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Pneumatic Flow Characterization Using ECT Tomography Images Analysis

pixel based correlation with dual plane ECT. Chemical Engineering Journal 130(2), 87 – 99 [4] Filipowicz, S. F. and Rymarczyk, T. (2012). Measurement methods and image reconstruction in electrical impedance tomography. Electrical Review 88(6), 247 – 250 [5] Grudzien, K. (2017). Visualization system for largescale silo flow monitoring based on ect technique. IEEE Sensors Journal 17(24), 8242–8250 [6] Jaworski, A. J. and Dyakowski, T. (2001). Application of electrical capacitance tomography for measurement of gas-solids flow characteristics in a

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Water Flow Forecasting and River Simulation for Flood Risk Analysis

References [1] P.-K. Chiang, P. Willems, J. Berlamont, "A conceptual river model to support real-time flood control," in Demer,’ River Flow 2010 - Dittrich, Koll, Aberle & Geisenhainer, 2010, pp. 1407-1414. [2] K.P. Georgakakos, R. Krzysztofowicz, "Probabilistic and Ensemble Forecasting (Editorial)," Journal of Hydrology, 249(1), 2001, pp.1-4. [3] C. Tucci, W. Collischonn, "Flood forecasting," WMO Bulletin 55 (3), 2006, pp. 179-184. [4] B. Pradhan, "Effective Flood Monitoring System Using

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P2P Traffic Identification Based on Host and Flow Behaviour Characteristics

SIGCOMM Computer Communication Review,Vol. 36, 2006, No 2, 23-26. 7. Chen, M., X. Wei. Hidden Markov Model-Based P2P Flow Identification: A Hidden Markov Model-Based P2P Flow Identification Method. - IET Communications, Vol. 6, 2012, No 13, 2091-2098. 8. Karagiannis, T., K. Papagiannaki, M. Faloutsos. BLINC: Multilevel Traffic Classification in the Dark. - In: Proceedings of the ACM SIGCOMM 2005 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, 2005, 229-237. 9 . Collins, M., M. Reiter

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Study of Flow Behavior of Granular Material Inside Cylindrical Silo Using Ultrafast X-Ray Imaging Technique

References [1] Babout, L., Grudzień, K., Maire, E., Withers, P. J. (2013). Influence of wall roughness and packing density on stagnant zone formation during funnel flow discharge from a silo: An X-ray imaging study. Chemical Engineering Science , 97, 210-224 [2] Bieberle, A., Frust, T., Wagner, M., Bieberle, M., Hampel, U. (2017). Data processing performance analysis for ultrafast electron beam X-ray CT using parallel processing hardware architectures. Flow Measurement and Instrumentation , 53, 180-188 [3] Brown, C.J., Nielsen, J. (Eds

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Algebraic Methods for Traffic Flow Densities Estimation

Dimensional Systems. - In: Lecture Notes in Control Informat. Sci. T. Meurer, K. Graichen, E. D. Gilles, Eds. Vol. 322. 2005, 217-233. 7. Fliess, M. Analyse Non Standard du Bruit. - C. R. Acad. Sci. Paris, Ser. I, Vol. 342, 2006. 8. Fliess, M., C. Join, H. Sira-Ramíre z. Non-Linear Estimation is Easy. - International Journal of Modelling, Identification and Control, Vol. 3, 2008, No 5. 9. Gazi s, D. C., C. H. Knap p. On-Line Estimation of Traffic Densities from Times-Series of Flow and Speed Data. - Transportation Science

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Algorithm for Monitoring Minimum Cost in Fuzzy Dynamic Networks

References [1] I. Chabini and M. Abou-Zeid, The minimum cost flow problem in capacitated dynamic networks. In Annual Meeting of the Transportation Research Board, pages 1-30, Washington, D.C., 2003. [2] L.R. Ford and D.R. Fulkerson, Constructing maximal dynamic flows from static flows, Oper. Res. 6, pages 419-433, 1958. [3] L.K. Fleischer and M. Skutella, Quickest flows over time, SIAM J. Comput. 36, pp. 1600-1630, 2007. [4] X, Cai, D. Sha and C. K. Wong, Time-varying minimum cost flow

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Minimize Traffic Congestion: An Application of Maximum Flow in Dynamic Networks

References Ahuja, R., Magnati, T., Orlin, J., Network Flows. Prentice-Hall, Englewood Cliffs, 1993. Aronson, J., A survey of dynamic network flows. Ann. Oper. Res., vol. 20, 1989, pp. 1-66. 395 M. A. Fonoberova, D. D. Lozovanu. Aumann, Y. And Rabani, Y. 1998. An O (log k ) approximate min-cut max-flow theorem and approximation algorithm. SIAM J. Comput. 27 , 1, 291-301. Aronson, J. E., A survey of dynamic network flows, Annals of Operations Research

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Color Analysis Supporting a Traffic Flow Measurement Based on Optical Flow

References [1] Aslani, S., Mahdavi-Nasab, H. (2013). Optical flow based moving object detection and tracking for traffic surveillance, International Journal of Electrical, Electronics, Communication, Energy Science and Engineering, 7(9), 789-793 [2] Głowacz, A., Mikrut, Z., Pawlik, P. (2012). Video detection algorithm using an optical flow calculation method, International Conference on Multimedia Cmmunications, Services and Security, 128-129 [3] Lien, C.C., Tsai, M.H. (2011). Real-Time Traffic Flow Analysis

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On a numerical flux for the pedestrian flow equations

based on a local variational principle. Computing and Visualization in Science 9, 2, 57–69. D ridi , M. H. 2015. Simulation of high density pedestrian flow: Microscopic model. Open Journal of Modelling and Simulation 3, 1, 81 – 95. E ymard , R., G allouët , T., and H erbin , R. 2000. Finite volume methods. In Handbook of Numerical Analysis , P. Ciarlet and J. Lions, Eds. Vol. VII. North-Holland, 713–1020. F eistauer , M., F elcman , J., and S traškraba , I. 2003. Mathematical and computational methods for compressible flow . Clarendon Press

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Material Flow Optimization with The Application of Generalized Network Flow Model


It is essential for every company to know their business processes well, because these companies must allocate their resources in an efficient way in order to keep or strengthen their market position. During the research we aimed at optimizing the material flow at a wooden box producer company with the use of the generalized network flow model as this model is widely used for modelling production processes. In the first part of our work we calculated the optimal material flows focusing on two objectives, and in the second part we determined a compromise solution. Finally, we compared and evaluated the results of the three models.

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