Informatica

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.

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 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.

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.

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.

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.

In this paper, we analyze the abstractions used for developing component-based domain generators. These include programming paradigms, programming languages, component models, and generator architecture models. On the basis of the analysis, we present a unified relationship model between the domain content, technological factors (structuring, composition, and generalization), and domain architecture. We argue that this model is manifested in the known software generator models, too.

Recently, Sun proposed a private-key encryption scheme based on the product codes with the capability of correcting a special type of structured errors. In this paper, we present a novel method to improve the information rate of Sun's scheme. This method uses the added error vector to carry additional information. Some information bits are mapped into an error vector with the special structure to be added to a codeword. Once the error vector can be identified, the additional information can be recovered.

The problem of recursive estimation of a state of dynamic systems in the presence of time-varying outliers in observations to be processed has been considered. A learning phase used in the state estimation is investigated, assuming that the observations of a noisy output signal and that of a training one are given. A technique based on robust filtering by means of a bank of parallel Kalman filters and on the procedure of optimization of the state estimation itself is used, choosing, at each time moment, a current estimate, that ensures a minimal absolute deviation from the current value of the teaching signal. An approach, based on the relation between the mean squared deviation of state estimates from the true state and innovation sequence variance as well as on the fact that both variables achieve their minimum for the same filter from the respective Kalman filter bank, is proposed here for a working phase, where a training signal will be absent. The recursive technique based on an adaptive state estimation with optimization procedure is worked out. The results of numerical simulation of the linear discrete-time invariant (LTI) system (56) by computer using a bank, consisting of Kalman filters are given (Figs. 1–5).

Two examples of open-loop differential games are considered in the paper. Starting with simplified dynamic Duel, further it was developed to differential economic Duel modelling problem.