Advertisement

Spline Evaluation for Railways

  • Dhananjay Singh
  • Madhusudan Singh
  • Zaynidinov Hakimjon
Chapter
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

This chapter explains the use of spline and B-splines in rail technology. It analyses and discusses the spline and B-spline signals and implementation results for rail technology. The use of spline functions leads to the improvement of accuracy of the results and to significant reduction of computation costs. A spline method of analysis, processing and determination of anomalies of rail structures, based on the use of spectral basic signals, was developed.

References

  1. 1.
    S. Bosse, M. Koerdt, D. Schmidt, Robust and adaptive signal segmentation for structural monitoring using autonomous agents, in Proceddings the 4th International Electronic Conference on Sensor and Applications, vol. 2, issue 3 (2017) p. 105.  https://doi.org/10.3390/ecsa-4-04917CrossRefGoogle Scholar
  2. 2.
    S.M. de Lima, L.V. Vareda, J.B.L. Liborio, High performance concrete applied to storage system buildings at low temperatures. Mater. Res. 11(2), 121–130 (2008). http://www.scielo.br/pdf/mr/v11n2/a03v11n2.pdfCrossRefGoogle Scholar
  3. 3.
    M. Koudstaal, F. Yao, From multiple Gaussian sequences to functional data and beyond: a Stein estimation approach. J. R. Stat. Soc., B 80, 319–342 (2018).  https://doi.org/10.1111/rssb.12255MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    B. Shillingford et al., Large scale visual speech recognition, arXiv:1807.05162V2 []cs.CV] (2018). https://arxiv.org/pdf/1807.05162.pdf
  5. 5.
    R. Sharma, L. Vignolo, G. Schlotthauer, M.A. Colominas, H.L. Rufiner, S.R.M. Prasanna, Empirical mode decomposition for adaptive AM-FM analysis of speech: a review. Speech Commun. 88, 39–64, ISSN 0167-6393, (2017).  https://doi.org/10.1016/j.specom.2016.12.004CrossRefGoogle Scholar
  6. 6.
    P.H.M. Bovendeerd, P.A. Van Steenhoven, F.N. van de Vosse, G. Vossers, Steady entry flow in curved pipe flow. J. Fluid Mech. 177, 233–246 (1987).  https://doi.org/10.1017/s0022112087000934CrossRefGoogle Scholar
  7. 7.
    A. Sotiras, C. Davatzikos, N. Paragios, Deformable Medical image registration: a survey. IEEE Trans. Med. Imaging 32(7), 1153–1190 (2013). http://doi.org/10.1109/TMI.2013.2265603. (PMC. Web. 28 Sept. 2018)CrossRefGoogle Scholar
  8. 8.
    D.B. Keele, Jr. (Don), Log sampling in time and frequency: preliminary theory and application. Audio Engineering Society Convention (1994). http://www.aes.org/e-lib/browse.cfm?elib=6297
  9. 9.
    D. Schillinger, E. Rank, An unfitted hp-adaptive finite element method based on hierarchical B-splines for interface problems of complex geometry. Comput. Methods Appl. Mech. Eng. 200(47–48), 3358–3380, ISSN 0045-7825, (2011).  https://doi.org/10.1016/j.cma.2011.08.002MathSciNetCrossRefGoogle Scholar
  10. 10.
    K.E. Emblem et al., Vessel architectural imaging identifies cancer patient responders to anti-angiogenic therapy. Nat. Med. 19(9), 1178–1183 (2013). http://doi.org/10.1038/nm.3289CrossRefGoogle Scholar
  11. 11.
    N. Gonga-Saholiariliva, Y. Gunnell, C. Petit, C. Mering, Techniques for quantifying the accuracy of gridded elevation models and for mapping uncertainty in digital terrain analysis. Prog. Phys. Geogr.: Earth Environ. 35(6), 739–764 (2011).  https://doi.org/10.1177/0309133311409086CrossRefGoogle Scholar
  12. 12.
    F. Gensun, Whittaker–Kotelnikov–Shannon Sampling theorem and aliasing error. J. Approx. Theory 85(2), 115–131, ISSN 0021-9045, (1996).  https://doi.org/10.1006/jath.1996.0033MathSciNetCrossRefGoogle Scholar
  13. 13.
    A.R. Amiri-Simkooei, M. Hosseini-Asl, A. Safari, Least squares 2D bi-cubic spline approximation: theory and applications. Measurement 127, 366–378, ISSN 0263-2241, (2018).  https://doi.org/10.1016/j.measurement.2018.06.005CrossRefGoogle Scholar
  14. 14.
    H.N. Zaynidinov, M.B. Zaynutdinova, E.S. Nazirova, Methods of reconstucting signals based on multivariate spline. Eur. J. Comput. Sci. Inf. Technol. 3(2), 56–63 (2015). http://www.eajournals.org/wp-content/uploads/Methods-of-reconstructing-signals-based-on-multivariate-spline2.pdf
  15. 15.
    C.H. Garcia-Capulin, F. Cuevas, G. Trejo-Caballero, H. Rostro, A hierarchical genetic algorithm approach for curve fitting with B-splines. Genet. Program. Evolvable Mach. 16, 151–166 (2014).  https://doi.org/10.1007/s10710-014-9231-3CrossRefGoogle Scholar
  16. 16.
    H. Zhou, T. Wenyi, W. Deng, Quasi-compact finite difference schemes for space fractional diffusion equations. J. Sci. Comput. 56(1), 45–66 (2013). http://dx.doi.org/10.1007/s10915-012-9661-0MathSciNetCrossRefGoogle Scholar
  17. 17.
    H. Zhang, X. Yang, The BDF orthogonal spline collocation method for the two-dimensional evolution equation with memory. Int. J. Comput. Math. 95(10), 2011–2025 (2018).  https://doi.org/10.1080/00207160.2017.1347259MathSciNetCrossRefGoogle Scholar
  18. 18.
    G. Zhang, A. Xiao, Exact and numerical stability analysis of reaction-diffusion equations with distributed delays. Front. Math. China 11(1), 189–205 (2016).  https://doi.org/10.1007/s11464-015-0506-7MathSciNetCrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Dhananjay Singh
    • 1
  • Madhusudan Singh
    • 2
  • Zaynidinov Hakimjon
    • 3
  1. 1.Department of Electronics EngineeringHankuk University of Foreign Studies (Global Campus)YonginKorea (Republic of)
  2. 2.School of Technology Studies, Endicott College of International StudiesWoosong UniversityDaejeonKorea (Republic of)
  3. 3.Head of Department of Information TechnologiesTashkent University of Information TechnologiesTashkentUzbekistan

Personalised recommendations