化工学报2020，Vol.71Issue(5)：2004-2016,13.DOI:10.11949/0438-1157.20200021
基于数据驱动的卷积神经网络电容层析成像图像重建
Data-driven image reconstruction of electrical capacitance tomography based on convolutional neural network
摘要
Abstract
A data-driven image reconstruction method based on convolutional neural networks is proposed for electrical capacitance tomography (ECT). According to the characteristics of the flow patterns of gas-solid two-phase flow, 60000 sets of particle distribution images are randomly generated by numerical simulation and the corresponding capacitance vectors are calculated by the finite element method, thereby creating a "capacitance vector-particle distribution" dataset. Then a convolutional neural network model is developed to learn and train the training dataset. The training result is verified and evaluated with the testing dataset. Further, static experiments and fluidized bed measurement experiments are performed on the ECT image reconstruction with the obtained convolutional neural network model. Simulation and experimental results show that the established convolutional neural network can well reconstruct ECT images and can be directly used for particle concentration distribution measurement in a fluidized bed.关键词
卷积神经网络/电容层析成像/图像重建/颗粒浓度分布Key words
convolutional neural network/electrical capacitance tomography/image reconstruction/particle concentration distribution分类
能源科技引用本文复制引用
孙先亮1,李健1,2,韩哲哲1,许传龙1..基于数据驱动的卷积神经网络电容层析成像图像重建[J].化工学报,2020,71(5):2004-2016,13.基金项目
国家自然科学基金项目(51676044) （51676044）
江苏省自然科学基金项目(BK20190366) （BK20190366）
中央高校基本科研业务费专项(2242019k30017) （2242019k30017）
