Data filtering using dynamic particles method

• Autorzy: Rauch Ł. , Kusiak J.
• Konferencja: Neural Networks and Soft Computing/International Symposium (30.06-02.07.2005 ; Cracow, Poland)
• Języki publikacji: EN

Abstrakty

EN

The identification of the industrial processes is a complex problem, especially in the case of signals denoising. The holistic approaches used for signal denoising processes are recently considered in various types of applications in the domain of experimental simulations, feature extraction and identification. A new signal filtering method based on the dynamic particles (DP) approach is presented. It employs physics principles for the signal smoothing. The presented method was validated in the identification of two kinds of input data sets: artificially generated data according to a given function y = f (X) and the data obtained in laboratory mechanical tests of metals. The algorithm of the DP method and the results of calculations are presented. The obtained results were compared with commonly used denoising techniques including weighted average, neural networks and wavelet analysis. Moreover the assessment of the results' quality is introduced.

Słowa kluczowe

EN

artificial neural networks; soft computing methods; dynamic particles method

PL

sztuczne sieci neuronowe; obliczenia

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Mechanics and Engineering Sciences
• Rocznik: 2007
• Tom: Vol. 14, No. 2
• Strony: 353--360
• Opis fizyczny: Bibliogr. 11 poz., tab., wykr.

Twórcy

autor

Rauch Ł.

autor

Kusiak J.

• Department of Applied Computer Science and Modelling AGH University of Science and Technology al. Mickiewicza 30, 30-059 Kraków, Poland

Bibliografia

• [1] R. Adelino, F. da Silva. Bayesian wavelet denoising and evolutionary calibration. Digital Signal Processing, 14: 566-589, 2004.
• [2] A. Buades, B. Coli, J.M. Morel. On image denoising methods. Centre de Matematiques et de Leurs Applications, http://www.cmla.ens-cachan.fr, 2004.
• [3] W. Dzwinel, W. Aida, D.A. Yuen. Cross-scale numerical simulations using discrete particie models. Molecular Simulation, 22: 397-421, 1999.
• [4] J. Falkus, J. Kusiak, P. Pietrzkiewicz, W. Pietrzyk. Filtering of the industrial data for the Artificial Neural Network Model of the Steel Oxygen Converter Process. A chapter in the monograph Intelligence in Small World- Nanomaterials for the 21th Century. CRC-PRESS, Boca Raton, Florida, 2003.
• [5] J. Gawad, J. Kusiak, M. Pietrzyk, S. Di Rosa, G. Nicol.Optimization methods used for identification of rheological model for brass. Proc. 6th ESAFORM Conf. on Material Forming, Salerno, Italy, 359-362, 2003.
• [6] R.M. Gray, L.D. Davisson. An Introduction to Statistical Signal Processing. Cambridge University Press, 2000.
• [7] S. Hara, T. Tsukada, K. Sasajirna. An in-line digital filtering algorithm for surface roughness profiles. Precision Engineering, 22: 190-195, 1998.
• [8] M. Piovoso, P.A. Laplante. Kalman filter recipes for real-time image processing. Real-time Image Processing, 9:433-439, 2003.
• [9] Ł. Rauch, J. Talar, T. Zak, J. Kusiak. Filtering of thermomagnetic data curve using artificial neural network and wavelet analysis. Proc. 7th ICAISC 2004 Conf., Zakopane, Poland. Springer-Verlag, 2004.
• [10] D. Szeliga, P. Matuszyk, R. Kuziak, M. Pietrzyk. Identification of rheological parameters on the basis of various types of plastometric tests. Journal of Materials Processing Technology, 125-126: 150-154, 2002.
• [11] Web Site: www.x-trade.biz, 2004.