Parameter estimation from tomographic data using self-organising maps

Trevor A. York, Aniekeme Ukpong, Saba Mylvaganam, Yan Ru

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    The paper reports on the potential of using a type of artificial neural network, the self-organising map, for processing tomographic data from pipe separators to estimate interface levels. This is motivated by a desire to estimate process parameters without recourse to image reconstruction. Results show direct quantitative estimation of volume fraction of two-component flow mixtures containing oil and water from electrical capacitance tomography measurements. Parameter extraction from the trained feature map is realised using Gaussian mixture modelling. Parametric information of a mixture is determined by using the probability estimation of sample map and comparing the result with the model's topology. The SOM Toolbox in MATLAB was used for training and developing the models. After preparing the training data the SOM mixture model can be trained in less than 20 seconds. 75% of the two-component mixture test samples are classified within 5% of the sample's true composition. © 2012 IEEE.
    Original languageEnglish
    Title of host publicationIST 2012 - 2012 IEEE International Conference on Imaging Systems and Techniques, Proceedings|IST - IEEE Int. Conf. Imaging Syst. Tech., Proc.
    PublisherIEEE
    Pages112-116
    Number of pages4
    ISBN (Print)9781457717741
    DOIs
    Publication statusPublished - 2012
    Event2012 IEEE International Conference on Imaging Systems and Techniques, IST 2012 - Manchester
    Duration: 1 Jul 2012 → …

    Conference

    Conference2012 IEEE International Conference on Imaging Systems and Techniques, IST 2012
    CityManchester
    Period1/07/12 → …

    Keywords

    • artificial neural network
    • electrical capacitance tomography
    • Gaussian mixture modeling
    • probability estimation of sample
    • self organising maps

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