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声源和障碍物联合反演问题研究

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针对声源和障碍物联合反演问题,提出一种并行神经网络方法,同时重建声源的位置和障碍物的位置、形状参数.首先,将远场数据作为并行神经网络的输入序列,声源和障碍物的相关参数作为并行神经网络的输出序列;其次,构建一个特征提取模块,该模块包括GRU模块和Res模块,分别用于提取远场数据的相对位置特征和绝对位置特征;最后,将相对位置特征和绝对位置特征进行融合,通过参数反演模块将融合的特征数据与声源和障碍物的相关参数建立映射关系.数值实验表明了该方法的有效性.
Research on Simultaneous Recovery of Sound Source and Obstacle
For the problem of simultaneous recovery of sound source and obstacle,a parallel neural network method is proposed to simultaneous recovery of the position parameters of sound source and the position and shape parameters of obstacle.Firstly,the far-field datas are taken as the input sequences of the parallel neural network,and the relevant parameters of sound source and obstacle are taken as the output sequences of the parallel neural network.Secondly,a feature extraction block is constructed,which includes a GRU block and a Res block for extracting relative position features and absolute position features of the far-field datas,respectively.Finally,the relative and absolute position features are fused,and the mapping relationship between the fused feature datas and the relevant parameters of sound source and obstacle is established by the parameter recovery block.Numerical experiments show the effectiveness of this method.

inverse scattering problemfar-field dataparallel neural networkfused feature

王莎莎、孟品超

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长春理工大学 数学与统计学院,长春 130022

反散射问题 远场数据 并行神经网络 特征融合

国家自然科学基金项目吉林省自然科学基金项目吉林省科技计划项目吉林省工业技术研发项目

1227120720220101040JCYDZJ202201ZYTS5852022C047-2

2024

长春理工大学学报(自然科学版)
长春理工大学

长春理工大学学报(自然科学版)

CSTPCD
影响因子:0.432
ISSN:1672-9870
年,卷(期):2024.47(5)