• Journal of Information Processing Systems
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• v.10 no.1
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• pp.23-35
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• 2014

Recently, novel application areas in digital geometry processing, such as simulation, dynamics, and medical surgery simulations, have necessitated the representation of not only the surface data but also the interior volume data of a given 3D object. In this paper, we present an efficient framework for the shape approximations of spherical solid objects based on trivariate B-splines. To do this, we first constructed a smooth correspondence between a given object and a unit solid cube by computing their harmonic mapping. We set the unit solid cube as a rectilinear parametric domain for trivariate B-splines and utilized the mapping to approximate the given object with B-splines in a coarse-to-fine manner. Specifically, our framework provides user-controllability of shape approximations, based on the control of the boundary condition of the harmonic parameterization and the level of B-spline fitting. Experimental results showed that our method is efficient enough to compute trivariate B-splines for several models, each of whose topology is identical to a solid sphere.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.287-299
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• 2022

In radiation epidemiology, the excess relative risk (ERR) model is used to determine the dose-response relationship. In general, the dose-response relationship for the ERR model is assumed to be linear, linear-quadratic, linear-threshold, quadratic, and so on. However, since none of these functions dominate other functions for expressing the dose-response relationship, a Bayesian semiparametric method using splines has recently been proposed. Thus, we improve the Bayesian semiparametric method for the selection of the tuning parameters for splines as the number and location of knots using a Bayesian knot selection method. Equally spaced knots cannot capture the characteristic of radiation exposed dose distribution which is highly skewed in general. Therefore, we propose a nonparametric Bayesian knot selection method based on a Dirichlet process mixture model. Inference of the spline coefficients after obtaining the number and location of knots is performed in the Bayesian framework. We apply this approach to the life span study cohort data from the radiation effects research foundation in Japan, and the results illustrate that the proposed method provides competitive curve estimates for the dose-response curve and relatively stable credible intervals for the curve.

• The Transactions of the Korea Information Processing Society
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• v.1 no.4
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• pp.531-540
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• 1994

Spline curve scheme is the most valuable tool for the CAD of industrial products. Hence, the development of a new, effective curve scheme can have immediate impact on the current design industries. This paper develops and implements a new methodology for the implementation of the visually continuous class of splines which can produce a more flexible and diverse curve shapes. This class of splines has advantaged over existing splines in that it can accommodate wider range of shapes while maintaining the interpolators property of the ordinary cardinal splines. Most importantly, we avoid using the previous method of implementing G$^1$ curves, where users must specify scalar values for the control of curve shapes. We derive and implement an easy-to -use transformation between the user-specified graphical tangent vectors and the actual parameters for the resulting curve. Since the resulting curve shape reflects original tangential direction faithfully, CAD users can simply represent approximate curve shapes with proper tangents. Consequently, a simple user interface device such as a mouse can effectively produce a various spline curves using the proposed spline tool.

• The Transactions of the Korea Information Processing Society
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• v.5 no.1
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• pp.181-190
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• 1998

특정한 부분에 정확성을 가지는 기하학 형태인식은 이미지 분석 응용에서 중요하게 다루어져 왔다. 또한, 부분적 혹은 변형된 모양을 계층화하는 새로운 접근방법에 대한 연구가 계속되고 있다. 그러므로 본 논문에서는 자유형태의 부드러운 곡선을 생성하는데 용이한 스플라인 공식들을 이용하여 새로운 기하학 형태 매칭 접근 방법을 제안한다. 이와 같은 방법에서, 여러개의 스플라인 공식들로 생성된 곡선들의 집합은 동일한 형태의 성질을 가진다. 본 논문은 곡선 설계를 위하여 일반적인 상호작용 방법과 다양한 스플라인 공식간에 관계들을 이용함으로서 간단한 설계점(design point)들의 이동으로 형태매칭 방법에 관한 관계성을 보인다. 그러므로, 본 논문에서는 3차 스플라인의 공식(B-splines, Bezier splines, Catmull-Rom splines)을 이용하여 형태 매칭 방법을 제안한다.

• Journal of the Korean Statistical Society
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• v.36 no.1
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• pp.57-76
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• 2007

Kriging is a nonparametric regression method used in geostatistics for estimating curves and surfaces for spatial data. It may come as a surprise that the Kriging estimator, normally derived as the best linear unbiased estimator, is also the solution of a particular variational problem. Thus, Kriging estimators can also be interpreted as generalized smoothing splines where the roughness penalty is determined by the covariance function of a spatial process. We build off the early work by Silverman (1982, 1984) and the analysis by Cox (1983, 1984), Messer (1991), Messer and Goldstein (1993) and others and develop an equivalent kernel interpretation of geostatistical estimators. Given this connection we show how a given covariance function influences the bias and variance of the Kriging estimate as well as the mean squared prediction error. Some specific asymptotic results are given in one dimension for Matern covariances that have as their limit cubic smoothing splines.

• Geomechanics and Engineering
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• v.14 no.4
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• pp.315-324
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• 2018

With rapid economic growth, numerous deep excavation projects for high-rise buildings and subway transportation networks have been constructed in the past two decades. Deep excavations particularly in thick deposits of soft clay may cause excessive ground movements and thus result in potential damage to adjacent buildings and supporting utilities. Extensive plane strain finite element analyses considering small strain effect have been carried out to examine the wall deflections for excavations in soft clay deposits supported by diaphragm walls and bracings. The excavation geometrical parameters, soil strength and stiffness properties, soil unit weight, the strut stiffness and wall stiffness were varied to study the wall deflection behaviour. Based on these results, a multivariate adaptive regression splines model was developed for estimating the maximum wall deflection. Parametric analyses were also performed to investigate the influence of the various design variables on wall deflections.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.35-44
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• 2005

The degree reduction of B-splines is necessary in exchanging parametric curves and surfaces of the different geometric modeling systems because some systems limit the supported maximal degree. In this paper, We provide an our experimental results in approximate conversion for B-splines apply to degree reduction. We utilize the existing Bezier degree reduction methods, and analyze the methods. Also, knot removal algorithm is used to reduce data in the degree reduction Process.

• Geomechanics and Engineering
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• v.7 no.4
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• pp.431-458
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• 2014

Construction of a new cavern close to an existing cavern will result in a modification of the state of stresses in a zone around the existing cavern as interaction between the twin caverns takes place. Extensive plane strain finite difference analyses were carried out to examine the deformations induced by excavation of underground twin caverns. From the numerical results, a fairly simple nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) has been used to relate the maximum key point displacement and the percent strain to various parameters including the rock quality, the cavern geometry and the in situ stress. Probabilistic assessments on the serviceability limit state of twin caverns can be performed using the First-order reliability spreadsheet method (FORM) based on the built MARS model. Parametric studies indicate that the probability of failure $P_f$ increases as the coefficient of variation of Q increases, and $P_f$ decreases with the widening of the pillar.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.543-553
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• 2005

Linear logistic regression is one of the most widely used method for credit scoring in credit risk management. This paper deals with credit scoring using splines based on Logistic regression. Linear splines and an automatic basis selection algorithm are adopted. The final model is an example of the generalized additive model. A simulation using a real data set is used to illustrate the performance of the spline method.

• Computers and Concrete
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• v.16 no.4
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• pp.569-585
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• 2015

Various computational tools are available for modeling highly nonlinear structural engineering problems that lack a precise analytical theory or understanding of the phenomena involved. This paper adopts a fairly simple nonparametric adaptive regression algorithm known as multivariate adaptive regression splines (MARS) to model the nonlinear interactions between variables. The MARS method makes no specific assumptions about the underlying functional relationship between the input variables and the response. Details of MARS methodology and its associated procedures are introduced first, followed by a number of examples including three practical structural engineering problems. These examples indicate that accuracy of the MARS prediction approach. Additionally, MARS is able to assess the relative importance of the designed variables. As MARS explicitly defines the intervals for the input variables, the model enables engineers to have an insight and understanding of where significant changes in the data may occur. An example is also presented to demonstrate how the MARS developed model can be used to carry out structural reliability analysis.