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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 15

Proceedings of the 2018 Federated Conference on Computer Science and Information Systems

Contextual processing of electrical capacitance tomography measurement data for temporal modeling of pneumatic conveying process

DOI: http://dx.doi.org/10.15439/2018F171

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 283286 ()

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Abstract. This work covers deployment of contextual processing of measurement data in application to temporal modeling of pneumatic conveying industrial process. Electrical capacitance tomography (ECT) used as a non-invasive process monitoring tool is supported by data mining for regularization of nonlinear inverse problem solution. Processing of a larger number of archived experimental datasets enables extracting additional constraints for inference. Contextual data processing model (CDPM) extracts demanded information from the data in order to incorporate it as an expert knowledge about the process temporal behavior. Then it is incorporated into the Bayesian inference framework. Comparative analysis with previous work and domain expert prepared baseline to the proposed approach is demonstrated. Additionally, simplified parameterization is tested and verified by the quantitative experimental analysis.


  1. M. S. Beck and R. A. Williams, “Process tomography: a european innovation and its applications,” Measurement Science and Technology, vol. 7, no. 3, p. 215, 1996.
  2. W. Q. Yang, A. L. Stott, M. S. Beck, and C. G. Xie, “Development of capacitance tomographic imaging systems for oil pipeline measurements,” Review of Scientific Instruments, vol. 66, no. 8, pp. 4326–4332, 1995.
  3. W. Fang, “Reconstruction of permittivity profile from boundary capacitance data,” Appl. Math. Comput., vol. 177, pp. 178–188, June 2006.
  4. K. Grudzien, A. Romanowski, D. Sankowski, and R. A. Williams, “Gravitational granular flow dynamics study based on tomographic data processing,” Particulate Science and Technology, vol. 26, no. 1, pp. 67–82, 2007.
  5. D. Wanta, J. Kryszyn, J. Buraczyk, and W. T. Smolik, “Www interface for an electrical capacitance tomography system,” in 2018 International Interdisciplinary PhD Workshop (IIPhDW), pp. 344–347, May 2018.
  6. M. Soleimani and W. R. B. Lionheart, “Nonlinear image reconstruction for electrical capacitance tomography using experimental data,” Measurement Science and Technology, vol. 16, no. 10, p. 1987, 2005.
  7. K. Grudzien, “Visualization system for large-scale silo flow monitoring based on ect technique,” IEEE Sensors Journal, vol. 17, pp. 8242–8250, December 2017.
  8. A. Romanowski, K. Grudzien, R. Aykroyd, and R. Williams, “Advanced statistical analysis as a novel tool to pneumatic conveying monitoring and control strategy development,” Particle & Particle Systems Characterization, vol. 23, no. 34, pp. 289–296, 2006.
  9. T. Rymarczyk, P. Tchorzewski, P. Adamkiewicz, K. Duda, and J. Szumowski, “Practical implementation of electrical tomography in a distributed system to examine the condition of objects,” IEEE Sensors Journal, vol. 7, no. 1, pp. 11–16, 2017.
  10. K. Grudzien, A. Romanowski, and R. Williams, “Application of a bayesian approach to the tomographic analysis of hopper flow,” Particle & Particle Systems Characterization, vol. 22, no. 4, pp. 246–253, 2006.
  11. A. Kowalska, R. Banasiak, R. Wajman, A. Romanowski, and D. Sankowski, “Towards high precision electrical capacitance tomography multilayer sensor structure using 3d modelling and 3d printing method,” in 2018 International Interdisciplinary PhD Workshop (IIPhDW), IEEE, pp. 238–243, 2018.
  12. R. Banasiak, R. Wajman, T. Jaworski, P. Fiderek, P. Kapusta, and D. Sankowski, “Two-phase flow regime three-dimensonal visualization using electrical capacitance tomography algorithms and software,” Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie rodowiska, vol. T. 7, nr 1, pp. 11–16, 2017.
  13. M. Panczyk, T. Rymarczyk, and J. Sikora, “Comparison of the inverse problem solutions for a 2d damp wall multilayer and nonhomogenous models,” in 2018 International Interdisciplinary PhD Workshop (IIPhDW), pp. 81–84, May 2018.
  14. V. Mosorov and D. Sankowski, “Estimation of the rotation angle of gas/solid swirl flow by subpixel image resizing,” Asia-Pacific Journal of Chemical Engineering, vol. 13, no. 2, p. e2177, 2018.
  15. A. Romanowski, “Big data-driven contextual processing methods for electrical capacitance tomography,” IEEE Transactions on Industrial Informatics, vol. http://dx.doi.org/10.1109/TII.2018.2855200, p. in press, 2018.
  16. A. Schmidt, “Implicit human computer interaction through context,” Personal Technologies, vol. 4, no. 2, pp. 191–199, 2000.
  17. E. Pascalau, G. Nalepa, and K. Kluza, “Towards a better understanding of context-aware applications,” in FedCSIS13, ACSIS, IEEE, p. 959962, 2013.
  18. A. Romanowski, K. Grudzien, Z. Chaniecki, and P. Wozniak, “Contextual processing of ECT measurement information towards detection of process emergency states,” in Hybrid Intelligent Systems (HIS), 2013 13th International Conference on, pp. 291–297, 2013.
  19. I. Jelliti, A. Romanowski, , and K. Grudzien, “Design of crowdsourcing system for analysis of gravitational flow using x-ray visualization,” in FedCSIS16, ACSIS, vol. 8. IEEE, p. 16131619, 2016.
  20. C. Chen, P. W. Woźniak, A. Romanowski, M. Obaid, T. Jaworski, J. Kucharski, K. Grudzień, S. Zhao, and M. Fjeld, “Using crowdsourcing for scientific analysis of industrial tomographic images,” ACM Trans. Intell. Syst. Technol., vol. 7, no. 4, pp. 52:1–52:25, 2016.
  21. M. Skuza and A. Romanowski, “Sentiment analysis of twitter data within big data distributed environment for stock prediction,” in 2015 FedCSIS’15, pp. 1349–1354, Sept 2015.
  22. H. Garbaa, L. Jackowska-Strumillo, K. Grudzien, and A. Romanowski, “Neural network approach to ect inverse problem solving for estimation of gravitational solids flow,” in 2014 Federated Conference on Computer Science and Information Systems, pp. 19–26, Sept 2014.
  23. R. A. Darwich and L. Babout, “Investigating local orientation methods to segment microstructure with 3d solid texture,” IET Image Processing, vol. 12, no. 7, pp. 1265–1272, 2018.
  24. S. Waktola, K. Grudzien, L. Babout, and J. Adrien, “Local concentration changes in eccentric and concentric silo discharging modes using x-ray tomography,” in 2018 International Interdisciplinary PhD Workshop (IIPhDW), pp. 377–380, May 2018.
  25. A. Wojciechowski and K. Fornalczyk, “Exponentially smoothed interactive gaze tracking method.,” Springer, Cham, In International Conference on Computer Vision and Graphics (pp. 645-652), 2014.
  26. A. Wojciechowski and R. Staniucha, “Mouth features extraction for emotion classification,” in FedCSIS16, ACSIS, vol. 8. IEEE, p. 16851692, IEEE, 2016.