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A Data Mining Approach To Improve Military Demand Forecasting

, vol. 22, no. 4, pp. 637–666, 2006. [6] G. Box, G. Jenkins, and G. Reinsel, Time Series Analysis: Forecasting and Control . 2008. [7] M. Downing, M. Chipulu, U. Ojiako, and D. Kaparis, “Forecasting in airforce supply chains,” International Journal of Logistics Management , vol. 22, no. 1, pp. 127–144, 2011. [8] R. Agrawal, T. Imieli´nski, and A. Swami, “Mining association rules between sets of items in large databases,” SIGMOD Rec. , vol. 22, no. 2, pp. 207–216, 1993. [9] H. Xiong, W. Zhou, M. Brodie, and S. Ma, “Top-k correlation

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Two New Sliding DTFT Algorithms for Phase Difference Measurement Based on a New Kind of Windows

flowmeter. Review of Scientific Instruments , 85 (6), 065116. [7] Knapp, C.H., Carter, G.C. (1976). The generalized correlation method foe estimation of time delay. IEEE Transactions on Acoustics, Speech and Signal Processing , 24 (4), 320-327. [8] Carter, G.C. (1993). Coherence and Time Delay Estimation: An Applied Tutorial for Research, Development, Test, and Evaluation Engineers . IEEE Press. [9] Maskell, D.L., Woods, G.S. (2005). Adaptive subsample delay estimation using a modified quadrature phase detector. IEEE Transactions on Circuits and Systems , 52 (2

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The Influence of the Soil Environment on the Corrosivity of Failure Infrastructure - Case Study of the Exemplary Water Network

Wrocławska, Wrocław. 6. V. Ondrejka Harbulakova, A. Estokova, M. Kovalcikova. 2017. “Correlation analysis between different types of corrosion of concrete containing sulfate resisting cement,” Environments, 4, pp. 1-14. 7. M. Kwietniewski, M. Roman, H. Kłoss-Trśbaczkiewicz. “Niezawodnośś wodociśgów i kanalizacji.” Reliability of water system and sewerage). Arkady, Warsaw, Poland. 1993. (In Polish). 8. B. Tchórzewska-Cieślak, K. Pietrucha-Urbanik, M. Urbanik, “Analysis of the gas network failure and failure prediction using

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Enhancing Constructive Neural Network Performance Using Functionally Expanded Input Data

-8. [4] J. R. Bertini Jr. and M. C. Nicoletti, A constructive neural network algorithm based on the geometric concept of barycenter of convex hull, In: Computational Intelligence: Methods and Applications, IEEE Comp. Intelligence Society, Poland, 2008, pp. 1-12. [5] N. Burgess, A constructive algorithm that converges for real-valued input patterns, International Journal of Neural Systems, vol. 5, no. 1, 1994, pp. 59-66. [6] S. Fahlman and C. Lebiere, The cascade correlation architecture, in Advances in Neural Information Processing

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Assessment of Skid Resistance of Asphalt Mixtures in Laboratory Conditions

References 1. B. Allen, P. Philips, D. Woodward, A. Woodside, Prediction of UK surfacing skid resistance usingWehner Schulze and PSV. 2nd International safer roads conference, 12-16 May, Cheltenham, United Kingdom, 2008. 2. H. Arampamoorthy, J. Partick, Potential of Wehner-Schulze test to predict the on-road friction performanceof aggregate. NZ Transport Agency research report 443. New Zealand, 2011. 3. M. Bustos, T. Echaveguren, H. Solminihac, A. Caroca, Development of correlation equations betweendifferent

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Rogowski Coil for Current Measurement in a Cryogenic Environment

. (1993). Quality of electric energy supply: Harmonics in public supply network. GB/T 14549-93. Beijing. [13] Isa, M., Elkalashy, N.I., Lehtonen, M., et al. (2012). Multi-end correlation-based PD location technique for medium voltage covered-conductor lines. IEEE Transations on Dielectrics and Electrical Insulation, 19 (3), 936-946. [14] Marracci, M., Tellini, B., Zappacosta, C., et al. (2011). Critical parameters for mutual inductance between Rogowski coil and primary conductor. IEEE Transations on

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Fusion Research of Electrical Tomography with Other Sensors for Two-phase Flow Measurement

Measurement and Instrumentation , 22 (4), 272-278. Deng, X., Peng, L. H., Yao, D. Y., Zhang, B. F. (2004). Velocity distribution measurement using pixel-pixel cross-correlation of electrical tomography. Chinese Journal of Electronics , 13 (3), 548-551. He, Y. B. (2006). Research on Techniques of Dual-Mode Electrical Resistance & Capacitance Tomography. Unpublished Doctoral Dissertation, Tianjin University, Tianjin, China. Deng, X., Chen, Y., Yan, Z., Wei, Z. (2009). AC impedance model of array

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Quality Parameter Assessment on iris Images

.,: The effect of predator appetite, prey warning coloration and luminance on predator foraging decisions, Behaviour, vol.147, No. 9., 1121-1143, BRILL, 2010 [8] Du, Y. and Belcher, C. and Zhou, Z. and Ives, R.,: Feature correlation evaluation approach for iris feature quality measure, Signal processing, Vol. 90, No. 4, 1176-1187, Elsevier, 2010 [9] Nill, N. B, IQF (Image Quality of Fingerprint) Software Application, The MITRE Corporation, 2007 [10] Crete, F., Dolmiere,T., Ladret, P. and Nicolas, M.: The blur effect

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Mechanism of Fatigue Crack Growth of Bridge Steel Structures

R eferences 1. L. Reid. “Repairing and preserving bridge and steel structure using an innovative crack arrest repair system”, Advanced Materials Research, 891-892:1217-1222, 2014. 2. H. Y. Lee, J. B. Kim, W. G. Kim, et al. “Creep-fatigue crack behaviour of a mod. 9Cr1Mo steel structure with weldments”, Transactions of the Indian Institute of Metals 63(2):245-250, 2010. 3. E. Sgambitterra, S. Lesci, C. Maletta. “Effects of higher order terms in fracture mechanics of shape memory alloys bydigital image correlation”, Procedia Engineering, 109

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On Merging and Dividing Social Graphs

Letters, vol. 54, p. 436â˘A ¸S442, May 2001. [11] E. N. Gilbert, “Random graphs,” The Annals of Mathematical Statistics, pp. 1141-1144, 1959. [12] R. Kruse, C. Borgelt, F. Klawonn, C. Moewes, M. Steinbrecher, and P. Held, Computational Intelligence: A Methodological Introduction, ser. Texts in Computer Science. New York: Springer, 2013. [13] D. Zwillinger and S. Kokoska, CRC standard probability and statistics tables and formulae. CRC Press, 1999. [14] M. G. Kendall, “A new measure of rank correlation

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