The existence of a huge amount of features for pattern recognition problems brings to the overloading of the training and exploitation steps of the recognition; also, highly correlated features affect the accuracy of the designed systems negatively. One of the most used ways for tackling this problem is the application of genetic algorithms for the solution of the binary optimization problems that appeared during the features subset selection process. In this paper was used parallel genetic algorithms for the selection of the most informative features in Azerbaijani hand-printed character recognition system by using opportunities of the distributed cluster computing. In this way after the given number of generations most appropriate features with the high recognition rate were selected from the features database.
Was investigated the process of the displacement of oil with water in the horizontally located two-dimensional layer, which is described with the double-phased filtration model of incompressible liquid in non-deformable porous media. Within this model was set inverse problem for the definition flow rate of exploitative wells. Meanwhile, as additional conditions are set downhole pressure in injection wells. For the numerical solution of the problem, firstly was conducted discretization by time. As a result original problem comes down to two independent problems which are solved sequentially in each layer of time: the inverse problem for the definition of pressure distribution and flow rate of exploitative wells and forward problem for the definition of saturation of displacing phase. For the solution of the forward problem was offered particular fission, which gives an opportunity parallelization of received differential problems. In the base of the offered numerical method were carried out experimental results for model tasks. For parallelization of computing, processes was applied Open MP technology.
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