多尺度卷积神经网络的电阻层析成像算法
MULTI-SCALE CONVOLUTIONAL NEURAL NETWORK ALGORITHM FOR ELECTRICAL RESISTANCE TOMOGRAPHY
仝卫国 1曾世超 1张立峰1
作者信息
- 1. 华北电力大学自动化系 河北保定 071003
- 折叠
摘要
针对电阻层析成像(ERT)经典算法(LBP、Landweber等)在复杂流型情况下成像精度低的问题,提出一种基于多尺度卷积神经网络(Multi-scale Convolutional Neural Network,MS-CNN)的电阻层析成像图像重建算法.根据气液两相流流型特点,构建有限元模型得到20 000组包含"边界电压向量-电导率分布"的数据集.在典型卷积神经网络Resnet50和Vgg16的基础上,构建针对ERT图像重建问题的MS-CNN.仿真实验结果表明,与Landweber迭代算法和单尺度卷积神经网络算法相比,MS-CNN算法的ICC分别提升了 0.715和0.023,RIE分别降低了 0.812和0.057.抗噪性测试与静态测试结果表明,MS-CNN算法具有良好的图像重建结果和鲁棒性.
Abstract
Aimed at the problem of low imaging accuracy of classical algorithms(LBP,Landweber,etc.)for electrical resistance tomography(ERT)in complex flow patterns,an image reconstruction algorithm based on multi-scale convolutional neural network(MS-CNN)for electrical resistance tomography is proposed.According to the characteristics of gas-liquid two-phase flow pattern,a finite element model was built to obtain 20,000 data sets containing"boundary voltage vector-conductivity distribution".On the basis of typical convolutional neural networks Resnet50 and VGG16,MS-CNN for ERT image reconstruction was constructed.The simulation results show that compared with Landweber iterative algorithm and single-scale convolutional neural network algorithm,the ICC of MS-CNN algorithm is improved by 0.715 and 0.023,and the RIE is decreased by 0.812 and 0.057 respectively.The anti-noise test and static test results show that the MS-CNN algorithm has good image reconstruction results and robustness.
关键词
卷积神经网络/计量学/电阻层析成像/Landweber/电导率分布Key words
Convolutional neural network/Metrology/Electrical resistance tomography/Landweber/Electrical conductivity distribution引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据