• Journal of the Chungcheong Mathematical Society
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• v.27 no.2
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• pp.211-218
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• 2014

In 3-dimensional Euclidean space, the geometric figures of a regular curve are completely determined by the curvature function and the torsion function of the curve, and surfaces are the fundamental curved spaces for pioneering study in modern geometry as well as in classical differential geometry. In this paper, we define parametrizations for surface by using parametric functions whose images are in the normal plane of each point on a given curve, and then obtain some results relating the Gaussian curvature of the surface with curvature and torsion of the given curve. In particular, we find some conditions for the surface to have either nonpositive Gaussian curvature or nonnegative Gaussian curvature.

• Transactions on Electrical and Electronic Materials
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• v.17 no.5
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• pp.270-274
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• 2016

This article investigates the use of the Gaussian-channel doping profile for the control of the short-channel effects in the double-gate MOSFET whereby a two-dimensional (2D) quantum simulation was used. The simulations were completed through a self-consistent solving of the 2D Poisson equation and the Schrodinger equation within the non-equilibrium Green’s function (NEGF) formalism. The impacts of the p-type-channel Gaussian-doping profile parameters such as the peak doping concentration and the straggle parameter were studied in terms of the drain current, on-current, off-current, sub-threshold swing (SS), and drain-induced barrier lowering (DIBL). The simulation results show that the short-channel effects were improved in correspondence with incremental changes of the straggle parameter and the peak doping concentration.

• Journal of the Semiconductor & Display Technology
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• v.11 no.3
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• pp.19-25
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• 2012

The accuracy of Shape From Focus (SFF) technique depends on the quality of the focus measurements which are computed through a focus measure operator. In this paper, we introduce a new approach to estimate 3D shape of an object based on Gaussian process regression. First, initial depth is estimated by applying a conventional focus measure on image sequence and maximizing it in the optical direction. In second step, input feature vectors consisting of eginvalues are computed from 3D neighborhood around the initial depth. Finally, by utilizing these features, a latent function is developed through Gaussian process regression to estimate accurate depth. The proposed approach takes advantages of the multivariate statistical features and covariance function. The proposed method is tested by using image sequences of various objects. Experimental results demonstrate the efficacy of the proposed scheme.

• Journal of the Korean Mathematical Society
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• v.53 no.5
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• pp.991-1017
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• 2016

In this paper we define a generalized analytic Fourier-Feynman transform associated with Gaussian process on the function space $C_{a,b}[0,T]$. We establish the existence of the generalized analytic Fourier-Feynman transform for certain bounded functionals on $C_{a,b}[0,T]$. We then proceed to establish a translation theorem for the generalized transform associated with Gaussian process.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.60-65
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• 2009

In this paper, we propose a new exponential pulse shaping function based on Chebychev identity equation and Bessel coefficients. The proposed pulse shaping function can produce various pulses with the different characteristics in the time and frequency domain by changing its two parameters. By differentiating the exponential pulse shaping function, we obtain new different pulse functions, in which the even order derivatives of the exponential pulse shaping function are orthogonal to its odd order derivatives. To find the efficiency of the proposed exponential pulse shaping function we analyze its essential characteristics and compare them with those of the conventional Gaussian pulses. We can choose the most suitable exponential pulse waveform according to the design criteria of communication systems.

• Journal of Magnetics
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• v.2 no.3
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• pp.105-109
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• 1997

The Preisach model neds a density function or Everett function for the hysterisis operator to simulate the hysteresis phenomena. To obtain the function, many experimental data for the first order transition curves are required. However, it needs so much efforts to measure the curves, especially for the hard magnetic materials. By the way, it is well known that the density function has the Gaussian distribution for the interaction axis on the Preisach plane. In this paper, we propose a simple technique to determine the distribution function or Everett function analytically. The initial magnetization curve is used for the distribution of the Everett function for the coercivity axis. A major, minor loop and the initial curve are used to get the Everett function for the interaction axis using the Gaussian distribution function and acceptable results were obtained.

• Journal of the Chungcheong Mathematical Society
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• v.26 no.2
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• pp.411-420
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• 2013

We consider fractional Gussian noise and FARIMA sequence with Gaussian innovations and show that the suitably scaled distributions of the FARIMA sequences converge to fractional Gaussian noise in the sense of finite dimensional distributions. Finally, we figure out ACF function and estimate the self-similarity parameter H of FARIMA(0, $d$, 0) by using R/S method.

• Honam Mathematical Journal
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• v.34 no.3
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• pp.341-349
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• 2012

In this paper, we discuss a constructive approximation by Gaussian neural networks. We show that it is possible to construct Gaussian neural networks with integer weights that approximate arbitrarily well for functions in $C_c(\mathbb{R}^s)$. We demonstrate numerical experiments to support our theoretical results.

• Journal of the Korean Mathematical Society
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• v.42 no.6
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• pp.1265-1278
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• 2005

In this paper, we establish limit theorems containing both the moduli of continuity and the large incremental results for finite dimensional Gaussian processes with N parameters, via estimating upper bounds of large deviation probabilities on suprema of the Gaussian processes.

• Journal of the Korean Statistical Society
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• v.25 no.4
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• pp.515-527
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• 1996

It has been known for mean field models that the limiting distribution reflecting the asymptotic behavior of the system is non-Gaussian at the critical state. Recently, however, Papangelow showed for the critical Curie-Weiss mean field model that there exist Gaussian structures in the asymptotic behavior of the total magnetization. We construct Gaussian structures existing in the internal fluctuation of the system for the critical case of a generalized Curie-Weiss mean field model.