• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1732-1733
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• 2011

This paper presents observer-based controller design for interval type-2 fuzzy system with staircase membership approximation. In type-2 fuzzy case, membership function is itself fuzzy set itself. Thus, type-2 fuzzy system can deal with parametric uncertainties of nonlinear system by capturing the uncertainties in membership function. Likewise, stabilization condition of type-2 fuzzy system is derived from quadratic Lyapunov function, and it goes to linear matrix inequality. Furthermore, in this paper, to relax the conservativeness of stabilization condition, staircase membership function approximating method is applied. Observer-based control method is adopted to control system which has some unmeasurable states. To prove suitability of our proposed method, numerical example is presented.

• 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.

• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.885-898
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• 2012

In this paper, we consider Bayesian approaches to partially linear models, in which a regression function is represented by a semiparametric additive form of a parametric linear regression function and a nonparametric regression function. We make a comparative study on the performance of widely used Bayesian partially linear models in terms of empirical analysis. Specifically, we deal with three Bayesian methods to estimate the nonparametric regression function, one method using Fourier series representation, the other method based on Gaussian process regression approach, and the third method based on the smoothness of the function and differencing. We compare the numerical performance of three methods by the root mean squared error(RMSE). For empirical analysis, we consider synthetic data with simulation studies and real data application by fitting each of them with three Bayesian methods and comparing the RMSEs.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.849-860
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• 1999

This paper addresses the issue of estimating regression function with errors in variables using wavelets. We adopt a nonparametric approach in assuming that the regression function has no specific parametric form, To account for errors in covariates deconvolution is involved in the construction of a new class of linear wavelet estimators. using the wavelet characterization of Besov spaces the question of regression estimation with Besov constraint can be reduced to a problem in a space of sequences. Rates of convergence are studied over Besov function classes $B_{spq}$ using $L_2$ error measure. It is shown that the rates of convergence depend on the smoothness s of the regression function and the decay rate of characteristic function of the contaminating error.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.1
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• pp.87-94
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• 2017

Reliability analysis(RA) and Reliability-based design optimization(RBDO) require statistical modeling of input random variables, which is parametrically or nonparametrically determined based on experimental data. For the parametric method, goodness-of-fit (GOF) test and model selection method are widely used, and a sequential statistical modeling method combining the merits of the two methods has been recently proposed. Kernel density estimation(KDE) is often used as a nonparametric method, and it well describes a distribution function when the number of data is small or a density function has multimodal distribution. Although accurate statistical models are needed to obtain accurate RA and RBDO results, accurate statistical modeling is difficult when the number of data is small. In this study, the accuracy of two statistical modeling methods, SSM and KDE, were compared according to the number of data. Through numerical examples, the RA results using the input models modeled by two methods were compared, and appropriate modeling method was proposed according to the number of data.

• Composites Research
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• v.12 no.1
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• pp.1-10
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• 1999

In order to predict the dynamic behavior of composite laminated plate accurately, the parametric identification is performed using its mechanical properties- $E_1,\;E_2,\;V_{12},\;G_{12}$ as design parameters. After natural frequencies are measured through simple vibration test, the objective function consists of the sum of errors between experimental and numerical frequencies of a structure. As optimization algorithm, conjugate gradient method is used to minimize the objective function. Sensitivity Analysis is performed to update design parameters during this process and can explain the result of parametric identification. In order to check the propriety of result, mode shapes are compared before and after identification. The improved prediction of natural frequencies of composite laminated plate is obtained with updated properties. For the application of result, updated properties is applied to the composite laminated plate that has different stacking sequence.

• Journal of the Society of Naval Architects of Korea
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• v.53 no.5
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• pp.343-352
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• 2016

In this study, a pre-swirl duct for the 180,000 DWT bulk carrier has been designed from a propulsion standpoint using CFD. The stern duct - designed by NMRI - was selected as the initial duct. The objective function is to minimize the value of delivered power in model scale. Design variables of the duct include duct angle, diameter, chord length, and vertical and horizontal displacements from the center. Design variables of the stators are blade number, arrangement angle, chord length, and pitch angle. A parametric design was carried out with the objective function obtained using CFD. Reynolds averaged Navier-Stokes equations have been solved; and the Reynolds stress model applied for the turbulent closure. A double body model is used for the treatment of free-surface. MRF and sliding mesh models have been applied to simulate the actuating propeller. A self-propulsion point has been obtained from the results of towing and self-propelled computations, i.e., form factor obtained from towing computation and towing forces obtained from self-propelled computations of two propeller rotating speeds. The reduction rate of the delivered power of the improved stern duct is 2.9%, whereas that of the initial stern duct is 1.3%. The pre-swirl duct with one inner stator in upper starboard and three outer stators in portside has been designed. The delivered power due to the designed pre-swirl duct is reduced by 5.8%.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.34-42
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• 2010

The DOA(direction of arrival), which is based on parametric and nonparametric estimation algorithm, and adaptive beamforming algorithm for mobile communication environments are researched and analyzed. In parametric estimation algorithm, eigenvalues of the signal component and the noise component are obtained from correlation matrix of received signal by array antenna and power spectrum of the received signal is discriminated from them. Otherwise, in nonparametric estimation algorithm, we minimize a regularized objective function for finding a estimate of the signal energy as a function of angle, using nonquadratic norm which leads to supper resolution and noise suppression. And then, DOA is estimated by the signal and noise spatial steering vector, and adaptive beam-forming pattern is improved by weight vectors obtained from the spatial vector. Therefore, the improved directional estimation algorithm with regularizing sparsity constraints offers super-resolution and noise suppression compared to other algorithms.

• Advances in aircraft and spacecraft science
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• v.7 no.4
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• pp.353-369
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• 2020

This paper presents the flutter analysis and optimum design of axially functionally graded box beam cantilever wing section by considering various geometric and material parameters. The coupled dynamic equations of the continuous model of wing system in terms of material and cross-sectional properties are formulated based on extended Hamilton's principle. By expressing the lift and pitching moment in terms of plunge and pitch displacements, the resultant two continuous equations are simplified using Galerkin's reduced order model. The flutter velocity is predicted from the solution of resultant damped eigenvalue problem. Parametric studies are conducted to know the effects of geometric factors such as taper ratio, thickness, sweep angle as well as material volume fractions and functional grading index on the flutter velocity. A generalized surrogate model is constructed by training the radial basis function network with the parametric data. The optimized material and geometric parameters of the section are predicted by solving the constrained optimal problem using firefly metaheuristics algorithm that employs the developed surrogate model for the function evaluations. The trapezoidal hollow box beam section design with axial functional grading concept is illustrated with combination of aluminium alloy and aluminium with silicon carbide particulates. A good improvement in flutter velocity is noticed by the optimization.

• Journal of Korea Multimedia Society
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• v.10 no.9
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• pp.1185-1196
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• 2007

To enhance competitiveness of game industry and added value, it needs scientific research and bases that suggest satisfaction standard which quantitatively evaluates satisfaction to develop games which have high satisfaction for demanders. Specially, scoring of satisfaction factors and estimation of population distribution are important task. This allows which a game have high or low satisfaction compared to other games. Also we predict improvable factors and technical aspects to promote satisfaction. For it, in this paper we discuss ways to normalization of score distribution for satisfaction factors and estimate its density function using parametric density estimation by simulation.