Image Reconstructing in Electrical Capacitance Tomography of Manufacturing Processes Using Genetic Programming

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

@MastersThesis{Al-Afeef:mastersthesis,
  author =       "Ala' S. Al-Afeef",
  title =        "Image Reconstructing in Electrical Capacitance
                 Tomography of Manufacturing Processes Using Genetic
                 Programming",
  school =       "Al-Balqa Applied University",
  year =         "2010",
  address =      "Al-Salt, Jordan",
  month =        jul,
  email =        "alaa.afeef@gmail.com",
  keywords =     "genetic algorithms, genetic programming, Image
                 Reconstructing, Electrical Capacitance Tomography",
  URL =          "https://sites.google.com/site/alaaalfeef/home/Alaa_afeef_Thesis_Final.pdf",
  size =         "137",
  abstract =     "Electrical capacitance tomography is considered the
                 most attractive technique for industrial process
                 imaging because of its low construction cost, safety,
                 fast data acquisition , non-invasiveness,
                 non-intrusiveness, simple structure, wide application
                 field and suitability for most kinds of flask and
                 vessels, however, the low accuracy of the reconstructed
                 images is the main limitation of implementing an ECT
                 system. In order to improve the imaging accuracy, one
                 may 1) increase the number of measurements by raising
                 number of electrodes, 2) improve the reconstruction
                 algorithm so that more information can be extracted
                 from the captured data, however, increasing the number
                 of electrodes has a limited impact on the imaging
                 accuracy improvement. This means that, in order to
                 improve the reconstructed image, more accurate
                 reconstruction algorithms must be developed. In fact,
                 ECT image reconstruction is still an inefficiently
                 resolved problem because of many limitations, mainly
                 the Soft-field and Ill-condition characteristic of ECT.
                 Although there are many algorithms to solve the image
                 reconstruction problem, these algorithms are not yet
                 able to present a single model that can relate between
                 image pixels and capacitance measurements in a
                 mathematical relationship. The originality of this
                 thesis lies in introducing a new technique for solving
                 the non-linear inverse problem in ECT based on Genetic
                 Programming (GP) to handle the ECT imaging for
                 conductive materials. GP is a technique that has not
                 been applied to ECT. GP found to be efficient in
                 dealing with the Non-linear relation between the
                 measured capacitance and permittivity distribution in
                 ECT. This thesis provides new implemented software that
                 can handle the ECT based GP problem with a
                 user-friendly interface. The developed simulation
                 results are promising.",
}

Genetic Programming entries for Alaa Al-Afeef

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