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2 February 2001 Effects of inaccuracies in fluid dynamical models in state estimation of process tomography
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Proceedings Volume 4188, Process Imaging for Automatic Control; (2001)
Event: Intelligent Systems and Smart Manufacturing, 2000, Boston, MA, United States
Process tomography consists of tomographic imaging of systems, such as process pipes in industry. One typical feature for the industrial processes is that the state of the system changes fast. If the changes are very fast in comparison to data acquisition rate, the ordinary computational methods in tomography can not provide feasible reconstructions. We use state estimation in process tomography and take into account the time dependence of the object. Especially, we consider the case of the electric imaging of the moving fluid. We use the convection-diffusion equation in modeling time dependence of the target. The Kalman smoother algorithm is used for estimating the state of the object. We have previously shown that the state estimation works well in process tomography in the cases in which the fluid dynamics of the system is modeled correctly. However, in the real case the velocity field can not usually be determined accurately. This may be caused e.g. by complex nature of the flow, the turbulence, discretization, etc. In this paper we consider how the inaccuracies in the fluid dynamical model affect the state estimates in process tomography.
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Aku Seppanen, Marko J. Vauhkonen, Erkki Somersalo, and Jari P. Kaipio "Effects of inaccuracies in fluid dynamical models in state estimation of process tomography", Proc. SPIE 4188, Process Imaging for Automatic Control, (2 February 2001);

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