基于双通道混合网络融合支持向量机的电容层析成像流型辨识  

Flow pattern identification of capacitance tomography based on dual-channel hybrid network fusion support vector machine

作　　者：马敏 李继伟 曾田 Ma Min;Li Jiwei;Zeng Tian(College of Electronic Information and Automation,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学电子信息与自动化学院,天津300300

出　　处：《电子测量技术》2022年第4期153-159,共7页Electronic Measurement Technology

基　　金：国家基金面上项目(61871379)资助。

摘　　要：针对两相流流型辨识精度低的问题,提出一种基于双通道混合网络融合支持向量机的流型辨识算法。通过多尺度卷积核对电容向量进行多尺度特征提取丰富特征层信息,利用压缩激励网络(SENet)关注卷积核通道上重要特征张量,调整各通道的重要占比,此外引入多头自注意力机制对电容向量的深度特征进行学习。将带有SENet的多尺度卷积通道与多头自注意力通道进行特征融合形成双通道辨识模型,最后将双通道模型有效捕捉到的电容向量特征的特征送入支持向量机中进行训练并测试。仿真实验结果表明,相比于BP神经网络、SVM、1DCNN算法,所提算法在流型辨识中的平均辨识率显著提升,高达98.6%。To solve the problem of low identification accuracy of two-phase flow pattern,a flow pattern identification algorithm based on two-channel hybrid network fusion support vector machine was proposed.Multi-scale feature extraction of rich feature layer information was carried out by multi-scale convolution check of capacitance vector.Squeeze-and-excitation networks was used to focus on the important feature tensor of convolutional kernel channel and adjust the importance proportion of each channel.In addition,multi-scale attention mechanism was introduced to learn the depth feature of capacitance vector.The multi-scale convolutional channel with SENet and multi-attention channel were fused to form a two-channel identification model.Finally,the features of capacitance vector effectively captured by the two-channel model were sent to support vector machine for training and testing.Simulation results show that compared with BP neural network,SVM and 1DCNN algorithms,the average identification rate of the proposed algorithm in flow pattern identification is significantly improved,reaching 98.6%.

关 键 词：多尺度特征 压缩激励网络 支持向量机 电容层析成像 流型辨识 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]