• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.5
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• pp.1165-1172
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• 1998

In this paper, we present a construction for binary pseudorandom sequences of period $2^{m}-1$ with ideal autocorraltion property using the polynomial $z^{d}+(z+1)^{d}$. We show that the sequence obtained from the polynomial becomes an m-sequence for certain values of d. We also find a few values of d which yield new binary sequences with ideal autocorrelation property when m is $3k{\pm}1$, where k is a positive integer. These new sequences are represented using trace function and the results are tabulated.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.43-56
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• 2005

In this study, a robust estimation method for the first-order autocorrelation coefficient in the time series model following AR(l) process with additive outlier(AO) is investigated. We propose the L-type trimmed least squares estimation method using the preliminary estimator (PE) suggested by Rupport and Carroll (1980) in multiple regression model. In addition, using Mallows' weight function in order to down-weight the outlier of X-axis, the bounded-influence PE (BIPE) estimator is obtained and the mean squared error (MSE) performance of various estimators for autocorrelation coefficient are compared using Monte Carlo experiments. From the results of Monte-Carlo study, the efficiency of BIPE(LAD) estimator using the generalized-LAD to preliminary estimator performs well relative to other estimators.

• Earthquakes and Structures
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• v.3 no.3_4
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• pp.507-518
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• 2012

It is now widely accepted that the resulting displacement field within elastic, inhomogeneous, anisotropic solids subjected to equipartitioned, uniform illumination from uncorrelated sources, has intensities that follow diffusion-like equations. Typically, coda waves are invoked to illustrate this concept. These waves arrive later as a consequence of multiple scattering and appear at "the tail" (coda, in Latin) of seismograms and are usually considered an example of diffuse field. It has been demonstrated that the average correlations of motions within a diffuse field, in frequency domain, is proportional to the imaginary part of Green's function tensor. If only one station is available, the average autocorrelation is equal to the average squared amplitudes or the average power spectrum and this gives the Green's function at the source itself. Several works address this point from theoretical and experimental point of view. However, a complete and explicit analytical description is lacking. In this work we study analytically some properties of the Green's function, specifically the imaginary part of Green's function for 2D antiplane problems. This choice is guided by the fact that these scalar problems have a closed analytical solution (Kausel 2006). We assume the diffusiveness of the field and explore its analytical consequences.

• Journal of the Korean Society of Radiology
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• v.7 no.1
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• pp.25-30
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• 2013

In this work, we investigate the statistical properties of gamma exposure rates using well-known analysis methods, such as Autocorrelation Function Analysis(ACF), Rescaled Range Analysis(R/S Analysis), and Detrended Fluctuation Analysis(DFA). Especially, DFA is an important method to reliably detect long-range correlations in non-stationary time series. Our data are measured by Gangneung regional radiation monitoring station over the period of 1998 to 2011. First, we find a crossover indicating two different governing regimes in fluctuations of gamma exposure rates. Within a year, they show a strong long-ranged memory while this property vanishes over the range of time period longer than one year. Second, our finding is very securely supported by a variety of analysis tools. Those tools yield many relevant exponents which satisfies the well known relation between them.

• Journal of Ocean Engineering and Technology
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• v.33 no.3
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• pp.222-228
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• 2019

An extreme value analysis (EVA) is essential to obtain a design value for highly nonlinear variables such as long-term environmental data for wind and waves, and slamming or sloshing impact pressures. According to the extreme value theory (EVT), the extreme value distribution is derived by multiplying the initial cumulative distribution functions for independent and identically distributed (IID) random variables. However, in the position mooring of DNVGL, the sampled global maxima of the mooring line tension are assumed to be IID stochastic variables without checking their independence. The ITTC Recommended Procedures and Guidelines for Sloshing Model Tests never deal with the independence of the sampling data. Hence, a design value estimated without the IID check would be under- or over-estimated because of considering observations far away from a Weibull or generalized Pareto distribution (GPD) as outliers. In this study, the IID sampling data are first checked in an EVA. With no IID random variables, an automatic resampling scheme is recommended using the block maxima approach for a generalized extreme value (GEV) distribution and peaks-over-threshold (POT) approach for a GPD. A partial autocorrelation function (PACF) is used to check the IID variables. In this study, only one 5 h sample of sloshing test results was used for a feasibility study of the resampling IID variables approach. Based on this study, the resampling IID variables may reduce the number of outliers, and the statistically more appropriate design value could be achieved with independent samples.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.383-388
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• 2024

This paper is a study to derive the predicted value of power generation based on the photovoltaic power generation data measured in Jeollanam-do, South Korea. Multivariate variables such as direct current, alternating current, and environmental data were measured in the inverter to measure the amount of power generation, and pre-processing was performed to ensure the stability and reliability of the measured values. Correlation analysis used only data with high correlation with power generation in time series data for prediction using partial autocorrelation function (PACF). Deep learning models were used to measure the amount of power generation to predict the amount of photovoltaic power generation, and the results of correlation analysis of each multivariate variable were used to increase the prediction accuracy. Learning using refined data was more stable than when existing data were used as it was, and the solar power generation prediction algorithm was improved by using only highly correlated variables among multivariate variables by reflecting the correlation analysis results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.7
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• pp.559-565
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• 2014

In this paper, we propose an unambiguous correlation function for time-multiplexed binary offset carrier (TMBOC) signal tracking. Specifically, considering that the TMBOC modulation transmits two kinds of sine-phased BOC signals in time domain alternatively, we generate sub-correlation functions for each of the BOC signals by using split sine-phased BOC signals, and then, obtain a correlation function with no side-peak by recombining the sub-correlation functions. From numerical results, we confirm that the proposed correlation function offers an improved tracking error standard deviation performance than the TMBOC autocorrelation function.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.5
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• pp.1290-1299
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• 1990

Characteristics of flow patterns in horizontal gas-liquid two-phase flow for two different sizes of pipe were investigated based upon a statistical analysis of differential pressure fluctuations at an orifice. The probability density function and the power spectral density function of the traces indicate peculiar shapes depending upon the two-phase flow regime. Mixed and separated flows also could be identified by the autocorrelation function. The transition region from separated flow to mixed flow also could be identified by these statistical properties. The experimental data determined by this method were compared with the flow pattern maps suggested by other investigators. The result indicates that the statistical characteristics of differential pressure fluctuations at orifices may be a useful tool for identifying flow patterns of horizontal gas-liquid two-phase flow.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.49-60
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• 2024

Ground-penetrating radar (GPR) enables rapid data acquisition over extensive areas, but interpreting the obtained data requires specialized knowledge. Numerous studies have utilized numerical analysis methods to examine GPR signal characteristics under various conditions. To develop more realistic numerical models, the heterogeneous nature of the ground, which causes clutter, must be considered. Clutter refers to signals reflected by objects other than the target. The Peplinski material model and fractal techniques can simulate these heterogeneous characteristics, yet there is a shortage of research on the necessary input parameters. Moreover, methods for quantitatively evaluating the similarity between field and analytical data are not well established. In this study, we calculated the autocorrelation coefficient of field data and determined the correlation length using the autocorrelation function. The correlation length represented the temporal or spatial distance over which data exhibited similarity. By comparing the correlation length of field data with that of the numerical model incorporating fractal weights, we quantitatively evaluated a numerical model for heterogeneous ground. Consequently, the results of this study demonstrated a numerical modeling technique that reflected the clutter characteristics of the field through correlation length.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.33-41
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• 1994

A single channel adaptive noise canceling (ANC) technique with a recursive time delay estimator (RTDE) is presented for removing effects of additive noise on the speech signal. While the conventional method makes a reference signal for the adaptive filter using the pitch estimated on a frame basis from the input speech, the proposed method makes the reference signal using the delay estimated recursively on a sample-by-sample basis. As the RTDEs, the recursion formulae of autocorrelation function (ACF) and average magnitude difference function (AMDF) are derived. The normalized least mean square (NLMS) and recursive least square (RLS) algorithms are applied for adaptation of filter coefficients. Experimental results with noisy speech demonstrate that the proposed method improves the perceived speech quality as well as the signal-to-noise ratio and cepstral distance when compared with the conventional method.