• Journal of the Chungcheong Mathematical Society
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• v.17 no.1
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• pp.77-83
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• 2004

The aim of this work is to generalize parametric approximation in order to apply them to an one-sided $L_1$-approximation. A natural question now arises : when is the parameter map $$P:f{\rightarrow}P_{K(f)}(f)$$ continuous on $C_1(X)$ ? We find some results with a monotone decreasing sequence about above question.

• Communications of the Korean Mathematical Society
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• v.37 no.4
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• pp.1259-1267
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• 2022

In this paper we present a full circle approximation method using parametric polynomial curves with algebraic coefficients which are curvature continuous at both endpoints. Our method yields the n-th degree parametric polynomial curves which have a total number of 2n contacts with the full circle at both endpoints and the midpoint. The parametric polynomial approximants have algebraic coefficients involving rational numbers and radicals for degree higher than four. We obtain the exact Hausdorff distances between the circle and the approximation curves.

• The Pure and Applied Mathematics
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• v.8 no.1
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• pp.9-14
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• 2001

In some problems of abstract approximation theory the approximating set depends on some parameter p. In this paper, we make a set M(f) depends on the element f, $\phi$ and then best approximations are sought from a subset M(f) of M.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.580-585
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• 2001

A response surface method(RSM) is utilized for structural optimization and implemented on a parametric CAD platform. Once an approximation of the performance function is made, no formal design sensitivity analysis is necessary. The approximation gives the designer the sensitivity information and furthermore intuition on the performance functions. The scheme for the design of experiment chosen for the RSM has a large influence on the accuracy of converged solutions and the amount of computation. The D-optimal design criterion as implemented in this paper is found efficient for the structural optimization. The program is developed on a parametric CAD platform and tested using several shape design problems of such as a torque arm and a belt clip. It is observed that the RSM used provides a faster convergence than other approximation methods for design sensitivity.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.616-625
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• 2021

Deep neural networks are an approximation method that approximates an arbitrary function to a linear model and then repeats additional approximation using a nonlinear active function. In this process, the method of evaluating the performance of approximation uses the loss function. Existing in-depth learning methods implement approximation that takes into account loss functions in the linear approximation process, but non-linear approximation phases that use active functions use non-linear transformation that is not related to reduction of loss functions of loss. This study proposes parametric activation functions that introduce scale parameters that can change the scale of activation functions and location parameters that can change the location of activation functions. By introducing parametric activation functions based on scale and location parameters, the performance of nonlinear approximation using activation functions can be improved. The scale and location parameters in each hidden layer can improve the performance of the deep neural network by determining parameters that minimize the loss function value through the learning process using the primary differential coefficient of the loss function for the parameters in the backpropagation. Through MNIST classification problems and XOR problems, parametric activation functions have been found to have superior performance over existing activation functions.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1389-1397
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• 2016

Semi-parametric frailty model with nonparametric baseline hazards has been widely used for the analyses of clustered survival-time data. The frailty models can be fitted via an auxiliary Poisson hierarchical generalized linear model (HGLM). For the inferences of the frailty model marginal likelihood, which gives MLE, is often used. The marginal likelihood is usually obtained by integrating out random effects, but it often requires an intractable integration. In this paper, we propose to obtain the MLE via Laplace approximation using a Poisson HGLM approach for semi-parametric frailty model. The proposed HGLM approach uses hierarchical-likelihood (h-likelihood), which avoids integration itself. The proposed method is illustrated using a numerical study.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.8
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• pp.727-731
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• 2000

This paper presents a new practical autopilot design approach to acceleration control for tail-controlled STT(Skid-to-Turn) missiles. The approach is novel in that the proposed parametric affine missile model adopts acceleration as th controlled output and considers the couplings between the forces as well as the moments and control fin deflections. The aerodynamic coefficients in the proposed model are expressed in a closed form with fittable parameters over the whole operating range. The parameters are fitted from aerodynamic coefficient look-up tables by the function approximation technique which is based on the combination of local parametric models through curve fitting using the corresponding influence functions. In this paper in order to employ the results of parametric affine modeling in the autopilot controller design we derived a parametric affine missile model and designed a feedback linearizing controller for the obtained model. Stability analysis for the overall closed loop sys-tem is provided considering the uncertainties arising from approximation errors. the validity of the proposed modeling and control approach is demonstrated through simulations for an STT missile.

• Journal of the Society of Naval Architects of Korea
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• v.52 no.3
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• pp.187-197
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• 2015

This study aims the development of a semi-analytic method for the parametric roll of large containerships advancing in longitudinal waves. A 1.5 Degree-of-Freedom(DOF) model is proposed to account the change of transverse stability induced by wave elevations and vertical motions (heave and pitch). By approximating the nonlinearity of restoring moment at large heel angles, the magnitude of roll amplitude is predicted as well as susceptibility check for parametric roll occurrence. In order to increase the accuracy of the prediction, the relationship between righting arm(GZ) and metacentric height(GM) is examined in the presence of incident waves, and then a new formula is proposed. Based on the linear approximation of the mean and first harmonic component of GM, the equation of parametric roll in irregular wave excitations is introduced, and the computational results of the proposed model are validated by comparing those of weakly nonlinear simulation based on an impulse-response-function method combined with strip theory. The present semi-analytic doesn’ t require heavy computational effort, so that it is very efficient particularly when numerous sea conditions for the analysis of parametric roll should be considered.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1882-1887
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• 2003

The response surface method (RSM) became one of famous meta modeling techniques, however its approximation errors give designers several restrictions. Classical RSM uses the least squares method (LSM) to find the best fitting approximation models from the all given data. This paper discusses how to construct RSM efficiently and accurately using moving least squares method (MLSM) with sensitivity information. In this method, several parameters should be determined during the construction of RSM. Parametric study and optimization for these parameters are performed. Several difficulties during approximation processes are described and numerical examples are demonstrated to verify the efficiency of this method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.139-146
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• 2009

Parametric array for audio applications is analyzed by numerical modeling and analytical approximation. The nonlinear wave equations are used to provide design guidelines for the audio parametric array. A time domain finite difference code that accurately solves the KZK (Khokhlov-Zabolotskaya-Kuznetsov) nonlinear parabolic wave equation is used to predict the response of the parametric array. The time domain code relates the source size and the carrier frequency to the audible signal response including the output level and beamwidth to considering the implementation issues for audio applications of the parametric array, the emphasis is given to the frequency response and distortion. We use the time domain code to find out the optimal parameters that will help produce the parametric array with highest achievable output in terms of the average power within the demodulated signal. Parameters such as primary input frequency, audio source radius and the modulation method are given utmost importance. The output effect of those parameters are demonstrated through the numerical simulation.