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Archives of Electrical Engineering

Tytuł artykułu

Application of electrical capacitance tomography and artificial neural networks to rapid estimation of cylindrical shape parameters of industrial flow structure

Autorzy Garbaa, H.  Jackowska-Strumiłło, L.  Grudzień, K.  Romanowski, A. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN A new approach to solve the inverse problem in electrical capacitance tomography is presented. The proposed method is based on an artificial neural network to estimate three different parameters of a circular object present inside a pipeline, i.e. radius and 2D position coordinates. This information allows the estimation of the distribution of material inside a pipe and determination of the characteristic parameters of a range of flows, which are characterised by a circular objects emerging within a cross section such as funnel flow in a silo gravitational discharging process. The main advantages of the proposed approach are explicitly: the desired characteristic flow parameters are estimated directly from the measured capacitances and rapidity, which in turn is crucial for online flow monitoring. In a classic approach in order to obtain these parameters in the first step the image is reconstructed and then the parameters are estimated with the use of image processing methods. The obtained results showed significant reduction of computations time in comparison to the iterative LBP or Levenberg-Marquard algorithms.
Słowa kluczowe
EN artificial neural networks   electrical capacitance tomography   flow parameters estimation   inverse problem  
Wydawca Polish Academy of Sciences, Electrical Engineering Committee
Czasopismo Archives of Electrical Engineering
Rocznik 2016
Tom Vol. 65, nr 4
Strony 657--669
Opis fizyczny Bibliogr. 26 poz., rys., tab., wz.
autor Garbaa, H.
  • Lodz University of Technology, Institute of Applied Computer Science Stefanowskiego, 18/22, 90-924, Łódź, Poland,
  • REsearch Groups in Intelligent Machines (REGIM-Lab) University of Sfax, National School of Engineers (ENIS) BP 1173, Sfax 3038, Tunisia
autor Jackowska-Strumiłło, L.
  • Lodz University of Technology, Institute of Applied Computer Science Stefanowskiego, 18/22, 90-924, Łódź, Poland,
autor Grudzień, K.
  • Lodz University of Technology, Institute of Applied Computer Science Stefanowskiego, 18/22, 90-924, Łódź, Poland,
autor Romanowski, A.
  • Lodz University of Technology, Institute of Applied Computer Science Stefanowskiego, 18/22, 90-924, Łódź, Poland,
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Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-ad883572-bb68-4f5d-8d3d-862355a86b94
DOI 10.1515/aee-2016-0046