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Distributed Computing and Artificial Intelligence, 13th International Conference pp 313–321Cite as

Fast Intelligent Image Reconstruction Algorithm for ECT Systems

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 474))

Abstract

Electrical Capacitance Tomography (ECT) has more attention in the last few decades due to its importance in many industrial and medical processes. Research has various directions in this field such how reconstruct accurate images of the object under consideration, hardware implantation of both the recognition system and/or the image viewing devices. In this paper, a novel single-stage intelligent approach is designed for reconstructing images that describe the materials distribution of the multi-phase flow in industrial pipelines. The proposed algorithm utilizes Fuzzy Inference System (FIS) to overcome the nonlinear response of the ECT system. The proposed algorithm is fast since it does not need solving the forward problem to update the sensitivity matrix. The reported results show that the proposed FIS image reconstruction algorithm has high accuracy and promising technique.

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References

  1. Wang, A., Marashdeh, Q., Fan, L.-S.: ECVT imaging and model analysis of the liquid distribution inside a horizontally installed passive cyclonic gas–liquid separator. Chem. Eng. Sci. 141, 231–239 (2016)

    Article  Google Scholar 

  2. Rasteiro, M.G., Silva, R.C.C., Garcia, F.A.P., Faia, P.M.: Electrical Tomography: a review of Configurations and Applications to Particulate Processes. KONA Powder Part. J. 29(29), 67–80 (2011)

    Article  Google Scholar 

  3. Wang, A., Marashdeh, Q., Motil, B.J., Fan, L.-S.: Electrical capacitance volume tomography for imaging of pulsating flows in a trickle bed. Chem. Eng. Sci. 119 (2014)

    Google Scholar 

  4. Wang, Z., Chen, Q., Wang, X., Li, Z., Han, Z.: Dynamic Visualization Approach of the Multiphase Flow Using Electrical Capacitance Tomography. Chinese J. Chem. Eng. 20(2), 380–388 (2012)

    Article  Google Scholar 

  5. Yang, Y., Jia, J., Mccann, H.: A Faster Measurement Strategy of Electrical Capacitance Tomography Using Less Sensing Data, (2) (2015)

    Google Scholar 

  6. Isaksen, Ø.: A review of reconstruction techniques for capacitance tomography. Meas. Sci. Technol. 7(3), 325–337 (1996)

    Article  MathSciNet  Google Scholar 

  7. Weber, J.M., Layfield, K.J., Van Essendelft, D.T., Mei, J.S.: Fluid bed characterization using Electrical Capacitance Volume Tomography (ECVT), compared to CPFD Software’s Barracuda. Powder Technol. 250, 138–146 (2013)

    Article  Google Scholar 

  8. Yang, W.Q., Peng, L.: Image reconstruction algorithms for electrical capacitance tomography. Meas. Sci. Technol. 14(1), R1–R13 (2003)

    Article  MathSciNet  Google Scholar 

  9. Rahman, N.A.A., Rahim, R.A., Bawi, A.M., Leow, P.L., Pusppanathan, J., Mohamad, E.J., Chan, K.S., Din, S.M., Ayob, N.M.N., Yunus, R.R.M.: A Review on Electrical Capacitance Tomography Sensor Development. J. Teknol. 73(3), 35–41 (2015)

    Google Scholar 

  10. Liu, X., Wang, X., Hu, H., Li, L., Yang, X.: An extreme learning machine combined with Landweber iteration algorithm for the inverse problem of electrical capacitance tomography. Flow Meas. Instrum. 45, 348–356 (2015)

    Article  Google Scholar 

  11. Taylor, S.H., Garimella, S.V.: An explicit conditioning method for image reconstruction in electrical capacitance tomography. Flow Meas. Instrum. 46, 155–162 (2015)

    Article  Google Scholar 

  12. Deabes, W.A., Abdelrahman, M.A.: A nonlinear fuzzy assisted image reconstruction algorithm for electrical capacitance tomography. ISA Trans. 49(1), 10–18 (2010)

    Article  Google Scholar 

  13. Ye, J., Wang, H., Yang, W.: Image Reconstruction for Electrical Capacitance Tomography Based on Sparse Representation. IEEE Trans. Instrum. Meas. 64(1) (2015)

    Google Scholar 

  14. Abdelrahman, M.A., Gupta, A., Deabes, W.A.: A Feature-Based Solution to Forward Problem in Electrical Capacitance Tomography of Conductive Materials. IEEE Trans. Instrum. Meas. 60(2), 430–441 (2011)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science Department, University College, Umm Al-Qura University, Makkah, Kingdom of Saudi Arabia

    Wael A. Deabes & Hesham H. Amin

  2. Computer and System Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Wael A. Deabes

  3. Computer Systems Department, Faculty of Engineering, Aswan University, Aswan, Egypt

    Hesham H. Amin

Authors
  1. Wael A. Deabes
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  2. Hesham H. Amin
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Corresponding author

Correspondence to Hesham H. Amin .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, Osaka Institute of Technology Faculty of Engineering, Osaka, Osaka, Japan

    Sigeru Omatu

  2. Faculty of Computer Science & Informatio, Universiti Teknologi Malaysia (UTM) Faculty of Computer Science & Informatio, Baharu, Malaysia

    Ali Semalat

  3. Department of Electronics and Compu, Koszalin University of Technology Department of Electronics and Compu, Koszalin, Poland

    Grzegorz Bocewicz

  4. Faculty of Electrical Engineering and Cs, Kielce University of Technology Faculty of Electrical Engineering and Cs, Kielce, Poland

    Paweł Sitek

  5. Faculty of Engineering and Science, Aalborg University Faculty of Engineering and Science, Aalborg, Denmark

    Izabela E. Nielsen

  6. ETS Ingeniería Informática, University of Sevilla ETS Ingeniería Informática, Sevilla, Spain

    Julián A. García García

  7. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid Departamento de Inteligencia Artificial, Madrid, Spain

    Javier Bajo

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© 2016 Springer International Publishing Switzerland

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Cite this paper

Deabes, W.A., Amin, H.H. (2016). Fast Intelligent Image Reconstruction Algorithm for ECT Systems. In: Omatu, S., et al. Distributed Computing and Artificial Intelligence, 13th International Conference. Advances in Intelligent Systems and Computing, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-319-40162-1_34

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  • DOI: https://doi.org/10.1007/978-3-319-40162-1_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40161-4

  • Online ISBN: 978-3-319-40162-1

  • eBook Packages: EngineeringEngineering (R0)

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