• Journal of Mechanical Science and Technology
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• v.16 no.10
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• pp.1320-1326
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• 2002

In a one-dimensional heat conduction domain with heated and insulated walls, an integral approach is proposed to estimate time-dependent boundary heat flux without internal measurements. It is assumed that the expression of the heat flux is not known a priori. Hence, the present inverse heat conduction problem is classified as a function estimation problem. The spatial temperature distribution is approximated as a third-order polynomial of position, whose four coefficients are determined from the heat fluxes and the temperatures at both ends at each measurement. After integrating the heat conduction equation over spatial and time domain, respectively, a simple and non-iterative recursive equation to estimate the time-dependent boundary heat flux is derived. Several examples are introduced to show the effectiveness of the present approach.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.527-530
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• 1996

The Kalman filter has been used as a self-localization method for the mobile robot. To satisfy the assumptions inherent in the Kalman filter, we should calibrate the sensors of the robot before use of them. However, it is generally hard to find exact sensor parameters, and the parameters may change during the robot task as the environment varies. Thus we need to perform on-line sensor calibration, by which we can obtain more credible location of the mobile robot. In this paper, we present an on-line sensor calibration scheme which estimates the unknown sensor bias and the current position of the robot. To this end, first we find out the calibration errors of the sensor from redundant sensory data using the parity vector and recursive minimum variance estimation. Then we calculate the current position of the robot by weighted least square estimation without internal encoder data. The performance of the proposed method is evaluated through computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.408-413
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• 2005

A new methodology for discrete non-Gaussian probability density estimation is investigated in this paper based on a dynamic Bayesian network (DBN) and kernel functions. The estimator consists of a DBN in which the transition distribution is represented with kernel functions. The estimator parameters are determined through a recursive learning algorithm according to the maximum likelihood (ML) scheme. A discrete-type Poisson distribution is generated in a simulation experiment to evaluate the proposed method. In addition, an unknown probability density generated by nonlinear transformation of a Poisson random variable is simulated. Computer simulations numerically demonstrate that the method successfully estimates the unknown probability distribution function (PDF).

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.495-498
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• 2004

In this paper, passive telemetry capacitive humidity sensor system using a RLSE(Recursive Least Square Estimation) technique Is proposed. To overcome the problem like power limits and complications that general passive telemetry sensor system including IC chip has, the principle of inductive coupling is applied to model the sensor system. Specially, by applying the forgetting factor, we show that the accuracy of its estimation can be improved even in the case of time varying parameter and also the convergence time can be reduced.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.1045-1050
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• 1988

The problem of applying the linear integral filter in analysis and parameter estimation of linear continuous systems is discussed. A discrete-time model, which is equivalent to that obtained using the bilinear z transformation, is derived and employed to predict system output. It is shown that the output error can be controlled through the sampling interval. In order to obtain unbiased estimates, an instrumental variable (IV) method is proposed, where the instrumental variables are constituted using adaptive filtering. Some problems on implementation of the recursive IV algorithm are discussed. Both theoretical analysis and simulation study are given to illustrate the proposed methods.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.170-175
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• 1992

A leak detection method for diagnosis of the leak position in a pipeline was developed using an estimation theory with the assumption that the measured flow rates and pressures are stochastic processes. A notch filter was designed using power spectral density analysis of measurements to reduce the effects of disturbances. The noise model dimension was determined by hypothesis testing and then recursive extended least square method was applied to estimate the leak position in real time. The proposed method was applied to an experimental system for evaluation of its performance.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.65-86
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• 2000

The estimates from an EM when it is applied to a large causal model of 10 or more categorical variables are often subject to the initial values for the estimates. This phenomenon becomes more serious as the model structure becomes more serious as the model structure becomes more complicated involving more variables. In this regard Wu(1983) recommends among others that EMs are implemented several times with different sets of initial values to obtain more appropriate estimates. in this paper a new approach for initial values is proposed. The main idea is that we use initials that are calibrated to data. A simulation result strongly indicates that the calibrated initials give rise to the estimates that are far closer to the true values than the initials that are not calibrated.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.600-602
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• 1997

The kalman filter which has good estimating capabilities by means of the recursive computation from the previously known of obtained data is usually used for the system estimation in the case of not being directly measurable. The best estimating technique is still open issues on the PWR reactor control system to increase operating contingencies and to predict the safety margins for safer reactor operation. This paper addressed its estimating technique using kalman filter for the more flexible reactor control and showed the reasonable approach for discretization of the continuos-time system for reduction of computation errors.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.2
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• pp.211-219
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• 1988

In this paper effective block matching algorithms are proposed to find the motion vector. There are two approaches to the estimation of the motion vector in MCC (motion compensated coding), i.e.pel(pixel element) recursive algorithm and block matching algorithm. The search algorithm in this paper is based on the block matching method. The advantage of this algorithm is the reduction of the computation time. In order to reduce the computation time, three mathods are proposed in this paper. These new algorithms are faster than other methods. Compared with the three step algorithm by Koga et al., the average ratio of the computational savings obtained from the proposed algorithm is about 3-4.

• Journal of Biomedical Engineering Research
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• v.17 no.1
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• pp.99-108
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• 1996

Diverse digital filters have been designed previously to eliminate powerline(AC) interference in the electrocardiouam. This paper describes filtering methods which have been developed recursive notch, adaptive, IEF(Incremental Estimation Filter) and proposes a new AIEF(Advanced Incremental Estimation Filter) method. The performances of these filters are compared on artificial signals as well as actual ECG signals with the aid of validated CSE(Common Standards for Quantitative Electrocardiowaphy). AC interference in this database is shown to exhibit two qualities especially relevant to filter design : considerable deviations from a nominal 60Hz frequency and substantial noise at higher harmonics.