• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.27-30
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• 1998

본 논문에서의 기존의 2선식 댁내 망을 이용하여 ISDN PRI(2.048MHz)급의 전송을 위해 DS-CDMA방식을 적용하는 방법을 제안한다. TE 와 NT 의 신호의 검출을 위해 자기의 확산코드와 정합된 단일 사용자 검출기를 사용할 수 있으나, 검출하려는 신호와 다른 사용자 신호간의 다원접속간섭(Multiple Access Interference)에 의해 시스템의 성능이 저하된다. 본 논문에서는 다원접속간섭을 효과적으로 제거할 수 있는 최소평균제곱오차 (Minimum Mean Squared Error) 다중사용자 간섭제거기를 사용하고 컴퓨터 모의실험을 통하여 성능을 분석한다.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.261-266
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• 2000

이변량 반복측정자료에서 Chinchilli 등(1996)이 제안한 가중일치상관계수는 두 변수의 일치성을 나타내는 측도이다. 기존에 제안된 가중일치상관계수 추정법은 변동효과 및 측정오차의 분산성분을 각각 최소제곱법으로 비편향 추정하여 구하는 것이다. 본 연구에서는 반복측정자료의 주변 우도함수를 설정한 후, 우도함수에 기초한 분산성분을 구하여 가중일치상관계수를 추정하는 방법을 제안한다. 이때, 각 분산성분은 유사/의사 우도함수 및 사후 분포에서 반복시행을 통하여 구해진다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.394-395
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• 2010

본 논문은 최소 평균 제곱 오차(minimum mean-square error: MMSE)에 기반한 비국소적 (nonlocal) 평균 영상 잡음 제거기법을 제안한다. 제안하는 기법에서는 기존의 비국소적 평균 기법에 추정 이론을 적용하여 잡음 제거에 사용되는 이웃 블록 또한 잡음을 포함하는 일반적인 경우로 확장하여 이웃 블록에 인가되는 가중치를 적응적으로 조절한다. 컴퓨터 모의실험을 통해 제안하는 알고리듬이 기존의 비국소적 기법에 비해 잡음 제거 성능이 향상됨을 확인한다.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.333-336
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• 2001

This paper reports that the Lambert W function applies to a non-iterative design of minimum mean square-error scalar quantizers for a Laplacian source. The contribution of the paper is in the reduction of the time needed for the design and the increased accuracy in resulting quantization points and thresholds, because the algorithm is non-iterative and the Lambert W function can be evaluated as accurately as desired.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2188-2191
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• 2003

이 논문에서는 라플라스 밀도함수에 대한 최적 홑양자기 지지역은 양자점의 개수와 로그선형 관계가 있음을 증명한다. 그리고, 극상한값을 유도하여 최적 지지역의 로그선형 증가가 어떤 상수값을 초과하지 않음을 증명한다. 이 결과들로부터, 학계에 경험적으로 알려져 왔던 최적 지지역의 로그선형 증가를 증명한다.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.251-260
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• 2009

In this study, we propose a new estimation method based on autocovariance for selecting optimal estimators of the regression coefficients in the simple linear regression model. Although this method does not seem to be intuitively attractive, these estimators are unbiased for the corresponding regression coefficients. When the exploratory variable takes the equally spaced values between 0 and 1, under mild conditions which are satisfied when errors follow an autoregressive moving average model, we show that these estimators have asymptotically the same distributions as the least squares estimators. Additionally, under the same conditions as before, we provide a self-contained proof that these estimators converge in probability to the corresponding regression coefficients.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.839-847
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• 2011

In this study, we derive an estimator based on autocovariance for the regression coefficients vector in the multiple linear regression model. This method is suggested by Park (2009), and although this method does not seem to be intuitively attractive, this estimator is unbiased for the regression coefficients vector. When the vectors of exploratory variables satisfy some regularity conditions, under mild conditions which are satisfied when errors are from autoregressive and moving average models, this estimator has asymptotically the same distribution as the least squares estimator and also converges in probability to the regression coefficients vector. Finally we provide a simulation study that the forementioned theoretical results hold for small sample cases.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2
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• pp.40-49
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• 1995

Application of the filtered-x LMS adaptive filter to active noise cancellation requires to estimate the transfer charactersitics between the output and the error signal of the adaptive canceler. In this paper, we derive the filtered-x adaptive noise cancellation algorithm and analyze its convergence behavior when the acoustic noise consists of multiple sinusoids. The results of the convergence analysis of the filtered-x LMS algorithm indicate that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components : Phase estimation error and estimated gain. In particular, the convergence is shown to strongly affected by the accuracy of the phase response estimate. Simulation results are presented to support the theoretical convergence analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6A
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• pp.524-532
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• 2002

This paper reports that the Lambert W function applies to a non-iterative design of minimum mean square-error scalar quantizers for a Laplacian source. Specifically, it considers a non-iterative design algorithm for optimum quantizers for a Laplacian source; it finds that the solution of the recursive nonlinear equation in the non-iterative design is elegantly expressed in term of the principal branch of the Lambert W function in a closed form; and it proves that the non-iterative algorithm applies only to exponential or Laplacian sources. The contribution of the paper is in the reduction of the time needed for the design and the increased accuracy in resulting quantization points and thresholds, because the algorithm is non-iterative and the Lambert W function can be evaluated as accurately as desired. Also, numerical results show how optimal quantization distortion converges monotonically to the Panter-Dite constant and help derive an approximation formula for the key parameters of optimum quantizers.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.47-54
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• 2009

Automatic object recognition in 3D measuring data is of great interest in many application fields e.g. computer vision, reverse engineering and digital factory. In this paper we present a software tool for a fully automatic object detection and parameter estimation in unordered and noisy point clouds with a large number of data points. The software consists of three interactive modules each for model selection, point segmentation and model fitting, in which the orthogonal distance fitting (ODF) plays an important role. The ODF algorithms estimate model parameters by minimizing the square sum of the shortest distances between model feature and measurement points. The local quadric surface fitted through ODF to a randomly touched small initial patch of the point cloud provides the necessary initial information for the overall procedures of model selection, point segmentation and model fitting. The performance of the presented software tool will be demonstrated by applying to point clouds.