• Journal of Environmental Science International
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• v.3 no.4
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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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• v.49 no.1
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• pp.24-29
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• 2012

In this paper, we propose a progressive filter for impulse noise reduction. The proposed method uses non-linear filter and linear filter progressively. Non-linear filter reduces abrupt noise pattern. Also, linear filter adjusts filtering direction according to an edge in the image which is filtered by non-linear filter. Thus, our proposed method not only preserves edge, but also reduces noise in uniform region. Experimental results show that our proposed method has better quality than those by existing non-linear and linear progressive filtering methods.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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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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• v.2 no.1
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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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• 1988.10a
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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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.2
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4A
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• pp.324-329
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• 2002

In the high-speed/low-power digital filter applications like wireless communication systems, canonical signed digit(CSD) linear phase finite impulse response(FIR) filter structures are widely investigated. In this paper, we propose a high-speed/low-power CSD linear phase FIR filter structure using vertical common sub-expression. In the conventional linear phase CSD filter, horizontal common sub-expressions are utilized due to the inherent horizontal common sub-expression of symmetrical filter coefficients. We use the fact that their MSBs are also equal since adjacent filter coefficients have similar values in the linear phase filter Through the examples, it is shown that our proposed structure is more efficient in case that precision of implementation is lower, and tap length are longer.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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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.