TWO-PHASE FLOW STRUCTURE IDENTIFICATION BASED ON FUZZY ASSESMENT OF 3D TOMOGRAPHICAL IMAGING


Abstract

The following paper presents results of research on automated two-phase flow pattern identification, which is based on a fuzzy assessment of registered spatial images. Such images are obtained from 3D tomography reconstruction algorithms and for each a set of fuzzy-based features is calculated. Finally, acquired features are used to classify obtained image to one of flow regime structures.


Keywords

fuzzy logic; fuzzy image assessment; image processing; spatial images

Al-sharhan S. , Karray F. , Gueaieb W., Basir O.: Fuzzy entropy: a brief survey in Fuzzy Systems. The 10th IEEE International Conference on, vol. 3, 2001, pp. 1135-1139.

Banasiak R. , Wajman R. , Fidos H. , Fiderek P. , Jaworski T. , Nowakowski J. , Sankowski D. : System trójwymiarowej tomografii pojemnościowej w zastosowaniu do wyznaczania udziału faz oraz identyfikacji struktur w przepływach mieszanin gaz-ciecz. IAPGOS, 2013, nr 3, 28-31.

Caniere H. , Bauwens B. , T’Joen C. , and Paepe M. D.: Probabilistic mapping of adiabatic horizontal two-phase flow by capacitance signal feature clustering. International Journal of Multiphase Flow, vol. 35, no. 7, 2009, pp. 650 – 660.

Cho K.-H., Kim S. , Lee Y.-J.: A fast eit image reconstruction method for the two-phase flow visualization. International Communications in Heat and Mass Transfer, vol. 26, no. 5, 1999, pp. 637 – 646.

Cho K. H., Kim S. , Lee Y. J.: Impedance imaging of two-phase flow field with mesh grouping method. Nuclear Engineering and Design, vol. 204, no. 1–3, 2001, pp. 57 – 67.

Cortes C. and Vapnik V. , Support-vector networks. Mach. Learn., vol. 20, Sept. 1995, pp. 273–297.

Dunn J. C.: A fuzzy relative of the ISODATA process and its use in detecting compact Well-Separated clusters. Journal of Cybernetics, vol. 3, no. 3, 1973, pp. 32–57.

Evgeniou T. , Pontil M. and Elisseeff A.: Leave one out error, stability, and generalization of voting combinations of classifiers. Mach. Learn., vol. 55, Apr. 2004, pp. 71–97.

Guyon I. , Weston J. , Barnhill S. , Vapnik V.: Gene selection for cancer classification using support vector machines. Mach. Learn., vol. 46, Mar. 2002, pp. 389–422.

Kohavi R. and Provost F.: Glossary of terms. Machine Learning, vol. 30, pp. 271–274, 1998. 10.1023/A:1017181826899.

Li H. , Zhou Z. , Hu C.: Measurement and evaluation of two-phase flow parameters. Instrumentation and Measurement, IEEE Transactions on, vol. 41, apr 1992, pp. 298 –303.

Rahmat M. F., Kamaruddin N. S., Isa M. D.: Flow regime identification in pneumatic conveyor using electrodynamic transducer and fuzzy logic method. Journal on Smart Sensing and Intelligent Systems, vol. 2, 2009, pp. 396–416.

Rifkin R. and Klautau A.: In defense of one-vs-all classification. J. Mach. Learn. Res., vol. 5, Dec. 2004, pp. 101–141.

Tsoukalas L. H., Ishii M. , and Mi Y.: A neurofuzzy methodology for impedance-based multiphase flow identification. Engineering Applications of Artificial Intelligence, vol. 10, no. 6, 1997, pp. 545 – 555.

Wajman R. , Banasiak R.: Nowa metoda tunelowego wyznaczania macierzy wrażliwości dla potrzeb procesu rekonstrukcji obrazów dla trójwymiarowej tomografii pojemnościowej. IAPGOS, 2013, nr 3, 32-37.


Published : 2013-07-24


Jaworski, T., Fiderek, P., Wajman, R., & Banasiak, R. (2013). TWO-PHASE FLOW STRUCTURE IDENTIFICATION BASED ON FUZZY ASSESMENT OF 3D TOMOGRAPHICAL IMAGING. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 3(3), 41-48. https://doi.org/10.35784/iapgos.1462

Tomasz Jaworski  tjaworski@kis.p.lodz.pl
Lodz University of Technology, Institute of Applied Computer Science  Poland
Paweł Fiderek 
Lodz University of Technology, Institute of Applied Computer Science  Poland
Radosław Wajman 
Lodz University of Technology, Institute of Applied Computer Science  Poland
Robert Banasiak 
Lodz University of Technology, Institute of Applied Computer Science  Poland