• Title/Summary/Keyword: 몬테-칼로 기법

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Importance Sampling Embedded Experimental Frame Design for Efficient Monte Carlo Simulation (효율적인 몬테 칼로 시뮬레이션을 위한 중요 샘플링 기법이 내장된 실험 틀 설계)

  • Seo, Kyung-Min;Song, Hae-Sang
    • The Journal of the Korea Contents Association
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    • v.13 no.4
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    • pp.53-63
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    • 2013
  • This paper presents an importance sampling(IS) embedded experimental frame(EF) design for efficient Monte Carlo (MC) simulation. To achieve IS principles, the proposed EF contains two embedded sub-models, which are classified into Importance Sampler(IS) and Bias Compensator(BC) models. The IS and BC models stand between the existing system model and EF, which leads to enhancement of model reusability. Furthermore, the proposed EF enables to achieve fast stochastic simulation as compared with the crude MC technique. From the abstract two case studies with the utilization of the proposed EF, we can gain interesting experimental results regarding remarkable enhancement of simulation performance. Finally, we expect that this work will serve various content areas for enhancing simulation performance, and besides, it will be utilized as a tool to understand and analyze social phenomena.

A Sequential Monte Carlo inference for longitudinal data with luespotted mud hopper data (짱뚱어 자료로 살펴본 장기 시계열 자료의 순차적 몬테 칼로 추론)

  • Choi, Il-Su
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.9 no.6
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    • pp.1341-1345
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    • 2005
  • Sequential Monte Carlo techniques are a set of powerful and versatile simulation-based methods to perform optimal state estimation in nonlinear non-Gaussian state-space models. We can use Monte Carlo particle filters adaptively, i.e. so that they simultaneously estimate the parameters and the signal. However, Sequential Monte Carlo techniques require the use of special panicle filtering techniques which suffer from several drawbacks. We consider here an alternative approach combining particle filtering and Sequential Hybrid Monte Carlo. We give some examples of applications in fisheries(luespotted mud hopper data).

Fast Execution of Monte Carlo Simulation with Random Walk (무작위 행보 방식의 몬테 칼로 시뮬레이션의 고속화)

  • Jeong, Ye-chan;Ryu, Seung-yo;Kim, Dongseung
    • Proceedings of the Korea Information Processing Society Conference
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    • 2015.10a
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    • pp.204-207
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    • 2015
  • 이 연구는 공학 및 실험과학에서 활용되는 몬테 칼로 시뮬레이션 기법 중 하나인 무작위 행보 알고리즘의 성능 개선을 목표로 하였다. 이를 위해 무작위 행보 과정에서 난수 발생부와 행보 진행부를 분리하여 처리 시간을 단축하는 방안과, 문제 영역의 계산 규모를 2단계로 분할하여 시뮬레이션의 수렴 속도를 향상 시키는 방안을 제안한다. 또한 대규모 문제를 병렬처리 가능하도록 구현하고, 서로 다른 작업 분할 방식을 혼합하여 최적화를 수행 하였다. 순차 알고리즘만으로 실험한 결과 단순 구현방법과 비교해 실행시간과 에너지 소모량이 각각 18%의 성능향상을 얻었으며, 병렬 알고리즘을 8개의 노드(16코어)의 클러스터에서 실행했을 때 행 분할 방식의 성능이 블록 분할 방식보다 8% 빨라지는 것을 확인하였다.

Optical Proximity Correction of Photomask with a Monte-Carlo Method (몬테-칼로 기법을 사용한 포토마스크의 결상 왜곡 보정)

  • 이재철;오용호;임성우
    • Journal of the Korean Institute of Telematics and Electronics D
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    • v.35D no.10
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    • pp.76-82
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    • 1998
  • As the minimum feature size of a semiconductor chip gets smaller, the inevitable distortion of patterned image by optical lithography becomes the limiting factor in the mass production of VLSI. The optical proximity correction (OPC), which corrects pattern distortion that originates from the resolution limit of optical lithography, is becoming indispensable technology. In this paper, we describe a program that corrects optical proximity effect and thus finds the optimum mask pattern with a Monte-Carlo method. The program was applied to real memory cell patterns to produce mask patterns that generate image patterns closer to object images than original mask patterns, and increase of process margin is expected, as well.

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ANISN-MCNP 코드를 이용한 월성2호기 반응도제어기구 방사선흐름해석

  • 김용일;진영권;김교윤
    • Proceedings of the Korean Nuclear Society Conference
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    • 1996.05a
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    • pp.269-274
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    • 1996
  • 월성원자력발전소 2호기와 같은 CANDU 6형 원자로의 반응도제어기구 설치대에는 여러 반응도제어기구가 삽입되기때문에 원자로심으로부터의 방사선흐름현상으로 인한 방사선피폭이 예상될 수 있는 위치이다. 좁고 긴 반응도제어기구 도관에서의 방사선 흐름으로 인한 반응도제어기구 설치대에서의 방사선량을 예측하기 위해 몬테 칼로 MCNP 코드를 1차원 각분할법 코드인 ANISN과 연계하여 사용하였다. 월성원자력2호기의 상단차폐해석을 위한 ANISN 계산, 도관의 방사선흐름을 평가하기 위한 MCNP 계산, 그리고 반응도제어기구 설치대에서의 방사선량율 평가를 위한 MCNP 계산등 3단계 계산 기법의 적응이 시도되었다.

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A Kill-Assessment Technique Using Hypothesis Testing and Kalman Filter (가설 검증과 칼만 필터를 이용한 격추평가 기법 연구)

  • Kim, Ho-Jeong;Lee, Dong-Gwan
    • Journal of the Korea Institute of Military Science and Technology
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    • v.9 no.4
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    • pp.5-14
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    • 2006
  • The correct and opportune decision of reengaging the intercepted target is required in order to enhance the engagement performance of the surface to air missile systems that has the ability to defense or attack against various targets at the same time. The engagement efficiency and success of these systems will be largely enhanced by assigning quickly its system resources to the intercepted target and minimizing the waste of system resources for the target which is not able to attack any more. The kill-assessment algorithm has to be able to evaluate automatically whether various targets intercepted by missiles are killed or not on the basis of the reasonable confidence level. The definition of kill assessment is discussed and the kill assessment algorithm is designed reliably by using Kalman filter and a probability theory. Finally its performance is evaluated and analyzed by the Monte Carlo simulation.

Genetic Algorithm and Clustering Technique for Optimization of Stochastic Simulation (유전자 알고리즘과 군집 분석을 이용한 확률적 시뮬레이션 최적화 기법)

  • 이동훈;허성필
    • Journal of the Korea Institute of Military Science and Technology
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    • v.2 no.1
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    • pp.90-100
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    • 1999
  • 유전자 알고리즘은 전통적인 등반 알고리즘을 이용하여 구하기 어려웠던 최적화 문제를 해결하기 위한 강인한(Robust) 탐색 기법이다. 특히 목적함수가 (1)여러 개의 국부 최대치를 가지는 경우, (2)수학적으로 표현이 불가능하거나 어려운 경우, (3)목적함수에 교란 항(disturbance term)이 섞여 있을 경우도 우수한 탐색 능력을 갖는 것으로 알려져 있다. 본 논문에서는 유전자 알고리즘을 이용하여 나타나는 다양한 해집합을 형성하는 개체군을 군집성 분석(cluster analysis)을 이용하여 군집화하고, 각 군집에 부여된 군집 적합도에 따라서 최적해를 구함으로써 단순 유전자 알고리즘에 의한 최적화보다 훨씬 향상된 탐색 알고리즘을 제안하였다. 반응표면의 형태가 정형화한 테스트 함수의 형태로 나타난다고 가정한 경우에 대하여 몬테 칼로 시뮬레이션을 통하여 본 알고리즘을 적용하여 평가하고 분석하였다.

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ESPI Simulation for the Vibration Modes of the Thin Right-Angled Plate (얇은 직각판의 진동 모드에 대한 ESPI 시뮬레이션)

  • 장순석
    • Journal of KSNVE
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    • v.9 no.3
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    • pp.509-516
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    • 1999
  • The ESPI (Electronic Speckle Pattern Interferometry) algorithm has been simulated to calculate vibrational modes of a thin right-angled STS304 plate. The phase transformation of the reference wave of the ESPI is carried out only one time during vibration in order to clarify ESPI speckle patterns. Two dimensional vibrational modes are calculated from one ESPI pattern before vibration onset and two ESPI patterns during vibrations but with and without the phase transformation. The ESPI harmonic results are compared with those derived from the finite element method (FEM), and they agree very well. Additionally a phase unwrapping algorithm has been newly developed to derive a displacement map from an ESPI phase map.

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On a robust analysis of variance based on winsorization (윈저화를 이용한 로버스트 분산분석)

  • 성내경
    • The Korean Journal of Applied Statistics
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    • v.8 no.1
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    • pp.119-131
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    • 1995
  • Based on Monte-Carlo simulation results we propose a robust analysis of variance procedure by utilizing trimmed mean and Winsorized variance. We deal with mainly the one-way classification case. We evaluate the empirical distribution of a pseudo-F statistic based on symmetrically Winsorized sum of squares when the population is normally distributed.

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Design of Occupant Protection Systems Using Global Optimization (전역 최적화기법을 이용한 승객보호장치의 설계)

  • Jeon, Sang-Ki;Park, Gyung-Jin
    • Transactions of the Korean Society of Automotive Engineers
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    • v.12 no.6
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    • pp.135-142
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    • 2004
  • The severe frontal crash tests are NCAP with belted occupant at 35mph and FMVSS 208 with unbelted occupant at 25mph, This paper describes the design process of occupant protection systems, airbag and seat belt, under the two tests. In this study, NCAP simulations are performed by Monte Carlo search method and cluster analysis. The Monte Carlo search method is a global optimization technique and requires execution of a series of deterministic analyses, The procedure is as follows. 1) Define the region of interest 2) Perform Monte Carlo simulation with uniform distribution 3) Transform output to obtain points grouped around the local minima 4) Perform cluster analysis to obtain groups that are close to each other 5) Define the several feasible design ranges. The several feasible designs are acquired and checked under FMVSS 208 simulation with unbelted occupant at 25mph.