• Korean Journal of Mathematics
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• v.29 no.2
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• pp.371-386
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• 2021

This paper investigates two discrete time queueing inventory models with positive service time and lead time. Customers arrive according to a Bernoulli process and service time and lead time follow geometric distributions. The first model under discussion based on replenishment of order upto S policy where as the second model is based on order placement by a fixed quantity Q, where Q = S - s, whenever the inventory level falls to s. We analyse this queueing systems using the matrix geometric method and derive an explicit expression for the stability condition. We obtain the steady-state behaviour of these systems and several system performance measures. The influence of various parameters on the systems performance measures and comparison on the cost analysis are also discussed through numerical example.

• Asian Journal of Innovation and Policy
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• v.11 no.3
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• pp.320-341
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• 2022

Empirical applications to explain criminogenic events are abundant. While much of the research in criminal studies concentrates on understanding the motivations of offenders and preventing victimization from a micro perspective, there have been recent theoretical advancements that give priority to the role of spatial factors in directly impacting crime rates. The primary purpose of this study is to investigate the empirical inference between violent crime incidence and spatial characteristics of local areas focusing particularly on spatial accessibility conditions in the areas. Applying discrete spatial econometrics models, this study reveals a significant relationship between spatial accessibility and the formation of violent crime hot spots in South Korea. Along with other variables, it is revealed that road accessibility has a clear association with violent crime hot spots. Based on the findings, this study suggests some policy implications such as effective surveillance systems, land use restrictions, and advanced street lighting.

• Journal of the Korean Society of Hazard Mitigation
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• v.6 no.2 s.21
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• pp.51-60
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• 2006

The objective of this research is to develop a least cost system design method for branched storm sewer systems while satisfying all the design constraints using heuristic techniques such as genetic algorithm and harmony search. Two sewer system models have been developed in this study. The SEWERGA and SEWERHS both determine the optimal discrete pipe installation depths as decision variables. Two models also determine the optimal diameter of sewer pipes using the discrete installation depths of the pipes while satisfying the discharge and velocity requirement constraints at each pipe. Two models are applied to the example that was originally solved by Mays and Yen (1975) using their dynamic programming(DP). The optimal costs obtained from SEWERGA and SEWERHS are about 4% lower than that of the DP approach.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.4
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• pp.138-146
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• 2007

Geotechnical performance at the soft ground is strongly dependent on the properties of the soil beneath and adjacent to the structure of interest. These soil properties can be described using deterministic and/or probabilistic models. Deterministic models typically use a single discrete descriptor for the parameter of interest. Probabilistic models describe parameters by using discrete statistical descriptors or probability distribution density functions. The consolidation process depends on several uncertain parameters including the coefficients of consolidation and coefficients of permeability in vertical and horizontal directions. The implication of this uncertain parameter in the design of prefabricated vertical drains for soil improvement is discussed. A sensitivity analysis of the degree of consolidation and calculation of settlements to these uncertain parameters is presented for clayey deposits.

• Proceedings of the Korea Water Resources Association Conference
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• 1992.07a
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• pp.411-418
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• 1992

The purpose of this study is to develop dynamic-stochastic models that can forecast the inflow into reservoir during low/drought periods and flood periods. For the formulation of the models, the discrete transfer function is utilized to construct the deterministic characteristics, and the ARIMA model is utilized to construct the stochastic characteristics of residuals. The stochastic variations and structures of time series on hydrological data are examined by employing the auto/cross covariance function and auto/cross correlation function. Also, general modeling processes and forecasting method are used the model building methods of Box and Jenkins. For the verifications and applications of the developed models, the Chungju multi-purpose reservoir which is located in the South Han river systems is selected. Input data required are the current and past reservoir inflow and Yungchun water levels. In order to transform the water level at Yungchon into streamflows, the water level-streamflows rating curves at low/drought periods and flood periods are estimated. The models are calibrated with the flood periods of 1988 and 1989 and hourly data for 1990 flood are analyzed. Also, for the low/drought periods, daily data of 1988 and 1989 are calibrated, and daily data for 1989 are analyzed.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.603-613
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• 2008

We often encounter the situation that discrete count data have a large portion of zeros. In this case, it is not appropriate to analyze the data based on standard regression models such as the poisson or negative binomial regression models. In this article, we consider Bayesian analysis for two commonly used models. They are zero-inflated poisson and negative binomial regression models. We use the Bayes factor as a model selection tool and computation is proceeded via Markov chain Monte Carlo methods. Crash count data are analyzed to support theoretical results.

• The Journal of the Korea Contents Association
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• v.6 no.11
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• pp.258-265
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• 2006

DEVS formalism is advantageous in modeling large-scale complex systems and it reveals good readability, because it can specify discrete event systems in a hierarchical manner. In contrast, it has drawback in that the simulation speed of DEVS models is comparably slow since it requires frequent message passing between the component models in run-time. This paper proposes a method, called model composition, for simulation speedup of DEVS models. The method is viewed as a compiled simulation technique which eliminates run-time interpretation of communication paths between component models. Experimental results show that the simulation speed of transformed DEVS models is about 18 times faster than original ones.

• Journal of the Korea Society for Simulation
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• v.11 no.2
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• pp.1-16
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• 2002

The need for network security is being increasing due to the development of information communication and internet technology. In this paper, firewall models, operating system models and other network component models are constructed. Each model is defined by basic or compound model, referencing DEVS formalism. These models and the simulation environment are implemented with MODSIM III, a general purpose, modular, block-structured high-level programming language which provides direct support for object-oriented programming and discrete-event simulation. In this simulation environment with representative attacks, the following three attacks are generated, SYN flooding and Smurf attack as an attack type of denial of service, Mail bomb attack as an attack type of e-mail. The simulation is performed with the models that exploited various security policies against these attacks. The results of this study show that the modeling method of packet filtering system, proxy system, unix and windows NT operating system. In addition, the results of the simulation show that the analysis of security performance according to various security policies, and the analysis of correlation between availability and confidentiality according to security empowerment.

• International Journal of CAD/CAM
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• v.7 no.1
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• pp.1-10
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• 2007

The compression of CAD models is a key technology for realizing Internet-based collaborative product development because big model sizes often prohibit us to achieve a rapid product information transmission. Although there exist some algorithms for compressing discrete CAD models, original precise CAD models are focused on in this paper. Here, the characteristics of hierarchical structures in CAD models and the distribution of their redundant data are exploited for developing a novel data encoding method. In the method, different encoding rules are applied to different types of data. Geometric data is a major concern for reducing model sizes. For geometric data, the control points of B-spline curves and surfaces are compressed with the second-order predictions in a local coordinate system. Based on analysis to the distortion induced by quantization, an efficient method for computation of the distortion is provided. The results indicate that the data size of CAD models can be decreased efficiently after compressed with the proposed method.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.895-906
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• 2015

Diffusion is a mathematical tool to explain the fluctuation of financial assets and the movement of particles in a micro time scale. There are ongoing statistical trials to develop an estimation method for diffusion models based on likelihood. When we estimate diffusion models by applying the maximum likelihood estimation method on data observed at discrete time points, we need to know the transition density of the diffusion. In order to approximate the transition densities of diffusion models, we suggests the method to approximate the path integral of the random process with normal random variables, and compare the numerical properties of the method with other approximation methods.