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采用 PDMS 和不同封装结构的 OMC-Sagnac 环设计,以提高传感性能并优化病态矩阵。

Design of OMC-Sagnac Loop Using PDMS and Different Package Structures to Improve Sensing Performance and Optimize the Ill-Conditioned Matrix.

机构信息

Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China.

College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China.

出版信息

Sensors (Basel). 2023 May 11;23(10):4655. doi: 10.3390/s23104655.

Abstract

In the process of ocean exploration, highly accurate and sensitive measurements of seawater temperature and pressure significantly impact the study of seawater's physical, chemical, and biological processes. In this paper, three different package structures, V-shape, square-shape, and semicircle-shape, are designed and fabricated, and an optical microfiber coupler combined Sagnac loop (OMCSL) is encapsulated in these structures with polydimethylsiloxane (PDMS). Then, the temperature and pressure response characteristics of the OMCSL, under different package structures, are analyzed by simulation and experiment. The experimental results show that structural change hardly affects temperature sensitivity, and square-shape has the highest pressure sensitivity. In addition, with an input error of 1% F.S., temperature and pressure errors were calculated, which shows that a semicircle-shape structure can increase the angle between lines in the sensitivity matrix method (SMM), and reduce the effect of the input error, thus optimizing the ill-conditioned matrix. Finally, this paper shows that using the machine learning method (MLM) effectively improves demodulation accuracy. In conclusion, this paper proposes to optimize the ill-conditioned matrix problem in SMM demodulation by improving sensitivity with structural optimization, which essentially explains the cause of the large errors for multiparameter cross-sensitivity. In addition, this paper proposes to use the MLM to solve the problem of large errors in the SMM, which provides a new method to solve the problem of the ill-conditioned matrix in SMM demodulation. These have practical implications for engineering an all-optical sensor that can be used for detection in the ocean environment.

摘要

在海洋探测过程中,对海水温度和压力进行高精度、高灵敏度的测量,对研究海水的物理、化学和生物过程具有重要意义。本文设计并制作了 V 型、方形和半圆形三种不同的封装结构,将光纤微环耦合 Sagnac 环(OMCSL)封装在聚二甲基硅氧烷(PDMS)中。然后,通过模拟和实验分析了 OMCSL 在不同封装结构下的温度和压力响应特性。实验结果表明,结构变化对温度灵敏度影响较小,方形结构具有最高的压力灵敏度。此外,在输入误差为 1% F.S.的情况下,计算了温度和压力误差,结果表明半圆形结构可以增加灵敏度矩阵方法(SMM)中线条之间的角度,减小输入误差的影响,从而优化病态矩阵。最后,本文表明,采用机器学习方法(MLM)可以有效提高解调精度。总之,本文提出了通过结构优化提高灵敏度来优化 SMM 解调中的病态矩阵问题,从本质上解释了多参数交叉灵敏度误差大的原因。此外,本文提出了利用 MLM 解决 SMM 中误差大的问题,为解决 SMM 解调中的病态矩阵问题提供了一种新方法。这些对于工程设计可用于海洋环境检测的全光传感器具有实际意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63ad/10220607/858ca1e9f24c/sensors-23-04655-g001.jpg

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