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神经网络在布里渊光时域分析仪温度提取中的应用(特邀)

The Application of Neural Network in Brillouin Optical Time Domain Analyzer for Temperature Extraction(Invited)

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摘要

文章回顾了本研究小组将人工神经网络(ANN)和深度神经网络(DNN)用于提取布里渊光时域分析仪(BOTDA)传感系统中的温度分布信息的研究工作。在对ANN或者DNN模型进行适当的训练之后,沿被测光纤的温度分布信息能够被训练完成的ANN或DNN从实验获得的布里渊增益谱(BGS)中直接提取出来,而不需要像传统的洛伦兹线型拟合(LCF)方法一样先对BGS拟合得到布里渊频移(BFS),再将其转换成温度信息。实验结果展示出了用ANN和DNN进行温度提取的方法相比于用传统LCF方法的优势。

Abstract

This paper reviews the research work of Artificial Neural Network (ANN) and Deep Neural Network (DNN) for extracting temperature distribution information in Brillouin Optical Time Domain Analyzer (BOTDA) sensing system. After proper training on ANN or DNN model, the Brillouin gain along the measured temperature distribution of optical fiber can be extracted from the Brillouin Gain Spectrum (BGS), rather than the traditional Lorenz Curve Fitting (LCF) method which fits the BGS as Brillouin Frequency Shift (BFS), and then convert it to the temperature information. The experimental results show the advantages of using ANN and DNN for temperature extraction compared with the traditional LCF method.

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中图分类号:TN915

DOI:10.13756/j.gtxyj.2017.06.007

所属栏目:特邀稿件

收稿日期:2017-10-19

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作者单位    点击查看

余长源:香港理工大学 电子及资讯工程学系,香港
王碧炜:香港理工大学 电子及资讯工程学系,香港
王亮:香港中文大学 电子工程学系,香港
吕超:香港理工大学 电子及资讯工程学系,香港

备注:余长源(1974-),男,福建福州人。副教授,博士。2005年至2015年在新加坡国立大学电子与计算机工程系任教,创立了新加坡国立大学光电系统研究小组,并兼任新加坡科技研究局资讯通信研究院高级研究员。2015年12月加入香港理工大学电子及资讯工程学系任教。至今已发表论文360篇(含66篇邀请报告,包括美国OFC2012),参与编写了6本学术专著。研究领域包括集成光电器件、光纤传感、光纤通信系统与网络以及光电生物医疗仪器。

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引用该论文

YU Chang-yuan,WANG Bi-wei,WANG Liang,Lü Chao. The Application of Neural Network in Brillouin Optical Time Domain Analyzer for Temperature Extraction(Invited)[J]. Study On Optical Communications, 2017, 43(6): 42-47

余长源,王碧炜,王亮,吕超. 神经网络在布里渊光时域分析仪温度提取中的应用(特邀)[J]. 光通信研究, 2017, 43(6): 42-47

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