Nondestructive Position Detection of a Metallic Target within Soil Substrate Using Electromagnetic Tomography

To determine the position of a metallic target in a cylindrical background made of soil, the electromagnetic tomography, as a nondestructive method, is employed. It is a difficult goal to produce precise electromagnetic tomography images using analytical methods. To cope with this issue, an artificial neural network is trained to mimic the electromagnetic tomography system. A hybrid optimization algorithm, which is a combination of intelligent global harmony search and Levenberg–Marquardt algorithms, is proposed to optimize the artificial neural network weights and biases. Simulation results show that the proposed method can estimate the position of target with an acceptable accurately.

[1]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[2]  Osama A. Mohammed,et al.  Detection of magnetic body using artificial neural network with modified simulated annealing , 1994 .

[3]  Peter J. Angeline,et al.  An evolutionary algorithm that constructs recurrent neural networks , 1994, IEEE Trans. Neural Networks.

[4]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[5]  V. V. Pickalov,et al.  1st World Congress on Industrial Process Tomography , 1999 .

[6]  International Conference on Machine Learning and Cybernetics, ICMLC 2010, Qingdao, China, July 11-14, 2010, Proceedings , 2010, ICMLC.

[7]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[8]  W. Marsden.  I and J , 2012 .

[9]  Jianhua Wu,et al.  Novel global harmony search algorithm for unconstrained problems , 2010, Neurocomputing.

[10]  Yanbiao Liao,et al.  Preconditioned Landweber iteration algorithm for electrical capacitance tomography , 2005 .

[11]  Yu Li,et al.  Particle swarm optimisation for evolving artificial neural network , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[12]  Ho-Chan Kim,et al.  Intelligent Optimization Algorithm Approach to Image Reconstruction in Electrical Impedance Tomography , 2006, ICNC.

[13]  Vanessa Rolnik,et al.  A specialized genetic algorithm for the electrical impedance tomography of two-phase flows , 2006 .

[14]  Ángel Fernando Kuri Morales,et al.  An Application of Neural Networks for Image Reconstruction in Electrical Capacitance Tomography Applied to Oil Industry , 2006, CIARP.

[15]  Michael R Neuman,et al.  PHYSIOLOGICAL MEASUREMENT , 1975, The Lancet.

[16]  Alberto Tesi,et al.  On the Problem of Local Minima in Backpropagation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Ryszard Palka,et al.  Inverse Problems in Magnetic Induction Tomography of Low Conductivity Materials , 2008 .

[18]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[19]  B. Mhamdi,et al.  Microwave imaging of dielectric cylinders from experimental scattering data based on the genetic algorithms, neural networks and a hybrid micro genetic algorithm with conjugate gradient , 2011 .

[20]  Benyebka Bou-Saïd,et al.  Flow Measurement and Instrumentation , 2013 .

[21]  Manuchehr Soleimani,et al.  Computational aspects of low frequency electrical and electromagnetic tomography: A review study , 2008 .

[22]  Chao Wang,et al.  RBF neural network image reconstruction for electrical impedance tomography , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[23]  E. Talbi,et al.  Studies in Computational Intelligence 424 , 2012 .

[24]  Xin Yao,et al.  A review of evolutionary artificial neural networks , 1993, Int. J. Intell. Syst..

[25]  Alan F. Murray,et al.  IEEE International Conference on Neural Networks , 1997 .

[26]  S. Priori,et al.  A genetic algorithm approach to image reconstruction in electrical impedance tomography , 2000, IEEE Trans. Evol. Comput..

[27]  Richard A Williams,et al.  Process tomography : principles, techniques and applications , 1995 .