• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.519-533
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• 2004

In this paper, we suggest quasi-likelihood estimation (QLE) method and its robust version in estimating spatial dependence modelled through variogram used for spatial data modelling. We compare the statistical characteristics of the estimators with other popular least squares estimators of parameters for variogram model by simulation study. The QLE method for estimating spatial dependence has the advantages that it does not need the concept of lags commonly required for least squares estimation methods as well as its statistical superiority. The QLE method also shows the statistical superiority to the other methods for the tested Gaussian and non-Gaussian spatial processes.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.187-196
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• 2002

Testing for normality has always been a center of practical and theoretical interest in statistical research. In this paper a test statistic for bivariate normality is proposed. The underlying idea is to investigate all the possible linear combinations that reduce to the standard normal distribution under the null hypothesis and compare the order statistics of them with the theoretical normal quantiles. The suggested statistic is invariant with respect to nonsingular matrix multiplication and vector addition. We show that the limit distribution of an approximation to the suggested statistic is represented as the supremum over an index set of the integral of a suitable Gaussian Process. We also simulate the null distribution of the statistic and give some critical values of the distribution and power results.

• Wind and Structures
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• v.3 no.2
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• pp.117-132
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• 2000

Under special conditions of turbulent wind, suspension and cable-stayed bridges could reach instability conditions. In various instances the bridge deck, as like a bluff body, could exhibit single-degree torsional instability. In the present study the turbulent component of flow has been considered as a solution of a differential stochastic linear equation. The input process is represented by a Gaussian zero-mean white noise. In this paper the analytical solution of the dynamic response of the bridge has been determined. The solution has been obtained with a technique of closing on the order of the moments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.9 no.1
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• pp.18-24
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• 1984

Calculation of error probabilities for a coherent phase-shilft keyed communication system operating in a transionospheric scintillation channel is accomplished by means of the Gauss-quadrature integration formula. The channel model used, patterned after Rino's work, is slowly flat fading wherein the envelope of the received signal is modeled as the envelope of correlated Gaussian quadrature random processes. The error probability for the scintillation channel is calculated using actual ionospheric scintillation data for transmission in the UHF region(30-300MHz).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.5 no.1
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• pp.34-39
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• 1980

This paper discribes a effect of Radio signals in Noise and Interference for the Communication systems and Generation of diffusion Noise from the Solid state Devices, and in Jection it to the Radio Reciving systems for probability of Signal Detection. The error performance depends on level of the Noise spectral density by Random processes between average signal energy. This experimental result are given by the performance of the correlation receiver for detecting Completely known signals in additive white Gaussian Noise.

• Structural Engineering and Mechanics
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• v.76 no.5
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• pp.631-641
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• 2020

In the present work, an approach for the multiple time probabilistic characterization of the response of linear structural systems subjected to random non-Gaussian processes is presented. Its fundamental property is working directly on the multiple time probability density functions of the actions and of the response. This avoids of passing through the evaluation of the response statistical moments at multiple time or correlations, reducing the computational effort in a consistent measure. This approach is the extension to the multiple time case of a previously published dynamic Probability Transformation Method (PTM) working on a single evolution of the response statistics. The application to some simple examples has revealed the efficiency of the method, both in terms of computational effort and in terms of accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2261-2268
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• 2010

The information captured by imaging devices such as CCD or CIS may contain external noises through the processes of passing signals or storing images. In this paper, we propose a Modified Sigma Filter (MSF) algorithm to reduce such noises. In experiment, we verified that our MSF algorithm showed better performance in PSNR and 1D plot of simulation results compared with Gaussian Filter (GF), Local Sigma Filter (LSF). Tested images include random Gaussian Noises.

• Journal of the Korea Computer Industry Society
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• v.8 no.3
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• pp.173-180
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• 2007

This paper describes the point-detector arrays system to processes the fields of signal and noise of the turbulent atmosphere. By using aboves, the maxmum output of direct-detection shows a little differences between experimental datas and theoretical datas. As a whole the experimental datas are agreed with the joint Gaussian theoretical curves and K-distribution curves.

• Journal of the Korean Mathematical Society
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• v.53 no.1
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• pp.57-72
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• 2016

We establish maximal moment inequalities of partial sums under ${\psi}$-weak dependence, which has been proposed by Doukhan and Louhichi [P. Doukhan and S. Louhichi, A new weak dependence condition and application to moment inequality, Stochastic Process. Appl. 84 (1999), 313-342], to unify weak dependence such as mixing, association, Gaussian sequences and Bernoulli shifts. As an application of maximal moment inequalities, a functional central limit theorem is developed for linear processes with ${\psi}$-weakly dependent innovations.

• Journal of applied mathematics & informatics
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• v.33 no.1_2
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• pp.203-210
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• 2015

We consider fractional Brownian motion and FARIMA process with Gaussian innovations and show that the suitably scaled distributions of the FARIMA processes converge to fractional Brownian motion in the sense of finite dimensional distributions. We figure out ACF function and estimate the self-similarity parameter H of FARIMA(0, d, 0) by using R/S method. Finally, we display power spectrum density of FARIMA process.