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Journal articles on the topic 'Spline methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Kuželka, K., and R. Marušák. "Use of nonparametric regression methods for developing a local stem form model." Journal of Forest Science 60, No. 11 (2014): 464–71. http://dx.doi.org/10.17221/56/2014-jfs.

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A local mean stem curve of spruce was represented using regression splines. Abilities of smoothing spline and P-spline to model the mean stem curve were evaluated using data of 85 carefully measured stems of Norway spruce. For both techniques the optimal amount of smoothing was investigated in dependence on the number of training stems using a cross-validation method. Representatives of main groups of parametric models &ndash; single models, segmented models and models with variable coefficient &ndash; were compared with spline models using five statistic criteria. Both regression spli

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2

Boehm, Wolfgang. "Multivariate spline methods in CAGD." Computer-Aided Design 18, no. 2 (1986): 102–4. http://dx.doi.org/10.1016/0010-4485(86)90158-2.

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3

Wang, Bin, Wenzhong Shi, and Zelang Miao. "Comparative Analysis for Robust Penalized Spline Smoothing Methods." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/642475.

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Smoothing noisy data is commonly encountered in engineering domain, and currently robust penalized regression spline models are perceived to be the most promising methods for coping with this issue, due to their flexibilities in capturing the nonlinear trends in the data and effectively alleviating the disturbance from the outliers. Against such a background, this paper conducts a thoroughly comparative analysis of two popular robust smoothing techniques, theM-type estimator andS-estimation for penalized regression splines, both of which are reelaborated starting from their origins, with their

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4

Buffa, A., G. Sangalli, and R. Vázquez. "Isogeometric methods for computational electromagnetics: B-spline and T-spline discretizations." Journal of Computational Physics 257 (January 2014): 1291–320. http://dx.doi.org/10.1016/j.jcp.2013.08.015.

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5

da Veiga, L. Beirão, A. Buffa, G. Sangalli, and R. Vázquez. "Mathematical analysis of variational isogeometric methods." Acta Numerica 23 (May 2014): 157–287. http://dx.doi.org/10.1017/s096249291400004x.

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This review paper collects several results that form part of the theoretical foundation of isogeometric methods. We analyse variational techniques for the numerical resolution of PDEs based on splines or NURBS and we provide optimal approximation and error estimates in several cases of interest. The theory presented also includes estimates for T-splines, which are an extension of splines allowing for local refinement. In particular, we focus our attention on elliptic and saddle point problems, and we define spline edge and face elements. Our theoretical results are demonstrated by a rich set o

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6

Sapidis, N. "Methods of Shape-Preserving Spline Approximation." Computer-Aided Design 33, no. 10 (2001): 747–48. http://dx.doi.org/10.1016/s0010-4485(01)00062-8.

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7

Grigorieff, R. D., and I. H. Solan. "Spline petrov-galerkin methods with quadrature." Numerical Functional Analysis and Optimization 17, no. 7-8 (1996): 755–84. http://dx.doi.org/10.1080/01630569608816723.

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8

Botella, Olivier, and Karim Shariff. "B-spline Methods in Fluid Dynamics." International Journal of Computational Fluid Dynamics 17, no. 2 (2003): 133–49. http://dx.doi.org/10.1080/1061856031000104879.

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9

Roch, Steffen. "Spline Approximation Methods Cutting Off Singularities." Zeitschrift für Analysis und ihre Anwendungen 13, no. 2 (1994): 329–45. http://dx.doi.org/10.4171/zaa/510.

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10

Yuyukin, Igor V. "GEOID APPROXIMATION BY SPLINE FUNCTIONS METHODS." Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S. O. Makarova 12, no. 2 (2020): 262–71. http://dx.doi.org/10.21821/2309-5180-2020-12-2-262-271.

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11

Holtz, Markus, and Angela Kunoth. "B‐Spline‐Based Monotone Multigrid Methods." SIAM Journal on Numerical Analysis 45, no. 3 (2007): 1175–99. http://dx.doi.org/10.1137/050642575.

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12

Hu, Xiaolan, Bo Hu, Feitie Zhang, Bing Fu, Hangyang Li, and Yunshan Zhou. "Influences of spline assembly methods on nonlinear characteristics of spline–gear system." Mechanism and Machine Theory 127 (September 2018): 33–51. http://dx.doi.org/10.1016/j.mechmachtheory.2018.04.029.

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13

Sarfraz, Muhammad, Munaza Ishaq, and Malik Zawwar Hussain. "Shape Designing of Engineering Images Using Rational Spline Interpolation." Advances in Materials Science and Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/260587.

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In modern days, engineers encounter a remarkable range of different engineering problems like study of structure, structure properties, and designing of different engineering images, for example, automotive images, aerospace industrial images, architectural designs, shipbuilding, and so forth. This paper purposes an interactive curve scheme for designing engineering images. The purposed scheme furnishes object designing not just in the area of engineering, but it is equally useful for other areas including image processing (IP), Computer Graphics (CG), Computer-Aided Engineering (CAE), Compute

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14

Lamichhane, Bishnu P., Elizabeth Harris, and Quoc Thong Le Gia. "Approximation of noisy data using multivariate splines and finite element methods." Journal of Algorithms & Computational Technology 15 (January 2021): 174830262110084. http://dx.doi.org/10.1177/17483026211008405.

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We compare a recently proposed multivariate spline based on mixed partial derivatives with two other standard splines for the scattered data smoothing problem. The splines are defined as the minimiser of a penalised least squares functional. The penalties are based on partial differential operators, and are integrated using the finite element method. We compare three methods to two problems: to remove the mixture of Gaussian and impulsive noise from an image, and to recover a continuous function from a set of noisy observations.

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15

Foucher, Françoise, and Paul Sablonnière. "Quadratic spline quasi-interpolants and collocation methods." Mathematics and Computers in Simulation 79, no. 12 (2009): 3455–65. http://dx.doi.org/10.1016/j.matcom.2009.04.004.

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16

Grant, I. P. "B-spline methods for radial Dirac equations." Journal of Physics B: Atomic, Molecular and Optical Physics 42, no. 5 (2009): 055002. http://dx.doi.org/10.1088/0953-4075/42/5/055002.

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17

Fischer, Charlotte Froese, W. Guo, and Z. Shen. "Spline methods for multiconfiguration Hartree-Fock calculations." International Journal of Quantum Chemistry 42, no. 4 (1992): 849–67. http://dx.doi.org/10.1002/qua.560420422.

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18

Sun, Weiwei, Jiming Wu, and Xiaoping Zhang. "Nonconforming spline collocation methods in irregular domains." Numerical Methods for Partial Differential Equations 23, no. 6 (2007): 1509–29. http://dx.doi.org/10.1002/num.20238.

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19

Morrison, J. C., and C. Bottcher. "Spline collocation methods for calculating orbital energies." Journal of Physics B: Atomic, Molecular and Optical Physics 26, no. 22 (1993): 3999–4006. http://dx.doi.org/10.1088/0953-4075/26/22/007.

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20

Chandler, G. A., and I. H. Sloan. "Spline qualocation methods for boundary integral equations." Numerische Mathematik 58, no. 1 (1990): 537–67. http://dx.doi.org/10.1007/bf01385639.

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21

Chandler, G. A., and I. H. Sloan. "Spline qualocation methods for boundary integral equations." Numerische Mathematik 62, no. 1 (1992): 295. http://dx.doi.org/10.1007/bf01396230.

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22

Lang, Feng-Gong, and Xiao-Ping Xu. "Spline methods for a Birkhoff interpolation problem." Calcolo 51, no. 3 (2013): 485–504. http://dx.doi.org/10.1007/s10092-013-0096-2.

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23

Verotta, Davide. "Two constrained deconvolution methods using spline functions." Journal of Pharmacokinetics and Biopharmaceutics 21, no. 5 (1993): 609–36. http://dx.doi.org/10.1007/bf01059117.

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24

Lou, Zhou-Ming, Bernard Bialecki, and Graeme Fairweather. "Orthogonal spline collocation methods for biharmonic problems." Numerische Mathematik 80, no. 2 (1998): 267–303. http://dx.doi.org/10.1007/s002110050368.

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25

Korotkiy, Viktor, and Igor' Vitovtov. "Approximation of Physical Spline with Large Deflections." Geometry & Graphics 9, no. 1 (2021): 3–19. http://dx.doi.org/10.12737/2308-4898-2021-9-1-3-19.

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Physical spline is a resilient element whose cross-sectional dimensions are very small compared to its axis’s length and radius of curvature. Such a resilient element, passing through given points, acquires a "nature-like" form, having a minimum energy of internal stresses, and, as a consequence, a minimum of average curvature. For example, a flexible metal ruler, previously used to construct smooth curves passing through given coplanar points, can be considered as a physical spline. The theoretical search for the equation of physical spline’s axis is a complex mathematical problem with no ele

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26

Jia, Jian Jun, Hong Xi Wang, and Ling Wang. "Precision Generating Cutting Research of Rectangle Spline Based on Parameters Superposition." Advanced Materials Research 411 (November 2011): 117–20. http://dx.doi.org/10.4028/www.scientific.net/amr.411.117.

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Aiming at the precision generating cutting problem of rectangle spline shaft, the multi-parameter fusion method to modified cutting edge of rectangle spline hob is presented. The method based on the analysis of workpiece machining errors to establish a fusion model about the rectangle spline hob cutting parameters and the rectangle spline shaft deformation quantity, the model can indicate the connection between rectangle spling hob theoretics profile and actual cutting profile, therefore, the rectangle spline hob’s profile is modified and to simulate by polynomials. The simulation experiment i

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27

Kotsiubivska, Kateryna, Olena Chaikovska, Maryna Tolmach, and Svitlana Khrushch. "Images compression by using cubic spline-functions methods." Technology audit and production reserves 3, no. 2(41) (2018): 4–10. http://dx.doi.org/10.15587/2312-8372.2018.134978.

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28

Sun, Weiwei. "Hermite cubic spline collocation methods with upwind features." ANZIAM Journal 42 (December 25, 2000): 1379. http://dx.doi.org/10.21914/anziamj.v42i0.650.

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29

El-Hawary, H. M., and S. M. Mahmoud. "Spline collocation methods for solving delay-differential equations." Applied Mathematics and Computation 146, no. 2-3 (2003): 359–72. http://dx.doi.org/10.1016/s0096-3003(02)00586-6.

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30

Bialecki, B., and G. Fairweather. "Orthogonal spline collocation methods for partial differential equations." Journal of Computational and Applied Mathematics 128, no. 1-2 (2001): 55–82. http://dx.doi.org/10.1016/s0377-0427(00)00509-4.

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31

Fischer, C. F., and M. Idrees. "Spline methods for resonances in photoionisation cross sections." Journal of Physics B: Atomic, Molecular and Optical Physics 23, no. 4 (1990): 679–91. http://dx.doi.org/10.1088/0953-4075/23/4/002.

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32

Yanik, Elizabeth Greenwell. "A Schwarz alternating procedure using spline collocation methods." International Journal for Numerical Methods in Engineering 28, no. 3 (1989): 621–27. http://dx.doi.org/10.1002/nme.1620280310.

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33

Zhou, Tingting, Michael R. Elliott, and Roderick J. A. Little. "Penalized Spline of Propensity Methods for Treatment Comparison." Journal of the American Statistical Association 114, no. 525 (2019): 1–19. http://dx.doi.org/10.1080/01621459.2018.1518234.

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34

Zamyatin, A. V., and E. A. Zamyatina. "Formation of Reflecting Surfaces Based on Spline Methods." IOP Conference Series: Materials Science and Engineering 262 (November 2017): 012107. http://dx.doi.org/10.1088/1757-899x/262/1/012107.

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35

Bialecki, B., G. Fairweather, A. Karageorghis, and Q. N. Nguyen. "Optimal superconvergent one step quadratic spline collocation methods." BIT Numerical Mathematics 48, no. 3 (2008): 449–72. http://dx.doi.org/10.1007/s10543-008-0188-6.

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36

Zeng, Xiaoyan, Peter Kritzer, and Fred J. Hickernell. "Spline Methods Using Integration Lattices and Digital Nets." Constructive Approximation 30, no. 3 (2009): 529–55. http://dx.doi.org/10.1007/s00365-009-9072-0.

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37

Hu, Xianliang, Danfu Han, and Jiang Zhu. "Spline element methods allowing multiple level hanging nodes." Journal of Computational and Applied Mathematics 337 (August 2018): 125–34. http://dx.doi.org/10.1016/j.cam.2018.01.013.

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38

Fuchs, P. M. "A-stable spline-collocation methods of multivalue type." BIT 29, no. 2 (1989): 295–310. http://dx.doi.org/10.1007/bf01952684.

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39

Ke, Ruan, Xia De-lin, and Yan Pu-liu. "Fingerprint representation methods based on B-Spline functions." Wuhan University Journal of Natural Sciences 9, no. 2 (2004): 193–97. http://dx.doi.org/10.1007/bf02830601.

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40

Li, Can, Tinggang Zhao, Weihua Deng, and Yujiang Wu. "Orthogonal spline collocation methods for the subdiffusion equation." Journal of Computational and Applied Mathematics 255 (January 2014): 517–28. http://dx.doi.org/10.1016/j.cam.2013.05.022.

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41

Christara, Christina C., and Barry Smith. "Multigrid and multilevel methods for quadratic spline collocation." BIT Numerical Mathematics 37, no. 4 (1997): 781–803. http://dx.doi.org/10.1007/bf02510352.

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42

Romanovskii, K. M., and V. D. Trush. "Spline function methods in event-related potential studies." Electroencephalography and Clinical Neurophysiology 61, no. 3 (1985): S229. http://dx.doi.org/10.1016/0013-4694(85)90865-x.

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43

Pr�ssdorf, S., and R. Schneider. "Spline approximation methods for multidimensional periodic pseudodifferential equations." Integral Equations and Operator Theory 15, no. 4 (1992): 626–72. http://dx.doi.org/10.1007/bf01195782.

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44

Rashidinia, J., R. Jalilian, and V. Kazemi. "Spline methods for the solutions of hyperbolic equations." Applied Mathematics and Computation 190, no. 1 (2007): 882–86. http://dx.doi.org/10.1016/j.amc.2007.01.082.

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45

Shariff, Karim, and Robert D. Moser. "Two-Dimensional Mesh Embedding for B-spline Methods." Journal of Computational Physics 145, no. 2 (1998): 471–88. http://dx.doi.org/10.1006/jcph.1998.6053.

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46

WELLS, J. C., V. E. OBERACKER, M. R. STRAYER, and A. S. UMAR. "SPECTRAL PROPERTIES OF DERIVATIVE OPERATORS IN THE BASIS-SPLINE COLLOCATION METHOD." International Journal of Modern Physics C 06, no. 01 (1995): 143–67. http://dx.doi.org/10.1142/s0129183195000125.

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We discuss the basis-spline collocation method for the lattice solution of boundary-value differential equations, drawing particular attention to the difference between lattice and continuous collocation methods. Spectral properties of the basis-spline lattice representation of the first and second spatial derivatives are studied for the case of periodic boundary conditions with homogeneous lattice spacing and compared to spectra obtained using traditional finite-difference schemes. Basis-spline representations are shown to give excellent resolution on small-length scales and to satisfy the ch

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47

Buffa, Annalisa, and Carlotta Giannelli. "Adaptive isogeometric methods with hierarchical splines: Optimality and convergence rates." Mathematical Models and Methods in Applied Sciences 27, no. 14 (2017): 2781–802. http://dx.doi.org/10.1142/s0218202517500580.

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We consider an adaptive isogeometric method (AIGM) based on (truncated) hierarchical B-splines and continue the study of its numerical properties. We prove that our AIGM is optimal in the sense that delivers optimal convergence rates as soon as the solution of the underlying partial differential equation belongs to a suitable approximation class. The main tool we use is the theory of adaptive methods, together with a local upper bound for the residual error indicators based on suitable properties of a well selected quasi-interpolation operator on hierarchical spline spaces.

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48

Liu, Xiaoyan, Jin Xie, Zhi Liu, and Jiahuan Huang. "The Cardinal Spline Methods for the Numerical Solution of Nonlinear Integral Equations." Journal of Chemistry 2020 (February 12, 2020): 1–7. http://dx.doi.org/10.1155/2020/3236813.

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In this study, an effective technique is presented for solving nonlinear Volterra integral equations. The method is based on application of cardinal spline functions on small compact supports. The integral equation is reduced to a system of algebra equations. Since the matrix for the system is triangular, it is relatively straightforward to solve for the unknowns and an approximation of the original solution with high accuracy is accomplished. Several cardinal splines are employed in the paper to enhance the accuracy. The sufficient condition for the existence of the inverse matrix is examined

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49

Sande, Espen, Carla Manni, and Hendrik Speleers. "Sharp error estimates for spline approximation: Explicit constants, n-widths, and eigenfunction convergence." Mathematical Models and Methods in Applied Sciences 29, no. 06 (2019): 1175–205. http://dx.doi.org/10.1142/s0218202519500192.

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In this paper, we provide a priori error estimates in standard Sobolev (semi-)norms for approximation in spline spaces of maximal smoothness on arbitrary grids. The error estimates are expressed in terms of a power of the maximal grid spacing, an appropriate derivative of the function to be approximated, and an explicit constant which is, in many cases, sharp. Some of these error estimates also hold in proper spline subspaces, which additionally enjoy inverse inequalities. Furthermore, we address spline approximation of eigenfunctions of a large class of differential operators, with a particul

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50

Rahayu, Putri Indi, and Pardomuan Robinson Sihombing. "PENERAPAN REGRESI NONPARAMETRIK KERNEL DAN SPLINE DALAM MEMODELKAN RETURN ON ASSET (ROA) BANK SYARIAH DI INDONESIA." JURNAL MATEMATIKA MURNI DAN TERAPAN EPSILON 14, no. 2 (2021): 115. http://dx.doi.org/10.20527/epsilon.v14i2.2968.

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Sharia Bank Return On Assets (ROA) modeling in Indonesia in 2018 aims to analyze the relationship pattern of Retturn On Assets (ROA) with interest rates. The analysis that is often used for modeling is regression analysis. Regression analysis is divided into two, namely parametric and nonparametric. The most commonly used nonparametric regression methods are kernel and spline regression. In this study, the nonparametric regression used was kernel regression with the Nadaraya-Watson (NWE) estimator and Local Polynomial (LPE) estimator, while the spline regression was smoothing spline and B-spli

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