Image reconstruction of a metal fill industrial process using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@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