• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.9
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• pp.381-388
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• 2002

This paper suggests the activation-only VSIMM estimator, applied mainly to target tracking problems. This algorithm is much simpler and easier to implement than the ordinary VSIMM algorithm. Also the activation-only VSIMM algorithm provides a substantial reduction in computation while having identical performance with the ordinary VSIMM estimator and the FSIMM estimator. More importantly, the drawbacks related to the improper termination and activation inherent to the VSIMM algorithm are eliminated in this algorithm. The performance of this estimator will be shown through a Monte Carlo simulation for maneuvering target tracking in comparison with the FSIMM and the VSIMM.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.5
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• pp.1291-1298
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• 1998

In this paper, a simple algorithm for detection of timing error of a synchronous, band-limited, multi-level data stream is proposed. The proposed algorithm can be applied to multi-level PAM, M-ary PSK, or M-ary QAM. The proposed algorithm for M-ary PSK requires only two samples per symbol for its operation, and it is based on the concept of transition logic table and transition level table. In orer to prove the steady-state operation of the proposed algorithm, its performance is evaluated and compared to BECM by Monte Carlo simulation method under Gaussian noise and fading noise channel environments. The comparison results confirm that the perforance of proposed algorithm is superior to that of BECM in jitter characteristics.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1826-1835
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• 2007

In this paper, we present a new approach for automatic detection and tracking for multiple targets. We combine a highest probability data association(HPDA) algorithm for target detection with a particle filter for multiple target tracking. The proposed approach evaluates the probabilities of one-to-one assignments of measurement-to-track and the measurement with the highest probability is selected to be target- originated, and the measurement is used for probabilistic weight update of particle filtering. The performance of the proposed algorithm for target tracking in clutter is compared with the existing clustering algorithm and the sequential monte carlo method for probability hypothesis density(SMC PHD) algorithm for multi-target detection and tracking. Computer simulation studies demonstrate that the HPDA algorithm is robust in performing automatic detection and tracking for multiple targets even though the environment is hostile in terms of high clutter density and low target detection probability.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.291-304
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• 2002

This article is concerned with an effective algorithm for the identification of multiple outliers in linear regression. It proposes a hybrid algorithm which employs the least median of squares estimator, instead of the least squares estimator, to construct an Initial clean subset in the stepwise forward search scheme. The performance of the proposed algorithm is evaluated and compared with the existing competitor via an extensive Monte Carlo simulation. The algorithm appears to be superior to the competitor for the most of scenarios explored in the simulation study. Particularly it copes with the masking problem quite well. In addition, the orthogonal decomposition and Its updating techniques are considered to improve the computational efficiency and numerical stability of the algorithm.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.443-451
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• 2005

The studied in this paper is a new algorithm for searching the maximum likelihood estimate(MLE) in which probability density function is not explicitly expressed. Newton-Raphson's root-finding routine and a nonlinear numerical optimization algorithm with constraint (so-called feasible sequential quadratic programming) are used. This algorithm is applied to the Wakeby distribution which is importantly used in hydrology and water resource research for analysis of extreme rainfall. The performance comparison between maximum likelihood estimates and method of L-moment estimates (L-ME) is studied by Monte-carlo simulation. The recommended methods are L-ME for up to 300 observations and MLE for over the sample size, respectively. Methods for speeding up the algorithm and for computing variances of estimates are discussed.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.1006-1008
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• 2005

최근에 계산금융 분야에서 복잡한 수식을 이용한 연산이 증가하고 있다. 그리고 계산금융 분야에서 몬테카를로 시뮬레이션은 대표적인 계산방법 중에 하나이다. 그러나 몬테카를로 시뮬레이션은 많은 반복연산을 수행하므로 연산시간이 오래 걸리는 문제점이 있다. 이러한 문제점을 해결하기 위하여 본 논문에서는 몬테카를로 시뮬레이션과 스프레드시트를 병렬로 처리하였다. 또한 실험을 통하여 병렬 스프레드시트의 계산 노드가 증가함에 따라 파생상품의 계산 시간이 단축되는 것을 보였다.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.171-174
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• 1998

A class of non-parametric detectors based on quantized m-dimensional noise sample space is introduced. Due to assuming the nongaussian noise as a channel model, it is not easy to design the detector through estimating the unknown functional form of noise; instead equiprobably partitioning m-dimensional noise into a finite number of regions, using a VQ and quantiles obtained by RMSA algorithm is used in this paper to design detectors. To show the comparison of performance between single sample detector and system suggested here, Monte-Carlo simulations were used. The effect of signal pulse shape on the receiver performance is analyzed too.

• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.379-391
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• 2001

본 연구에서는 관심거리가 되고 있는 마코프인쇄 몬테칼로(Markov Chain Monte Carlo, MCMC)방법에 근거한 공간 확률난수 (spatial random variate)생성법과 깁스표본추출법(Gibbs sampling)에 의한 베이지안 분석 방법에 대한 기술적 사항들에 관하여 검토하였다. 먼저 기본적인 확률난수 생성법과 관련된 사항을 살펴보고, 다음으로 조건부명시법(conditional specification)을 이용한 공간 확률난수 생성법을 예를 들어 살펴보기로한다. 다음으로는 이렇게 생성된 공간자료를 분석하기 위하여 깁스표본추출법을 이용한 베이지안 사후분포를 구하는 방법을 살펴보았다.

• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.443-455
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• 1999

A simulation-based approach to estimating the probability of an arbitrary region under a multivariate normal distribution is developed. In specific, the probability is expressed as the ratio of the unrestricted and the restricted multivariate normal density functions, where the restriction is given by the region whose probability is of interest. The density function of the restricted distribution is then estimated by using a sample generated from the Gibbs sampling algorithm.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.621-633
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• 2006

In this paper, we estimate multiple change-points when the data follow the normal distributions in the Bayesian way. Evolutionary Monte Carlo (EMC) algorithm is applied into general Bayesian model with variable-dimension parameters and shows its usefulness and efficiency as a promising tool especially for computational issues. The method is applied to the humidity data of Seoul and the final model is determined based on BIC.