• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.951-959
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• 2014

Recently Hazelton and Turlach (2009) proposed a weighted kernel density estimator for the deconvolution problem. In the case of Gaussian kernels and measurement error, they argued that the weighted kernel density estimator is a competitive estimator over the classical deconvolution kernel estimator. In this paper we consider weighted kernel density estimators when sample observations are contaminated by double exponentially distributed errors. The performance of the weighted kernel density estimators is compared over the classical deconvolution kernel estimator and the kernel density estimator based on the support vector regression method by means of a simulation study. The weighted density estimator with the Gaussian kernel shows numerical instability in practical implementation of optimization function. However the weighted density estimates with the double exponential kernel has very similar patterns to the classical kernel density estimates in the simulations, but the shape is less satisfactory than the classical kernel density estimator with the Gaussian kernel.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.3
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• pp.69-75
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• 2013

During the magnetic abrasive polishing of a curved surface, the improvement in surface roughness varies with the maximum value and distribution of magnetic flux density. Thus, in this study, the magnetic flux density on the curved surface was simulated according to curvature radii of magnetic tool. As a result of the simulation, the 14.5mm of the magnetic tool had a higher maximum magnetic flux density and it showed a large weighted magnetic flux density. The weighted magnetic flux density means the highest value for the magnetic flux density in the curvature of the magnetic tool. From the experimental verification, the better improvement in surface roughness was observed on wider area at the 14.5mm radius of the magnetic tool than other radii.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.57.2-57.2
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• 2014

Protostellar jets and outflows are signatures of star formation and promising mechanisms for driving supersonic turbulence in molecular clouds. We quantify outflow-driven turbulence through three-dimensional numerical simulations using an isothermal version of the total variation diminishing code. We drive turbulence in real space using a simplified spherical outflow model, analyze the data through density probability distribution functions (PDFs), and investigate density and velocity power spectra. The real-space turbulence-driving method produces a negatively skewed density PDF possessing an enhanced tail on the low-density side. It deviates from the log-normal distributions typically obtained from Fourier-space turbulence driving at low densities, but can provide a good fit at high densities, particularly in terms of mass-weighted rather than volume-weighted density PDF. We find shallow density power-spectra of -1.2. It is attributed to spherical shocks of outflows themselves or shocks formed by the interaction of outflows. The total velocity power-spectrum is found to be -2.0, representative of the shock dominated Burger's turbulence model. Our density weighted velocity power spectrum is measured as -1.6, slightly less that the Kolmogorov scaling values found in previous works.

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.219-225
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• 2012

We derive approximate maximum likelihood estimators of two parameters in a weighted exponential distribution, and derive the density function for the ratio Y=(X+Y) of two independent weighted exponential random variables X and Y, and then observe the skewness of the ratio density.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1639-1649
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• 2004

In this paper, a method for detection of forged signatures based on spectral analysis of directional gradient density function and a weighted fuzzy classifier is proposed. The well defined outline of an incoming signature image is extracted in a preprocessing stage which includes noise reduction, automatic thresholding, image restoration and erosion process. The directional gradient density function derived from extracted signature outline is highly related to the overall shape of signature image, and thus its frequency spectrum is used as a feature set. With this spectral feature set, having a property to be invariant in size, shift, and rotation, a weighted fuzzy classifier is evaluated for the verification of freehand and random forgeries. Experiments show that less than 5% averaged error rate can be achieved on a database of 500 signature samples.

• Bulletin of the Korean Mathematical Society
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• v.60 no.2
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• pp.325-338
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• 2023

In this paper, we derive a Reilly-type inequality for the Laplacian with density on weighted manifolds with boundary. As its applications, we obtain some new Poincaré-type inequalities not only on weighted manifolds, but more interestingly, also on their boundary. Furthermore, some mean-curvature type inequalities on the boundary are also given.

• Honam Mathematical Journal
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• v.41 no.4
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• pp.683-695
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• 2019

In the present paper, we study curves in 𝔼³ with density $e^{ax^2+by^2}$, where a, b ∈ ℝ not all zero constants and give the parametric expressions of the curves with vanishing weighted curvature. Also, we create ruled surfaces whose base curves are the curve with vanishing weighted curvature and the ruling curves are Smarandache curves of this curve. Then, we give some characterizations about these ruled surfaces by obtaining the mean curvatures, Gaussian curvatures, distribution parameters and striction curves of them.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1984.10a
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• pp.92-95
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• 1984

This paper describes the output P.D.F. of various Compandors, optimum, -law and A-law for the Gaussian and Laplacian Density as an input analog signal. Also we consider the truncated densities compensated by weighted impulse or density coefficient.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.743-752
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• 2002

The estimation of the response curve is the important problem in the quantal bioassay. When we estimate the response curve, we determine the design points in advance of the experiment. Then naturally we have a question of which design would be optimal. As a response curve estimator, locally weighted quasi-likelihood estimator has several more appealing features than the traditional nonparametric estimators. The optimal design density for the locally weighted quasi-likelihood estimator is derived and its ability both in theoretical and in empirical point of view are investigated.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.451-457
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• 2010

This paper considers the problem of nonparametric deconvolution density estimation when sample observa-tions are contaminated by double exponentially distributed errors. Three different deconvolution density estima-tors are introduced: a weighted kernel density estimator, a kernel density estimator based on the support vector regression method in a RKHS, and a classical kernel density estimator. The performance of these deconvolution density estimators is compared by means of a simulation study.