Nondestructive Dynamic Process Monitoring Using Electrical Capacitance Tomography

2006 ◽  
Vol 321-323 ◽  
pp. 1671-1674 ◽  
Author(s):  
U.Z. Ijaz ◽  
J.H. Kim ◽  
M.C. Kim ◽  
Sin Kim ◽  
J.W. Park ◽  
...  
Keyword(s):  
Measurement Data ◽  
Synthetic Data ◽  
Reconstruction Algorithm ◽  
State Equation ◽  
Electrical Capacitance Tomography ◽  
Time Varying ◽  
Electrical Capacitance ◽  
Independent Measurement ◽  
Reconstruction Performance ◽  
Capacitance Tomography

In this paper, we propose a dynamic Electrical Capacitance Tomography (ECT) image reconstruction algorithm based on the extended Kalman filter (EKF) to estimate the rapidly time-varying changes in the permittivity within the time taken to acquire a full set of independent measurement data. The ECT inverse problem is formulated as a state estimation problem in which the system is modeled with the state equation and the observation equation. Computer simulation with synthetic data is provided and comparison is done with existing modified Newton Raphson (mNR) method to illustrate the reconstruction performance of the proposed algorithm.

Split Bregman iteration based reconstruction algorithm for electrical capacitance tomography

2018 ◽  
Vol 41 (9) ◽  
pp. 2389-2399 ◽  
Author(s):  
Lian Lu ◽  
Guowei Tong ◽  
Ge Guo ◽  
Shi Liu
Keyword(s):  
Cost Function ◽  
Tikhonov Regularization ◽  
Measurement Data ◽  
Reconstruction Algorithm ◽  
Electrical Capacitance Tomography ◽  
Reconstruction Technique ◽  
Electrical Capacitance ◽  
Reconstruction Algorithms ◽  
Split Bregman ◽  
Capacitance Tomography

The electrical capacitance tomography (ECT) technique uses the measured capacitance data to reconstruct the permittivity distribution in a specific measurement area, in which the performances of reconstruction algorithms play a crucial role in the reliability of measurement results. According to the Tikhonov regularization technique, a new cost function with the total least squares technique and the ℓ1-norm based regularizer is presented, in which measurement noises, model deviations and the influence of the outliers in the measurement data are simultaneously considered. The split Bregman technique and the fast-iterative shrinkage-thresholding method are combined into a new iterative scheme to solve the proposed cost function efficiently. Numerical experiment results show that the proposed algorithm achieves the boost in the precision of reconstruction, and under the noise-free condition the image errors for the imaging targets simulated in this paper, that is, 8.4%, 12.4%, 13.5% and 6.4%, are smaller than the linear backprojection (LBP) algorithm, the Tikhonov regularization (TR) algorithm, the truncated singular value decomposition (TSVD) algorithm, the Landweber algorithm and the algebraic reconstruction technique (ART).

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