• Title/Summary/Keyword: Monte-Carlo algorithm

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Design of Equal-Cost Bifurcated Routing Algorithm : A Case Study Using Closure Approximation (클로즈 근사화를 이용한 등가 라우팅 알고리즘의 설계)

  • Lee, Bong-Hwan
    • The Transactions of the Korea Information Processing Society
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    • v.1 no.3
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    • pp.380-390
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    • 1994
  • In this paper, we propose an equal-cost bifurcated routing algorithm which may be useful in practical computer network design problem. The performance of the routing algorithm is evaluated using the conventional Monte Carlo simulation and a transient queueing approximation. The relative errors between the closure approximation and the Monte Carlo simulation was fairly small. The closure approximation may be used to evaluate the performance of the load splitting algorithms, which results in considerable execution time reduction. The performance of the proposed algorithm is compared to that of the known algorithms based on average packet delay. For networks that have many non-disjoint equal-paths, the proposed algorithm performed better than other algorithms.

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Domain Decomposition Strategy for Pin-wise Full-Core Monte Carlo Depletion Calculation with the Reactor Monte Carlo Code

  • Liang, Jingang;Wang, Kan;Qiu, Yishu;Chai, Xiaoming;Qiang, Shenglong
    • Nuclear Engineering and Technology
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    • v.48 no.3
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    • pp.635-641
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    • 2016
  • Because of prohibitive data storage requirements in large-scale simulations, the memory problem is an obstacle for Monte Carlo (MC) codes in accomplishing pin-wise three-dimensional (3D) full-core calculations, particularly for whole-core depletion analyses. Various kinds of data are evaluated and quantificational total memory requirements are analyzed based on the Reactor Monte Carlo (RMC) code, showing that tally data, material data, and isotope densities in depletion are three major parts of memory storage. The domain decomposition method is investigated as a means of saving memory, by dividing spatial geometry into domains that are simulated separately by parallel processors. For the validity of particle tracking during transport simulations, particles need to be communicated between domains. In consideration of efficiency, an asynchronous particle communication algorithm is designed and implemented. Furthermore, we couple the domain decomposition method with MC burnup process, under a strategy of utilizing consistent domain partition in both transport and depletion modules. A numerical test of 3D full-core burnup calculations is carried out, indicating that the RMC code, with the domain decomposition method, is capable of pin-wise full-core burnup calculations with millions of depletion regions.

A Study on Decoding Algorithm of TCM by Path Back Method (Path Back 방식을 이용한 TCM의 복호 알고리즘에 관한 연구)

  • 정지원;장청룡;이인숙;원동호
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.17 no.12
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    • pp.1401-1412
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    • 1992
  • On band limited channel like satellite communication and voice communication. TCM(Trellis Coded Modulation) is a communication method that has coding gain which combines modulation with channel coding without bandwidth expansion. In this paper, we apply PAM, PSK, QAM to TCM, and propose the extended path back method decoding algorithm which improved drawback of viterbi decoding algorithm and apply to TCM this decoding algorithm. Using Monte Carlo simulation, we analyze performance of each modulation technique and efficiency of decoding algorithm.

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Monte Carlo Simulation Based Digitally Reconstructed Radiographs

  • Kakinohana, Yasumasa;Ogawa, Kazuhiko;Toita, Takafumi;Murayama, Sadayuki
    • Proceedings of the Korean Society of Medical Physics Conference
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    • 2002.09a
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    • pp.436-438
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    • 2002
  • As the use of virtual simulation expands, digitally reconstructed radiographs (DRRs), which mimic conventional simulation films, play an increasingly important role as reference images in the verification of treatment fields. The purpose of our study is to develop an algorithm for computation of digitally reconstructed radiographs based on Monte Carlo simulation that take into account almost all possible physical processes by which photons interact with matter. The Monte Carlo simulation based DRRs have the following features. 1) Account has been taken of almost all possible physical processes of interaction of photons with matter, including a detector (film) response. In principle, this is equivalent to X-ray radiography. 2) Arbitrary photon energies (from diagnostic to therapeutic) can be used to produce DRRs. One can even use electrons as the source. 3) It is easy to produce a double exposure, which mimics the double exposure portal image and may have superior visual appeal for treatment field verification, with weighting within the treatment field.

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A Bayesian Approach for Accelerated Failure Time Model with Skewed Normal Error

  • Kim, Chansoo
    • Communications for Statistical Applications and Methods
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    • v.10 no.2
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    • pp.268-275
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    • 2003
  • We consider the Bayesian accelerated failure time model. The error distribution is assigned a skewed normal distribution which is including normal distribution. For noninformative priors of regression coefficients, we show the propriety of posterior distribution. A Markov Chain Monte Carlo algorithm(i.e., Gibbs Sampler) is used to obtain a predictive distribution for a future observation and Bayes estimates of regression coefficients.

Bayesian estimation for finite population proportion under selection bias via surrogate samples

  • Choi, Seong Mi;Kim, Dal Ho
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.6
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    • pp.1543-1550
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    • 2013
  • In this paper, we study Bayesian estimation for the finite population proportion in binary data under selection bias. We use a Bayesian nonignorable selection model to accommodate the selection mechanism. We compare four possible estimators of the finite population proportions based on data analysis as well as Monte Carlo simulation. It turns out that nonignorable selection model might be useful for weekly biased samples.

A Circuit design with Yield Maximization (Yield 최대화를 고려한 회로설계)

  • 김희석;임재석
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.22 no.5
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    • pp.102-109
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    • 1985
  • A new yield maximization procedure by investigating method of the multidimensional Monte Carlo integration is presented. And then maximum yield is obtained by the new modified weight selection algorithm applied to objective function of MOSFET NAND GATE Also this yield maximization procedure can be applied to nonconvex objective function.

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System Co-existence Analysis Using Monte-Carlo Method (몬테-카를로(Monte-Carlo) 방법을 적용한 시스템 양립성 분석)

  • KIm, Young-Hwan;Eo, Pill-Seon;Yang, Hoon-Gee;Park, Seung-Keun;Cho, Pyung-Dong
    • Proceedings of the IEEK Conference
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    • 2005.11a
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    • pp.193-196
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    • 2005
  • This paper presents a Monte-Carlo based method to obtain a probability of interference among systems. We show an efficient algorithm to calculate not only in-band interference for a given emission mask, but out-of-band interference, which depends on the blocking performance of a victim receiver filter. Applying the proposed method to an arbitrary system, we show the simulation results by Matlab and compare them with those by a SEAMCAT software

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Diffusion Coefficients for Electrons in SF6-Ar Gas Mixtures by MCS-BEq (MCSBEq에 의한 SF6-Ar혼합기체의 확산계수)

  • Kim, Sang-Nam
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.64 no.3
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    • pp.125-129
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    • 2015
  • Energy distribution function for electrons in SF6-Ar mixtures gas used by MCS-BEq algorithm has been analysed over the E/N range 30~300[Td] by a two term Boltzmann equation and a Monte Carlo Simulation using a set of electron cross sections determined by other authors experimentally the electron swarm parameters for 0.2[%] and 0.5[%] $SF_6-Ar$ mixtures were measured by time-of-flight(TOF) method, The results show that the deduced longitudinal diffusion coefficients and transverse diffusion coefficients agree reasonably well with theoretical for a rang of E/N values. The results obtained from Boltzmann equation method and Monte Carlo simulation have been compared with present and previously obtained data and respective set of electron collision cross sections of the molecules.

Pedestrian Detection and Tracking Method for Autonomous Navigation Vehicle using Markov chain Monte Carlo Algorithm (MCMC 방법을 이용한 자율주행 차량의 보행자 탐지 및 추적방법)

  • Hwang, Jung-Won;Kim, Nam-Hoon;Yoon, Jeong-Yeon;Kim, Chang-Hwan
    • The Journal of Korea Robotics Society
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    • v.7 no.2
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    • pp.113-119
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    • 2012
  • In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.