• 조명전기설비학회논문지
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• 제17권4호
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• pp.81-86
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• 2003

피크용 분산전원은 계통의 전체 운영비용을 줄일 수 있고, 대기용 분산전원은 수용가의 신뢰도를 향상시킬 수 있다. 피크용 분산전원과 대기용 분산전원은 해석하는데 있어서 다른 모델링을 세워야 하고 그에 따라 다른 방식으로 해석해야 하는데 이를 구현하기 위해서는 Monte-Carlo시뮬레이션 기법이 적합하다고 할 수 있다. 본 논문에서는 순차적 시뮬레이션 기법을 통해 두 가지 분산전원의 영향을 신뢰도 지수로 표현하고 이를 통해 두 가지 분산전원의 장단점을 살펴보도록 하겠다.

• 대한토목학회논문집
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• 제6권3호
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• pp.63-74
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• 1986

댐 지점(地點)에서의 저유량(抵流量)의 지속기간(持續期間)과 재현기간(再現期間)을 고려하여 저수지(貯水池)의 활용(活用) 저수용량(貯水容量)을 확률론적(確率論的)으로 결정하는 방법(方法)을 제안하였다. 확률론적(確率論的) 분석(分析)의 신뢰도를 제고시키기 위해 월유량(月流量) 자료계별(資料系別)의 모의발생(模擬發生)에 흔히 사용되는 모의모형(模擬模型) 중 Monte Carlo 모형(模型)과 Thomas Fiering 모형(模型)의 적합성(適合性)을 비교 검토하였으며 그 결과 월유량(月流量) 자료계열(資料系列)을 표준화(標準化)한 계열월유량자료(系列月流量資料)를 근거로 하는 Monte Carlo 모형(模型)(Monte Carlo-B)이 최적모형(最適模型)으로 선정되었다. Monte Carlo-Bah 모형(模型)에 의해 홍천(洪川)댐 지점(地點)에 대한 200 년간(年間)의 월유량계열(月流量系列)을 발생시켰으며 이로 부터 각종(各種) 지속기간별(持續期間別) 저유하량(抵流下量) 계열(系列)을 작성하였다. 여러 크기의 용수수요(用水需要)를 표시하는 상시유량(常時流量)에 대하여 지속기간별(持續期間別) 저유하량(抵流下量) 계열(系列)의 누가용적해석(累加容積解析)을 실시함으로서 활용저수용량(活用貯水容量)-상시유량(常時流量)-재현기간(再現期間)(Active Storage Draft Recurrence Interval) 관계가 수립되었으며 이 관계(關係)는 조발재현기간별(早魃再現期間別)로 용수수요(用水需要)를 보장할 수 있는 저수지(貯水池)의 활용용량(活用容量)을 결정하는 기준(基準)을 제공하게 된다. 또한, 이 관계(關係)를 사용하여 저수지(貯水池)의 설계재현기간별(設計再現期間別) 만수위(滿水位) 표고별(標高別) 상시가용수량(常時可用水量)을 추정하는 방법(方法)과 특정(特定) 저수지(貯水池)의 용수공급능력(用水供給能力)을 평가(評價)하는 방법(方法)을 예시(例示)하였다.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.257-266
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• 2003

Distribution-free estimation methods are proposed for slope, $\beta$ in the simple linear regression model. In this paper, we suggest the point estimators using the sequential slope based on sign test and Wilcoxon signed rank test. Also confidence intervals are presented for each estimation methods. Monte Carlo simulation study is carried out to compare the efficiency of these methods with least square method and Theil´s method. Some properties for the proposed methods are discussed.

• 한국섬유공학회:학술대회논문집
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• 한국섬유공학회 1995년도 추계 학술발표회
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• pp.164-165
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• 1995

• Nuclear Engineering and Technology
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• 제52권7호
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• pp.1587-1596
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• 2020

The location selection for nuclear power plants (NPP) is a strategic decision, which has significant impact operation of the plant and sustainable development of the region. Further, the ranking of the alternative locations and selection of the most suitable and efficient locations for NPPs is an important multi-criteria decision-making problem. In this paper, the non-sequential Monte Carlo probabilistic method and the Latin hypercube sampling probabilistic method are used to evaluate and select the optimal locations for NPP. These locations are identified by the power plant's onsite loads and the average of the lowest number of relay protection after the NPP's trip, based on electricity considerations. The results obtained from the proposed method indicate that in selecting the optimal location for an NPP after a power plant trip with the purpose of internal onsite loads of the power plant and the average of the lowest number of relay protection power system, on the IEEE RTS 24-bus system network given. This paper provides an effective and systematic study of the decision-making process for evaluating and selecting optimal locations for an NPP.

• Advances in robotics research
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• 제2권2호
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• pp.161-182
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• 2018

This paper proposes a novel receding horizon control (RHC) algorithm for formation control of a swarm of unmanned surface vehicles (USVs) using particle swarm optimization (PSO). The proposed control algorithm provides the coordinated path tracking of multi-agent USVs while preventing collisions and considering external disturbances such as ocean currents. A three degrees-of-freedom kinematic model of the USV is used for the RHC with guaranteed stability and convergence by incorporating a sequential Monte Carlo (SMC)-based particle initialization. An ocean current model-based estimator is designed to compensate for the effect of ocean currents on the USVs. This method is compared with the PSO-based RHC algorithms to demonstrate the performance of the formation control and the collision avoidance in the presence of ocean currents through numerical simulations.

• Smart Structures and Systems
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• 제21권5호
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• 대한전기학회논문지:전력기술부문A
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• 제55권1호
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• pp.45-51
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• 2006

This paper proposes a method to evaluate the Total Transfer Capability (TTC) by considering uncertainty of weather conditions. TTC is limited not only by the violation of system thermal and voltage limits, but also restricted by transient stability limit. Impact of the contingency on the power system performance could not be addressed in a deterministic way because of the random nature of the system equipment outage and the increase of outage probability according to the weather conditions. For these reasons, probabilistic approach is necessary to realize evaluation of the TTC. This method uses a sequential Monte Carlo simulation (MCS). In sequential simulation, the chronological behavior of the system is simulated by sampling sequence of the system operating states based on the probability distribution of the component state duration. Therefore, MCS is used to accomplish the probabilistic calculation of the TTC with consideration of the weather conditions.

• 한국지능시스템학회논문지
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• 제21권3호
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• pp.329-334
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• 2011

본 논문은 실내형 이동로봇에 적용하기 위한 위치인식과 장애물 추적 방법을 제안한다. 제안된 방법을 구현하기 위해 레이저 스캐너가 사용되었으며 로봇이 운행되는 공간의 지도정보를 미리 알고 있다고 가정한다. 레이저 스캐너의 측정치를 지도정보와 매칭해가며 Sequential Monte Carlo (SMC)방법을 이용하여 로봇의 위치를 파악하고 파악된 위치에서 주변 장애물의 위치를 인식하고 다중 물체 추적 알고리즘을 이용함으로써 장애물과의 충돌 위험성 등을 미리 파악할 수 있다. 마지막으로, 본 논문에서 제안한 방법을 실험을 통해 검증한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.666-670
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• 2004

This paper presents a new approach for determining an accurate incentive levels of Direct Load Control (DLC) program using sequential Monte Carlo Simulation (MCS) techniques. The economic analysis of DLC resources needs to identify the hourly-by-hourly expected energy-not-served resulting from the random outage characteristics of generators as well as to reflect the availability and duration of DLC resources, which results the computational explosion. Therefore, the conventional methods are based on the scenario approaches to reduce the computation time as well as to avoid the complexity of economic studies. In this paper, we have developed a new technique based on the sequential MCS to evaluate the required expected load control amount in each hour and to decide the incentive level satisfying the economic constraints. And also the proposed approach has been considered multi-state as well as two-state of the generating units. In addition, we have applied the variance reduction technique to enhance the efficiency of the simulation. To show the efficiency and effectiveness of the suggested method the numerical studies have been performed for the modified IEEE reliability test system.