• Tunnel and Underground Space
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• v.11 no.2
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• pp.156-166
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• 2001

A stochastic continuum(SC) modeling technique was developed to simulate the groundwater flow pathway in fractured rocks. This model was developed to overcome the disadvantageous points of discrete fracture network(DFN) modes which has the limitation of fracture numbers. Besides, SC model is able to perform probabilistic analysis and to simulate the conductive groundwater pathway as discrete fracture network model. The SC model was formulated based on the discrete fracture network(DFN) model. The spatial distribution of permeability in the stochastic continuum model was defined by the probability distribution and variogram functions defined from the permeabilities of subdivided smaller blocks of the DFN model. The analysis of groundwater travel time was performed to show the consistency between DFN and SC models by the numerical experiment. It was found that the stochastic continuum modes was an appropriate way to provide the probability density distribution of groundwater velocity which is required for the probabilistic safety assessment of a radioactive waste disposal facility.

• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.217-229
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• 2013

As customers' demands for diversified small-quantity products have been increased, there have been great efforts for a firm to respond to customers' demands flexibly and minimize the cost of inventory at the same time. To achieve that goal, in SCM perspective, many firms have tried to control the inventory efficiently. We present an mathematical model to determine the near optimal (s, S) policy of the supply chain, composed of multi suppliers, a warehouse and multi retailers. (s, S) policy is to order the quantity up to target inventory level when inventory level falls below the reorder point. But it is difficult to analyze inventory level because it is varied with stochastic demand of customers. To reflect stochastic demand of customers in our model, we do the analyses in the following order. First, the analysis of inventory in retailers is done at the mathematical model that we present. Then, the analysis of demand pattern in a warehouse is performed as the inventory of a warehouse is much effected by retailers' order. After that, the analysis of inventory in a warehouse is followed. Finally, the integrated mathematical model is presented. It is not easy to get the solution of the mathematical model, because it includes many stochastic factors. Thus, we get the solutions after the stochastic demand is approximated, then they are verified by the simulations.

• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1503-1514
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• 2006

Today, consumers can access Internet from everywhere, therefore most commercial and other organizations provide their services on the Web. As the result, countless Web service systems are already on the Internet and more systems are under construction. Therefore, many researches of verifying that the system to be constructed will not have any deadlock and will run successfully without any problem at the early stage of design have been performed. Several Petri net based verification methods have also been published. However, they have focused on building Petri net models of Web service systems and none of them introduces efficient analysis methods. As a mathematical technique with which we can find the minimum duration time needed to fire all the transitions at least once and coming back to the initial marking in a timed net, the minimum cycle time method has been widely used in computer system analysis. A timed net is a modified version of a Petri net where a transition is associated with a delay time. A delay time used in a timed net is a constant even though the duration time associated with an event in the real world is a stochastic number in general. Therefore, this paper proposes 'Mixedly Distributed Stochastic Timed Net' where a transition can be associated with a stochastic number and introduce a minimum cycle time analysis method for 'Mixedly Distributed Stochastic Timed Net'. We also introduce a method of analysing a Web service system's response time with the minimum cycle time analysis method for 'Mixedly Distributed Stochastic Timed Net.'.

• Journal of the Society of Naval Architects of Korea
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• v.32 no.1
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• pp.37-41
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• 1995

In order to analyze the rolling motion of a ship in random beam waves we use the partial stochastic linearization method. The quadratic damping and the nonlinear restoring moments given by the odd polynomials up to the 11th order are added to a single degree of freedom linear equation of roll motion. The irregular excitation moment is assumed to be the Gaussian white noise. The statistical characteristics of the response by the partial stochastic linearization method is compared with results by the equivalent linearization method and Monte Carlo simulation. It is fecund that the partial stochastic linearization method is not necessarily superior to the equivalent linearization method.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.3
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• pp.145-156
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• 2002

Business managers are often required to use LP problems to deal with uncertainty inherent in decision making due to rapid changes in today＇s business environments. Uncertain parameters can be easily formulated in the two-stage stochastic LP problems. However, since solution methods are complex and time-consuming, a common approach has been to use modified formulations to provide upper and lower bounds on the two-stage stochastic LP problem. One approach is to use an expected value problem, which provides upper and lower bounds. Another approach is to use “walt-and-see” problem to provide upper and lower bounds. The objective of this paper is to propose a modified approach of “wait-and-see” problem to provide an upper bound and to compare the relative error of optimal value with various upper and lower bounds. A computing experiment is implemented to show the relative error of optimal value with various upper and lower bounds and computing times.

• Wind and Structures
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• v.7 no.3
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• pp.173-186
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• 2004

Flutter derivatives provide the basis of predicting the critical wind speed in flutter and buffeting analysis of long-span cable-supported bridges. In this paper, one popular stochastic system identification technique, covariance-driven Stochastic Subspace Identification(SSI in short), is firstly presented for estimation of the flutter derivatives of bridge decks from their random responses in turbulent flow. Secondly, wind tunnel tests of a streamlined thin plate model and a ${\Pi}$ type blunt bridge section model are conducted in turbulent flow and the flutter derivatives are determined by SSI. The flutter derivatives of the thin plate model identified by SSI are very comparable to those identified by the unifying least-square method and Theodorson's theoretical values. As to the ${\Pi}$ type section model, the effect of turbulence on aerodynamic damping seems to be somewhat notable, therefore perhaps the wind tunnel tests for flutter derivative estimation of those models with similar blunt sections should be conducted in turbulent flow.

• Interaction and multiscale mechanics
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• v.3 no.4
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• pp.333-342
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• 2010

The Pseudo-Excitation Method (PEM) is applied to study the stochastic space vibration responses of train-bridge coupling system. Each vehicle is modeled as a four-wheel mass-spring-damper system with two layers of suspension system possessing 15 degrees-of- freedom. The bridge is modeled as a spatial beam element, and the track irregularity is assumed to be a uniform random process. The motion equations of the vehicle system are established based on the d'Alembertian principle, and the motion equations of the bridge system are established based on the Hamilton variational principle. Separate iteration is applied in the solution of equations. Comparisons with the Monte Carlo simulations show the effectiveness and satisfactory accuracy of the proposed method. The PSD of the 3-span simply-supported girder bridge responses, vehicle responses and wheel/rail forces are obtained. Based on the $3{\sigma}$ rule for Gaussian stochastic processes, the maximum responses of the coupling system are suggested.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.11
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• pp.1314-1319
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• 2009

The design methods of mechanical systems are largely classified into deterministic methods and stochastic methods. In deterministic methods, design parameters are assumed to have fixed values. On the other hand, in stochastic methods, design parameters are assumed to be statistically distributed. When a stochastic method is employed, statistical characteristics of the populations of design variables are assumed to be known. However, very often, it is almost impossible or very expensive to obtain the statistical characteristics of the populations. Therefore a sample survey method is usually employed for stochastic methods. This paper describes the procedure of estimating the statistical characteristics of populations by employing sample data sets. An example of AFM micro cantilever beam is employed to show the effectiveness of the procedure.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.653-657
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• 1994

A symbolic performance analysis approach for flexible for manufactring systems (FMS) can be formulated based on the integration of Petri Nets (PN) and moment generating function (MGF) concept. In this method, generalized stochastic Petri Nets are used to define performance models for FMS, then MGF nased approach for evaluating stochastic PN is used to derive performance parameters of PN, and finally system performance is calculated. A GSPN model of machine cell is shown to illustrate the proposed method for evaluating such performance indices as production rate, utilization, work-in-process and lead time. The major advantage of this method over existing performance evaluation of FMS is the ability to compute symbolic solutions for performance. Finally future research toward automating performance measure for GSPN models of FMS is discussed.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1993.04a
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• pp.117-124
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• 1993

The Neumann Expansion method has been used for evaluating the response variability of three dimensional truss structure resulting from the spatial variability of material properties with the aid of the finite element method, and in conjunction with the direct Monte Carlo simulation methods. The spatial variabilites are modeled as three-dimensional stochastic field. Yamazaki 〔1〕 has extended the Neumann Expansion method to the plane-strain problem to obtain the response variability of 2 dimensional stochastic systems. This paper presents the extension of the Neumann Expansion method to 3 dimensional stochastic systems. The results by the NEM are compared with those by the deterministic finite element analysis and by the direct Monte Carlo simulation method