• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.3
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• pp.350-357
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• 1997

The transient incompressible flow behind the axisymmetric bluff body is numerically simulated using the random vortex method(RVM). Based on the vorticity formulation of the unsteady Navier-Stokes equations, the Lagrangian approach with a stochastic simulation of diffusion using random walk technique is employed to account for the transport processes of the vortex elements. The numerical solutions for 2-dimensional recirculating flow behind a backward-facing step in the laminar range of Reynolds number are compared with experimental data. The present simulation focuses on the transitional flow regime where the recirculation zone behind the bluff body becomes highly unsteady and large-scale vortex eddies are shed from the bluff body wake due to intrinsic shear layer instabilities. The unsteady vertical flow structures and the mixing characteristics behind the bluff body are discussed in detail.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.136-141
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• 1991

An Overlapping Decomposed Filter(ODF) accomplishing an initial alignment of an INS is proposed in this paper. The proposed filter improves the observable condition and reduces the filtering computation time. Its good performance has been verified by simulation. Completely observable and controllable conditions of INS error model derived from psi-angle approach are introduced under varying sensor characteristics vary. The east components of gyro and accelerometer have to be the first order markov process and the rest of them are the characteristics of the random walk or first order markov process.

• 한국연소학회:학술대회논문집
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• 1999.10a
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• pp.139-148
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• 1999

In oder to analyze the effect of operating conditions on pulverized coal combustion, a numerical study is conducted at the pulverized coal combustor. Eulerian approach is used for the gas phase, whereas Lagrangian approach is used for the particle phase. Turbulence is modeled using standard ${\kappa}-{\varepsilon}$ model. The description of species transport and combustion chemistry is based on the mixture fraction/probability density function(PDF) approach. Radiation is modeled using P-l model. The turbulent dispersion of particles is modeled using discrete random walk model. Swirl number of secondary air affects the flame front, particle residence time and carbon conversion. Primary/Secondary air mass ratio also affects the flame front but little affects the carbon conversion and particle residence time. Air-fuel ratio only affects the flame front due to lack of oxygen. Radiation strongly affects the flame front and gas temperature distribution because pulverized coal flame of high temperature is considered.

• Communications of the Korean Mathematical Society
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• v.25 no.1
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• pp.119-128
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• 2010

Let ${X_n,\;n\geq1}$ be a sequence of independent identically distributed (i.i.d.) random variables (r.vs.), defined on a probability space ($\Omega$,A,P), and let ${N_n,\;n\geq1}$ be a sequence of positive integer-valued r.vs., defined on the same probability space ($\Omega$,A,P). Furthermore, we assume that the r.vs. $N_n$, $n\geq1$ are independent of all r.vs. $X_n$, $n\geq1$. In present paper we are interested in asymptotic behaviors of the random sum $S_{N_n}=X_1+X_2+\cdots+X_{N_n}$, $S_0=0$, where the r.vs. $N_n$, $n\geq1$ obey some defined probability laws. Since the appearance of the Robbins's results in 1948 ([8]), the random sums $S_{N_n}$ have been investigated in the theory probability and stochastic processes for quite some time (see [1], [4], [2], [3], [5]). Recently, the random sum approach is used in some applied problems of stochastic processes, stochastic modeling, random walk, queue theory, theory of network or theory of estimation (see [10], [12]). The main aim of this paper is to establish some results related to the asymptotic behaviors of the random sum $S_{N_n}$, in cases when the $N_n$, $n\geq1$ are assumed to follow concrete probability laws as Poisson, Bernoulli, binomial or geometry.

• Structural Engineering and Mechanics
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• v.81 no.1
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• pp.103-115
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• 2022

The prediction error variances for frequencies are usually considered as unknown in the Bayesian system identification process. However, the error variances for mode shapes are taken as known to reduce the dimension of an identification problem. The present study attempts to explore the effectiveness of Bayesian approach of model parameters updating using Markov Chain Monte Carlo (MCMC) technique considering the prediction error variances for both the frequencies and mode shapes. To remove the ergodicity of Markov Chain, the posterior distribution is obtained by Gaussian Random walk over the proposal distribution. The prior distributions of prediction error variances of modal evidences are implemented through inverse gamma distribution to assess the effectiveness of estimation of posterior values of model parameters. The issue of incomplete data that makes the problem ill-conditioned and the associated singularity problem is prudently dealt in by adopting a regularization technique. The proposed approach is demonstrated numerically by considering an eight-storey frame model with both complete and incomplete modal data sets. Further, to study the effectiveness of the proposed approach, a comparative study with regard to accuracy and computational efficacy of the proposed approach is made with the Sequential Monte Carlo approach of model parameter updating.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.4
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• pp.467-478
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• 2016

In this paper, we try to find the optimal locations of NeNB(Nomadic evolved NodeB)s for maximizing the overall throughput of the PS-LTE networks. Since finding optimal locations of all NeNBs in a given area is NP-hard(Non-deterministic Polynomial time-hard) problem, we proposed a PSO-based heuristic approach. In order to evaluate the performance, we conducted two experiments. We compared performance with other schemes such as Exhaustive Search, Random Walk Search, and locating neighboring NeNBs with the same NeNB-to-NeNB distance. The proposed method showed the similar results to the exhaustive search method in terms of locating optimal position and user's data throughput. The proposed method, however, has the fast and consistent convergence time.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_1
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• pp.575-581
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• 2012

In general, various modeling and estimation techniques have been proposed to extract the tropospheric and ionospheric delays from the GPS CORS. In this study, Kalman filter approach is adopted to estimate the tropospheric and ionospheric delays and the proper modeling for the state vector and the variance-covariance matrix for the process noises are performed. The coordinates of reference stations and the zenith wet delays are estimated with the assumption of random walk stochastic process. Also, the first-order Gauss-Markov stochastic process is applied to compute the ionospheric effects. For the evaluation of the proposed modeling technique, Kalman filter algorithm is implemented and the numerical test is performed with the CORS data. The results show that the atmospheric effects can be estimated successfully and, as a consequence, can be used for the generation of VRS data.

• International Journal of Contents
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• v.2 no.2
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• pp.17-24
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• 2006

The work presented in this paper is divided into two parts. The first part presents finite urn problems which generate truncated negative binomial random variables. Some combinatorial identities that arose from the negative binomial sampling and truncated negative binomial sampling are established. These identities are constructed and serve important roles when we deal with these distributions and their characteristics. Other important results including cumulants and moments of the distributions are given in somewhat simple forms. Second, the distributions of the maximum of two chi-square variables and the distributions of the maximum correlated F-variables are then derived within the negative binomial sampling scheme. Although multinomial theory applied to order statistics and standard transformation techniques can be used to derive these distributions, the negative binomial sampling approach provides more information and deeper insight regarding the nature of the relationship between the sampling vehicle and the probability distributions of these functions of chi-square variables. We also provide an algorithm to compute the percentage points of these distributions. We supplement our findings with exact simple computational methods where no interpolations are involved.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.8
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• pp.47-53
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• 2014

This paper presents an Differential Search(DS) Algorithm for solving the economic load dispatch(ELD) problems with Valve-Point loading constraints. DS algorithm simulates the Brownian-like random-walk movement used by an organism to migrate. Numerical results on a test system consisting of 13 units show that the proposed approach is faster, more robust and powerful than conventional algorithms. Case studies show the simulation results are better than Lagrange method, the Hopfield neural networks and GA.

• Journal of Sasang Constitutional Medicine
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• v.35 no.4
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• pp.10-22
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• 2023

Objectives The aim of this study is to explore the mechanism of action and target diseases of Sasang constitutional prescriptions using a multiscale interactome approach. Methods The compound and target information of Sasang constitutional prescriptions were retrieved from various databases such as the TM-MC, STITCH, and TTD. Key targets for Sasang constitutional prescriptions were identified by selecting the top 100 targets based on the number of simple paths within the constructed network. Diffusion profiles for Sasang constitutional prescriptions and diseases were calculated based on a biased random walk algorithm. Potential diseases and key mechanisms of Sasang constitutional prescriptions were identified by analyzing diffusion profiles. Results We identified 144 Sasang constitutional prescriptions and their targets, finding 80 herbs with effective biological targets. A cluster analysis based on selecting up to 100 key targets for each prescription revealed a more cohesive grouping of prescriptions according to Sasang constitution. We then predicted potential diseases for 62 Sasang constitutional prescriptions using diffusion profiles calculated on a multiscale interactome. Finally, our analysis of diffusion profiles revealed key targets and biological functions of prescriptions in obesity and diabetes. Conclusions This study demonstrates the effectiveness of a multiscale interactome approach in elucidating the complex mechanisms and potential therapeutic applications of prescriptions in Sasang constitutional medicine.