Abstract: The article presents the development of a reliability simulation program for electrical power stations, using Monte Carlo method. Based on the electrical power station scheme, the system's characteristic equation is generated which can be specified as program input data, increasing the program's flexibility. The simulation yields the values of reliability indicators and the reliability function diagram. The article also presents a case

This paper presents a decision support system, DietMix, running under MicrosoftWindows, and implementing a linear programming model for solving general diet/feed mixproblems. The program is part of the LINMOD package.

There is a great diversity of ways to use fuzzy inference in robot control systems, either in the place where it is applied in the control scheme or in the form or type of inference algorithms used. On the other hand, artificial neural networks ability to simulate nonlinear systems is used in different researches in order to develop automated control systems of industrial processes. In these applications of neural networks, there are two important

In this paper we will present the utilization of neuro - fuzzy models as prediction of some events, more exactly, realizing of some applications viewing the time intervals prediction in which incidents can appear in an electrical energy distribution system. It was realized the duration analyzes between two incidents, with the aim to estimate the frequency of the incidences in the future. The time intervals prediction where may appear incidences was

We propose an object-oriented design of an information integrated system for a SME. Our design is based on a kernel which implements CRM functions. This kernel is conceived as an independent subsystem and it is the first to be implemented. The others added subsystems are designed in a way that they will gravitate around the kernel. This type of integrated information system is developed in the iterative and incremental steps. CRM kernel implements

In this paper the authors describe the results of experiments for surface roughness image acquisition and processing in order to develop an automated roughness control system. This implies the finding of a characteristic roughness parameter (for example Ra) on the bases of information contained in the image of the surface. To achieve this goal we use quasi-fractal characteristics and fuzzy clustering methods.