Estimation of void boundaries in flow field using expectation–maximization algorithm

Khambampati, Anil Kumar; Rashid, Ahmar; Lee, Jeong Seong; Kim, Bong Seok; Liu, Dong; Kim, Sin; Kim, Kyung Youn

doi:10.1016/j.ces.2010.10.029

Cited by 7 publications

(1 citation statement)

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“…One of the major limitations of both the EKF and UKF is that the coefficients of state and measurement noise covariance matrices, which are integral parts of these algorithms, need to be manually tuned and may lead to suboptimal performance, if they are not tuned well. Based upon the assumption that it is possible to model the initial conditions, the state evolution and the likelihood of the measurement data using a Gaussian distribution, Khambampati et al (2011) employed the expectation-maximization (EM) algorithm to track the void boundaries in the flow process. EM, which calculates the mode of the proposed likelihood function through the expectation and maximization steps, guarantees convergence (Dempster et al 1977, Shumway andStoffer 1982).…”

Section: Introductionmentioning

confidence: 99%

A dynamic oppositional biogeography-based optimization approach for time-varying electrical impedance tomography

et al. 2016

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Dynamic electrical impedance tomography-based image reconstruction using conventional algorithms such as the extended Kalman filter often exhibits inferior performance due to the presence of measurement noise, the inherent ill-posed nature of the problem and its critical dependence on the selection of the initial guess as well as the state evolution model. Moreover, many of these conventional algorithms require the calculation of a Jacobian matrix. This paper proposes a dynamic oppositional biogeography-based optimization (OBBO) technique to estimate the shape, size and location of the non-stationary region boundaries, expressed as coefficients of truncated Fourier series, inside an object domain using electrical impedance tomography. The conductivity of the object domain is assumed to be known a priori. Dynamic OBBO is a novel addition to the family of dynamic evolutionary algorithms. Moreover, it is the first such study on the application of dynamic evolutionary algorithms for dynamic electrical impedance tomography-based image reconstruction. The performance of the algorithm is tested through numerical simulations and experimental study and is compared with state-of-the-art gradient-based extended Kalman filter. The dynamic OBBO is shown to be far superior compared to the extended Kalman filter. It is found to be robust to measurement noise as well as the initial guess, and does not rely on a priori knowledge of the state evolution model.

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Section: Introductionmentioning

confidence: 99%