scholarly journals Key-Point Interpolation: A Sparse Data Interpolation Algorithm based on B-splines

2021 ◽  
Vol 2068 (1) ◽  
pp. 012010
Author(s):  
Bolun Wang ◽  
Xin Jiang ◽  
Guanying Huo ◽  
Cheng Su ◽  
Dongming Yan ◽  
...  
Keyword(s):  
Sparse Data ◽  
Shanxi Province ◽  
Interpolation Algorithm ◽  
Data Interpolation ◽  
Dynamic Surface ◽  
B Spline ◽  
B Splines ◽  
Surface Interpolation ◽  
Spline Surface ◽  
Point Interpolation

Abstract B-splines are widely used in the fields of reverse engineering and computer-aided design, due to their superior properties. Traditional B-spline surface interpolation algorithms usually assume regularity of the data distribution. In this paper, we introduce a novel B-spline surface interpolation algorithm: KPI, which can interpolate sparsely and non-uniformly distributed data points. As a two-stage algorithm, our method generates the dataset out of the sparse data using Kriging, and uses the proposed KPI (Key-Point Interpolation) method to generate the control points. Our algorithm can be extended to higher dimensional data interpolation, such as reconstructing dynamic surfaces. We apply the method to interpolating the temperature of Shanxi Province. The generated dynamic surface accurately interpolates the temperature data provided by the weather stations, and the preserved dynamic characteristics can be useful for meteorology studies.

The Synthesis of Follower Motions of Camoids Using Nonparametric B-Splines

1996 ◽  
Vol 118 (1) ◽  
pp. 138-143 ◽  
Author(s):  
Der Min Tsay ◽  
Guan Shyong Hwang
Keyword(s):  
Spline Functions ◽  
B Spline ◽  
Numerical Examples ◽  
B Splines ◽  
Surface Interpolation ◽  
Spline Surface ◽  
Motion Function ◽  
Knot Sequences ◽  
Independent Parameters

This paper proposes a tool to synthesize the motion functions of the camoid-follower mechanisms. The characteristics of these kinds of motion functions are that they possess two independent parameters. To implement the work, this study applies the nonparametric B-spline surface interpolation, whose spline functions are constructed by the closed periodic B-splines and the de Boor’s knot sequences in the two parametric directions of the motion function, respectively. The rules and the restrictions needed to be noticed in the process of synthesis are established. Numerical examples are also given to verify the feasibility of the proposed method.

Generation and Modification of Non-Uniform B-Spline Surface Approximations to PDE Surfaces Using the Finite Element Method

1990 ◽  
Author(s):  
Joanna M. Brown ◽  
Malcolm I. G. Bloor ◽  
M. Susan Bloor ◽  
Michael J. Wilson
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Spline Approximation ◽  
The Finite Element Method ◽  
B Spline ◽  
B Splines ◽  
Spline Surface ◽  
Spline Surfaces ◽  
Spline Basis ◽  
Element Method

Abstract A PDE surface is generated by solving partial differential equations subject to boundary conditions. To obtain an approximation of the PDE surface in the form of a B-spline surface the finite element method, with the basis formed from B-spline basis functions, can be used to solve the equations. The procedure is simplest when uniform B-splines are used, but it is also feasible, and in some cases desirable, to use non-uniform B-splines. It will also be shown that it is possible, if required, to modify the non-uniform B-spline approximation in a variety of ways, using the properties of B-spline surfaces.

scholarly journals Hierarchical Genetic Algorithm for B-Spline Surface Approximation of Smooth Explicit Data

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
C. H. Garcia-Capulin ◽  
F. J. Cuevas ◽  
G. Trejo-Caballero ◽  
H. Rostro-Gonzalez
Keyword(s):  
Genetic Algorithm ◽  
Geometric Modeling ◽  
Data Set ◽  
Surface Approximation ◽  
B Spline ◽  
B Splines ◽  
Spline Surface ◽  
Spline Surfaces ◽  
Surface Dimension ◽  
Hierarchical Genetic Algorithm

B-spline surface approximation has been widely used in many applications such as CAD, medical imaging, reverse engineering, and geometric modeling. Given a data set of measures, the surface approximation aims to find a surface that optimally fits the data set. One of the main problems associated with surface approximation by B-splines is the adequate selection of the number and location of the knots, as well as the solution of the system of equations generated by tensor product spline surfaces. In this work, we use a hierarchical genetic algorithm (HGA) to tackle the B-spline surface approximation of smooth explicit data. The proposed approach is based on a novel hierarchical gene structure for the chromosomal representation, which allows us to determine the number and location of the knots for each surface dimension and the B-spline coefficients simultaneously. The method is fully based on genetic algorithms and does not require subjective parameters like smooth factor or knot locations to perform the solution. In order to validate the efficacy of the proposed approach, simulation results from several tests on smooth surfaces and comparison with a successful method have been included.

Parallel B-Spline Surface Interpolation on a Mesh-Connected Processor Array

1995 ◽  
Vol 24 (2) ◽  
pp. 224-229
Author(s):  
F.H. Cheng ◽  
G.W. Wasilkowski ◽  
J.Y. Wang ◽  
C.M. Zhang ◽  
W.P. Wang
Keyword(s):  
Processor Array ◽  
B Spline ◽  
Surface Interpolation ◽  
Spline Surface

scholarly journals A Reconstruction Algorithm for Blade Surface Based on Less Measured Points

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jia Liu ◽  
Ji Zhao ◽  
Xu Yang ◽  
Jiming Liu ◽  
Xingtian Qu ◽  
...  
Keyword(s):  
Reconstruction Algorithm ◽  
Surface Model ◽  
Machining Accuracy ◽  
Blade Surface ◽  
B Spline ◽  
Design Concepts ◽  
Surface Interpolation ◽  
Spline Surface ◽  
Machining Allowance ◽  
Curvature Continuity

A reconstruction algorithm for blade surface from less measured points of section curves is given based on B-spline surface interpolation. The less measured points are divided into different segments by the key geometric points and throat points which are defined according to design concepts. The segmentations are performed by different fitting algorithms with consideration of curvature continuity as their boundary condition to avoid flow disturbance. Finally, a high-quality reconstruction surface model is obtained by using the B-spline curve meshes constructed by paired points. The advantage of this algorithm is the simplicity and effectivity reconstruction of blade surface to ensure the aerodynamic performance. Moreover, the obtained paired points can be regarded as measured points to measure and reconstruct the blade surface directly. Experimental results show that the reconstruction blade surface is suitable for precisely representing blade, evaluating machining accuracy, and analyzing machining allowance.

Curve and Surface Representation by Iterative B-Spline Fit to a Data Point Set

1987 ◽  
Vol 16 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Marilyn Lord
Keyword(s):  
Computer Aided Design ◽  
Control Point ◽  
The Body ◽  
B Spline ◽  
B Splines ◽  
Spline Surface ◽  
Data Points ◽  
Point Set ◽  
Support Surfaces ◽  
Aided Design

The method of B-splines provides a very powerful way of representing curves and curved surfaces. The definition is ideally suited to applications in Computer Aided Design (CAD) where the designer is required to remodel the surface by reference to interactive graphics. This particular facility can be advantageous in CAD of body support surfaces, such as design of sockets of limb prostheses, shoe insoles, and custom seating. The B-spline surface is defined by a polygon of control points which in general do not lie on the surface, but which form a convex hull enclosing the surface. Each control point can be adjusted to remodel the surface locally. The resultant curves are well behaved. However, in these biomedical applications the original surface prior to modification is usually defined by a limited set of point measurements from the body segment in question. Thus there is a need initially to define a B-spline surface which interpolates this set of data points. In this paper, a computer-iterative method of fitting a B-spline surface to a given set of data points is outlined, and the technique is demonstrated for a curve. Extension to a surface is conceptually straightforward.

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