• KCI Concrete Journal
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• v.12 no.2
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• pp.11-21
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• 2000

It is not easy to correctly predict deflections of reinforced concrete beams and one-way slabs due to the variability of parameters involved in the calculation of deflections. Monte Carlo simulation is used to assess the variability of deflections with known statistical data and probability distributions of variables. A deterministic deflection value is obtained using the layered beam model based on the finite element approach in which a finite element is divided into a number of layers over the depth. The model takes into account nonlinear effects such as cracking, creep and shrinkage. Statistical parameters were obtained from the literature. For the assessment of variability of deflections, 12 cases of one-way slabs and T-beams are designed on the basis of ultimate moment capacity. Several results of a probabilistic study are presented to indicate general trends indicated by results and demonstrate the effect of certain design parameters on the variability of deflections. From simulation results, the variability of deflections relies primarily on the ratio of applied moment to cracking moment and the corre-sponding reinforcement ratio.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.663-676
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• 1997

We obtain the simultaneous unit roots test statistics for both regular and seasonal unit roots in a time series with possible seasonal deterministic trends. The limiting distributions of the proposed test statistics are derived and empirical percentiles of the test statistics are tabulated for some seasonal periods. The power and size of the test statistics are examined for finite samples through a Monte Carlo simulation and Compared with those of the Lagrange multiplier test.

• Electronics and Telecommunications Trends
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• v.34 no.6
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• pp.108-122
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• 2019

Ultra-low latency networking is a technology that reduces the end-to-end latency related to transport time-sensitive or mission-critical traffic in a network. As the proliferation of the fourth industrial revolution and 5G mobile communications continues, ultra-low latency networking is emerging as an essential technology for supporting various network applications (such as industrial control, tele-surgery, and unmanned vehicles). In this report, we introduce the ultra-low-latency networking technologies that are in progress, categorized by application area, and examine their up-to-date standard status.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.2
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• pp.133-139
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• 2011

Estimation of biomass carbon stock is an important research for estimation of public benefit of forest. Previous studies about biomass carbon stock estimation have limitations, which come from the used deterministic models. The most serious problem of deterministic models is that deterministic models do not provide any explanation about the relevant effects of errors. In this study, the effects of location errors were analyzed in order to estimation of biomass carbon stock of Danyang area using Monte Carlo simulation method. More specifically, the k-Nearest Neighbor(kNN) algorithm was used for basic estimation. In this procedure, random and systematic errors were added on the location of Sample plot, and effects on estimation error were analyzed by checking the changes of RMSE. As a result of random error simulation, mean RMSE of estimation was increased from 24.8 tonC/ha to 26 tonC/ha when 0.5~1 pixel location errors were added. However, mean RMSE was converged after the location errors were added 0.8 pixel, because of characteristic of study site. In case of the systematic error simulation, any significant trends of RMSE were not detected in the test data.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.383-386
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• 2001

The ability to make good cost overruns predictions is a very important aspect of in major construction project. The probabilistic cost models can provide more reliable than traditional cost models which have been used in korea to prepare Bill of Quantities, if the actual cost data are sufficient enough to analyze the trends of the variables. The paper considers non-deterministic methods in a cost estimate. The method(referred to as the 'Monte Carlo simulation' method) interprets cost data indirectly, to generate a probability distribution for total costs from the deficient elemental experience cost distribution. The objectives of this research is to develop a method to forecast the probabilistic total construction cost and the elemental work cost

• International Journal of Railway
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• v.8 no.1
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• pp.5-9
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• 2015

Reliability based limit state design method is replacing traditional deterministic designs such as allowable stress design and/or ultimate strength design methods in world trends. European design code(Eurocode) has adopted limit state design, and Korea road bridge design standard has also recently been transferred to limit state design method. In this trend, Korea railroad design standard is also preparing for adopting the same design concept. While safety factors are determined empirically in traditional design, load combinations as well as load factors are determined by solving limit state equations. General partial safety factors are evaluated by using AFORM(Advanced First Order Reliability Method) in the reliability based limit state design method. In this study sensitivity analysis is carried out for a dead load factor and a live load factor. Relative precisions of the dead load and the live load factors are discussed prior to the AFORM analysis. Furthermore the sectional forces of design and the material quantities required by two different design methods are compared for a PSC box girder railway bridge.

• Korean Journal of Construction Engineering and Management
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• v.4 no.4 s.16
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• pp.192-200
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• 2003

The object of this research is to develop a computerized algorithm of cost estimation method to forecast the total construction cost in the bidding stage by the historical and elemental work cost data. Traditional cost models to prepare Bill of Quantities in the korea construction industry since 1970 are not helpful to forecast the project total cost in the bidding stage because the BOQ is always constant data according to the design factors of a particular project. On the contrary, statistical models can provide cost quicker and more reliable than traditional ones if the collected cost data are sufficient enough to analyze the trends of the variables. The estimation system considers non-deterministic methods which referred to as the 'Monte Carlo simulation. The method interprets cost data to generate a probabilistic distribution for total costs from the deficient elemental experience cost distribution.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.303-313
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• 2018

This study considers time series models to forecast drug expenditures in national health insurance. We adopt autoregressive error model (ARE) and transfer function model (TFM) with segmented level and trends (before and after 2012) in order to reflect drug price reduction in 2012. The ARE has only a segmented deterministic term to increase the forecasting performance, while the TFM explains a causality mechanism of drug expenditure with closely related exogenous variables. The mechanism is developed by cross-correlations of drug expenditures and exogenous variables. In both models, the level change appears significant and the number of drug users and ratio of elderly patients variables are significant in the TFM. The ARE tends to produce relatively low forecasts that have been influenced by a drug price reduction; however, the TFM does relatively high forecasts that have appropriately reflected the effects of exogenous variables. The ARIMA model without the exogenous variables produce the highest forecasts.

• Geomechanics and Engineering
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• v.2 no.4
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• pp.253-280
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• 2010

A new prediction model is derived for the uplift capacity of suction caissons using a hybrid method coupling genetic programming (GP) and simulated annealing (SA), called GP/SA. The predictor variables included in the analysis are the aspect ratio of caisson, shear strength of clayey soil, load point of application, load inclination angle, soil permeability, and loading rate. The proposed model is developed based on well established and widely dispersed experimental results gathered from the literature. To verify the applicability of the proposed model, it is employed to estimate the uplift capacity of parts of the test results that are not included in the modeling process. Traditional GP and multiple regression analyses are performed to benchmark the derived model. The external validation of the GP/SA and GP models was further verified using several statistical criteria recommended by researchers. Contributions of the parameters affecting the uplift capacity are evaluated through a sensitivity analysis. A subsequent parametric analysis is carried out and the obtained trends are confirmed with some previous studies. Based on the results, the GP/SA-based solution is effectively capable of estimating the horizontal, vertical and inclined uplift capacity of suction caissons. Furthermore, the GP/SA model provides a better prediction performance than the GP, regression and different models found in the literature. The proposed simplified formulation can reliably be employed for the pre-design of suction caissons. It may be also used as a quick check on solutions developed by more time consuming and in-depth deterministic analyses.

• Journal of Ecology and Environment
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• v.44 no.4
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• pp.241-255
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• 2020

Background: The stem exclusion stage is a stage of forest development that is important for understanding the subsequent understory reinitiation stage and maturation stage during which horizontal heterogeneity is formed. Over the past 11 years (2009-2019), we observed a deciduous broad-leaved forest in the Albongbunji Basin in Ulleungdo, South Korea in its stem exclusion stage, where Fagus engleriana (Engler's beech) is the dominant species, thereby analyzing the changes in the structure (density and size distributions), function (biomass and species richness), and demographics. Results: The mean stem density data presented a bell-shaped curve with initially increasing, peaking, and subsequently decreasing trends in stem density over time, and the mean biomass data showed a sigmoidal pattern indicating that the rate of biomass accumulation slowed over time. Changes in the density and biomass of Fagus engleriana showed a similar trend to the changes in density and biomass at the community level, which is indicative of the strong influence of this species on the changing patterns of forest structure and function. Around 2015, a shift between recruitment and mortality rates was observed. Deterministic processes were the predominant cause of tree mortality in our study; however, soil deposition that began in 2017 in some of the quadrats resulted in an increase in the contribution of stochastic processes (15% in 2019) to tree mortality. The development of horizontal heterogeneity was observed in forest gaps. Conclusions: Our observations showed a dramatic shift between the recruitment and mortality rates in the stem exclusion stage, and that disturbance increases the uncertainty in forest development increases. The minor changes in species composition are likely linked to regional species pool and the limited role of the life-history strategy of species such as shade tolerance and habitat affinity. Our midterm records of ecological succession exhibited detailed demographic dynamics and contributed to the improvement of an ecological perspective in the stem exclusion stage.