Vol. 11 No. 1 (2012): Revista UIS Ingenierías
Articles

Automatic identification of gas storage cylinders using hopfield neural networks

Luis Carlos Maldonado
Universidad de Pamplona
Bio
César Augusto Peña
Universidad de Pamplona
Bio
Oscar Gualdrón
Universidad de Pamplona
Bio

Published 2012-06-15

Keywords

  • Artificial Vision,
  • gas cylinders,
  • codes,
  • serials,
  • artificial neural networks,
  • hopfield
  • ...More
    Less

How to Cite

Maldonado, L. C., Peña, C. A., & Gualdrón, O. (2012). Automatic identification of gas storage cylinders using hopfield neural networks. Revista UIS Ingenierías, 11(1), 103–111. Retrieved from https://revistas.uis.edu.co/index.php/revistauisingenierias/article/view/103-111

Abstract

Companies engaged in the manufacture, marketing and maintenance of cylinders for liquefied petroleum gas in Colombia, stamped steel plates and welded to the product a unique serial code to be identified within the cylinder of the country park. Currently, the identification process is manual and checked approximately 7000 cylinders per day in a single factory. The main objective of this paper is to present a vision system that uses artificial neural networks to recognize the code. This system consists physically of a portable device that controls light environment and scene for the acquisition of images. Another component of the system is to adjust the image. The adjustment is based on median filtering, binarization, label, and segmentation, this processing allows more meaningful information and image discrimination. Finally, the intelligent component identification is performed with Hopfield neural networks and an algorithm that checks the development of image recognition. The effectiveness of the system was reported with experimental results obtained on the basis of error with a significant number of samples.

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