• The Pure and Applied Mathematics
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• v.5 no.1
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• pp.55-63
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• 1998

A Gauss map of m-dimensional distribution on a Riemannian manifold M is called a harmonic Gauss map if it is a harmonic map from the manifold into its Grassmann bundle $G_m$(TM) of m-dimensional tangent subspace. We calculate the tension field of the Gauss map of m-dimensional distribution and especially show that the Hopf fibrations on $S^{4n＋3}$ are the harmonic Gauss map of 3-dimensional distribution.

• Journal of Computational Design and Engineering
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• v.1 no.1
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• pp.48-54
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• 2014

In this paper, the authors propose a system for assisting mold designers of plastic parts. With a CAD model of a part, the system automatically determines the optimal ejecting direction of the part with minimum undercuts. Since plastic parts are generally very thin, many rib features are placed on the inner side of the part to give sufficient structural strength. Our system extracts the rib features from the CAD model of the part, and determines the possible ejecting directions based on the geometric properties of the features. The system then selects the optimal direction with minimum undercuts. Possible ejecting directions are represented as discrete points on a Gauss map. Our new point distribution method for the Gauss map is based on the concept of the architectural geodesic dome. A hierarchical structure is also introduced in the point distribution, with a higher level "rough" Gauss map with rather sparse point distribution and another lower level "fine" Gauss map with much denser point distribution. A system is implemented and computational experiments are performed. Our system requires less than 10 seconds to determine the optimal ejecting direction of a CAD model with more than 1 million polygons.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.1-11
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• 2002

This paper discusses problems of the Poisson Mixture model which Is widely used to decide the effective words in judging relevant document. Gamma Distribution model and Gauss Patterns model as an alternative of the Poisson Mixture model are studied. Classification experiments by using TREC sub-collection, WSJ[1,2] with MGQUERY and AidSearch3.0 system are discussed.

• Journal for History of Mathematics
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• v.12 no.1
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• pp.1-12
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• 1999

The first part of this thesis discusses the types and the properties of errors, one of which makes up systematic errors of measurements, removable by detecting their causes, the other errors of accidental causes which can not be removed. The final part of this thesis deals with the historical background of the Gaussian distribution by Hershel, Hagen, Laplace and Gauss from the late 18th century to the early 19th century. It can be concluded that the accidental idea and the treatment of accidental error distribution by Gauss Is the best one based on the assumption that the most probable value of true value is the arithmetic mean of data, obtained by repeated measurements of a given quantity.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.303-312
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• 1995

We consider the following type of general semi-parametric non-linear regression model : $y_i = f_i(\theta) + \epsilon_i, i=1, \cdots, n$ where ${f_i(\cdot)}$ represents the set of non-linear functions of the unknown parameter vector $\theta' = (\theta_1, \cdots, \theta_p)$ and ${\epsilon_i}$ represents the set of measurement errors with unknown distribution. Under suitable finite-sample, small-dispersion asymptotic framework, we derive a general lower bound for the asymptotic mean squared error (AMSE) matrix of the Gauss-consistent estimator of $\theta$. We then prove the fundamental result that the general non-linear least squares estimator (NLSE) is an optimal estimator within the class of all regular Gauss-consistent estimators irrespective of the type of the distribution of the measurement errors.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.3
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• pp.8-13
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• 1989

The parameters of bipolar random square-wave signal process, amplitude and phase with unknown probability distribution are shown to be simultaneously estimated by using Gauss-Markov estimator so that transmitted digital data can be recovered under the additive Gaussinan noise environment. However, we see that the preprocessing stage using the correlator composed of the multiplier and the running integrator is needed to convert the received process into the sampled sequences and to obtain the observed data vectors, which can be used for Gauss-Markov estimation.

• Korean Journal of Optics and Photonics
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• v.8 no.2
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• pp.95-98
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• 1997

The Gaussian diffraction pattern initially assumed in the diffraction inverse problem is further sharply defined by multiplying $e^{-q{\omega}_0$\mid${\chi}$\mid$}$. The modified pupil function is obtained and the diffraction intensity distribution for the finite aperture ($-{\omega}_0~{\times}{\omega}_0$ is obtained, and then the OTF is derived analytically. It is found the OTF is equal to or less than the $(OTF)_{q=0}$, namely the modulation is not useful. It is shown that the narrowing down the initial Gaussian diffraction pattern does not give the anticipated improvement in OTF and the reason is clarified.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.6 no.2
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• pp.1-8
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• 2002

Let $T_{\phi}$ be a generalized Gauss transformation and $[a_1,\;a_2,\;{\cdots}]_{T_{\phi}}$ be a symbolic representation of $x{\in}[0,\;1)$ induced by $T_{\phi}$, i.e., generalized continued fraction expansion induced by $T_{\phi}$. It is shown that the distribution of relative frequency of [$k_1,\;{\cdots},\;k_n$] in $[a_1,\;a_2,\;{\cdots}]_{T_p}$ satisfies Central Limit Theorem where $k_i{\in}{\mathbb{N}}$ for $1{\leq}i{\leq}n$.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.49 no.11
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• pp.729-736
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• 2000

This paper deals with the characteristic analysis of magnetizer for convergence purity magnet by the finite element method. The analysis utilizes combined method of the time-stepped finite element analysis and the Preisach model with hysteresis phenomena. In the finite element analysis, the non-linearity and the eddy current of the magnetizing fixure and permanent-magnet are taken account. The magnetization distribution in the permanent magnet is determined by using Preisach model which are composed of Everett function table and the first order transition curves is obtained by the Vibrating Sample Magnetometer. The calculated flux density values on the surface of the permanent magnet are led to the approximated gauss density values measured by the gauss meter. As a result, winding current, copper loss, eddy current loss of the magnetizing yoke, flux plot, surface gauss plot, temperature rise of the coil and resistor variation, vector diagram of magnetization distribution are shown.

• Journal of IKEEE
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• v.19 no.2
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• pp.219-224
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• 2015

Electrical impedance tomography is an imaging technique to reconstruct the internal conductivity distribution based on applied currents and measured voltages in a domain of interest. In this paper, a modified Gauss-Newton method is proposed for conductivity image reconstruction. In the proposed method, the dimension of the inverse term is reduced by replacing the number of elements with the number of measurement data in the conductivity updating equation of the conventional Gauss-Newton method. Therefore, the computation time is greatly reduced as compared to the conventional Gauss-Newton method. Moreover, the regularization parameter is selected by computing the minimum-maximum from the diagonal components of the Jacobian matrix at every iteration. The numerical experiments with several scenarios were carried out to evaluate the reconstruction performance of the proposed method.