CFP last date
20 August 2024
Reseach Article

Performance Analysis of Grey Level Fitting Mechanism based Gompertz Function for Image Reconstruction Algorithms in Electrical Capacitance Tomography Measurement System

by Josiah Nombo, Alfred Mwambela, Michael Kisangiri
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 109 - Number 15
Year of Publication: 2015
Authors: Josiah Nombo, Alfred Mwambela, Michael Kisangiri
10.5120/19263-0960

Josiah Nombo, Alfred Mwambela, Michael Kisangiri . Performance Analysis of Grey Level Fitting Mechanism based Gompertz Function for Image Reconstruction Algorithms in Electrical Capacitance Tomography Measurement System. International Journal of Computer Applications. 109, 15 ( January 2015), 9-14. DOI=10.5120/19263-0960

@article{ 10.5120/19263-0960,
author = { Josiah Nombo, Alfred Mwambela, Michael Kisangiri },
title = { Performance Analysis of Grey Level Fitting Mechanism based Gompertz Function for Image Reconstruction Algorithms in Electrical Capacitance Tomography Measurement System },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 109 },
number = { 15 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 9-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume109/number15/19263-0960/ },
doi = { 10.5120/19263-0960 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:44:51.869214+05:30
%A Josiah Nombo
%A Alfred Mwambela
%A Michael Kisangiri
%T Performance Analysis of Grey Level Fitting Mechanism based Gompertz Function for Image Reconstruction Algorithms in Electrical Capacitance Tomography Measurement System
%J International Journal of Computer Applications
%@ 0975-8887
%V 109
%N 15
%P 9-14
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper analyses the performance of grey level fitting mechanism based on Gompertz function used in Electrical Capacitance Tomography measurement system. In order to evaluate its performance, the data fitting mechanism has been applied to common image reconstruction algorithms which include; Linear Back Projection, Singular Value Decomposition, Tikhonov Regularization, Iterative Tikhonov Regularization, Landweber iteration and Projected Landweber iteration. Images were reconstructed using measured capacitance data for annular and stratified flows, and qualitative and quantitative evaluation were done on the reconstructed images in comparison with respective reference images. Results show that this grey level fitting mechanism is better in terms of improving image spatial resolution, minimizing relative image error and distribution error and maximizing correlation coefficient.

References
  1. Yang W Q and Peng L 2003 Image reconstruction algorithms for electrical capacitance tomography Meas. Sci. Technol. 14 R1
  2. Marashdeh Q and Fan L 2008 Electrical capacitance tomography-a perspective Ind. Eng. Chem. Res. 47 3708–19
  3. Isaksen Ø 1996 A review of reconstruction techniques for capacitance tomography Meas. Sci. Technol. 7 325
  4. Chandrasekera T and Wang A 2012 A comparison of magnetic resonance imaging and electrical capacitance tomography: An air jet through a bed of particles Powder Technol. 227 86–95
  5. Wang H, Zhu X and Zhang L 2005 Conjugate gradient algorithm for electrical capacitance tomography. Tianjin Daxue Xuebao(J. Tianjin Univ. Sci. Technol. )
  6. Liu S, Fu L and Yang W 2004 Prior-online iteration for image reconstruction with electrical capacitance tomography Sci. Meas. Technol. IEE Proceeding 151 195–200
  7. Rasteiro M and Silva R 2011 Electrical Tomography: a review of Configurations and Applications to Particulate Processes KONA Powder Part. J. 29 67–70
  8. Thorn R 1997 Recent developments in three-phase flow measurement Meas. Sci. Technol. 8 691
  9. CHEN Y, GAO B, ZHANG L, CHEN D and YU X 2010 Image reconstruction based on weighted SVD truncation conjugate gradient algorithm for electrical capacitance tomography Opt. Precis. Eng. 3
  10. Yang W Q, Spink D M, York T A and McCann H 1999 An image-reconstruction algorithm based on Landweber's iteration method for electrical-capacitance tomography Meas. Sci. Technol. 10 1065
  11. Lu G, Peng L, Zhang B and Liao Y 2005 Preconditioned Landweber iteration algorithm for electrical capacitance tomography Flow Meas. Instrum. 16 163–7
  12. Jang J D, Lee S H, Kim K Y and Choi B Y 2006 Modified iterative Landweber method in electrical capacitance tomography Meas. Sci. Technol. 17 1909
  13. Mou C, Peng L, Yao D and Xiao D 2005 Image reconstruction using a genetic algorithm for electrical capacitance tomography Tsinghua Sci. Technol. 10 587–92
  14. Warsito W and Fan L 2003 Neural network multi-criteria optimization image reconstruction technique (NN-MOIRT) for linear and non-linear process tomography Chem. Eng. Process. Process Intensif. 42 663–74
  15. Marashdeh Q and Warsito W 2006 Nonlinear forward problem solution for electrical capacitance tomography using feed-forward neural network Sensors Journal, IEEE 6 pp. 441–9
  16. Huang S M, Plaskowski A B, Xie C G and Beck M S 1989 Tomographic imaging of two-component flow using capacitance sensors J. Phys. E. 22 173–7
  17. Xie C G, Huang S M, Beck M S, Hoyle B S, Thorn R, Lenn C and Snowden D 1992 Electrical capacitance tomography for flow imaging: system model for development of image reconstruction algorithms and design of primary sensors IEE Proc. G (Circuits, Devices Syst. 139 89–98
  18. Hua Yan, Chunting Liu and Jing Gao 2004 Electrical capacitance tomography image reconstruction based on singular value decomposition Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No. 04EX788) vol 4 (IEEE) pp 3783–6
  19. Tikhonov A and Arsenin V 1977 Solutions of ill-posed problems (Washington, DC?: Winston & Sons)
  20. Wang H, Tang L and Cao Z 2007 An image reconstruction algorithm based on total variation with adaptive mesh refinement for ECT Flow Meas. Instrum. 18 pp. 262–7
  21. Björck A 1996 Numerical methods for least squares problems (Siam)
  22. Nombo J 2014 A Grey Level Fitting Mechanism based on Gompertz Function for Two Phase Flow Imaging using Electrical Capacitance Tomography Measurement Int. J. Comput. Appl. 101 7–12
  23. Demidenko E Z 1989 Optimization and regression Moscow. Publ. House "Nauka". Main Ed. Phys. Math. Lit. 293
  24. Fletcher R 2013 Practical methods of optimization (John Wiley & Sons)
  25. Isaksen Ø and Nordtvedt J E 1994 An implicit model based reconstruction algorithm for use with a capacitance tomography system Proc. European Concerted Action on Process Tomography, Oporto pp 215–26
  26. Xie C G, Huang S M, Lenn C P, Stott A L and Beck M S 1994 Experimental evaluation of capacitance tomographic flow imaging systems using physical models Circuits, Devices and Systems, IEE Proceedings- vol 141 (IET) pp 357–68
Index Terms

Computer Science
Information Sciences

Keywords

Electrical Capacitance Tomography Image Reconstruction Algorithms Data Fitting Gompertz function.