• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.2 s.95
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• pp.169-175
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• 2005

In this paper, a novel vibration control scheme for a single-link flexible manipulator system without using a vibration feedback sensor is proposed. In order to achieve the vibration information of the flexible link, a reaction moment estimator based on the dynamic characteristics of the flexible manipulator is proposed. While the manipulator is maneuvering the reaction moment is reciprocally acting on the flexible link and the hub inertia due to the vibration of the link. A sliding mode controller based on the equivalent rigid body dynamics corresponding to the proposed flexible manipulator is then augmented with the reaction moment estimator to realize a decentralized control system. The reaction moment estimator is implemented via the first order low pass filter. The performance of the proposed control scheme is verified by computer simulation and experiment.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.859-866
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• 2001

The problem of wavelet density estimation is studied when the sample observations are contaminated with random noise. In this paper a linear wavelet estimator based on Meyer-type wavelets is shown to be L$_1$ strongly consistent for f(x) with bounded support when Fourier transform of random noise has polynomial descent or exponential descent.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.1
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• pp.101-104
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• 2013

We computed Cramer-Rao bound for estimating amplitude and decay parameters of exponentially decaying function under Poisson noise. Since Cramer-Rao bound is the lowest variance bound for any unbiased estimator, the computed Cramer-Rao bound can be used for evaluating the performance of estimators under Poisson noise. In addition, we show that the performance of maximum-likelihood estimator is close to the Cramer-Rao bound by simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.5
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• pp.415-419
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• 2008

Detecting and correcting defects on LCD glasses early in the manufacturing process becomes important for panel makers to reduce the manufacturing costs and to improve productivity. Many attempts have been made and were successfully applied to detect and identify simple defects such as scratches, dents, and foreign objects on glasses. However, it is still difficult to robustly detect low-contrast defect region, called Mura or blemish area on glasses. Typically, these defect areas are roughly defined as relatively large, several millimeters of diameter, and relatively dark and/or bright region of low Signal-to-Noise Ratio (SNR) against background of low-frequency signal. The aim of this article is to present a robust algorithm to segment these blemish defects. Early 90's, a highly robust estimator, known as the Model-Fitting (MF) estimator was developed by X. Zhuang et. al. and have been successfully used in many computer vision application. Compared to the conventional Least-Square (LS) estimator the MF estimator can successfully estimate model parameters from a dataset of contaminated Gaussian mixture. Such a noise model is defined as a regular white Gaussian noise model with probability $1-\varepsilon$ plus an outlier process with probability $varepsilon$. In the sense of robust estimation, the blemish defect in images can be considered as being a group of outliers in the process of estimating image background model parameters. The algorithm developed in this paper uses a modified MF estimator to robustly estimate the background model and as a by-product to segment the blemish defects, the outliers.

• Journal of KSNVE
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• v.9 no.6
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• pp.1240-1246
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• 1999

A prior error estimator which is solving structural dynamic problems and which is based on the generalized-method, is developed. Since the proposed error estimator is computed with only previous information, the time step size can be adaptively selected without the feedback mechanism. This paper shows that the automatic time stepping algorithm using the error estimator performs an efficient time integration. To verify its efficiency, several examples are numerically investigated.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.151-156
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• 2012

In this paper robust nonlinear image denoising algorithms are introduced for the distribution which is Gaussian in the center and Laplacian in the tails. The distribution is known as the least favorable ${\epsilon}$-contaminated normal distribution that maximizes the asymptotic variance. The proposed filter proves to be the maximum likelihood estimator under the heavy-tailed Gaussian noise environments. It is optimal in the respect of maximizing the efficacy under the above noise environment. Another filter for reducing impulsive noise is proposed by mixing with the myriad filter to propose an amplitude-limited myriad filter. Extensive experiment is conducted with images corrupted with ${\alpha}$-stable noise to analyze the behavior and performance of the proposed filters.

• Journal of Digital Convergence
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• v.11 no.7
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• pp.135-140
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• 2013

Noise estimation and detection algorithm to adapt quickly to changing noise environment using the LMS Filter. However, the LMS Filter for noise estimation for a certain period of time and need time to adapt. If the signal changes occur, have the disadvantage of being more adaptive time-consuming. Therefore, noise removal method is proposed to a frame basis AELMS Filter to compensate. In this paper, we split the input signal on a frame basis in noisy environments. Remove the LMS Filter by configuring noise predictions using the mean and variance. Noise, even if the environment changes fast adaptation time to remove the noise. Remove noise and environmental noise and speech input signal is mixed to maintain the unique characteristics of the voice is a way to reduce the damage of voice information. Noise removal method using a frame basis AELMS Filter To evaluate the performance of the noise removal. Experimental results, the attenuation obtained by removing the noise of the changing environment was improved by an average of 6.8dB.

• Journal of the Korean Statistical Society
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• v.27 no.2
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• pp.165-187
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• 1998

Shin and Sarkar (1993, 1994) studied the problem of testing for a unit root in an AR(p) signal observed with MA(q) noise when the MA parameters are known. In this paper we consider the case when the MA parameters are unknown and to be estimated. Test statistics are defined using unit root parameter estimates based on three different estimation methods of Hannan and Rissanen (1982), Kohn (1979) and Shin and Sarkar (1995). An AR(p) process contaminated by MA(q) noise is a .estricted ARMA model, for which Shin and Sarkar (1995) derived an easy-to-compute Newton- Raphson estimator The two-stage estimation p.ocedu.e of Hannan and Rissanen (1982) is used to compute initial parameter estimates in implementing the iterative estimation methods of both Shin and Sarkar (1995) and Kohn (1979). In a simulation study we compare the relative performance of these unit root tests with respect to both size and power for p=q=1.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.12 no.5
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• pp.519-526
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• 1987

An adaptive estimation system which operates propoerly in the environments corrupted by additive impulse noise in addition to the white Gaussian noise has been proposed. A feed forward loop is inserted into the adaptive estimator proposed by R. L. Moose for a system with an unknown measurement bias by which the improved adaptive estimator is processed successfully without the sum of the time varying weights being zero even when the measurement system is added impulue noise. Successfully processed adaptive estimator has been obtained under the large impulse noise in addition to randomly varying unknown biases condition by giving sufficient large value to the elements of discrete vector on the computer simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5A
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• pp.298-304
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• 2012

In this paper, we propose robust estimators for the frequency offset of orthogonal frequency division multiplexing in non-Gaussian noise environments. We first propose a maximum-likelihood (ML) estimator in non-Gaussian noise modeled as a complex isotropic Cauchy process, and then, we present a simpler suboptimal estimator based on the ML estimator. From numerical results, it is demonstrated that the proposed estimators not only outperform the conventional estimators, but also have a robustness in non-Gaussian noise environments.