• 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).

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.49.1-49
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• 2001

The skin color model is a very important concept in face detection, face recognition and face tracking. Usually, this model is obtained by estimating a probability density function of skin color distribution. In many cases, it is assumed that the underlying density function follows a Gaussian distribution. In this paper, a new method for non-parametric estimation of the probability density function, by using feed-forward neural network, is used to estimate the underlying skin color model. By using this method, the resulting skin color model is better than the Gaussian estimation and substantially approaches the real distribution. Applications to face detection and face ...

• Journal of the military operations research society of Korea
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• v.28 no.2
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• pp.56-69
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• 2002

A technique of finding both probability density and distribution function for interkilling times is considered and demonstrated. An important result is that any arbitrary interfiring time random variables fit to this study, The interfiring renewal density function given a certain interfiring probability density function can be applied to obtain the corresponding interkilling renewal density function which helps us to estimate the expected number of killing events in a time period. The numerical inversion of Laplace transformation makes these possible and the results appear to be excellent. In case of ammunition supply is limited, an alternative way of getting the probability density function of time to the killing is investigated. The convolution technique may give us a means of settling for this new problem.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.9 no.4
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• pp.163-169
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• 1984

We propose a new parameter estimation algorithm that converges 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 even though the parameters we estimate are multiparameter case. And the results are shown by the computer simulation.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.2
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• pp.231-235
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• 2013

The purpose of this paper is to compare with estimation of equivalent fatigue load in time domain and frequency domain and estimate the fatigue life of structure with multi-axial vibration loading. The fatigue analysis with two methods is implemented with various signals like random, sinusoidal signals. Also an equivalent fatigue life estimated by rainflow cycle counting in time domain is compared with results estimated with probability density function of each signal in frequency domain. In case of frequency domain, equivalent fatigue life can estimate through Dirlik's method with probability density function. And the work proposed in this paper compared the fatigue damage accumulated under uni-axial loading to that induced by multi-axial loading. The comparison is preformed for a simple cantilever beam, which is exposed to vibrations of several directions. For verification of estimation performance of fatigue life, results are compared to those of FEM analysis (ANSYS).

• Journal of the Korean Solar Energy Society
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• v.30 no.2
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• pp.54-64
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• 2010

Interest in new and renewable energies like solar energy and wind energy is increasing throughout the world due to the rapidly expanding energy consumption and environmental reasons. An essential requirement for wind force power generation is estimating the size of wind energy accurately. Wind energy is estimated usually using meteorological data or field measurement. This study attempted to estimate wind energy density using meteorological data on daily mean wind speed and the Weibull parameters in Seoul, a representative inland city where over 60% of 15 story or higher apartments in Korea are situated, and Busan, Incheon, Ulsan and Jeju that are major coastal cities in Korea. According to the results of analysis, the monthly mean probability density distribution based on the daily mean wind speed agreed well with the monthly mean probability density distribution based on the Weibull parameters. This finding suggests that the Weibull parameters, which is highly applicable and convenient, can be utilized to estimate the wind energy density distribution of each area. Another finding was that wind energy density was higher in coastal cities Busan and Incheon than in inland city Seoul.

• Journal of the Korean Statistical Society
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• v.29 no.1
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• pp.1-8
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• 2000

A spectrum minimizing the frequency-domain Kullback-Leibler information number has been proposed and used to modify a spectrum estimate. Some numerical examples have illustrated the KL spectrum estimate is superior to the initial estimate, i.e., the autocovariances obtained by the inverse Fourier transformation of the KL spectrum estimate are closer to the sample autocovariances of the given observations than those of the initial spectrum estimate. Also, it has been shown that a Gaussian autoregressive process associated with the KL spectrum is the closest in the timedomain Kullback-Leibler sense to a Gaussian white noise process subject to given autocovariance constraints. In this paper a corresponding conditional probability theorem is presented, which gives another rationale to the KL spectrum.

• Journal of Ocean Engineering and Technology
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• v.13 no.3 s.33
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• pp.29-35
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• 1999

At the design of Mark III membrane type LNG tank, an analytical and experimental approach on the fatigue strengths of membrane and its welds are very important in order to assist designers and surveyors. In this study, fatigue tests of lap weld of Mark III membrane type LNG tank were carried out and cumulative damage factor was calculated in order to estimate the fatigue life by probability density function and rule methods. It contained the following tests and reviews : 1) The fatigue tests of lap weld of stainless steel according to statistical testing method recommended by JSME, 2)Preparation of S-N curve for lap welds considering the statistical properties of the results of fatigue tests. 3) Procedure for estimating the initiation life of fatigue crack of lap welds under variable loads by the rule lf classification society and probability density function, 4) Guideline for inspection of lap welds fo membrane type LNG tank.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5095-5111
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• 2016

The Probability Hypothesis Density (PHD) filter is a suboptimal approximation and tractable alternative to the multi-target Bayesian filter based on random finite sets. However, the PHD filter fails to track newborn targets when the target birth intensity is unknown prior to tracking. In this paper, a dual detection-guided newborn target intensity PHD algorithm is developed to solve the problem, where two schemes, namely, a newborn target intensity estimation scheme and improved measurement-driven scheme, are proposed. First, the newborn target intensity estimation scheme, consisting of the Dirichlet distribution with the negative exponent parameter and target velocity feature, is used to recursively estimate the target birth intensity. Then, an improved measurement-driven scheme is introduced to reduce the errors of the estimated number of targets and computational load. Simulation results demonstrate that the proposed algorithm can achieve good performance in terms of target states, target number and computational load when the newborn target intensity is not predefined in multi-target tracking systems.