Dariusz Grabowski

Publications

• Analysis of deterministic model of electric arc furnace

  D. Grabowski, J. Walczak

  Environment and Electrical Engineering (EEEIC), 2011 10th International Conference on, Rome, Italy; 01/2011

  A closed form of the solution to a differential equation used for the AC electric arc modeling has been derived in the paper. A special case of periodic excitations has been considered and the appropriate formulae have been given. Having a closed form of the solution rather than the numerical one en... [more] A closed form of the solution to a differential equation used for the AC electric arc modeling has been derived in the paper. A special case of periodic excitations has been considered and the appropriate formulae have been given. Having a closed form of the solution rather than the numerical one enables better understanding of the phenomena described by the equation. Moreover, it makes easier the extension of the arc model in order to cover the time-varying character of the arc waveforms, e.g. using stochastic approach. Theoretical considerations have been illustrated by a simulation experiment.

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• Stochastic power process for nonlinear inertialess elements

  D Grabowski

  01/2010; 86:147-150.

• Probabilistic representations of active currents

  D Grabowski, J Walczak

  01/2007; 83:86-89.

• Application of genetic algorithms in production line classification systems

  C. Adapa, C. Cristalli, D. Grabowski

  Industrial Technology, 2003 IEEE International Conference on; 01/2004

  This work aims at simplifying the 'optimal feature set selection' step in the design of quality control systems used in the production line processes by means of genetic algorithms (GA). The goals of the work are (i) to save the engineer hours spent on examining sample distributions in the f... [more] This work aims at simplifying the 'optimal feature set selection' step in the design of quality control systems used in the production line processes by means of genetic algorithms (GA). The goals of the work are (i) to save the engineer hours spent on examining sample distributions in the feature space to select the right set of features (ii) to extract the smallest possible feature set that returns agreeable classification accuracy, so that the system finally developed adheres to the temporal limitations imposed by the production line. The idea is to encode feature subsets directly or indirectly as chromosomes and then to assign each chromosome a fitness depending upon the number of features it uses and the testing accuracy obtained by using the classifier constructed on the chromosome's features. The paper also discusses a genetic method that discretizes continuous features while simultaneously selecting optimal features for the decision tree classifier. In the last section the performance of this method has been demonstrated by exemplifying a production line diagnosis system that has been built on the feature set prescribed by it.

• Application of time-frequency representations to classification of vibration signals of electric motors

  D Grabowski, EP Tomasini

  01/2004: pages 143-152;

• Neural approach to time-frequency signal decomposition

  D Grabowski, J Walczak, L Rutkowski, J Siekmann, R Tadeusiewicz, LA Zadeh

  01/2004: pages 1118-1123;

• Generalized spectrum analysis by means of neural networks

  D Grabowski, J Walczak, L Rutkowski, J Kacprzyk

  01/2003: pages 704-709;

• Optimization methods of synthesis of linear and nonlinear electrical systems

  Dariusz Grabowski

  01/1999

  Degree: Ph.D.

  Supervisor: Prof. Janusz Walczak