• Bulletin of the Korean Chemical Society
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• v.19 no.4
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• pp.422-428
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• 1998

In the present paper, we report a molecular dynamics (MD) simulation of non-rigid zeolite-A framework only as the base case for a consistent study of the role of intraframework interaction on several zeolite-A systems using the same technique in our previous studies of rigid zeolite-A frameworks. Usual bond stretching, bond angle bending, torsional rotational, and non-bonded Lennard-Jones and electrostatic interactions are considered as intraframework interaction potentials. The comparison of experimental and calculated structural parameters confirms the validity of our MD simulation for zeolite-A framework. The radial distribution functions of non-rigid zeolite-A framework atoms characterize the vibrational motion of the framework atoms. Mean square displacements are all periodic with a short period of 0.08 ps and a slow change in the amplitude of the vibration with a long period of 0.53 ps. The displacement auto-correlation (DAC) and neighbor-correlation (DNC) functions describe the up-and-down motion of the framework atoms from the center of α-cage and the back-and-forth motion on each ring window from the center of each window. The DAC and DNC functions of the framework atoms from the center of α-cage at the 8-ring windows have the same period of the up-and-down motion, but those functions from the center of 8-ring window at the 8-ring windows are of different periods of the back-and-forth motion.

• Journal of Korean Society for Quality Management
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• v.21 no.1
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• pp.181-189
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• 1993

Over the past two decades, forecasting has gained widespread acceptance as an integral part of business planning and decision making. Accurate forecasting is a prerequisite to successful planning. Accordingly, recent advances in forecasting techniques are of exceptional value to corporate planners. But most of forecasting mothods are reveal its limit and problem for precision and reliability duing to each relationship for raw data and possibility of explanation for each variable. Therefore, to construct the Integrated Forecasting Model(IFM) for Information Factor Analysis, it shoud be considered that whether law data has time lag and variables are explained. For this. following several method can be used : Least Square Method, Markov Process, Fibonacci series, Auto-Correlation, Cross-Correlation, Serial Correlation and Random Walk Theory. Thus, the unified property of these several functions scales the safety and growth of the system which may be varied time-to-time.

• Convergence Security Journal
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• v.24 no.2
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• pp.123-132
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• 2024

In the era of the Internet of Things, 7 billion diverse devices have been interconnected worldwide. Ensuring information security across these varied devices is crucial in this hyper-connected age. To achieve essential security functions such as confidentiality, integrity, and authentication, it is imperative to implement true random number generators (TRNGs). Therefore, this study proposes a method to rapidly characterize the randomness of TRNGs. While there are international standards for formally characterizing the randomness of TRNGs, adhering to these standards often requires significant time and resources. This study aims to help TRNG developers enhance efficiency in both time and cost by characterizing rough randomness and unpredictability. Firstly, we propose applying auto-correlation and cross-correlation metrics for analog signals. Secondly, we suggest adopting joint entropy and mutual information metrics for digital signals.

• Bulletin of the Korean Chemical Society
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• v.24 no.2
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• pp.178-182
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• 2003

The stress and the heat-flux auto-correlation functions in the Green-Kubo formulas for shear viscosity and thermal conductivity have non-decaying long-time tails. This problem can be overcome by improving the statistical accuracy by N (number of particles) times, considering the stress and the heat-flux of the system as properties of each particle. The mean square stress and the heat-flux displacements in the Einstein formulas for shear viscosity and thermal conductivity are non linear functions of time since the quantities in the mean square stress and the heat-flux displacements are not continuous under periodic boundary conditions. An alternative to these quantities is to integrate the stress and the heat-flux with respect to time, but the resulting mean square stress and heat-flux displacements are still not linear versus time. This problem can be also overcome by improving the statistical accuracy. The results for transport coefficients of liquid argon obtained are discussed.

• Bulletin of the Korean Chemical Society
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• v.12 no.3
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• pp.309-315
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• 1991

The thermal transport coefficients-the self-diffusion coefficient, shear viscosity, and thermal conductivity-of liquid argon at 94.4 K and 1 atm are calculated by non-equilibrium molecular dynamics (NEMD) simulations of a Lennard-Jones potential and compared with those obtained from Green-Kubo relations using equilibrium molecular dynamics (EMD) simulations and with experimental data. The time-correlation functions-the velocity, pressure, and heat flux auto-correlation functions-of liquid argon obtained from the EMD simulations show well-behaved smooth curves which are not oscillating and decaying fast around 1.5 ps. The calculated self-diffusion coefficient from our NEMD simulation is found to be approximately 40% higher than the experimental result. The Lagrange extrapolated shear viscosity is in good agreement with the experimental result and the asymptotic formula of the calculated shear viscosities seems to be an exponential form rather than the square-root form predicted by other NEMD studies of shear viscosity. The agreement for thermal conductivity between the simulation results (NEMD and EMD) and the experimental result is within statistical error. In conclusion, through our NEMD and EMD simulations, the overall agreement is quite good, which means that the Green-Kubo relations and the NEMD algorithms of thermal transport coefficients for simple liquids are valid.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4C
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• pp.321-328
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• 2010

A Rayleigh fading channel simulator that realizes given correlation properties between two channels such as downlink/uplink is described. We propose an improved channel simulator that provides more accurate match between reference correlation and simulated one. The proposed channel simulator is based on a sum-of-sinusoids (SOS) method that handles finite sums of weighted sinusoid functions. SOS method is widely accepted in generating fading signals and obtain the exact match with the reference model if infinite number of weighted sinusoids are used. The proposed channel simulator minimizes mean-square-error (MSE) to obtain a close matched correlation properties.

• Bulletin of the Korean Chemical Society
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• v.12 no.3
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• pp.315-322
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• 1991

In a recent $paper^1$ we reported equilibrium (EMD) and non-equilibrium (NEMD) molecular dynamics simulations of liquid argon using the Green-Kubo relations and NEMD algorithms to calculate the thermal transport coefficients-the self-diffusion coefficient, shear viscosity, and thermal conductivity. The overall agreement with experimental data is quite good. In this paper the same technique is applied to calculate the thermal transport coefficients of liquid water at 298.15 K and 1 atm using TIP4P model for the interaction between water molecules. The EMD results show difficulty to apply the Green-Kubo relations since the time-correlation functions of liquid water are oscillating and not decaying rapidly enough except the velocity auto-correlation function. The NEMD results are found to be within approximately ${\pm}$30-40% error bars, which makes it possible to apply the NEMD technique to other molecular liquids.

• Economic and Environmental Geology
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• v.39 no.3 s.178
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• pp.269-284
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• 2006

Aquifer responses to the river stage fluctuations were analyzed between the groundwater level and the river stage in an alluvial aquifer. The auto-correlation and cross-correlation as a time series analysis were applied. Study site is a floodplain in the Mangyeong river. Groundwater levels in each depth representing the silt and sand(SS), gravel and sand(GS), and weathered zone(WZ) layer were monitored. The groundwater levels were more sensitive to the river stage fluctuations than rainfall. Since the river stages are influenced by the gate operations downstream and tide, show periodic patterns, and the correlation coefficients with rainfall is low. Cross-correlation function between groundwater level in each depth do not show any delay time, then response time is very short to each depth. Cross-correlation analysis were performed to estimate the response time of groundwater levels to river stage fluctuations. Groundwater levels respond to the river stage within 30 minutes to 1 hour in wells near the stream. Short time lag between groundwater level and river stage indicates the quick response. A different response time imply the hydraulic inhomogeneity of the site, and a probable high permeability zone between river and aquifer can be inferred. Mangyeong-river in study site is a gaining stream normally, and river stage rising by gate operation or floods makes river water flow into groundwater. The auto-correlation and cross-correlation functions as a time series analysis can be a good tool to interpret the aquifer responses to stream stage fluctuation

• Journal of KIISE:Information Networking
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• v.32 no.4
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• pp.433-442
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• 2005

An increase In traffic has a large Influence on the performance of a total network. Therefore, traffic management has become an important issue of network management. In this paper, we propose a new routing algorithm that attempts to analyze network conditions using time series prediction models and to propose predictive optimal routing decisions. Traffic congestion is assumed when the predicting result is bigger than the permitted bandwidth. By collecting traffic in real network, the predictable model is obtained when it minimizes statistical errors. In order to predict network traffic based on time series models, we assume that models satisfy a stationary assumption. The stationary assumption can be evaluated by using ACF(Auto Correlation Function) and PACF(Partial Auto Correlation Function). We can obtain the result of these two functions when it satisfies the stationary assumption. We modify routing oaths by predicting traffic in order to avoid traffic congestion through experiments. As a result, Predicting traffic and balancing load by modifying paths allows us to avoid path congestion and increase network performance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.2
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• pp.123-129
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• 2015

The Kalman filter has been widely used in the kinematic GPS positioning due to its flexibility and efficiency in computational points of view. At the same time, the relative positioning technique also provided the high precision positioning results by removing the systematic errors in the measurements significantly. However, the positioning quality may be degraded following to longer in baseline length. For this case, it is required that the remaining atmospheric effects, such as double-difference ionospheric delay and zenith wet delay, should be properly modeled by examining the characteristics of the stochastic processes. In general, atmospheric effects are estimated with the assumption of random walk, or the first-order Gauss-Markov stochastic process, which requires the precise modeling on the corresponding process noises. Therefore, we determined and provided the parameters for modelling the process noises for atmospheric effects. The auto-correlation functions are empirically determined at first, and then the parameters are extracted from the empirical auto-correlation function. In fact, the test results can be either applied directly, or used as guidance values for the modeling of process noises in the kinematic GPS positioning.