基于圆柱平移窗扩展和堆叠 3D-CNN 层的自动编码器的圆柱近场声全息方法。

A Cylindrical Near-Field Acoustical Holography Method Based on Cylindrical Translation Window Expansion and an Autoencoder Stacked with 3D-CNN Layers.

机构信息

State Key Laboratory of Digital Manufacturing and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.

School of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China.

出版信息

Sensors (Basel). 2023 Apr 20;23(8):4146. doi: 10.3390/s23084146.

DOI:10.3390/s23084146

PMID:37112486

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10142147/

Abstract

The performance of near-field acoustic holography (NAH) with a sparse sampling rate will be affected by spatial aliasing or inverse ill-posed equations. Through a 3D convolution neural network (CNN) and stacked autoencoder framework (CSA), the data-driven CSA-NAH method can solve this problem by utilizing the information from data in each dimension. In this paper, the cylindrical translation window (CTW) is introduced to truncate and roll out the cylindrical image to compensate for the loss of circumferential features at the truncation edge. Combined with the CSA-NAH method, a cylindrical NAH method based on stacked 3D-CNN layers (CS3C) for sparse sampling is proposed, and its feasibility is verified numerically. In addition, the planar NAH method based on the Paulis-Gerchberg extrapolation interpolation algorithm (PGa) is introduced into the cylindrical coordinate system, and compared with the proposed method. The results show that, under the same conditions, the reconstruction error rate of the CS3C-NAH method is reduced by nearly 50%, and the effect is significant.

摘要

近场声全息（NAH）的稀疏采样率性能会受到空间混叠或逆不适定方程的影响。通过三维卷积神经网络（CNN）和堆叠自动编码器框架（CSA），数据驱动的 CSA-NAH 方法可以利用每个维度数据中的信息来解决这个问题。在本文中，引入了圆柱平移窗口（CTW）来截断和展开圆柱图像，以补偿在截断边缘处的圆周特征的损失。结合 CSA-NAH 方法，提出了一种基于堆叠 3D-CNN 层的圆柱稀疏采样 NAH 方法（CS3C），并通过数值验证了其可行性。此外，将基于 Paulis-Gerchberg 外推插值算法（PGa）的平面 NAH 方法引入圆柱坐标系，并与所提出的方法进行了比较。结果表明，在相同条件下，CS3C-NAH 方法的重建误差率降低了近 50%，效果显著。