• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.10a
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• pp.627-632
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• 2011

The internal broadband noise of a centrifugal fan in a household refrigerator is predicted by using hybrid CAA technique based on stochastic turbulent synthetic model. First, the unsteady flow field around the centrifugal fan is predicted using Computational Fluid Dynamics (CFD) method. Then, the turbulent flow field is synthesized by applying the stochastic turbulent synthetic technique to the predicted flow field. The aerodynamic noise sources of the centrifugal fan are modeled on a basis of the synthesized turbulent field. Finally, the broadband noise of the centrifugal fan is predicted using Boundary Element Method (BEM) and the modeled sources. The predicted result is compared with the experimental data. It is found that the predicted result closely follows the experimental data. The proposed method can be used as an effective tool for designing low-noise fans without expensive computational cost required generally for the LES and DNS simulations to resolve the turbulence flow field responsible for the broadband noise.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.1-9
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• 2024

Predicting the spread of COVID-19 remains a challenge due to the complexity of the disease and its evolving nature. This study presents an integrated approach using the classic SIR model for infectious diseases, enhanced by the chemical master equation (CME). We employ a Monte Carlo method (SSA) to solve the model, revealing unique aspects of the SARS-CoV-2 virus transmission. The study, a first of its kind in Korea, adopts a step-by-step and complementary approach to model prediction. It starts by analyzing the epidemic's trajectory at local government levels using both basic and stochastic SIR models. These models capture the impact of public health policies on the epidemic's dynamics. Further, the study extends its scope from a single-infected individual model to a more comprehensive model that accounts for multiple infections using the jump SIR prediction model. The practical application of this approach involves applying these layered and complementary SIR models to forecast the course of the COVID-19 epidemic in small to medium-sized local governments, particularly in Gangnam-gu, Seoul. The results from these models are then compared and analyzed.

• MALSORI
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• no.35_36
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• pp.145-155
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• 1998

This study intended to determine the most appropriate POS(Part of Speech) sets for predicting prosodic boundary strength efficiently. We used 3-level POB bets which Kim(1997), one of the authors, has devised. Three POS sets differ from each other according to how much grammatical information they have: the first set has maximal syntactic and morphological information which possibly affects prosodic phrasing, and the third set has minimal one. We hand-labelled 150 sentences using each of three POS sets and conducted perception test. Based on the results of the test, stochastic language modeling method was used to predict prosodic boundary strength. The results showed that the use of each POS set led to not too much different efficiency in the prediction, but the second set was a little more efficient than the other two. As far as the complexity in stochastic language modeling is concerned, however, the third set may be also preferable.

• Journal of information and communication convergence engineering
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• v.11 no.1
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• pp.30-44
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• 2013

Traffic congestion has become a serious problem with the recent exponential increase in the number of vehicles. In urban areas, almost all traffic congestion occurs at intersections. One of the ways to solve this problem is road expansion, but it is difficult to realize in urban areas because of the high cost and long construction period. In such cases, traffic signal control is a reasonable method for reducing traffic jams. In an actual situation, the traffic flow changes randomly and its randomness makes the control of traffic signals difficult. A prediction of traffic jams is, therefore, necessary and effective for reducing traffic jams. In addition, an autonomous distributed (stand-alone) point control of each traffic light individually is better than the wide and/or line control of traffic lights from the perspective of real-time control. This paper describes a stochastic optimum control of crossroads and multi-way traffic signals. First, a stochastic model of traffic flows and traffic jams is constructed by using a Bayesian network. Secondly, the probabilistic distributions of the traffic flows are estimated by using a cellular automaton, and then the probabilistic distributions of traffic jams are predicted. Thirdly, optimum traffic signals of crossroads and multi-way intersection are searched by using a modified particle swarm optimization algorithm to realize real-time traffic control. Finally, simulations are carried out to confirm the effectiveness of the real-time stochastic optimum control of traffic signals.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.795-805
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• 1997

In this paper, we consider a Bayesian forecasting method for the analysis of repeated surveys. It is assumed that the parameters of the superpopulation model at each time follow a stochastic model. We propose Bayesian prediction procedures for the finite population total under dynamic generalized linear models. Some numerical studies are provided to illustrate the behavior of the proposed predictors.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.04a
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• pp.70-80
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• 2000

In order to reduce seismic hazard the characteristics of strong earthquakes are required. In the region where strong earthquakes do not happen frequently the stochastic simulation of strong motion is an alternative way to predict strong motions. this simulation required input parameters such as the quality factor the corner frequency the moment magnitude the stress drop and so on which can be obtained from analyses of records of small and intermediate earthquakes. Using those parameters obtained in the previous work the strong ground motions are predicted employing the stochastic method, . The results are compared to the two observed earthquakes-the Ulsan Offshore Earthquake and the Kyungju Earthquake. Although some deviations are found the predictions are similar to the observed data. Finally we computed attenuation equations for PGA PGV and ground accelerations for some frequencies using the results of predictions. These results can be used for earthquake engineering and more reliable results will come out as earthquake observations continue.

• Journal of Ocean Engineering and Technology
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• v.5 no.2
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• pp.191-191
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• 1991

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.8
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• pp.1487-1494
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• 2002

In this study, to propose the prediction method of the crack growth under flight-simulation loading, crack growth tests are conducted on 2124-7851 aluminum alloy specimens. The prediction of crack growth under flight-simulation loading is performed by the stochastic crack growth model which was developed in previous study. First of all, to reduce the complex load history into a number of constant amplitude events, rainflow counting is applied to the flight-simulation loading wave. The crack growth, then, is predicted by the stochastic crack growth model that can describe the load interaction effect as well as the variability in crack growth process. The material constants required in this model are obtained from crack growth tests under constant amplitude loading and single tensile overload. The curves predicted by the proposed model well describe the crack growth behavior under flight-simulation loading and agree with experimental data. In addition, this model well predicts the variability of fatigue lives.

• Wind and Structures
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• v.31 no.6
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• pp.549-560
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• 2020

Methods for stochastic simulation of non-Gaussian wind pressure have increasingly addressed the efficiency and accuracy contents to offer an accurate description of the extreme value estimation of the long-span and high-rise structures. This paper presents a linear prediction and z-transform (LPZ) based Cumulative distribution function (CDF) mapping algorithm for the simulation of multivariate non-Gaussian fluctuating wind pressure. The new algorithm generates realizations of non-Gaussian with prescribed marginal probability distribution function (PDF) and prescribed spectral density function (PSD). The inverse linear prediction and z-transform function (ILPZ) is deduced. LPZ is improved and applied to non-Gaussian wind pressure simulation for the first time. The new algorithm is demonstrated to be efficient, flexible, and more accurate in comparison with the FFT-based method and Hermite polynomial model method in two examples for transverse softening and longitudinal hardening non-Gaussian wind pressures.

• Structural Engineering and Mechanics
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• v.31 no.5
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• pp.481-506
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• 2009

The present study is concerned with the stochastic linear free vibration study of laminated composite plate embedded with piezoelectric layers with random material properties. The system equations are derived using higher order shear deformation theory. The lamina material properties of the laminate are modeled as basic random variables for accurate prediction of the system behavior. A $C^0$ finite element is used for spatial descretization of the laminate. First order Taylor series based mean centered perturbation technique in conjunction with finite element method is outlined for the problem. The outlined probabilistic approach is used to obtain typical numerical results, i.e., the mean and standard deviation of natural frequency. Different combinations of simply supported, clamped and free boundary conditions are considered. The effect of side to thickness ratio, aspect ratio, lamination scheme on scattering of natural frequency is studied. The results are compared with those available in literature and an independent Monte Carlo simulation.