• 한국환경과학회지
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• 제3권4호
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• pp.349-356
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• 1994

The purpose of this study is to develop a linear reservoir model with Kalman filter using Kalman filter theory which removes a physical uncertainty of :ainfall-runoff process. A linear reservoir model, which is the basic model of Kalman filter, is used to calculate runoff from rainfall in river basin. A linear reservoir model with Kalman filter is composed of a state-space model using a system model and a observation model. The state-vector of system model in linear. The average value of the ordinate of IUH for a linear reservoir model with Kalman filter is used as the initial value of state-vector. A .linear reservoir model with Kalman filter shows better results than those by linear reserevoir model, and decreases a physical uncertainty of rainfall-runoff process in river basin.

• 전자공학회논문지 IE
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• 제49권1호
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• pp.24-29
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• 2012

본 논문에서는 임펄스 잡음을 제거하는 프로그레시브 필터를 제안한다. 비선형 필터와 선형필터를 순차적으로 적용하는 방법을 제안한다. 비선형 필터는 급격한 잡음 패턴을 제거한다. 이에 따른 비선형필터링 된 영상을 선형 필터에서 에지에 따라 방향을 조절하여 필터링한다. 따라서 제안하는 방법은 에지를 유지할 뿐 아니라 일정한 지역에서 잡음을 효과적으로 제거한다. 실험결과, 제안하는 방법이 기존 선형, 비선형 프로그레시브 필터링 방법들 보다 향상된 결과를 보인다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.980-983
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• 1996

A new version of the discrete-time optimal FIR (finite impulse response) filter utilizing only the measurements of finite sliding estimation window is suggested for linear time-invariant state-space models. This filter is called the BLUFIR (best linear unbiased finite impulse response) filter since it provides the BLUE (best linear unbiased estimate) of the state obtained from the measurements of the estimation window. It is shown that the BLUFIR filter has the deadbeat property when there are no noises in the estimation window.

• Agricultural and Biosystems Engineering
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• 제2권1호
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• pp.15-23
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• 2001

On the assumption that the voices of the cows are produced by the linear prediction filter, we characterized the cows’voices. The order of this filter was determined by examining the voice characteristics both in time and frequency domains. The proposed order of the linear prediction filter is 15 for modeling voice production of the cow. The characteristics of the amplitude envelope of the voice signal was investigated by analyzing the sequence of the short time variance both in time and frequency domains, and the new parameters were defined. One of the coefficients o the linear prediction filter generating the voice signal, the fundamental frequency, the slope of the straight line regressed from the log-log spectra of the short time variance and the coefficients of the linear prediction filter generating the sequence of the short time variance of the voice signal can differentiate the two cows.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.172-177
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• 1988

In this paper, a new fast algorithm of FIR least squares filter with linear phase is presented. The general unknown statistics case is considered, whereby only sample records of the data are available. Taking advantage of the near-to-Toeplitz+Hankel structure of the resulting normal equation, a fast algorithm which gurantees the linear phase constraint, is developed that recursively produces the filter coefficient of linear phase FIR filter for a single block of data.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2006년도 춘계공동학술대회 논문집
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• pp.92-100
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• 2006

In many common control charting situations, the statistic to be charted can be viewed as the output of a linear filter applied to the sequence of process measurement data. In recent work that has generalized this concept, the charted statistic is the output of a general linear filter in impulse response form, and the filter is designed by selecting its impulse response coefficients in order to optimize its average run length performance. In this work, we restrict attention to the class of all second-order linear filters applied to the residuals of a time series model of the process data. We present an algorithm for optimizing the design of the second-order filter that is more computationally efficient and robust than the algorithm for optimizing the general linear filter. We demonstrate that the optimal second-order filter performs almost as well as the optimal general linear filter in many situations. Both methods share a number of interesting characteristics and are tuned to detect any distinct features of the process mean shift, as it manifests itself in the residuals.

• 전자공학회논문지B
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• 제31B권8호
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• pp.39-46
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• 1994

In this paper a state reconstruction filter for linear discrete-time stochastic systems with unknown inputs and noises is presented. The suggested filter can estimate the system state vector and the unknown inputs simultaneously As an extension of the filter a fault diagnosis filter for linear discrete-time stochastic systems with unknown inputs and noises is presented for each filters the optimal gain determination methods which minimize the variance of the state reconstruction errorare presented. Finally the usability of the filtersis shown via numerical examples.

• 제어로봇시스템학회논문지
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• 제12권2호
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• pp.93-100
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• 2006

In this paper, a new-type Extended Kalman Filter (EKF) is proposed as a robust nonlinear filter for a stochastic nonlinear system. The original EKF is widely used for various nonlinear system applications. But it is fragile to its estimation errors because they give rise to linearization errors that affect the system mode1 as the modeling errors. The linearization errors are nonlinear functions of the estimation errors therefore it is very difficult to obtain the accurate error covariance of the EKF using the linear form. The inaccurately estimated error covariance hinders the EKF from being a sub-optimal estimator. The proposed filter tries to obtain the upper bound of the error covariance tolerating the uncertainty of the error covariance instead of trying to obtain the accurate one. It treats the linearization errors as uncertain modeling errors that can be handled by the robust linear filtering. In order to be more robust to the estimation errors than the original EKF, the proposed filter minimizes the upper bound like the robust linear filter that is applied to the linear model with uncertainty. The in-flight alignment problem of the inertial navigation system with GPS position measurements is a good example that the proposed robust filter is applicable to. The simulation results show the efficiency of the proposed filter in the robustness to initial estimation errors of the filter.

• 한국통신학회논문지
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• 제27권4A호
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• pp.324-329
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• 2002

Digital If(Intermediate frequency) 처리단과 같은 고속과 저전력을 요구하는 필터에서 덧셈기만을 사용하여 CSD(Canonical Signed Digit)형의 필터계수들을 구현하는 구조가 널리 연구되고 있다. 본 논문에서는 CSD형의 선형위상 FIR(Finite Impulse Response) 필터에서 수직의 공통패턴을 공유하는 구조를 제안한다. 선형위상 FIR 필터를 CSD형의 코드를 사용하여 구현할 때에, 선형위상의 계수대칭의 특성 때문에 수평 공통패턴의 방식이 사용되어 왔다. 그러나 본 논문에서는 선형위상 필터는 근접해 있는 계수들끼리 근사의 값을 갖기 때문에 MSB가 같다는 것을 이용하여 수직 공통패턴을 사용하는 방식을 제안하였다. 제안된 방식은 구현의 정세도가 낮을수록, 구현하는 탭의 길이가 길수록 더욱 효과가 큼을 예제를 통하여 보였다. 따라서 제안된 방식은 고 고속/저전력 구현을 요하는 이동 통신용 필터에서 사용하기에 적합한 필터임을 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.521-521
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• 2000

This paper investigates the problem of robust H$_{\infty}$ filter with FIR(Finite Impulse Response) structure for linear continuous time-varying systems with sampled-data measurements. It is assumed that the system is subject to real time-varying uncertainty which is represented by the state-space model having parameter uncertainty. The robust H$_{\infty}$ FIR filter is proposed for the continuous-time linear parameter uncertain systems. It is also derived from the equivalence relationship between the robust linear H$_{\infty}$ FIR filter and the robust linear H$_{\infty}$ filter with sampled-data measurements.