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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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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