical microfiber (HMF) structural parameters to ∆I, an artificial neuron network with good performance should be established at the beginning
The structural parameter that has the smallest values of the root-mean-square error (RMSE) (1.94×10-2 dB by backward stepwise elimination (BSE) and 1.69×10-2 dB by holdback input randomization (HIPR)) is α

Summary

Introduction

Optical sensors based on micro/nanofibers (MNFs), which have flexible and various sensing units, could provide high sensitivity and have become one of the most important applications of MNFs. In recent decades, a variety of sensing structures constructed by MNFs are proposed, from the simple geometry, such as biconical microfibers [1], [2] and U-type microfibers [3], to the delicate construction, for instance, gratings [4], interferometers [5] and coupled microrings [6], [7], [8]. When perceiving the same physical quantity, MNF sensors with different types of sensing units often obtain different optical signals and response relationships [9]. Even if the types of sensitive units are the same, there are some differences in the optical response due to the fabrication errors and geometric deformation of MNFs [10]. It has been shown that the optical properties of these MNF sensors are susceptible to their sensing structures. A deeper analysis of the structural characteristics should be carried out to improve the performance of the MNF sensors

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Conclusion