Abstract
EIT image reconstruction is an ill-posed problem, the spatial resolution of the estimated conductivity distribution is usually poor and the external voltage measurements are subject to variable noise. Therefore, EIT conductivity estimation cannot be used in the raw form to correctly estimate the shape and size of complex shaped regional anomalies. An efficient algorithm employing a shape based estimation scheme is needed. The performance of traditional inverse algorithms, such as the Newton Raphson method, used for this purpose is below par and depends upon the initial guess and the gradient of the cost functional. This paper presents the application of differential evolution (DE) algorithm to estimate complex shaped region boundaries, expressed as coefficients of truncated Fourier series, using EIT. DE is a simple yet powerful population-based, heuristic algorithm with the desired features to solve global optimization problems under realistic conditions. The performance of the algorithm has been tested through numerical simulations, comparing its results with that of the traditional modified Newton Raphson (mNR) method.
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References
Vauhkonen, M.: Electrical impedance tomography and prior information. PhD Thesis University of Kuopio, Finland (1997), http://venda.uku.fi/~mvauhkon
Kim, B.S., Isaacson, D., Xia, H., Kao, T., Newell, J., Saulnier, G.: A method for analyzing electrical impedance spectroscopy data from breast cancer patients. Physiol. Meas. 28, S237–S246 (2007)
Brown, B.H., Barber, D.C., Morica, A.H., Leathard, A.D.: Cardiac and respiratory related electric impedance changes in the human thorax. IEEE Trans. Biomed. Eng. 41, 729–733 (1994)
Deibele, J.M., Luepschen, H., Leonhardt, S.: Dynamic separation of pulmonary and cardiac changes in electrical impedance tomography. Physiol. Meas. 29, S1–S14 (2008)
Mann, R., Dickin, F.J., Wang, M., Dyakowski, T., Williams, R.A., Edwards, R.B., et al.: Application of electrical resistance tomography to interrogate mixing processes at plant scale. Chem. Eng. Sci. 52, 2087–2097 (1997)
Khambampati, A.K., Rashid, A., Kim, S., Soleimani, M., Kim, K.Y.: Unscented Kalman filter approach to track moving interfacial boundary in sedimentation process using three-dimensional electrical impedance tomography. Phil. Trans. R. Soc. A. 367, 3095–3120 (2009)
Han, D.K., Prosperetti, A.: A shape decomposition technique in electrical impedance tomography. J. Comput. Phys. 155, 75–95 (1999)
Kolehmainen, V., Voutilainen, A., Kaipio, J.P.: Estimation of non-stationary region boundaries in EIT—state estimation approach. Inverse Problems 17, 1937–1956 (2001)
Rashid, A., Kim, B.S., Khambampati, A.K., Liu, D., Kim, S., Kim, K.Y.: Dynamic boundary estimation of human heart within a complete cardiac cycle using electrical impedance tomography. J.Phys.: Conf. Ser. 224, 012042 (2010)
Back, T., Schwefel, H.-P.: An overview of evolutionary algorithms for parameter optimization. Evolut. Comput. 1, 1–23 (1993)
Storn, R., Price, K.: Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. J. of Global Optim. 11, 341–359 (1997)
Spears, W.: Crossover or Mutation? In: Foundations of Genetic Algorithms Workshop, vol. 2, pp. 221–237 (1992)
Brest, J., Boškovic, B., Greiner, S., Žumer, V., Sepesy Maucec, M.: Performance comparison of self-adaptive and adaptive differential evolution algorithms. Soft. Comput. 11, 617–629 (2007)
Brest, J., Greiner, S., Boškovic, B., Mernik, M., Žumer, V.: Selfadapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10, 646–657 (2006)
Goldberg, D.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company, Reading (1989)
Rolnik, V.P., Seleghim Jr. P.: A specialized genetic algorithm for the electrical impedance tomography of two-phase flows. J. Braz. Soc. Mech. Sci. & Eng. 28, 378–389 (2006)
Ali, M.M., Törn, A.: Population set-based global optimization algorithms: some modifications and numerical studies. Comput. Oper. Res. 31, 1703–1725 (2004)
Gong, W., Cai, Z., Ling, C.X.: DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Comput, 1–21 (2010)
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Rashid, A., Khambampati, A.K., Kim, B.S., Liu, D., Kim, S., Kim, K.Y. (2010). A Differential Evolution Based Approach to Estimate the Shape and Size of Complex Shaped Anomalies Using EIT Measurements. In: Kim, Th., Yau, S.S., Gervasi, O., Kang, BH., Stoica, A., Ślęzak, D. (eds) Grid and Distributed Computing, Control and Automation. GDC CA 2010 2010. Communications in Computer and Information Science, vol 121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17625-8_21
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DOI: https://doi.org/10.1007/978-3-642-17625-8_21
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