• The Journal of the Acoustical Society of Korea
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• v.12 no.1
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• pp.20-26
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• 1993

본 논문은 Reed-Solomon부호의 복호가능어 가중치 분포에 대한 명시적 식과 근사식을 구하여 이를 복호기 오류확률 PE(u)에 적용하고, 복호기 오류확률의 상한식을 구하고 분석하였다. t+1개 이상의 오류가 발생했을 때 복호기 오류확률의 추정치 Q와 Q'를 개선하여 식 Q를 제안하고, 컴퓨터 시뮬레이션을 수행한 결과 가중치 u가 커질 때 복호기 오류확률은 추정치 Q와 Q'에는 접근하였으나, 본 논문에서 제안한 Q와는 일치됨을 확인하였다. 그리고, 가중치 u가 부호의 길이 n에 접근할 때, 복호가능어의 명시적 식 Du와 근사식 Du'가 서로 일치하고, 복호기 오류확률 Pe(u)와 근사오류확률 Pe(u')가 일치함을 보였다. 또하 t+1개 이상의 오류가 발생했을 때 복호기 오류확률은 1/t!보다 작으며, 가중치분포 Au에 Vn(t)를 곱한 결과는 근사복호가능어 Du'와 일치함도 확인하였다.

• Journal of the Earthquake Engineering Society of Korea
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• v.5 no.2
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• pp.67-72
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• 2001

대상으로 하는 시스템의 입출력신호에 근거하여, 시스템의 수학적 모델을 결정하는 것을 총칭하여 시스템식별이라 한다. 본 논문에서는 지진응답 관측치를 입출력신호로 하여 조건부대치를 최적치로 판단하는 비선형근사필터법을 사용한 건축구조물의 지진응답추정 및 파라미터식별에 관하여 논한다. 비선형근사필터법에 의한 건축구조물식별의 유효성의 적용성을 판단하기 위해, 진동대를 사용하여 강구조시험체의 진동실험을 행하고 결과적으로 얻어진 시험체의 수학적 모델에 대한 지진응답 수치해석결과와 진동실험에서의 관측기록을 비교하여 본 식별법의 타당성을 보인다.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.219-228
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• 2004

Confidence interval estimators for proportions using normal approximation have been commonly used for coverage analysis of simulation output even though alternative approximate estimators of confidence intervals for proportions were proposed. This is -because the normal approximation was easier to use in practice than the other approximate estimators. Computing technology has no problem with dealing these alternative estimators. Recently, one of the approximation methods for coverage analysis which is based on arcsin transformation has been used for estimating proportion and for controlling the required precision in [12]. In this paper, we compare three approximate interval estimators, based on a normal distribution approximation, an arcsin transformation and an F-distribution approximation, of a single proportion. Three estimators were applied to sequential coverage analysis of steady-state means, in simulations of the M/M/1/$\infty$ and W/D/l/$\infty$ queueing systems on a single processor and multiple processors.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1171-1180
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• 2010

It is well known that the asymptotic convergence rates of nonparametric regression estimator gets worse as the dimension of covariates gets larger. One possible way to overcome this problem is reducing the dimension of covariates by using single index models. Two coefficient estimation methods in single index models are introduced. One is semiparametric least square estimation method, which tries to find approximate solution by using iterative computation. The other one is weighted average derivative estimation method, which is non-iterative method. Both of these methods offer the parametric convergence rate to normal distribution. However, practical comparison of these two methods has not been done yet. In this article, we compare these methods by examining the variances of estimators in various models.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.887-896
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• 2016

The Neyman-Scott Rectangular Pulse (NSRP) model is used to model the hourly rainfall series. This model uses a modest number of parameters to represent the rainfall processes and underlying physical phenomena such as the arrival of a storm or rain cells. In this paper, we proposed approximated likelihood function for the NSRP model and applied the proposed method to precipitation data in Seoul.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.95-102
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• 2017

Gamma generalized linear models have received less attention than Poisson and binomial generalized linear models. Therefore, many old-established statistical techniques are still used in gamma generalized linear models. In particular, existing literature and textbooks still use approximate estimates for the dispersion parameter. In this paper we study the efficiency of various dispersion parameter estimators in gamma generalized linear models and perform numerical simulations. Numerical studies show that the maximum likelihood estimator and Cox-Reid adjusted maximum likelihood estimator are recommended and that approximate estimates should be avoided in practice.

• The Journal of the Acoustical Society of Korea
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• v.27 no.5
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• pp.221-228
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• 2008

To estimate array shape with reference sources in SONAR systems, nearfield signal modeling is required for the reference sources near a towed array. Array shape estimation method based on the nearfield signal modeling generally exploits the spatial covariance matrix of the received reference sources. Among those method, nearfield eigenvector method uses the eigenvector corresponding to the maximum eigenvalue as a steering vector of the reference source. In this paper, we propose a simplified subspace fitting method based on the nearfield signal modeling with spherical wave modeling. Furthermore, we analyze performance of the array shape estimation methods based on the nearfield signal modeling for various environments. The results of the numerical experiments indicate that the simplified subspace fitting method and the nearfield eigenvector method with single reference source shows almost similar performance. Furthermore, the simplified subspace fitting method with 2 reference sources consistently estimates the shape of the array regardless of the incident angle of the reference sources, whereas the nearfield eigenvector method cannot apply for the case of 2 reference sources.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.226-233
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• 2012

In this paper, we propose a 'linear approximation method' which is an accelerated BER (Bit Error Rate) test method for high speed interfaces, based on an analytical BER model. Both the conventional 'Q-factor estimation method' and 'linear approximation method' can predict a timing margin for $10^{-13}$ BER with an error of about 0.03UI. This linear approximation method is implemented on a hardware as an accelerated Built-In Self Test (BIST) with an internal BERT (BET Tester). While a direct measurement of a timing margin in a 3Gbps interface takes about 5.6 hours with $10^{-13}$ BER requirement and 95% confidence level, the accelerated BIST estimates a timing margin within 0.6 second without a considerable loss of accuracy. The test results show that the error between the estimated timing margin and the timing margin from an actual measurement using the internal BERT is less than 0.045UI.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.1
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• pp.15-22
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• 2015

Estimating the number of distinct values is really well-known problems in network data measurement and many effective algorithms are suggested. Recent works have built upon technique called Linear Counting to solve the estimation problem for massive sets or spreaders in small memory. Sampling is used to reduce the measurement data, and it is assumed that sampling gives bad effect on the accuracy. In this paper, however, we show that the sampling on multi-set estimation sometimes gives better results for CSE with sampling than for MCSE that examines all the packets without sampling in terms of accuracy and estimation range. To prove this, we presented mathematical analysis, conducted experiment with real data, and compared the results of CSE, MCSE, and CSES.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.17-27
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• 2014

As the importance of Cooperative Engagement Capability and network-centric warfare has been dramatically increasing, it is necessary to develop distributed tracking systems. Under the development of distributed tracking systems, it requires tracking filters and data fusion theory for nonlinear systems. Therefore, in this paper, the problem of nonlinear track fusion, which is suitable for distributed networks, is formulated, four algorithms to solve the problem of nonlinear track fusion are introduced, and performance of introduced algorithms are analyzed. It is a main problem of nonlinear track fusion that cross-covarinaces among multiple platforms are unknown. Thus, in order to solve the problem, two techniques are introduced; a simplification technique and a approximation technique. The simplification technique that help to ignore cross-covariances includes two algorithms, i.e. the sample mean algorithm and the Millman formula algorithm, and the approximation technique to obtain approximated cross-covariances utilizes two approaches, by using analytical linearization and statistical linearization based on the sigma point approach. In simulations, BCS fusion is the most efficient scheme because it reduces RMSE by approximating cross-covariances with low complexity.