• Journal of applied mathematics & informatics
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• v.5 no.2
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• pp.373-382
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• 1998

Most of the works in Time Series Analysis are based on the Auto Regressive Integrated Moving Average (ARIMA) models presented by Box and Jeckins(1976). If the data exhibits no ap-parent deviation from stationarity and if it has rapidly decreasing autocorrelation function then a suitable ARIMA(p,q) model is fit to the given data. Selection of the orders of p and q is one of the crucial steps in Time Series Analysis. Most of the methods to determine p and q are based on the autocorrelation function and partial autocor-relation function as suggested by Box and Jenkins (1976). many new techniques have emerged in the literature and it is found that most of them are over very little use in determining the orders of p and q when both of them are non-zero. The Durbin-Levinson algorithm and Innovation algorithm (Brockwell and Davis 1987) are used as recur-sive methods for computing best linear predictors in an ARMA(p,q)model. These algorithms are modified to yield an effective method for ARMA model identification so that the values of order p and q can be determined from them. The new method is developed and its validity and usefulness is illustrated by many theoretical examples. This method can also be applied to an real world data.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.155-158
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• 2001

A new method for simultaneous multiple transmit focusing using orthogonal weighted FM chirp is proposed. Weighted chirp signals focused at different depths are transmitted at the same time. These chirp signals are mutually orthogonal in the approximate sense that the autocorrelation function of each signal has a narrow mainlobe width and low sidelobe levels, and the crosscorrellation function of any pair of the signals has smaller values than the sidelobe levels of each autocorrelation function. This means that each weighted chirp signal can be separately compressed into a short pulse, focused individually and combined with other focused beams to form a frame of image. Theoretically, any two chirp signals defined in two nonoverlapped frequency bands are mutually orthogonal. In the present work, however, a fractional overlap of adjacent frequency bands, by up to 25%, were permitted to design more chirp signals within a given transducer bandwidth. The crosscorrelation values due to the frequency overlap could be reduced by alternating the direction of frequency sweep of the adjacent chirp signals. The simulation results show that this method can improve the lateral resolution of image without sacrifice in the frame rate compared with the conventional pulse system.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.949-954
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• 2009

The autoregressive process is applied in this paper to kernel regression in order to infer nonlinear models for predicting responses. We propose a kernel method for the autoregressive data which estimates the mean function by kernel machines. We also present the model selection method which employs the cross validation techniques for choosing the hyper-parameters which affect the performance of kernel regression. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of mean function in the presence of autocorrelation between data.

• Bulletin of the Korean Chemical Society
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• v.31 no.4
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• pp.959-964
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• 2010

In this work, equilibrium molecular dynamics (MD) simulations in a microcanonical ensemble are performed to evaluate the friction coefficient of a Brownian particle (BP) in a Lennard-Jones (LJ) solvent. The friction coefficients are determined from the time dependent friction coefficients and the momentum autocorrelation functions of the BP with its infinite mass at various ratios of LJ size parameters of the BP and solvent, ${\sigma}_B/{\sigma}_s$. The determination of the friction coefficients from the decay rates of the momentum autocorrelation functions and from the slopes of the time dependent friction coefficients is difficult due to the fast decay rates of the correlation functions in the momentum-conserved MD simulation and due to the scaling of the slope as 1/N (N: the number of the solvent particle), respectively. On the other hand, the friction coefficient can be determined correctly from the time dependent friction coefficient by measuring the extrapolation of its long time decay to t=0 and also from the decay rate of the momentum autocorrelation function, which is obtained by time integration of the time dependent friction coefficient. It is found that while the friction coefficient increases quadratically with the ratio of ${\sigma}_B/{\sigma}_s$ for all ${\sigma}_B$, for a given ${\sigma}_s$ the friction coefficient increases linearly with ${\sigma}_B$.

• Journal of Korea Multimedia Society
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• v.15 no.11
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• pp.1330-1340
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• 2012

In this paper, a new algorithm is proposed for the heart valves stenosis region detection using heart sounds. Many researches for detecting primary components or removing heart murmurs have been studied, but their performances are degraded at abnormal heart sounds such as aortic stenosis and mitral stenosis because of large heart murmurs. In this paper, heart murmur detection method is proposed based on noise intensity function. The proposed noise intensity function detect the primary components S1, S2, then set session up using S1, S2. And then noise intensity function was computed using autocorrelation value of each session. The proposed noise intensity function estimated noise intensity of each sessions and detected heart murmurs. According to simulation results, the proposed algorithm has better performance than former study for detecting heart valve stenosis region.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.187-199
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• 2022

Kriging metamodel, as a flexible machine learning method for approximating deterministic analysis models of an engineering system, has been widely used for efficiently estimating slope reliability in recent years. However, the autocorrelation function (ACF), a key input to Kriging that affects the accuracy of reliability estimation, is usually selected based on empiricism. This paper proposes an adaption of the Kriging method, named as Genetic Algorithm optimized Whittle-Matérn Kriging (GAWMK), for addressing this issue. The non-classical two-parameter Whittle-Matérn (WM) function, which can represent different ACFs in the Matérn family by controlling a smoothness parameter, is adopted in GAWMK to avoid subjectively selecting ACFs. The genetic algorithm is used to optimize the WM model to adaptively select the optimal autocorrelation structure of the GAWMK model. Monte Carlo simulation is then performed based on GAWMK for a subsequent slope reliability analysis. Applications to one explicit analytical example and two slope examples are presented to illustrate and validate the proposed method. It is found that reliability results estimated by the Kriging models using randomly chosen ACFs might be biased. The proposed method performs reasonably well in slope reliability estimation.

• Korean Journal of Mathematics
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• v.17 no.4
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• pp.399-409
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• 2009

We study the weak convergence of various models to Fractional Brownian motion. First, we consider arima process and ON/OFF source model which allows for long packet trains and long inter-train distances. Finally, we figure out power spectrum density as a Fourier transform of autocorrelation function of arima model and binary signal model.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.271-271
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• 2004

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.521-525
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• 1996

An attempt is made to characterize and synthesize engineered surfaces. The proposed method is not only an analytical tool to characterize but alsoto generate/synthesize three-dimensional surfaces. The developed method expresses important engineered surface characteristics such as the autocorrelation or pwoer spectrum density functions in terms of the two-dimensional autoregressive coefficients.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1991.10a
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• pp.303-304
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• 1991