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Convolutional neural networks for estimating transport parameters of fibrous materials based on micro-computerized tomography images
The Journal of the Acoustical Society of America ( IF 2.4 ) Pub Date : 2021-04-26 , DOI: 10.1121/10.0004768
Ju Hyun Jeon 1 , Elias Chemali 1 , Sung Soo Yang 1 , Yeon June Kang 1
Affiliation  

This study proposes a method for estimating the transport parameters of fibrous materials from x-ray micro-computed tomography (CT) images using convolutional neural networks (CNNs). Two-dimensional (2-D) micro-CT images and numerically obtained transport parameters were used to train the CNNs; Stokes flow and potential flow were used to numerically obtain the transport parameters using geometrical models extracted from the raw CT images. Then, analogously to constructing a three-dimensional image of the fibrous material by stacking the 2-D slice images, the volumetric transport parameters of the fibrous materials were calculated using the parameters of each 2-D image predicted by the trained CNN models. The transport parameters of the fibrous volume predicted by the CNN models showed good agreement with the measured values. In addition, the sound absorption coefficient was calculated by applying both the predicted and measured transport parameters to the semi-phenomenological sound propagation model and compared with the measured sound absorption coefficient. The results of the study confirm the feasibility of predicting transport parameters of fibrous materials using a neural network model based on raw micro-CT images.

中文翻译:

基于微计算机断层图像估计纤维材料传输参数的卷积神经网络

这项研究提出了一种使用卷积神经网络(CNN)从X射线微计算机断层扫描(CT)图像估计纤维材料的传输参数的方法。二维(2-D)显微CT图像和数值获得的传输参数用于训练CNN;使用斯托克斯流和势流,使用从原始CT图像中提取的几何模型,以数值方式获得运输参数。然后,类似于通过堆叠二维切片图像来构造纤维材料的三维图像,使用由训练后的CNN模型预测的每个二维图像的参数来计算纤维材料的体积传输参数。CNN模型预测的纤维体积的传输参数与测量值显示出良好的一致性。此外,通过将预测和测量的传输参数应用于半现象学的声音传播模型来计算吸声系数,并将其与测得的吸声系数进行比较。研究结果证实了使用基于原始微CT图像的神经网络模型预测纤维材料传输参数的可行性。
更新日期:2021-04-27
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