Informatica

We study the stochastic model for bioremediation in a bioreactor with ideal mixing. The dynamics of the examined system is described by stochastic differential equations. We consider an optimal control problem with quadratic costs functional for the linearized model of a well-stirred bioreactor. The optimal control is based on the optimal robust state estimates. The approximate optimal solution is obtained as a linear feedback.

A tool for modeling the propagation of optical beams is proposed and investigated. Truncated Laguerre–Gauss polynomial series are used for approximation of the field at any point in free space. Aposteriori error estimates in various norms are calculated using errors for input functions. The accumulation of truncation errors during space transition is investigated theoretically. The convergence rate of truncated LG series is obtained numerically for super-Gaussian beams. An optimization of algorithm realization costs is done by choosing parameters in such a way that the error reaches minimum value. Results of numerical experiments are presented.

This paper is concerned with design, implementation and verification of persistent purely functional data structures which are motivated by the representation of natural numbers using positional number systems. A new implementation of random-access list based on redundant segmented binary numbers is described. It uses 4 digits and an invariant which guarantees constant worst-case bounds for cons, head, and tail list operations as well as logarithmic time for lookup and update. The relationship of random-access list with positional number system is formalized and benefits of this analogy are demonstrated.

The objective of expert systems is the use of Artificial Intelligence tools so as to solve problems within specific prefixed applications. Even when such systems are widely applied in diverse applications, as manufacturing or control systems, until now, there is an important gap in the development of a theory being applicable to a description of the involved problems in a unified way. This paper is an attempt in supplying a simple formal description of expert systems together with an application to a robot manipulator case.

Five methods for count data clusterization based on Poisson mixture models are described. Two of them are parametric, the others are semi-parametric. The methods emlploy the plug-in Bayes classification rule. Their performance is investigated by making use of computer simulation and compared mainly by the clusterization error rate. We also apply the clusterization procedures to real count data and discuss the results.

The problem of supervised classification of the realisation of the stationary univariate Gaussian random field into one of two populations with different means and factorised covariance matrices is considered. Unknown means and the common covariance matrix of the feature vector components are estimated from spatially correlated training samples assuming spatial correlation to be known. For the estimation of unknown parameters two methods, namely, maximum likelihood and ordinary least squares are used. The performance of the plug-in discriminant functions is evaluated by the asymptotic expansion of the misclassification error. A set of numerical calculations is done for the spherical spatial correlation function.

The new strategy for non-uniform initial FE mesh generation is presented in this paper. The main focus is set to a priori procedures that define the sizing function independent on the mesh generation algorithm. The sizing function used by the mesh generation algorithm is established by control sphere and control space concepts and fully controls mesh gradation in the complex 2D problem domains.