• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.3
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• pp.247-254
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• 2008

The double-peak normal distribution function (DPDF) suggested by Cho et al.(2004) has the problems that the extremely high and low tidal elevations are frequently generated in the Monte-Carlo simulation processes because the upper and lower limits of the DPDF are unbounded in spite of the excellent goodness-offit results. In this study, the modified DPDF is suggested by introducing the upper and lower value parameters and re-scale parameters in order to remove these problems. These new parameters of the DPDF are optimally estimated by the non-linear optimization problem solver using the Levenberg-Marquardt scheme. This modified DPDF can remove completely the unrealistically generated tidal levations and give a slightly better fit than the existing DRDF. Based on the DPDF's characteristic power, the over- and under estimation problems of the design factors are also automatically intercepted, too.

• Journal of Korea Water Resources Association
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• v.37 no.9
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• pp.719-727
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• 2004

Water quality models can be applied to manage the regional water quality problems and to estimate the target and allowable pollution load in watershed effectively. The optimization of state variables in the given water quality model Is necessary to build up more effective model. The least-squares method is applied to fit field observations in QUAL2E developed by U.S. EPA, which is most widely used one in the world to simulate the stream water quality, and the optimization model with constraints is constructed to estimate the parameters. The objective function of the optimization model is solved by Solver in Microsoft Excel and Monte Carlo simulation is conducted to know the influence of parameter in conventional pollutants. It is found that this technique is easily implemented and rapidly convergent computational procedure to calibrate the parameters after appling this approach in Anyang stream located in Kyonggi province mainly.

• Journal of Korea Water Resources Association
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• v.40 no.2 s.175
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• pp.171-182
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• 2007

In this study, as a method for decreasing the confidence interval of the estimates of Clark hydrograph's concentration time and storage coefficient, regression equations of these parameters with respect to those of rainfall, meteorology, and basin characteristics are derived and analyzed using the Monte Carlo simulation technique. The results are also reviewed by comparing them with those derived by applying the Bootstrap technique and empirical equations. Results derived from this research are summarized as follows. (1) Even in case of limited rainfall events are available, it is possible to estimate the mean runoff characteristics by considering the affecting factors to runoff characteristics. (2) It is also possible to use the Monte Carlo simulation technique for estimating and evaluating the confidence intervals for concentration time and storage coefficient. The confidence intervals estimated in this study were found much narrower than those of Yoo et al. (2006). (3) A supporting result could also be derived using the Bootstrap technique. However, at least 20 independent rainfall events are necessary to get a rather significant result for concentration time and storage coefficient. (4) No empirical equations are found to be properly applicable for the study basin. However, empirical equations like the Kraven(I) and Kraven(II) are found valid for the estimation of concentration time, on the other hand the Linsley is found valid for the storage coefficient In this study basin. But users of these empirical formula should be careful as these also provide a wide range of possible values.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5B
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• pp.501-509
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• 2006

The safety of levee that depends on the river flood elevation has been regarded as very important keys to build up various flood prevention systems. However, deterministic methods for computation of water surface profile cannot reflect the effect of possible inaccuracies in the input parameters. The purpose of this study is to develop a methodology of uncertainty computation of design flood level based on steady flow analysis and Monte Carlo simulation. This study addresses the uncertainty of water surface elevation by Manning's coefficients, design discharges, river cross sections and boundary condition. Monte Carlo simulation with the variations of these parameters is performed to quantify the variations of water surface elevations in a river. The proposed model has been applied to the Kumho-river. The reliability analysis was performed within 38.5 km (95 sections) reach considered the variations of the above-mentioned parameters. Overtopping risks were evaluated by comparing the elevations of the flood condition with the those of the levees. The results show that there is a necessity which will raise the levee elevation between 1 cm and 56 cm at 7 sections. The model can be used for preparing flood risk maps, flood forecasting systems and establishing flood disaster mitigation plans as well as complement of conventional levee design.

• Journal of the Korea Society for Simulation
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• v.29 no.1
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• pp.47-55
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• 2020

This paper presents a real-time autonomous computation of shot numbers and aiming points against multiple soft targets on grounds by applying an unsupervised learning, k-mean clustering and Monte carlo simulation. For this computation, a 100 × 200 square meters size of virtual battlefield is created where an augmented enemy infantry platoon unit attacks, defences, and is scatted, and a virtual weapon with a lethal range of 15m is modeled. In order to determine damage types of the enemy unit: no damage, light wound, heavy wound and death, Monte carlo simulation is performed to apply the Carlton damage function for the damage effect of the soft targets. In addition, in order to achieve the damage effectiveness of the enemy units in line with the commander's intention, the optimal shot numbers and aiming point locations are calculated in less than 0.4 seconds by applying the k-mean clustering and repetitive Monte carlo simulation. It is hoped that this study will help to develop a system that reduces the decision time for 'detection-decision-shoot' process in battalion-scaled combat units operating Dronebot combat system.

• Journal of Korea Water Resources Association
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• v.55 no.4
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• pp.279-289
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• 2022

Since the frequency of heavy rainfall is increasing due to climate change, water levels in the river exceed past historical records. The rating-curve is to convert water level into flow dicscharge from the regression analysis of the water level and corresponding flow discharges. However, the rating-curve involves many uncertainties because of the limited data especially when observed water level exceed past historical water levels. In order to compensate for insufficient data and increase the accuracy of flow discharge data, this study estimates the flow discharge in the river computed mathematically using Monte Carlo simulation based on a 1D hydrodynamic numerical model. Based on the existing rating curve, a random combination of coefficients constituting the rating-curve creates a number of virtual rating curve. From the computed results of the hydrodynamic model, it is possible to estimate flow discharge which reproduces best fit to the observed water level. Based on the statistical evaluation of these samples, a method for mathematically estimating the water level and flow discharge of all cross sections is porposed. The proposed methodology is applied to the junction of Yochoen Stream in the Seomjin River. As a result, it is confirmed that the water level reproducibility was greatly improved. Also, the water level and flow discharge can be calculated mathematically when the proposed method is applied.

• Journal of Astronomy and Space Sciences
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• v.15 no.2
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• pp.341-358
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• 1998

The X-ray astronomy team of the Korea Astronomy Observatory(KAO) is planning to develop a multi-cell proportional counter, adopting an anti-coincidence method to reduce its internal background. We have developed a Monte Carlo code to determine the X-ray detection efficiency of the counter. As a check of our code, we successfully reproduced the detection efficiency of Ginga/LAC. In this paper, we report the simulation results for the counter being considered in KAO.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.10
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• pp.107-114
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• 1996

Monte carlo method is especially a useful method for the analysis of thermal noise of semiconductor devices since the time dependence of microscopic details is simulated directly. Recently, a mthod for the calculation of the instantaneous currents of 2-dimensional devices, which is numerically more accurate than the conventional method, has been proposed using the generalized ramo-shockley theorem. Using this mehtod we investage the validity of the existing thermal noise theories of field-effect transistors. First, the 1-dimensional analysis of thermal noise theories of field-effect transistors. First, the 1-dimensional analysis of thermal noise theories of field-effect transistors. First, the 1-dimensional analysis of thermal noise using ramo-shockley theorem is shown to be applicable to 2 dimensional devices if the frequency of interest is low enough. The correlation between electrons in different regions of th echannel is shown not to be negligible. And we also obtian the spatial map of the noise in the channel region. By doing so, we show that the steady state nyquist theorem is the correct theory rather than the theory by van der ziel et.al.

• The KIPS Transactions:PartB
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• v.8B no.3
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• pp.231-236
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• 2001

일반적인 다층 신경망에서 가중치의 갱신 알고리즘으로 사용하는 오류 역전과 방식은 가중치 갱신 결과를 고정된(fixed) 한 개의 값으로 결정한다. 이는 여러 갱신의 가능성을 오직 한 개의 값으로 고정하기 때문에 다양한 가능성들을 모두 수용하지 못하는 면이 있다. 하지만 모든 가능성을 확률적 분포로 표현하는 갱신 알고리즘을 도입하면 이런 문제는 해결된다. 이러한 알고리즘을 사용한 베이지안 신경망 모형(Bayesian Neural Networks Models)은 주어진 입력값(Input)에 대해 블랙 박스(Black-Box)와같은 신경망 구조의 각 층(Layer)을 거친 출력값(Out put)을 계산한다. 이 때 주어진 입력 데이터에 대한 결과의 예측값은 사후분포(posterior distribution)의 기댓값(mean)에 의해 계산할 수 있다. 주어진 사전분포(prior distribution)와 학습데이터에 의한 우도함수(likelihood functions)에 의해 계산한 사후확률의 함수는 매우 복잡한 구조를 가짐으로 기댓값의 적분계산에 대한 어려움이 발생한다. 따라서 수치해석적인 방법보다는 확률적 추정에 의한 근사 방법인 몬테 칼로 시뮬레이션을 이용할 수 있다. 이러한 방법으로서 Hybrid Monte Carlo 알고리즘은 좋은 결과를 제공하여준다(Neal 1996). 본 논문에서는 Hybrid Monte Carlo 알고리즘을 적용한 신경망이 기존의 CHAID, CART 그리고 QUEST와 같은 여러 가지 분류 알고리즘에 비해서 우수한 결과를 제공하는 것을 나타내고 있다.

• Journal of radiological science and technology
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• v.23 no.2
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• pp.75-79
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• 2000

This study was performed for the clinical applications applying the Monte Carlo methods. In this study we calculated the absorbed dose distributions for the 6 MeV electron beam in water phantom and compared the results with measured values. The energy data of electron beam used in Monte Carlo calculation is the energy distribution for 6 MeV electron beam which is assumed as a Gaussian form. We calculated percent depth doses and beam profiles for three field sizes of $10{\times}10,\;15{\times}15$, and $20{\times}20\;cm^2$ in water phantom using Monte Carlo methods and measured those data using a semiconductor detector and other devices. We found that the calculated percent depth doses and beam profiles agree with the measured values approximately. However, the calculated beam profiles at the edge of the fields were estimated to be lower than the measured values. The reason for that result is that we did not consider the angular distributions of the electrons in phantom surface and contamination of X-rays in our calculations. In conclusion, in order to apply the Monte Carlo methods to the clinical calculations we are to study the source models for electron beam of the linear accelerator beforehand.