• Title/Summary/Keyword: 분산오차

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Network Traffic Control for War-game Simulation in Distributed Computing Environment (분산 컴퓨팅 환경에서의 워게임 시뮬레이션을 위한 네트워크 트래픽 제어)

  • Jang, Sung-Ho;Kim, Tae-Young;Lee, Jong-Sik
    • Journal of the Korea Society for Simulation
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    • v.18 no.4
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    • pp.1-8
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    • 2009
  • The distributed war-game simulation system has been used to represent the virtual battlefield environment. In order to produce a simulation result, simulators connected from a network transfer messages with location information of simulated objects to a central simulation server. This network traffic is an immediate cause of system performance degradation. Therefore, the paper proposes a system to manage and control network traffic generated from distributed war-game simulation. The proposed system determines the moving distance of simulated objects and filters location messages by a distance threshold which is controlled according to system conditions like network traffic and location error. And, the system predicts the next location of simulated objects to minimize location error caused by message filtering. Experimental results demonstrate that the proposed system is effective to control the network traffic of distributed war-game simulation systems and reduce the location error of simulated objects.

Improved Confidence Intervals on Total Variance in a Regression Model with Unbalanced Nested Error Structure

  • 박동준;이수진
    • Proceedings of the Korean Statistical Society Conference
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    • 2004.11a
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    • pp.265-270
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    • 2004
  • 불균형중첩오차구조를 갖는 단순선형회귀모형에서 나타나는 두 분산의 합에 대한 신뢰구간을 구하기 위하여 Ting et al.(1990) 방법과 Graybill and Wang(1980) 방법과 Tsui and Weerahandi(1989)가 제안한 일반화 축량(generalized pivotal quantity)방법을 이용한 두 가지 방법 등 모두 네 가지 신뢰구간을 제안한다. 신뢰구간의 적절성을 판단하기 위하여 여러 가지 불균형 설계에 대하여 SAS/IML로 시뮬레이션을 실행하고 신뢰계수와 신뢰구간의 평균 길이를 비교한다. 불균형중첩오차구조를 갖는 단순선형회귀모형의 두 분산의 합에 대한 네 가지 신뢰구간들이 주샘플링 단위의 변화에 따라 어느 방법이 적절한 신뢰구간을 구축하는지 추천하고, 실제 예제를 적용하여 시뮬레이션의 결과와 일관성이 있는지를 확인한다.

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Preliminary test estimation method accounting for error variance structure in nonlinear regression models (비선형 회귀모형에서 오차의 분산에 따른 예비검정 추정방법)

  • Yu, Hyewon;Lim, Changwon
    • The Korean Journal of Applied Statistics
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    • v.29 no.4
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    • pp.595-611
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    • 2016
  • We use nonlinear regression models (such as the Hill Model) when we analyze data in toxicology and/or pharmacology. In nonlinear regression models an estimator of parameters and estimation of measurement about uncertainty of the estimator are influenced by the variance structure of the error. Thus, estimation methods should be different depending on whether the data are homoscedastic or heteroscedastic. However, we do not know the variance structure of the error until we actually analyze the data. Therefore, developing estimation methods robust to the variance structure of the error is an important problem. In this paper we propose a method to estimate parameters in nonlinear regression models based on a preliminary test. We define an estimator which uses either the ordinary least square estimation method or the iterative weighted least square estimation method according to the results of a simple preliminary test for the equality of the error variance. The performance of the proposed estimator is compared to those of existing estimators by simulation studies. We also compare estimation methods using real data obtained from the National Toxicology program of the United States.

Efficiency of Variance Estimators for Two-stage PPS Systematic Sampling (2단 크기비례 계통추출법의 분산추정량 효율성 비교)

  • Kim, Young-Won;Kim, Yeny;Han, Hye-Eun;Kwak, Eun-Sun
    • The Korean Journal of Applied Statistics
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    • v.26 no.6
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    • pp.1033-1041
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    • 2013
  • In this paper, we investigate several variance estimators for pps systematic sampling. Unfortunately, there is no unbiased variance estimators for a systematic sample because systematic sampling can be regarded as a random selection of one cluster. This study provides guidance on which variance estimator may be more appropriate than others in several circumstances. We judge the efficiency of variance estimators for systematic sampling based on of their relative biases and relative mean square error. Also, we investigate variance estimation problems for two-stage systematic sampling applied for the Food Raw Material Consumption Survey and the Establishment Labor Force Survey simulation study, in order to consider the popular two-stage pps systematic sample design for establishment and household survey in Korea.

비모수 회귀모형의 차분에 기저한 분산의 추정에 대한 고찰

  • 김종태
    • Communications for Statistical Applications and Methods
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    • v.5 no.1
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    • pp.121-131
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    • 1998
  • 이 논문의 목적은 비모수 회귀모형에 있어서의 오차의 분산을 추정하는 방법들 중 차분에 기저한 방법 (difference-based methods)을 이용한 기존의 추정량들을 비교 분석하는데 있다. 특히 점근적인 최적 이차 차분에 기저한 Hall과 Kay, Titterington(1990)의 HKT 추정량에 대한 그들의 추정량에 대한 문제점들을 제시하고, HKT추정량과, GSJS추정량, Rice추정량에 대하여 모의 실험을 이용하여 모수에 대한 수렴 속도를 비교 분석 하였다. 또한 GSJS 추정량에 대한 일치성과 수렴 속도를 보였다.

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An improved frequency offset estimation technique for an OFDM system (OFDM 시스템을 위한 개선된 주파수 옵셋 추정 기법)

  • 최종호;조용수
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.23 no.5
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    • pp.1270-1281
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    • 1998
  • Frequency offset in an orthogonal frequency division multiplexing (OFDM) system is known to cause the inter-channel interference (ICI), amplitude and phase distortion of a received signal, resulting in a severe performance degradation of the total system. In this paper, we propose an improved pilot-based masimum likelihood frequency offset estimation technique, which uses the predefined sync-subchannels, and derive the error performance of the proposed frequency offset estimator analytically. The proposed technique improves the performance of the frequency offset estimator by adding up the frequency offset caused by coherent phase changes and averaging out the effect caused by random phase error. It is confirmed by computer simulations that the upper bound of error variance for the proposed frequency offset estimator analytically derived in this paper is correct, and that the proposed estimator has better performance than the previous ones in terms of error variance, tracking range, and time-varying characteristics of a channel.

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Truncation Error Problem of Error Diffusion Method (오차 확산 방법의 절삭 오차 문제)

  • Jho, Cheung-Woon
    • Journal of Advanced Navigation Technology
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    • v.15 no.5
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    • pp.850-856
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    • 2011
  • The error diffusion method is one of the digital halftoning methods that diffuses quantization errors of current processing pixel to neighboring pixels and get a high-quality black-white image. This method has the problematic case which partially increase or decrease summation of diffused errors in the process of diffusing the quantization error. In this paper, we analyze Floyd-Steinberg method, Jarvis-Judice-Ninke method, Stucki method, and Shiau-Fan method as a representative case of error diffusion methods and propose a solution method of this problem.

Impact of Heterogeneous Dispersion Parameter on the Expected Crash Frequency (이질적 과분산계수가 기대 교통사고건수 추정에 미치는 영향)

  • Shin, Kangwon
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.15 no.9
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    • pp.5585-5593
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    • 2014
  • This study tested the hypothesis that the significance of the heterogeneous dispersion parameter in safety performance function (SPF) used to estimate the expected crashes is affected by the endogenous heterogeneous prior distributions, and analyzed the impacts of the mis-specified dispersion parameter on the evaluation results for traffic safety countermeasures. In particular, this study simulated the Poisson means based on the heterogeneous dispersion parameters and estimated the SPFs using both the negative binomial (NB) model and the heterogeneous negative binomial (HNB) model for analyzing the impacts of the model mis-specification on the mean and dispersion functions in SPF. In addition, this study analyzed the characteristics of errors in the crash reduction factors (CRFs) obtained when the two models are used to estimate the posterior means and variances, which are essentially estimated through the estimated hyper-parameters in the heterogeneous prior distributions. The simulation study results showed that a mis-estimation on the heterogeneous dispersion parameters through the NB model does not affect the coefficient of the mean functions, but the variances of the prior distribution are seriously mis-estimated when the NB model is used to develop SPFs without considering the heterogeneity in dispersion. Consequently, when the NB model is used erroneously to estimate the prior distributions with heterogeneous dispersion parameters, the mis-estimated posterior mean can produce large errors in CRFs up to 120%.

Analysis of Flight Performance Reserve for Upper Stage of Satellite Launch Vehicles (위성발사체 상단의 비행성능여유 분석)

  • Song, Eun-Jung;Choi, Jiyoung;Cho, Sang-bum;Sun, Byung-Chan
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.45 no.5
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    • pp.386-392
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    • 2017
  • This paper considers the analysis of the flight performance reserve, which is required propellant to compensate various launch vehicle performance deviations, to inject the payload of a 3-staged launch vehicle to a circular sun synchronous orbit at a height of 700 km. The various error sources, which affect the orbit injection accuracy, and their uncertainty are defined first. Then the sensitivity analysis, which has the advantage that each error source effect can be investigated independently, is performed for the extreme ${\pm}3{\sigma}$ conditions of the launch vehicle performance errors. Monte carlo simulations are also conducted to compute the propellant reserve, which can consider the combined effects of each error source. Finally the obtained flight performance reserves by the two approaches are compared and it is confirmed that they show similar results.

Adaptive Kernel Estimation for Learning Algorithms based on Euclidean Distance between Error Distributions (오차분포 유클리드 거리 기반 학습법의 커널 사이즈 적응)

  • Kim, Namyong
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.22 no.5
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    • pp.561-566
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    • 2021
  • The optimum kernel size for error-distribution estimation with given error samples cannot be used in the weight adjustment of minimum Euclidean distance between error distributions (MED) algorithms. In this paper, a new adaptive kernel estimation method for convergence enhancement of MED algorithms is proposed. The proposed method uses the average rate of change in error power with respect to a small interval of the kernel width for weight adjustment of the MED learning algorithm. The proposed kernel adjustment method is applied to experiments in communication channel compensation, and performance improvement is demonstrated. Unlike the conventional method yielding a very small kernel calculated through optimum estimation of error distribution, the proposed method converges to an appropriate kernel size for weight adjustment of the MED algorithm. The experimental results confirm that the proposed kernel estimation method for MED can be considered a method that can solve the sensitivity problem from choosing an appropriate kernel size for the MED algorithm.