Image reconstruction of a metal fill industrial process using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Al-Afeef:2010:ISDA,
  author =       "Alaa Al-Afeef and Alaa F. Sheta and Adnan Al-Rabea",
  title =        "Image reconstruction of a metal fill industrial
                 process using Genetic Programming",
  booktitle =    "10th International Conference on Intelligent Systems
                 Design and Applications (ISDA), 2010",
  year =         "2010",
  pages =        "12--17",
  address =      "Cairo",
  month =        "29 " # nov # "-1 " # dec,
  email =        "alaa.afeef@gmail.com",
  keywords =     "genetic algorithms, genetic programming, electrical
                 capacitance tomography, ill-condition characteristic,
                 image reconstruction, industrial process imaging, metal
                 fill industrial process, soft-field characteristic,
                 genetic algorithms, image reconstruction, industrial
                 engineering, tomography, Process Tomography",
  isbn13 =       "978-1-4244-8134-7",
  URL =          "http://sites.google.com/site/alaaalfeef/home/8.pdf",
  DOI =          "doi:10.1109/ISDA.2010.5687299",
  size =         "6 pages",
  abstract =     "Electrical Capacitance Tomography (ECT) is one of the
                 most attractive technique for industrial process
                 imaging because of its low construction cost, safety,
                 non-invasiveness, non-intrusiveness, fast data
                 acquisition, simple structure, wide application field
                 and suitability for most kinds of flask and vessels.
                 However, image reconstruction based ECT suffers many
                 limitations. They include the Soft-field and
                 Ill-condition characteristic of ECT. The basic idea of
                 the ECT for image reconstruction for a metal fill
                 problem is to model the image pixels as a function of
                 the capacitance measurements. Developing this
                 relationship represents a challenge for systems
                 engineering community. In this paper, we presents our
                 innovative idea on solving the non-linear inverse
                 problem for conductive materials of the ECT using
                 Genetic Programming (GP). GP found to be a very
                 efficient algorithm in producing a mathematical model
                 of image pixels in the form of Lisp expression. The
                 reported results are promising.",
  notes =        "Also known as \cite{5687299}",
}

Genetic Programming entries for Alaa Al-Afeef Alaa Sheta Adnan Al-Rabea

Citations