A specialized genetic algorithm for the electrical impedance tomography of two-phase flows

The main objective of this work is to contribute to the development of a new tomographic reconstruction method well suited for processing signals obtained from electrical or other soft sensing field probes. The adopted approach consists in formulating the reconstruction problem in terms of an error function, which assesses the difference between a prospective and the actual internal contrast distribution (3D image), and searching for its minimum with the help of a specialized genetic algorithm (GA). Numerical simulations have been performed to demonstrate the feasibility of the proposed reconstruction method, as well as to emphasize the relation between the ill-posed nature of the problem and the topology of the minimization hyper-surface, and the importance of considering this relation when designing the numerical solution procedure. Results show that convergence is greatly enhanced when a priori information is introduced in the error function. Keywords : Electrical impedance tomography, genetic algorithm, inverse problem, optimization, multiphase-flow

[1]  Nail Gumerov,et al.  Application of a Hybrid Genetic/PowellAlgorithm and a Boundary Element Methodto Electrical Impedance Tomography , 2001 .

[2]  A. Nachman,et al.  Reconstructions from boundary measurements , 1988 .

[3]  William H. Press,et al.  Numerical Recipes: FORTRAN , 1988 .

[4]  Fadil Santosa,et al.  Stability and resolution analysis of a linearized problem in electrical impedance tomography , 1991 .

[5]  N. Holmer,et al.  Electrical Impedance Tomography , 1991 .

[6]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[7]  Khaled H. Hamed,et al.  Time-frequency analysis , 2003 .

[8]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[9]  Nail A. Gumerov,et al.  Application of a hybrid gentic/powell algorithm and a boundary element method to electical , 2001 .

[10]  Paulo Seleghim,et al.  Sensitivity analysis of different sensing strategies for electrical impedance imaging of two-phase flows , 2001, J. Electronic Imaging.

[11]  Alan C. Tripp,et al.  INVERSE CONDUCTIVITY PROBLEM FOR INACCURATE MEASUREMENTS , 1996 .

[12]  Nicholas J. Radcliffe,et al.  Genetic neural networks on MIMD computers , 1992 .

[13]  Ian Knowles.  A variational algorithm for electrical impedance tomography , 1998 .

[14]  Liliana Borcea,et al.  Electrical impedance tomography , 2002 .

[15]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[16]  Paulo Seleghim,et al.  Improving the determination of bubble size histograms by wavelet de-noising techniques , 2001 .

[17]  W. Spears,et al.  On the Virtues of Parameterized Uniform Crossover , 1995 .

[18]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[19]  David C. Dobson,et al.  Estimates on resolution and stabilization for the linearized inverse conductivity problem , 1992 .

[20]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

[21]  A. Nachman,et al.  Global uniqueness for a two-dimensional inverse boundary value problem , 1996 .

[22]  Sin Kim,et al.  Impedance imaging of two-phase flow field with mesh grouping method , 2001 .

[23]  Štefan Lányi.  Analysis of linearity errors of inverse capacitance position sensors , 1998 .

[25]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[26]  A. Calderón,et al.  On an inverse boundary value problem , 2006 .

[27]  Robert V. Kohn,et al.  Determining conductivity by boundary measurements , 1984 .

[28]  Zbigniew Michalewicz,et al.  Genetic algorithms + data structures = evolution programs (3rd ed.) , 1996 .