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Autor
Garbaa Hela (Lodz University of Technology, Poland), Jackowska-Strumiłło Lidia (Lodz University of Technology, Poland), Grudzień Krzysztof (Lodz University of Technology, Poland), Romanowski Andrzej (Lodz University of Technology, Poland)
Tytuł
Neural network approach to ECT inverse problem solving for estimation of gravitational solids flow
Źródło
Annals of Computer Science and Information Systems, 2014, vol. 2, s. 19-26, rys., tab., bibliogr. 22 poz.
Słowa kluczowe
Sztuczne sieci neuronowe (SSN), System MATLAB, Metody informatyczne
Artificial neural networks (ANN), MATLAB system, IT methods
Uwagi
summ.
Abstrakt
A new method to solve the inverse problem in electrical capacitance tomography is proposed. Our method is based on artificial neural network to estimate the radius of an object present inside a pipeline. This information is useful to predict the distribution of material inside the pipe. The capacitance data used to train and test the neural network is simulated on Matlab using the electrical capacitance tomography toolkit ECTsim. The provided accuracy is promising and shows efficiency to solve the inverse problem in a simple manner and on reduced computational time about 120 times when compared to the existing Landweber iterative algorithm for tomographic image reconstruction that can be encouraging for dynamic industrial applications.(original abstract)
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Bibliografia
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  7. Jianwei Li, Xiaoguang Yang, Youhua Wang and Ruzheng Pan , An Image Reconstruction Algorithm Based on RBF Neural Network for Electrical Capacitance Tomography, Sixth International Conference on Electromagnetic Field Problems and Applications (ICEF), 2012,pp1-4, 10.1109/ICEF.2012.6310416.
  8. Jing Lei , Shi Liu , Xueyao Wang and Qibin Liu ,An Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Robust Principle Component Analysis, Sensors2013, VOL.13, pp 2076-2092, 10.3390/s130202076.
  9. Jing Lei and Shi Liu, Dynamic Inversion Approach for ElectricalCapacitance Tomography, IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 62, NO. 11, November2013, pp3035-3049, 10.1109/TIM.2013.2270039.
  10. Lionheart WRB.: Review: Developments in EIT reconstruction algorithms: pitfalls, challenges and recent development. Physiol. Meas., Vol. 25, 125-142, 2004, 10.1088/0967-3334/25/1/021.
  11. Niedostatkiewicz M., Tejchman J., Chaniecki Z., Grudzień K.: Determination of bulk solid concentration changes during granular flow in a silo with ECT sensors. Chemical Engineering Science. 64, 2008, pp. 20-30.
  12. Norberto Flores, Ángel Kuri-Morales, Carlos Gamio , An Application of Neural Networks for Image Reconstruction in Electrical Capacitance Tomography Applied to Oil Industry, Progress in Pattern Recognition, Image Analysis and Applications,Lecture Notes in Computer Science, Vol 4225, 2006, pp 371-380, 10.1007/11892755_38.
  13. Norberto Flores, J Carlos Gamio, Carlos Ortiz-Alemán and Enrique Damián ,Sensor modeling for an electrical capacitance tomography system applied to oil industry, Excerpt from the Proceedings of the COMSOL Multiphysics User's Conference 2005 Boston.
  14. Qussai Marashdeh, Warsito Warsito, Liang-Shih Fan, and Fernando L. Teixeira, Nonlinear Forward Problem Solution for Electrical Capacitance Tomography Using Feed-Forward Neural Network, IEEE SENSORS JOURNAL, VOL. 6, NO. 2, APRIL 2006, pp441-449, 10.1109/JSEN.2005.860316.
  15. Romanowski A., K. Grudzien, R.A. Williams Analysis and interpretation of hopper flow behavior using electrical capacitance tomography Part. Part. Syst. Charact., 23 (2006), pp. 297-305, 10.1002/ppsc.200601060.
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  17. Smolik W and Radomski D, The matlab's toolbox for iterative image reconstruction in electrical capacitance tomography, 5th Int.Symp.on Process tomography (Poland), pp98-103.
  18. W Q Yang and Lihui Peng, Image reconstruction algorithms for electrical capacitance tomography, Measurement Science and Technology, Vol.44,No.1, January2003, 10.1088/0957-0233/14/1/201.
  19. Warsito W. and Fan L-S (2003) Development of 3-Dimensional Electrical Capacitance Tomography Based on Neural Network Multicriterion Optimization Image Reconstruction, Proc. of 3rd World Congress on Industrial Process Tomography (Banff), 2003, 942-947.
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  21. Yunjie Yang and Lihui Peng, Data Pattern With ECT Sensor and Its Impact on Image Reconstruction, IEEE SENSORS JOURNAL, VOL. 13, NO. 5, MAY 2013, pp1582-1593, 10.1109/JSEN.2013.2237763.
  22. Ziqiang Cui, Chengyi Yang, Benyuan Sun, Huaxiang Wang, Liquid film thichness estimation using electrical capacitance tomography, MEASUREMENT SCIENCE REVIEW, Volume 14, No. 1, 2014, pp8-15, 10.2478/msr-2014-0002.
Cytowane przez
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ISSN
2300-5963
Język
eng
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