基于CNN-LSTM和注意力机制的多芯光纤形状坐标预测  

Shape Coordinate Prediction of Multi-Core Optical Fiber Based on CNN-LSTM Network and Attention Mechanism

作　　者：王健健[1,2,3] 汤锐 Wang Jianjian;Tang Rui(Department of Electronic&Communication Engineering,North China Electric Power University,Baoding 071003,Hebei,China;Hebei Key Laboratory of Power Internet of Things Technology,North China Electric Power University,Baoding 071003,Hebei,China;Baoding Key Laboratory of Optical Fiber Sensing and Optical Communication Technology,North China Electric Power University,Baoding 071003,Hebei,China)

机构地区：[1]华北电力大学电子与通信工程系,河北保定071003 [2]华北电力大学河北省电力物联网技术重点实验室,河北保定071003 [3]华北电力大学保定市光纤传感与光通信技术重点实验室,河北保定071003

出　　处：《光学学报》2025年第1期29-36,共8页Acta Optica Sinica

基　　金：国家自然科学基金项目(62205105,62071181);河北省省级科技计划项目(SZX2020034);河北省自然科学基金(F2021201055)。

摘　　要：针对光纤形状传感过程中需要复杂的数值计算的问题,提出基于卷积神经网络(CNN)和长短时记忆(LSTM)网络并结合注意力机制的多芯光纤形状坐标预测模型,能够由多芯光纤纤芯的分布式应变直接获得其形状坐标,从而实现形状传感。CNN和LSTM用于提取应变信息中的时序特征,注意力机制弱化次要时序特征,最终获得光纤形状坐标。实验结果表明:对于不同曲率半径的曲线,所提方法能够避免复杂的数值计算而实现光纤形状传感,其均方根误差(RMSE)和平均绝对误差(MAE)均优于基于弗莱纳方程进行数值计算得到的结果。对于曲率半径为700 mm的曲线,RMSE仅为1.5739 mm,MAE仅为0.6919 mm,光纤形状传感精度分别较数值计算方法的结果提高了58.96%和32.14%。Objective Optical fiber shape sensing technology has gained widespread attention in the field of spatial shape perception due to its unique advantages.Strain sensing measurement,bending information computing,and shape reconstruction algorithms are key components of optical fiber shape sensing technology.The conventional numerical computation method for shape sensing is based on the geometric relationship of sensing fibers,which is cumbersome,and the accuracy of shape sensing may be influenced by various factors during the computation process.To avoid complex numerical calculations and potential errors,methods based on neural networks for shape sensing have become a research focus.However,current neural network methods have not established a direct mapping relationship between strain measurement results and fiber shape spatial coordinates,nor do they address the situation of distributed strain measurement.In this study,we propose a multi-core fiber shape coordinate prediction network model that integrates convolutional neural network-long short-term memory(CNN-LSTM)and an attention mechanism.This model effectively avoids complex numerical calculations and directly obtains shape coordinates from the distributed strains of the three cores in the multi-core fiber.A distributed strain measurement system based on optical frequency domain reflectometry(OFDR)technology is used to collect data and construct a dataset for network testing.The coordinate prediction and curve shape reconstruction results of the method proposed are compared and analyzed with numerical calculation methods,the LSTM network,and the CNN-LSTM network.Methods The input data of the proposed network model are the distributed strains of the three cores in a three-core optical fiber,and the output data are the spatial coordinates of the optical fiber.The input data are three-dimensional,and the output data are two-dimensional.The proposed network model includes a CNN module,an LSTM module,a Dropout layer,an attention mechanism layer,and two independent

关 键 词：光纤形状传感 多芯光纤 坐标预测 神经网络 注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]