• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.718-723
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• 1993

In this paper, we propose a neural network with fuzzy preprocessor not only for improving the classification accuracy but also for being able to classify objects whose attribute values do not have clear boundaries. The fuzzy input signal representation scheme is included as a preprocessing module. It transforms imprecise input in linguistic form and precisely stated numerical input into multidimensional numerical values. The transformed input is processed in the postprocessing module. The experimental results indicate the superiority of the backpropagation network with fuzzy preprocessor in comparison to the conventional backpropagation network.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1189-1191
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• 2005

This paper introduces the least-squares order-recursive lattice (LSORL) Laguerre smoother that has order-recursive smoothing structure based on the Laguerre signal representation. The LSORL Laguerre smoother gives excellent performance for a channel equalization problem with smaller order of tap-weights than its counterpart algorithm based on the transversal filter structure. Simulation results show that the LSORL Laguerre smoother gives better performance than the LSORL transversal smoother.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.134-136
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• 1986

In this paper, a new power spectral estimation technique is presented. At first, by transforming the original data with the Karhunen-Loeve Transform(KLT), we can reduce the amount of the redundant information. Next, by modeling the transformed data by means of the autoregressive(AR) model and then applying the least-squares parameter estimation algorithm to this model, even more accurate spectrum estimates can be obtained. The KLT is the optimum transform for signal representation with respect to the mean-square error criterion. And the least-squares method is used to overcome the inherent shortcomings of popular burg algorithm.

• Journal of Industrial Technology
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• v.18
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• pp.439-442
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• 1998

In this paper, we firstly suggested C&D (Common mode and Differential mode) model for the representation of a stereophonic signal. Then a measure of stereophonic channel separability is defined as the ratio of differential mode energy to total energy in frequency domain. After that, a new channel separability enhancement scheme is proposed by the control of common mode rejection. Finally, some experimental results are presented in order to verify our scheme.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.12
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• pp.1-11
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• 1993

This paper concentrates on models useful for analyzing the error performance of ML(Maximum Likelihood) estimators of a single unknown signal parameter: that is the error intensity model. We first develop the point process representation for the estimation error and the conditional distribution of the estimator as well as the distribution of error candidate point process. Then the error intensity function is defined as the probability dessity of the estimate and the general form of the error intensity function is derived. We then develop several intensity models depending on the way we choose the candidate error locations. For each case, we compute the explicit form of the intensity function and discuss the trade-off among models as well as the extendability to the case of multiple parameter estimation.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.369-372
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• 2000

An overview on applications of wavelet transform in power systems presented in this paper. Wavelet transform is capable of making trade-offs between time and frequency resolutions, which is a property that makes it appropriate for the analysis of non stationary signal. In recent years, wavelet transform is widely accepted as a technology offering an alternative way due to its flexibility in representation of non-stationary signal even in power systems. This paper presents various applications of wavelet transform in power systems. Wavelet transform has been used by the authors in the field of power system protection for the classification of transient signals, and forecasting of short term loads and system marginal price and so on. Various research works carried out by many researchers in power systems are summarized.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.755-762
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• 2004

In this research, EOG(Electrooculogram) signal was analyzed to predict the subject's intention using a fuzzy classifier. The fuzzy classifier is built automatically using the EOG data and evolutionary algorithms. An assistant robot manipulator in redundant configuration has been developed, which operates according to the EOG signal classification results. For automatic fuzzy model construction without any experts' knowledge, an evolutionary algorithm with the new representation scheme, design of adequate fitness function and evolutionary operators, is proposed. The proposed evolutionary algorithm can optimize the number of fuzzy rules, the number of fuzzy membership functions, parameter values for the each membership functions, and parameter values for the consequent parts. It is shown that the fuzzy classifier built by the proposed algorithm can classify the EOG data efficiently. Intelligent motion planner that consists of several neural networks are used for control of robot manipulator based upon EOG classification results.

• The Journal of the Acoustical Society of Korea
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• v.27 no.1E
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• pp.30-34
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• 2008

An improvement to the existing blind signal separation (BSS) method has been made in this paper. The proposed method models the inherent signal dependency observed in acoustic object to separate the real-world convolutive sound mixtures. The frequency domain approach requires solving the well known permutation problem, and the problem had been successfully solved by a vector representation of the sources whose multidimensional joint densities have a certain amount of dependency expressed by non-spherical distributions. Especially for speech signals, we observe strong dependencies across neighboring frequency bins and the decrease of those dependencies as the bins become far apart. The non-spherical joint density model proposed in this paper reflects this property of real-world speech signals. Experimental results show the improved performances over the spherical joint density representations.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.481-493
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• 1995

An ARIMA signal observed with measurement error is shown to have another ARIMA representation with nonlinear restrictions on parameters. For this model, the restricted Newton-Raphson estimator(RNRE) of the unit root is shown to have the same limiting distribution as the ordinary least squares estimator of the unit root in an AR(1) model tabulated by Dickey and Fuller (1979). The RNRE of parameters of the ARIMA(p,1,k) process and unit root tests base on the RNRE are developed.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.63-70
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• 2001

EZW(Embedded coding using Zero-trees of Wavelet coefficients) decreases symbol-position information using zero-trees, but threshold value fall lot raising resolution, then coding cost of significant coefficients is expensive. To avoide this fact, this paper uses quad-tree representing coefficient-position information. a magnitude of significant coefficient is represented on matrix used at EZW. the proposed algorithm is hoped for raising a coding cost.