The paper presents the investigation results of the effectiveness of applying wavelet methods and the Wigner-Ville transformation in states monitoring of typical elements of the AC drive. As an example the electric drive system consist of the voltage inverter powering the squirrel-cage induction motor was showed. A case of complex mechanical load was also investigated. An influence of the decomposition level and the selection of the basic wavelet shape as well as an influence of the selection of the time period of the Wigner-Ville method on the faults detection sensitivity and the effectiveness of the diagnostic inference were examined.

In dynamics of milles driven with electric engines issues of the drive transmission systems nonlinearity will matter greatly, especially in case of the need to conduct the identification of faults. The paper presents an example of applying wavelet analysis for investigating chosen faults in the non-linear arrangement of the drive transmission so as: appearing of backlash in mechanical connections and damaging a roller bearing. Also a case of non-linear damping in the arrangement of the mill was taken into account.

This paper presents the Poincaré-Lindstedt method for approximating periodic solutions of nonlinear
circuit equations. An accuracy analysis is done for an approximation obtained by applying this method. An application of described method to the van der Pol equation is also covered and the results are compared with numerical solutions.

In this paper Probabilistic Traveling Salesman Problem (PTSP) is considered and a hybrid algorithm is proposed, in which an evolutionary algorithm is combined with local optimization and parallelization techniques. Local optimization methods include 1-shift and 2-p-opt operators. Several basic variants of evolutionary and hybrid algorithms are experimentally tested and compared.

In this paper a new design tool is presented that is useful in automated synthesis of combinatorial logic. PKmin program is devoted for synthesis of 2-level circuits composed of gates and PLAs, multi-level circuits and a functional decomposition of logical functions for LUT-based logic implementations in FPGA. It has been built on the basis of the research conducted at Cracow University of Technology. In the paper design algorithms implemented in PKmin are mutually compared. Then, an experimental efficiency comparison of gate and PLA-based 2-level synthesis with PKmin and Espresso design tools is reported.

Nowadays, due to the growing dimensionality of optimisation problems, numerous studies are dedicated to reduction of metaheuristics computational requirements. Reducing size of the population during optimisation process is one of the promising research trends in the field of Evolutionary Algorithms. The purpose of this paper is to clarify the subject in form of a survey of population size reduction methods already proposed and to present preliminary results of a new method based on the clustering technique. Introduced method was implemented in the framework of Differential Evolution algorithm and verified on a set of real-parameter benchmark functions.

The aim of this paper is to present a complete procedure for extracting features from the real face picture. The procedure consists of several image processing algorithms such as binarization, erosion, filtration, gradient method and Haar-like Features algorithm. The obtained feature vector used in biometric identification with particular emphasis on the problem of authentication. A system processing the image obtained from a webcam operates in an on-line regime. The correctness of the proposed algorithm was tested using the database created with dozens of people. The tests were done from the camera images as well as artificially created images with different damage cases. The algorithm was tested for cases of various types of lighting, disturbances associated with the natural change of face appearance, the image rotation and distorted image.

The main goal of this paper is a description of clustering algorithm based on the particle swarm optimization algorithm, inspired on social behavior of animals and its application in fuzzy modeling. In the paper the idea of the heuristic swarm-based algorithm was presented, including a few modifications. Moreover, the results of the experimental evaluation were shown, both a selected optimization technique and its synthesi with a fuzzy modeling method referring to the k-means algorithm and the fuzzy control process.

This paper proposes a novel approach to consensus reaching process, which combines a mathematical model and socio-psychological factors. We attempt to correlate two decision support systems: one with the teacher managing the process of reaching consensus within the laboratory groups of individuals, and computer-based system, with mathematical presumptions as for the consistency of the model with the reality of a particular social group.

In this paper we present human-consistent approach of multi-model consensus reaching process supporting by group decision support systems. We consider the idea developed by Kacprzyk and Zadrożny [9, 10, 12] which is related to the “soft” consensus, and where the core of the system is based on fuzzy logic. Essentially, we attempt to stress the multi-model architecture of considering system and distinguish several aspects, i.e. model of agent, model of moderator, model of consensus achievement. Moreover, we present a novel concept based on fair consensus as a meaningful point of further development.

This paper deals with high-dimensional data analysis accomplished through supplementing standard feature extraction procedures with topology preservation measures. This approach is based on an observation that not all elements of an initial dataset are equally preserved in its low-dimensional embedding space representation. The contribution first overviews existing topology preservation measures, then their inclusion in the classical methods of exploratory data analysis is discussed. Finally, some illustrative examples of presented approach in the tasks of cluster analysis and classification are given.