Search Results

71 - 80 of 166 items :

  • "correlation" x
  • Electrical Engineering x
Clear All

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 Systems, vol. 2, 1990, pp. 524-532. [7] S. E. Fahlman, Faster-learning variations on

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 measurements of skid resistance in pavements. Indian

. 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,” Biometrika, vol. 30, no. 1-2, pp. 81-93, 1938. [15] P. Held, A. Dockhorn, and R. Kruse, “Generating events for dynamic social network simulations,” in Proceedings of 15th International Conference on Information Processing and Management of

by Use of Ring-CSOM and Frequency-Domain Local Correlation, IEICE Transactions, 92-C(1), pp.102–108, 2009. [5] Rajoo Pandey, Complex-Valued Neural Networks for Blind Equalization of Time-Varying Channels, International Journal of Signal Processing, 1(1), pp.1–8, 2004. [6] A. J. Noest, Associative Memory in Sparse Neural Networks, Europhysics Letters, 6(6), pp.469–474, 1988. [7] N. N. Aizenberg and I. N. Aizenberg, CNN Based on Multi-Valued Neuron as a Model of Associative Memory for Gray-Scale Images, Proceedings of the 2nd IEEE International Workshop on Cellular

, 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: perception and estimation with a new no-reference perceptual blur metric., Human Vision and Electronic Image in XII, 6492:64920I, 2007. [11] Li, Y.H., Savvides, M.: An

References [1] Thorn, R., Johansen, G.A., Hjertaker, B.T. (2013). Three-phase flow measurement in the petroleum industry. Measurement Science and Technology, 24, 012003. [2] Tan, C., Dong, F. (2010). Modification to mass flow rate correlation in oil-water two-phase flow by a Vcone flow meter in consideration of the oil-water viscosity ratio. Measurement Science and Technology, 21, 045403. [3] Li, Y., Yang, W., Xie, C., Huang, S., Wu, Z., Tsamakis, D., Lenn, C. (2013). Gas/oil/water flow measurement by electrical capacitance tomography. Measurement Science and

partial discharge measurements. IEEE Sensors Journal, 13 (3), 1081-1091. [11] Chiampi, M., Crotti, G., Morando, A. (2011). Evaluation of flexible Rogowski coil performances in power frequency applications. IEEE Transations on Instrumentation and Measurement, 60 (3), 854-862. [12] Chinese Standard. (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

pipelines using filter diagonalization method. IEEE Sensor Journal , 9 (11), 1605-1614. Deng, X., Li, G. Y., Wei, Z., Yan, Z. W., Yang, W. Q. (2011). Theoretical study of vertical slug flow measurement by data fusion from electromagnetic flowmeter and electrical resistance tomography. Flow 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

ROC curves. BMC Bioinformatics , 12, 77. [41] Model Evaluation: Quantifying the quality of predictions . https://scikit-learn.org/stable/modules/model_evaluation.html . [42] Boughorbel, S., Jarray, F., El-Anbari, M. (2017). Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS One , 12 (6), e0177678. [43] Iuchi, H. (2017). mccr: The Matthews correlation coefficient (v. 0.4.4) . https://CRAN.R-project.org/package=mccr . [44] Krizhevsky, A., Sutskever, I., Hinton, G.E. (2017). ImageNet classification with deep convolutional

analysis of Hilbert transform with band-pass FIR filters for robust brain computer interface, 2014 IEEE Symposium on CIBCI, Orlando, FL, 2014 [22] J. J. J. Davis, G. Gillett, and R. Kozma, Revisiting Brentano on Consciousness: Striking Correlations with Electrocorticogram Findings about the Action- Perception Cycle and the Emergence of Knowledge and Meaning, Mind and Matter, vol. 13, no. 1, pp. 45-69, 2015. [23] J. J. J. Davis, R. Kozma, and W. J. Freeman, The Art of Encephalography to Understand and Discriminate Higher Cognitive Functions Visualizing Big Data on Brain