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DOI: 10.14569/IJARAI.2012.010209
PDF

Automated Marble Plate Classification System Based On Different Neural Network Input Training Sets And PLC Implementation

Author 1: Irina Topalova

International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 1 Issue 2, 2012.

  • Abstract and Keywords
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Abstract: The process of sorting marble plates according to their surface texture is an important task in the automated marble plate production. Nowadays some inspection systems in marble industry that automate the classification tasks are too expensive and are compatible only with specific technological equipment in the plant. In this paper a new approach to the design of an Automated Marble Plate Classification System (AMPCS),based on different neural network input training sets is proposed, aiming at high classification accuracy using simple processing and application of only standard devices. It is based on training a classification MLP neural network with three different input training sets: extracted texture histograms, Discrete Cosine and Wavelet Transform over the histograms. The algorithm is implemented in a PLC for real-time operation. The performance of the system is assessed with each one of the input training sets. The experimental test results regarding classification accuracy and quick operation are represented and discussed.

Keywords: Automated classification; DCT; DWT; Neural network; PLC

Irina Topalova, “ Automated Marble Plate Classification System Based On Different Neural Network Input Training Sets And PLC Implementation” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 1(2), 2012. http://dx.doi.org/10.14569/IJARAI.2012.010209

@article{Topalova2012,
title = { Automated Marble Plate Classification System Based On Different Neural Network Input Training Sets And PLC Implementation},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2012.010209},
url = {http://dx.doi.org/10.14569/IJARAI.2012.010209},
year = {2012},
publisher = {The Science and Information Organization},
volume = {1},
number = {2},
author = {Irina Topalova}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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