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ECT image reconstruction method based on deconvolution network

A deconvolution network and image reconstruction technology, applied in image generation, image data processing, neural learning methods, etc., can solve the problems of incomplete sensitive field experience information, difficult to deal with complex sensitive field changes, etc., and achieve high-precision images. Reconstruction and increase the effect of spatial feature extraction

Pending Publication Date: 2020-07-28
XIAN UNIV OF SCI & TECH
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Problems solved by technology

The empirical information of the sensitive field detected by the traditional ECT image reconstruction method is incomplete, and it is difficult to deal with complex sensitive field changes

Method used

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  • ECT image reconstruction method based on deconvolution network
  • ECT image reconstruction method based on deconvolution network
  • ECT image reconstruction method based on deconvolution network

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Embodiment 1

[0039] Such as figure 1 Described, a kind of ECT image reconstruction method based on deconvolution network, is characterized in that, comprises the steps:

[0040] S1. Establish a mathematical model for ECT image reconstruction by solving the formula through the deconvolution network;

[0041] S2. Randomly generate geometric parameters to build a geometric model, and make label data and training data;

[0042] S3. Build a deconvolution network model;

[0043] S4. Use the built deconvolution network model for training.

[0044] S5. Realize ECT image reconstruction by using a deconvolution network.

[0045] For the ECT problem, the feature extraction ability of the shallow fully connected network is insufficient, and the nonlinear fitting ability is not strong; the parameters of the deep fully connected network are too many, and the training period is time-consuming and inefficient. Therefore, the present invention proposes a deconvolution network solution formula. Unlike t...

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Abstract

The invention relates to the field of motion rules of two-phase flow, in particular to an ECT image reconstruction method based on a deconvolution network. The method comprises the following steps: S1, establishing a mathematical model for ECT image reconstruction through a deconvolution network solution formula; S2, randomly generating geometric parameters to build a geometric model, and manufacturing label data and training data; S3, building a deconvolution network model; S4, training by using the built deconvolution network model; and S5, realizing ECT image reconstruction by using a deconvolution network. According to the invention, the deconvolution network is used to extract the truly distributed spatial features, the feature extraction capability is enhanced, and for a local application scene, high-precision image reconstruction can be realized without sensitive field priori. The quality of the image is better than that of the prior art, the 2D image of the pipeline section canbe dynamically presented, and monitoring personnel can conveniently analyze the motion law of the two-phase fluid.

Description

technical field [0001] The invention relates to the field of motion laws of two-phase flow, in particular to an ECT image reconstruction method based on a deconvolution network. Background technique [0002] In the two-phase flow pipeline transportation process, the detection of fluid motion parameters is of great significance for improving production efficiency and ensuring production safety. However, due to the complex force between the phases and the large changes in the physical properties of the phase surface, it is difficult to detect the motion mechanism and state of the two-phase flow through traditional means. In addition, pipe plugging and scaling on the pipe wall are unavoidable during the transportation process, which will increase the energy consumption of the pipeline and affect the safe operation of the pipeline. The pipeline is often a closed environment, so the visual observation technology for closed pipelines also needs to be solved urgently. [0003] In ...

Claims

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Application Information

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IPC IPC(8): G06T11/00G06N3/04G06N3/08
CPCG06T11/006G06N3/084G06T2211/424G06N3/045
Inventor 秦学斌纪晨晨王卓李明桥申昱瞳刘浪王湃张波王美赵玉娇
Owner XIAN UNIV OF SCI & TECH
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