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This paper proposes a probabilistic collocation method (PCM) to quantify the uncertainties in state estimation. Comparing to classic Monte-Carlo (MC) method, the proposed PCM is based on sparse grid points and uses a smaller number of sparse grid points to quantify the uncertainty. Thus, the proposed PCM can quantify a large number of uncertain power system variables with relatively lower computational...
The estimation of many unknown parameters is carried out using a simplified Sequential Importance Sampling (SIS) algorithm which is implemented in a graphic processing unit (GPU). The aim of the present work is to show technical points to bring out the performance of GPU. Using the implemented code, two numerical experiments are demonstrated. In the first demonstration, it is shown that a parameter...
An approach for statistical and reliability analyses of concrete structures is presented. It describes the virtual simulation used on the way from assessment of experimental results to reliability analysis. The whole approach is based on randomization of nonlinear fracture mechanics finite element analysis of concrete structures. Efficient techniques of both nonlinear numerical analysis of concrete...
The problem considered in this paper is detection and estimation of multiple radiation sources using a time series of radiation counts from a collection of sensors. A Bayesian framework is adopted. Source detection is approached as a model selection problem in which competing models are compared using partial Bayes factors. Given the number of sources, the posterior mean is the minimum mean square...
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