A least squares support vector regression coupled linear reconstruction algorithm for ECT. (March 2021)

• Record Type: Journal Article
• Authors: Xie, Huangjun; Xia, Tao; Tian, Zenan; Zheng, Xudong; Zhang, Xiaobin
• Is Part Of: Flow measurement and instrumentation. Volume 77(2021)
• Publication Date: 2021-03
• Subjects: ECT -- Least squares support vector regression -- Linearization error -- Nonlinear inverse problem -- Reconstruction algorithm; Fluid dynamic measurements -- Periodicals; Flow meters -- Periodicals; Fluides, Dynamique des -- Mesure -- Périodiques; Débitmètres -- Périodiques; 681.2805
• DOI: 10.1016/j.flowmeasinst.2020.101874
• Languages: English
• ISSNs: 0955-5986

Abstract

Abstract: Linearization error of the simplified linear electrical capacitance tomography (ECT) model is one of the leading causes of ECT reconstruction errors. In this paper, the least squares support vector regression (LSSVR) is used to fit the correlation between the capacitance vector and the linearization error. And it is trained by the training samples of typical phase distributions. When removing the linearization error from equations derived by the linear model, the reconstruction problem becomes an exact linear inverse problem because the nonlinearity of ECT is completely included in the linearization error. Then a reconstruction algorithm combining the LSSVR and the Landweber iteration is proposed. Numerical results show that the proposed algorithm achieves significantly better reconstruction accuracies than the linear back projection and the Landweber algorithm for both the noise-free and noisy cases. Compared with the Landweber algorithm, The image errors of the reconstructions are reduced by about 23%–68%, and the correlation coefficient increased by about 0.04–0.14. And the calculation time of the proposed algorithm for all the tested cases is about 0.4–0.6s, which makes it have the potential for real-time imaging. Static experimental results show that the reconstructions of the proposed algorithm have more accurate phase boundary shapes and fewer artifacts. Highlights: ECT linearization error is fitted by least squares support vector. Six typical phase distributions are set for training sample generation. Least squares support vector linear coupled reconstruction algorithm is established. Reconstructions based on noise-free and noisy data show good accuracies.