• Journal of Advanced Marine Engineering and Technology
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• v.36 no.7
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• pp.919-926
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• 2012

In order to control a LTI(Linear Time Invariant) system subjected to system noise and measurement noise, first of all, it is necessary to estimate the state of system with reliability. Kalman filtering technique has been widely used to estimate the state of the stochastic LTI system with stationary noise characteristics because of its estimation ability versus algorithm simplicity. However, it often fails to estimate the state of the LTI system of which system parameter uncertainty exists partly and/or input uncertainty exists. In this paper, a new estimation technique based on Kalman filter is suggested for stochastic LTI system under parameter uncertainty and/or input uncertainty. A fuzzy estimation algorithm against uncertainties is introduced so as to compensate the state estimate filtered by Kalman filter. In order to verify the state estimation performance of the suggested technique, several simulations are accomplished.

• Speech Sciences
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• v.11 no.2
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• pp.237-245
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• 2004

In general, Gaussian mixture model(GMM) is used to estimate the speaker model for speaker identification. The parameter estimates of the GMM are obtained by using the expectation-maximization (EM) algorithm for the maximum likelihood(ML) estimation. However, if the number of mixtures isn't defined well in the GMM, those parameters are obtained inappropriately. The problem to find the number of components is significant to estimate the optimal parameter in mixture model. In this paper, to estimate the optimal number of mixtures, we propose the method that starts from the sufficient mixtures, after, the number is reduced by investigating the mutual information between mixtures for GMM. In result, we can estimate the optimal number of mixtures. The effectiveness of the proposed method is shown by the experiment using artificial data. Also, we performed the speaker identification applying the proposed method comparing with other approaches.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1984.04a
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• pp.42-45
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• 1984

we propose a new parameter estimation algorithm that converge with probability one and in mean square, If the mean is the function of parameter of the probability density function. This recursive algorithm is applicable also ever the parameters we estimate are multiparameter case. And the results are shown by the computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.441-444
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• 1989

This correspondence presents a recursive estimation algorithm which, unlike conventional ones; updates the estimates only when a sufficient improvement can be obtained with a bounded noise assumption, the resulting sequence of estimates is a sequence of convex sets(ellipsoids) in the parameter space. For the cases studied, the algorithm use less than 20 percent of the. data to update, the estimate and still acquired good accuracy for spectral estimation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.433-439
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• 1998

In this paper, we present an approach to the structure identification based on the input space partition methods and to the parameter identification by hybrid learning method in neuro-fuzzy system. The structure identification can automatically estimate the number of membership function and fuzzy rule using grid partition, tree partition, scatter partition from numerical input-output data. And then the parameter identification is carried out by the hybrid learning scheme using back-propagation and least squares estimate. Finally, we sill show its usefulness for neuro-fuzzy modeling to truck backer-upper control.

• Journal of Applied Reliability
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• v.3 no.2
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• pp.117-125
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• 2003

In sampling survey, prior-information about population has been generally ignored to estimate parameters. But if there is some believable prior-information about population, it is very useful to get more efficiency estimators by using the prior-information. This paper shows how to estimate the parameter, to evaluate the variance of the estimator, and to un-biasness of the estimator by using multiple stratification with prior-information about survey population. The proposed method is illustrated with a set of hypothetical data. The results show that the proposed estimator is very efficiency and strongly recommendable.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.3009-3015
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• 2013

Next-generation sequencing (NGS) has enabled whole genome and transcriptome single nucleotide variant (SNV) discovery in cancer and method of the most fundamental being determining an individual's genotype from multiple aligned short read sequences at a position. Bayesian algorithm estimate parameter using posterior genotype probabilities and other method, EM algorithm, estimate parameter using maximum likelihood estimate method in observed data. Here, we propose a novel genotype-calling system and compare and analyze the effect of sample size(S = 50, 100 and 500) on posterior estimate of sequencing error rate, somatic mutation status and genotype probability. The result is that estimate applying Bayesian algorithm even for 50 of small sample size approached real parameter than estimate applying EM algorithm in small sample more accurately.

• Journal of Environmental Science International
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• v.4 no.5
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• pp.427-435
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• 1995

In order to discuss the differences among the SMP(Surface Moisture Availability Parameter), by previous researchers on the basis of their own theoretical and empirical background, we assessed the SMP according to the soil types and volumetric soil water contents. The results are as follows. There are differences among all the five SMAPs. There's a tendency that the larger grain size, the higher value of parameters. And they divided into two groups for their value: one group has parameters with exponential function and the other with cosine and linear function. The maximum difference between the two groups appears when the volumetric soil water contents are 0.07m3m-3 for sand, 0.l1m3m-3 for loam, 0.12 for clay, and 0.13m3m-3 for silt loam. So, these differences must be considered when we estimate the surface evaporation rate. From field data, the paddy field soil around Junam reservoir is classified as a silt has high wetness, 0.56. So, the parameter obtained from the field measurement is much higher than that of Clapp and Hornberger(1978)'s Table. This study treated the SMP for a certain point of time in winter season. But if we measured the soil water contents continuously, we could obtain better time-dependent parameter. Key words : SMAP(Surface Moisture Availability Parameter), Paddy field, Volumetric soil water content, Evaporation, Capillary potential.

• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.135-141
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• 2006

This paper presents offline estimation of equivalent physical damping parameter in haptic interaction systems where damping is the most important parameter for stability. Based on the previous energy bounding algorithm, an offline procedure is developed in order to estimate the physical damping parameter of a haptic device by measuring energy flow-in to the haptic device. The proposed method does not use force/torque sensor at the handgrip. Numerical simulation and experiments verified effectiveness of the proposed method.

• Journal of Mechanical Science and Technology
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• v.17 no.10
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• pp.1443-1449
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• 2003

Primary goal of adaptive observers would be to estimate the true states of a plant. Identification of unknown parameters is of secondary interest and is achieved frequently with the persistent excitation condition of some regressors. Nevertheless, two problems are linked to each other in the classical approaches to adaptive observers; as a result, we get a good state estimate once after a good parameter estimate is obtained. This paper focuses on the state estimation without parameter identification so that the state is estimated regardless of persistent excitation. In this direction of research, Besancon (2000) recently summarized that most of adaptive observers in the literature share one common canonical form, in which unknown parameters do not affect the unmeasured states. We enlarge the class of linear systems from the canonical form of (Besancon, 2000) by proposing an adaptive observer (with additional dynamics) that allows unknown parameters to affect those unmeasured states. A recursive algorithm is presented to design the proposed dynamic observer systematically. An example confirms the design procedure with a simulation result.