• Journal of the Korea Society for Simulation
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• v.23 no.3
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• pp.19-25
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• 2014

Demand forecasting is the basis of management activities including marketing strategy. Especially, the demand of a part is remarkably important in supply chain management (SCM). In the fields of various industries, the part demand usually has the intermittent characteristic. The intermittent characteristic implies a phenomenon that there frequently occurs zero demands. In the intermittent demands, non-zero demands have large variance and their appearances also have stochastic nature. Accordingly, in the intermittent demand forecasting, it is inappropriate to apply the traditional time series models and/or cause-effect methods such as linear regression; they cannot describe the behaviors of intermittent demand. Markov bootstrap method was developed to forecast the intermittent demand. It assumes that first-order autocorrelation and independence of lead time demands. To release the assumption of independent lead time demands, this paper proposes a modified bootstrap method. The method produces the pseudo data having the characteristics of historical data approximately. A numerical example for real data will be provided as a case study.

• Journal of the Korea Society for Simulation
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• v.20 no.1
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• pp.97-105
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• 2011

In rivers and streams, biofilms are thin layers of greenish-brown slime attached to rocks, plants, and other surfaces. Biofilms play key roles in primary production and cycling of nutrients, water quality remediation, suspended sediment removal, and energy flow to higher trophic levels. In the present study, we developed a two-dimensional cellular automata model to simulate mixed biofilms of toxin-sensitive and toxin-producing species in hydrodynamic flow. The flow was generated by a stochastic process for uniform flow and by using the Navier-Stokes equation for non-uniform flow. Minimized local rules governing reproduction and mortality of the species were executed in the self-organizing processes to elucidate interactions between toxin-producing and toxin-sensitive species in competition over nutrients. We briefly discuss the morphology of the simulated biofilm under different flow conditions.

• Journal of the Korean Geotechnical Society
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• v.33 no.7
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• pp.29-41
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• 2017

There are uncertainties about the seismic load caused by seismic waves, which cannot be predicted due to the characteristics of the earthquake occurrence. Therefore, it is necessary to consider these uncertainties by probabilistic analysis. In this paper, procedures to develop a fragility curve that is a representative method to evaluate the safety of a structure by stochastic analysis were proposed for cut slopes. Fragility curve that considers uncertainties of soil shear strength parameters was prepared by Monte Carlo Simulation using pseudo static analysis. The fragility curve considering the uncertainty of the input ground motion was developed by performing time-history seismic analysis using selected 30 real ground input motions and the Newmark type displacement evaluation analysis. Fragility curves are represented as the cumulative probability distribution function with lognormal distribution by using the maximum likelihood estimation method.

• Korean Journal of Remote Sensing
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• v.26 no.1
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• pp.29-38
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• 2010

It is necessary to develop an integration model which can account for various data acquired at different measurement scales in environmental thematic mapping with high-resolution ground survey data and relatively low-resolution remote sensing data. This paper presents and applies a multi-scale geostatistical methodology for downscaling of thematic maps generated from lowresolution remote sensing data. This methodology extends a traditional ordinary kriging system to a block kriging system which can account for both ground data and remote sensing data which can be regarded as point and block data, respectively. In addition, stochastic simulation based on block kriging is also applied to describe spatial uncertainty attached to the downscaling. Two downscaling experiments including SRTM DEM and MODIS Leaf Area Index (LAI) products were carried out to illustrate the applicability of the geostatistical methodology. Through the experiments, multi-scale geostatistics based on block kriging successfully generated relatively high-resolution thematic maps with reliable accuracy. Especially, it is expected that multiple realizations generated from simulation would be effectively used as input data for investigating the effects of uncertain input data on GIS model outputs.

• Land and Housing Review
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• v.15 no.1
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• pp.135-146
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• 2024

This study predicts the means sharing ratio of the urban air transportation (UAM) when the VertiHub of the UAM in the southern western part is built at Songjeong Station in Gwanju. Based on Monte Carlo simulation of the utility function and means selection logit model for each means of transportation, our findings indicate an average mode share of 0.95%, with a variability range from 0.07% to 4.7%. Moreover, 95% of the simulation outcomes fall below a 2.02% mode share. Sensitivity analysis, conducted via Tornado Plot, highlights that the mode share is principally influenced by factors such as the unit fare, cost parameter, basic fare, and the time required for takeoff and landing. Notably, a negative correlation exists for unit fare, basic fare, and takeoff and landing time, suggesting the necessity of setting an appropriate level of fair to enhance UAM utilization.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.3 no.2
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• pp.127-135
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• 1983

A new approach is developed to analyze the reliability of the earth retaining wall using the concept of probability of failure, instead of conventional factor of safety. Many uncertainties, which are included in the conventional stability analysis, can be excluded by using the stochastic approach. And the reliability, more consistent with the reality, can be obtained by the simulation. The strength parameters of soil properties are assumed to be random variables to follow a generalized beta distribution. The interval [A, B] of the random variables could be determined using the maximum likelihood estimation. The pseudo-random values corresponding to the proposed beta distribution are generated using the rejection method. The probability of failure defined as follows, is obtained by using the Monte Carlo Method. $$P_f=\frac{M}{N}$$ where, $P_f$ : Probability of failure N : Total number of trials M : Total number of failure out of N A computer program is developed for the computation procedure mentioned above. Finally, a numerical example is solved using the developed program.

• Journal of the Korea Society for Simulation
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• v.22 no.2
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• pp.101-107
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• 2013

Because forest fires are predicted to increase in severity and frequency under global climate change with important environmental implications, an understanding of fire dynamics is critical for mitigation of these negative effects. For the reason, researchers with different background, such as ecologists, physicists, and mathematical biologists, have developed the simulation models to mimic the forest fire spread patterns. In this study, we suggested a novel model considering the wind effect. Our theoretical forest was comprised of two different tree species with varying probabilities of transferring fire that were randomly distributed in space at densities ranging from 0.0 (low) to 1.0 (high). We then studied the distributional patterns of burnt trees using a two-dimensional stochastic cellular automata model with minimized local rules. We investigated the time, T, that the number of burnt trees reaches 25% of the whole trees for different values of the initial tree density, fire transition probability, and the degree of wind strength. Simulation results showed that the values of T decreased with the increase of tree density, and the wind effect decreased in the case of too high or low tree density. We believe that our model can be a useful tool to explore forest fire spreading patterns.

• Structural Engineering and Mechanics
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• v.28 no.1
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• pp.89-105
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• 2008

The paper presents a study on the effects of soil-structure-interaction (SSI) on the performance of the compliant liquid column damper (CLCD) for the seismic vibration control of short period structures. The frequency-domain formulation for the input-output relation of a flexible-base structure with CLCD has been derived. The superstructure has been modeled as a linear, single degreeof-freedom (SDOF) system. The foundation has been considered to be attached to the underlying soil medium through linear springs and viscous dashpots, the properties of which have been represented by complex valued impedance functions. By using a standard equivalent linearization technique, the nonlinear orifice damping of the CLCD has been replaced by equivalent linear viscous damping. A numerical stochastic study has been carried out to study the functioning of the CLCD for varying degrees of SSI. Comparison of the damper performance when it is tuned to the fixed-base structural frequency and when tuned to the flexible-base structural frequency has been made. The effects of SSI on the optimal value of the orifice damping coefficient of the damper has also been studied. A more convenient approach for designing the damper while considering SSI, by using an established model of a replacement oscillator for the structure-soil system has also been presented. Finally, a simulation study, using a recorded accelerogram, has been carried out on the CLCD performance for the flexible-base structure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2473-2492
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• 2012

Energy conservation is a vital issue in wireless sensor networks. Recently, employing mobile sinks for data gathering become a pervasive trend to deal with this problem. The sink can follow stochastic or pre-defined paths; however the controlled mobility pattern nowadays is taken more into consideration. In this method, the sink moves across the network autonomously and changes its position based on the energy factors. Although the sink mobility would reduce nodes' energy consumption and enhance the network lifetime, the overhead caused by topological changes could waste unnecessary power through the sensor field. In this paper, we proposed EEDARS, an energy-efficient dual-sink algorithm with role switching mechanism which utilizes both static and mobile sinks. The static sink is engaged to avoid any periodic flooding for sink localization, while the mobile sink adaptively moves towards the event region for data collection. Furthermore, a role switching mechanism is applied to the protocol in order to send the nearest sink to the recent event area, hence shorten the path. This algorithm could be employed in event-driven and multi-hop scenarios. Analytical model and extensive simulation results for EEDARS demonstrate a significant improvement on the network metrics especially the lifetime, the load and the end-to-end delay.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.138-141
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• 2003

Recently reinforcement learning has attracted attention of many researchers because of its simple and flexible learning ability for any environments. And so far many reinforcement learning methods have been proposed such as Q-learning, actor-critic, stochastic gradient ascent method and so on. The reinforcement learning system is able to adapt to changes of the environment because of the mutual action with it. However when the environment changes periodically, it is not able to adapt to its change well. In this paper we propose the reinforcement learning system that is able to adapt to periodical changes of the environment by introducing the time-varying parameters to be adjusted. It is shown that the proposed method works well through the simulation study of the maze problem with aisle that opens and closes periodically, although the conventional method with constant parameters to be adjusted does not works well in such environment.