• The Journal of the Acoustical Society of Korea
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• v.7 no.4
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• pp.54-76
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• 1988

In Part Ⅰ of the paper, we have developed various block least mean-square (BLMS) adaptive digital filters (ADF's) based on a unified matrix treatment. In Part Ⅱ we analyze the convergence behaviors of the self-orthogonalizing frequency-domain BLMS (FBLMS) ADF and the unconstrained FBLMS (UFBLMS) ADF both for the overlap-save and overlap-add sectioning methods. We first show that, unlike the FBLMS ADF with a constant convergence factor, the convergence behavior of the self-orthogonalizing FBLMS ADF is governed by the same autocorrelation matrix as that of the UFBLMS ADF. We then show that the optimum solution of the UFBLMS ADF is the same as that of the constrained FBLMS ADF when the filter length is sufficiently long. The mean of the weight vector of the UFBLMS ADF is also shown to converge to the optimum Wiener weight vector under a proper condition. However, the steady-state mean-squared error(MSE) of the UFBLMS ADF turns out to be slightly worse than that of the constrained algorithm if the same convergence constant is used in both cases. On the other hand, when the filter length is not sufficiently long, while the constrained FBLMS ADF yields poor performance, the performance of the UFBLMS ADF can be improved to some extent by utilizing its extended filter-length capability. As for the self-orthogonalizing FBLMS ADF, we study how we can approximate the autocorrelation matrix by a diagonal matrix in the frequency domain. We also analyze the steady-state MSE's of the self-orthogonalizing FBLMS ADF's with and without the constant. Finally, we present various simulation results to verify our analytical results.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.941-943
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• 1995

The convergence of adaptive algorithm depends mainly on the proper choice of the design factor called the covergence factor. In the paper, an optimal convergence factor involved in TRLMS algorithm, which is used to predict the coefficients of the ARMA predictor in ADPCM is presented. It is shown that such an optimal value can be generated by system signals such that the adaptive filter becomes self optimizing in terms of the convergence factor. This algorithm is applied to real image.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.697-699
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• 2005

연속 변수 함수 최적화를 위한 진화 연산에서는 전통적으로 확률 분포를 도입하여 새로운 세대를 생성하는 기법을 사용하고 있다. 최근 들어 이러한 확률 분포를 개체군으로부터 추정하여 보다 효율적으로 최적화를 해결하려는 연구가 진행되고 있다. 본 논문에서는 variational 베이지안 혼합 인자 분석 기법(Bayesian mixtures of factor analyzers)을 사용한 개체군의 분포 추정을 통해 연속 변수 함수의 최적화 문제를 해결하는 방법을 제안한다. 이 기법은 혼합 분포의 개수 추정을 자동화하여 개체군의 다양성을 유지할 수 있기 때문에 지역 최적점으로 일찍 수렴하는 현상을 방지할 수 있으며, 세부 개체군 내의 분포 추정을 통해 탐색을 효율적으로 수행할 수 있다. 잘 알려진 평가 함수들에 대하여 다른 분포 추정 진화 연산과 비교하여 제안하는 방법의 우수성을 검증하였다.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.2
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• pp.1-7
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• 2008

This paper presents a method to design a repetitive controller that is robust to variations in the system parameters. The uncertain parameters are specified probabilistically by their probability distribution functions. Instead of working with the distribution functions directly, the repetitive controller is designed from a set of models that are generated from the specified probability functions. With this multiple-model design approach, any number of uncertain parameters that follow any type of distribution functions can be treated. furthermore, the controller is derived by minimizing a frequency-domain based cost function that produces monotonic convergence of the tracking error as a function of repetition number. Numerical illustrations show how the proposed multiple-model design method produces a repetitive controller that is significantly more robust than an optimal repetitive controller designed from a single nominal model of the system.

• Journal of IKEEE
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• v.19 no.1
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• pp.33-40
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• 2015

Inverse problem in electrical impedance tomography (EIT) is highly ill-posed therefore prior information is used to mitigate the ill-posedness. Regularization methods are often adopted in solving EIT inverse problem to have satisfactory reconstruction performance. In solving the EIT inverse problem, iterative Gauss-Newton method is generally used due to its accuracy and fast convergence. However, its performance is still suboptimal and mainly depends on the selection of regularization parameter. Although, there are few methods available to determine the regularization parameter such as L-curve method they are sometimes not applicable for all cases. Moreover, regularization parameter is a scalar and it is fixed during iteration process. Therefore, in this paper, a novel method is used to determine the regularization parameter to improve reconstruction performance. Conductivity norm is calculated at each iteration step and it used to obtain the regularization parameter which is a diagonal matrix in this case. The proposed method is applied to human thorax imaging and the reconstruction performance is compared with traditional methods. From numerical results, improved performance of proposed method is seen as compared to conventional methods.

• Journal of Korea Multimedia Society
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• v.2 no.1
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• pp.99-108
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• 1999

In this paper, a new robust adaptive algorithm is proposed to improve the performance of AEC without computational burden. The proposed adaptive algorithm is based on NLMS algorithm, and its step-size is varied with the reference input signal power and the desired signal power. Its step-size is normalized by the sum of the powers of the reference input signal and the desired signal. When the near-end speaker's speech and noise are applied into the microphone, the step-size becomes small and the misalignment of coefficients are reduced. The convergence speed is comparable to NLMS algorithm at AEC application because the echo signals are attenuated about 10∼20 dBSPL. The characteristics of this algorithm is also analyzed and compared with conventional ones in this paper.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2005.04a
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• pp.312-315
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• 2005

Visual MODFLOW 프로그램 내 MT3D 패키지를 이용하여 다공 추적자 시험 해석 모델을 제시하였으며, 개념모델을 설정하여 추적자 해석 모델 모사 시 입력상수인 수리전도도(K), 비산출률$(S_y)$, 유효공극률$(n_e)$, 종분산지수$({\alpha}_L)$ 및 횡분산지수$({\alpha}_T)$ 등에 따른 민감도를 분석하였다. 또한, 이를 이용하여 부산과 이천지역의 현장 추적자 시험 자료를 해석하였다. 민감도 분석 결과, 유효공극률과 종분산지수의 민감도가 가장 크게 나타났으며, 비산출률의 민감도가 가장 작게 나타났다. 유효공극률은 관측정에서 측정되는 추적자의 최고농도를 결정하는 인자이며, 종분산지수는 추적자 최고농도가 되는 경과시간과 관계가 깊은 것으로 나타났다. 재순환 추적자 시험 해석 모델을 적용한 부산지역의 경우 유효공극률은 0.15, 종분산지수는 5m인 것으로 모사되었으며, 수렴 흐름 추적자 시험 해석 모델을 적용한 이천지역의 경우 유효공극률은 0.01, 종분산지수는 13m로 산정되었다.

• Proceedings of the KIEE Conference
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• 2001.04a
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• pp.75-77
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• 2001

전기기기 최적 설계에 있어서 결정론적 최적화 방법은 국부해를 빠른 속도로 찾을 수 있지만 최적값에 대한 보장이 어려우므로 비결정론적 방법인 진화전략 알고리즘을 많이 사용한다. 전기기기 최적화에 쓰이는 많은 확률적 알고리즘 중에서 진화 전략 알고리즘은 시뮬레이티드 어닐링과 유전 알고리즘을 결합한 방법으로, 전체 최적점 탐색이 가능할 뿐만 아니라 알고리즘이 비교적 간단하면서도 빠른 수렴 특성을 갖고 있다. 그리고, 종류 또한 다양하다. 진화 전략 알고리즘 중에서 중요한 것은 수렴속도와 성공률에 기여하는 파라미터들을 잘 선정하는 것이다. 본 논문에서는, 진화 전략 알고리즘의 중요한 인자인 자식 세대의 개수인 ${\lambda}$값과 ${\alpha}$값을 변화시켜 가면서 변수 개수에 따른 최적화된 조합을 제시한다. 본 논문의 결과는 전기기기 최적 설계에 응용하는데 도움이 될 것으로 사료된다.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.3-8
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• 2015

In this paper, we propose a blind equalization algorithm based on the error signal linearly combined a constellation-matched error (CME) and a non-constellation-matched error (non-CME). The new error signal was designed to include the non-CME term for reaching initial convergence and the CME term for improving intersymbol interference (ISI) performance of output signals, and it controls the error terms through a combining factor. By controlling the error terms, it generates an appropriate error signal for equalization process and improves convergence speed and ISI cancellation performance compared to those of conventional algorithms. In the simulation for 64-QAM and 256-QAM signals under the multipath channel and additive noise conditions, the proposed method was superior to CMA and CMA+DD concurrent equalization.

• Journal of Korea Multimedia Society
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• v.4 no.3
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• pp.230-237
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• 2001

Adaptive filters are widely used for acoustic echo canceler, adaptive equalizer and adaptive noise canceler. Coefficients of adaptive filters are updated by NLMS algorithm. However, Coefficients are misaligned by ambient noises when they are adapted by NLMS algorithm. In this Paper, a method determined the adaptation constant by low-pass filtered instantaneous gradient vector of LMS algorithm using orthognality principles of optimal filter is proposed. At initial states, instantaneous gradient vector, that is the cross-correlation of input signals and estimation error signals, has large value because input signals are remained in estimation error signals. When an adaptive filter is conversed, the cross-correlation will be close to zero. It isn's affected by ambient noises because ambient noises are uncorrelated with input signals. Determining adaptation constant with the cross-correlation, adaptive filters can be robust to ambient noises and the convergence rate doesn't slower As results of computer simulations, it is shown that the performance of proposed algorithm is betted than that of conventional algorithms.