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Chromatography PIV reconstruction method and device based on deep neural network

A deep neural network and convolutional neural network technology, applied in the field of tomographic PIV reconstruction based on deep neural network, can solve problems such as particle elongation, and achieve the goal of reducing elongation, improving reconstruction accuracy and high operating efficiency. Effect

Active Publication Date: 2019-08-30
ZHEJIANG UNIV
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Problems solved by technology

However, due to the angle of view and number of cameras, the projected gray scale cannot fully reflect the spatial shape of the particles, resulting in the particles being elongated in the direction of the camera axis (the direction of the thickness of the measuring body).

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  • Chromatography PIV reconstruction method and device based on deep neural network
  • Chromatography PIV reconstruction method and device based on deep neural network
  • Chromatography PIV reconstruction method and device based on deep neural network

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[0040] The present invention will be described in detail below according to the accompanying drawings and preferred embodiments, and the purpose and effects of the present invention will become clearer. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0041] The invention relates to a tomographic particle image velocimetry (PIV) reconstruction algorithm based on a deep neural network, in particular to a reconstruction method and device from projected particle images to spatial particle distribution in tomographic PIV experiments. The camera arrangement for tomo-PIV is as follows figure 1 shown, where the cameras are arranged in a '┼' shape. After the particles in the measured space E are illuminated by the laser, the projected image I is obtained in the four cameras 1 ,I 2 ,I 3 ,I 4 . The technology provided by the present invention is to reconstruct the spatia...

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Abstract

The invention discloses a chromatography particle image velocimetry (PIV) reconstruction method and device based on a deep neural network. Reconstruction from a projection particle image to space particle distribution is realized in a chromatography PIV experiment. The method comprises the following steps: generating a data set, building a neural network model and training, reading a projection image, reconstructing an image by adopting a multiplication algebraic reconstruction technique (MART) and performing correction processing by adopting a neural network. By adopting the reconstruction method disclosed by the invention, chromatography PIV space particle reconstruction precision can be improved, and the deep neural network adopted by the construction method has extremely high operational efficiency, so that extra computation time is hardly increased.

Description

technical field [0001] The invention relates to the field of tomographic particle image velocity measurement, in particular to a tomographic PIV reconstruction method and device based on a deep neural network. Background technique [0002] PIV is a modern laser velocimetry technology, mainly used for velocity measurement of fluid motion, and plays a vital role in the study of fluid dynamics theory and experiments. PIV obtains the global velocity field of the fluid by adding fluorescent tracer particles into the measured medium, and then using the movement of the tracer particles in the flow field. In recent years, tomographic particle image velocimetry (tomographic PIV) has successfully extended two-dimensional PIV to three-dimensional flow field measurement, and can obtain an instantaneous three-dimensional three-component (3D3C) velocity field. This technology reconstructs the true distribution of space particles through particle scattering imaging under different viewing...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01P5/20G06N3/04G06N3/08
CPCG01P5/20G06N3/08G06N3/045
Inventor 许超蔡声泽梁家铭高琪
Owner ZHEJIANG UNIV