• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.590-593
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• 2002

A study on gray image morphing using 2D spline interpolation and 2D wavelet transform is presented. The B-spline of splines is used for interframe specified points to determine and the wavelet transform of transforms is applied for generating interframe images. The results are a smoothing image transfer by 2D spline and a removed degrading images as a blotting by 2D wavelet transform is making a good morphing image.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1047-1058
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• 2008

Prediction of total sales in information security industry is considered. Exponential smoothing and spline smoothing is applied to the time series of annual sales data. Due to the different survey items of every year, we recollect the original survey data by some basic criterion and predict the sales to 2014. We show the total sales in infonnation security industry are increasing gradually by year.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.11
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• pp.38-41
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• 1990

A new spline function for resampling discrete signal adaptively is proposed. In general, B-spline function is used for an image interpolation because of its smoothness and continuity, but accompanies a large amount of blurring effect. Hence, we developed a new spline function to remedy this effect, with two procedures ; deblurring of Gaussian blurring and diminishing of aliasing effect caused by deblurring procedure. The proposed function has a parametric expression with $\alpha$ which is related to the variance of Gaussian blurring model. Locally adaptive resampling scheme is obtained by changing a according to statistical characteristics of an image. The proposed, interpolation function shows edge-sharpening effect as well as noise smoothing, with comparison to the conventional schemes.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.249-254
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• 1987

This paper presents a new method for generating smooth free-form surface by local correction. B-spline surface is used for its convenience of local correction, and the direction of surface correction is fixed to the average-surface-normal direction. The surface to be corrected is approximated into a uniform cubic B-spline surface. Then, the smoothness (curvature arrows, iso-parametric lines) of the approximated surface is displayed with B-spline control points. When a control point near the region that needs correction is selected, a new point 1 mm higher than the original control point in the direction of the average surface normal is displayed. And the surface is corrected by giving the amount of control point movement interactively. Since the direction of correction is given by the program and the amount of correction is selected by the user, the method is called semiautomatic. sufficiently smooth surface can be obtained by this method. Examples are given to illustrate the method.

• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.809-817
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• 2012

We propose a semiparametric inference for a generalized varying coefficient partially linear model(VCPLM) for negative binomial data. The VCPLM is useful to model real data in that varying coefficients are a special type of interaction between explanatory variables and partially linear models fit both parametric and nonparametric terms. The negative binomial distribution often arise in modelling count data which usually are overdispersed. The varying coefficient function estimators and regression parameters in generalized VCPLM are obtained by formulating a penalized likelihood through smoothing splines for negative binomial data when the shape parameter is known. The performance of the proposed method is then evaluated by simulations.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.363-376
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• 1998

The linear smoothing spline estimator is modified to remove boundary bias effects. The resulting estimator can be calculated efficiently using an O(n) algorithm that is developed for the computation of fitted values and associated smoothing parameter selection criteria. The asymptotic properties of the estimator are studied for the case of a uniform design. In this case the mean squared error properties of boundary corrected linear smoothing splines are seen to be asymptotically competitive with those for standard second order kernel smoothers.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.12
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• pp.1124-1132
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• 2011

This paper deals with smooth path generation using B-spline for fixed-wing unmanned aerial vehicles manuevering in 2D environment. Hexagonal cell representation is employed to model the 2D environment, which features increased connectivity among cells over square cell representation. Subsequently, hexagonal cell representation enables smoother path generation based on a discrete sequence of path from the path planner. In addition, we present an on-line path smoothing algorithm incorporating B-spline path templates. The path templates are computed off-line by taking into account all possible path sequences within finite horizon. During on-line implementation, the B-spline curves from the templates are stitched together repeatedly to come up with a reference trajectory for UAVs. This method is an effective way of generating smooth path with reduced on-line computation requirement, hence it can be implemented on a small low-cost autopilot that has limited computational resources.

• Journal of Mechanical Science and Technology
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• v.14 no.5
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• pp.518-529
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• 2000

A detailed and thorough parametric study of digital image correlation method is presented. A theoretical background and development of the method were introduced and the effects of various parameters on the determination of displacement outputs from the raw original and deformed image information were examined. Use of the normalized correlation coefficient, the use of 20 to 40 pixels for a searching window side, 6 variables searching, bi-cubic spline sub pixel interpolations and the use of coarse-fine search are some of the key choices among the results of parametric studies. The displacement outputs can be further processed with two dimensional curve fitting for the data noise reduction as well as displacement gradient calculation.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.713-722
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• 2009

The estimation of odds ratio and corresponding confidence intervals for case-control data have been done by traditional generalized linear models which assumed that the logarithm of odds ratio is linearly related to risk factors. We adapt a lower-dimensional approximation of Gu and Kim (2002) to provide a faster computation in nonparametric method for the estimation of odds ratio by allowing flexibility of the estimating function and its Bayesian confidence interval under the Bayes model for the lower-dimensional approximations. Simulation studies showed that taking larger samples with the lower-dimensional approximations help to improve the smoothing spline estimates of odds ratio in this settings. The proposed method can be used to analyze case-control data in medical studies.