Piotr Kulczycki

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In many applications of motion control, the occurrence of nonlinear friction constitutes a fundamental obstacle in the design of satisfactory controlling systems. Since it is seldom possible to obtain a relatively accurate model of resistance to motion, a solution more and more often applied in practice is to introduce approaches that incorporate the(More)
Methods based on kernel density estimation have been successfully applied for various data mining techniques. Their natural interpretation together with consistency properties make them an attractive tool in clustering problems. In this paper, the complete gradient clustering algorithm, based on the density of the data, is presented. The proposed method has(More)
The specification, based on experimental data, of functions which characterize an object under investigation, constitutes one of the main tasks in modern science and technological problems. A typical example here is the estimation of density function of random variable distribution from any given sample. The classical procedures rely here on arbitrary(More)
The aim of this paper is to provide a gradient clustering algorithm in its complete form, suitable for direct use without requiring a deeper statistical knowledge. The values of all parameters are effectively calculated using optimizing procedures. Moreover, an illustrative analysis of the meaning of particular parameters is shown, followed by the effects(More)
This paper describes the basic components of a research project aimed at the application of natural computing metaheuristics to optimize the horizontal scaling of databases. Column oriented databases were selected for the project because of their unique properties. A mathematical model has been created in order to align the problem of horizontal scalability(More)