• 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 Communications and Networks
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• v.16 no.3
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• pp.301-304
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• 2014

This paper proposes accurate interference probability and bit error rate formulas for cognitive relay networks with underlay spectrum sharing and channel estimation error (CEE). Numerous results reveal that the CEE not only degrades the performance of secondary systems (SSs) but also increases interference power caused by SSs to primary systems (PSs), eventually unfavorable to both systems. A solution to further protect PSs from this effect through reducing the power of secondary transmitters is investigated and analyzed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.10
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• pp.1847-1853
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• 2008

This paper presents stochastic methodology based fault detection algorithm for induction motor systems. We measure current of healthy induction motors by means of hall sensor systems and then establish its probability distribution. We propose online probability density estimation which is effective in real-time implementation due to its simplicity and low computational burden. In addition, we accomplish theoretical analysis to demonstrate convergence property of the proposed estimation by using statistical convergence and system stability theory. We apply our fault diagnosis approach to three-phase induction motors and achieve real-time experiment for evaluating its reliability and practicability in industrial fields.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.311-316
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• 2009

This paper presents the motion estimation algorithm on real-time for mobile surveillance robot using particle filter. the particle filter that based on the monte carlo's sampling method, use bayesian conditional probability model which having prior distribution probability and posterior distribution probability. However, the initial probability density was set to define randomly in the most of particle filter. In this paper, we find first the initial probability density using Sum of Absolute Difference(SAD). and we applied it in the partical filter. In result, more robust real-time estimation and tracking system on the randomly moving object was realized in the mobile surveillance robot environments.

• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.443-455
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• 1999

A simulation-based approach to estimating the probability of an arbitrary region under a multivariate normal distribution is developed. In specific, the probability is expressed as the ratio of the unrestricted and the restricted multivariate normal density functions, where the restriction is given by the region whose probability is of interest. The density function of the restricted distribution is then estimated by using a sample generated from the Gibbs sampling algorithm.

• Water Engineering Research
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• v.4 no.3
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• pp.155-173
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• 2003

Parameters and quantiles of the 2-parameter generalized Pareto distribution were estimated using the methods of regular moments, modified moments, probability weighted moments, linear moments, maximum likelihood, and entropy for Monte Carlo-generated samples. The performance of these seven estimators was statistically compared, with the objective of identifying the most robust estimator. It was found that in general the methods of probability-weighted moments and L-moments performed better than the methods of maximum likelihood estimation, moments and entropy, especially for smaller values of the coefficient of variation and probability of exceedance.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4B
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• pp.365-376
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• 2009

The importance of resonable treatment of tide characteristics has been shown by Kweon, et al. (2005, 2006) for the crest elevation estimation because of a big difference of tidal elevation along Korean coast. For the procedure of crest elevation design, the expected overtopping probability (EOP) was estimated by Kweon, et al. (2006). The comparisons on each sea showed that EOP was lower east, south and west sea in order. The results involved the assumption that the tide and design level wave height meet any time in a year. However, big waves mainly occur in summer or winter in Korean coast, the study focuses on the encounter probability of big waves and seasonal tide level. A theory of the encounter probability is not derived by the present study but it shows reasonable acceptability of the proposed scenario in which the expected overtopping probability could be an index for the crest elevation estimation in Korean coast. The calculation based on the scenario gives the possibility range for the crest elevation estimation which has no tendency of each sea along Korean peninsular. The range is within the expected overtopping probability of 1% in the whole coast of Korea.

• Journal of Korean Academy of Nursing
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• v.41 no.6
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• pp.750-757
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• 2011

Purpose: Cost-benefit analysis is one of the most commonly used economic evaluation methods, which helps to inform the economic value of a program to decision makers. However, the selection of a correct benefit estimation method remains critical for accurate cost-benefit analysis. This paper compared benefit estimations among three different benefit estimation models. Methods: Data from community-based chronic hypertension management programs in a city in South Korea were used. Three different benefit estimation methods were compared. The first was a standard deterministic estimation model; second, a repeated-measures deterministic estimation model; and third, a transitional probability estimation model. Results: The estimated net benefit of the three different methods were $1,273.01, $-3,749.42, and $-5,122.55 respectively. Conclusion: The transitional probability estimation model showed the most correct and realistic benefit estimation, as it traced possible paths of changing status between time points and it accounted for both positive and negative benefits.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.1
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• pp.15-24
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• 2006

In simulation input modeling, it is important to identify a probability distribution to represent the input process of interest. In this paper, an appropriate sample size is determined for parameter estimation associated with some typical probability distributions frequently encountered in simulation input modeling. For this purpose, a statistical measure is proposed to evaluate the effect of sample size on the precision as well as the accuracy related to the parameter estimation, square rooted mean square error to parameter ratio. Based on this evaluation measure, this sample size effect can be not only analyzed dimensionlessly against parameter's unit but also scaled regardless of parameter's magnitude. In the Monte Carlo simulation experiments, three continuous and one discrete probability distributions are investigated such as ; 1) exponential ; 2) gamma ; 3) normal ; and 4) poisson. The parameter's magnitudes tested are designed in order to represent distinct skewness respectively. Results show that ; 1) the evaluation measure drastically improves until the sample size approaches around 200 ; 2) up to the sample size about 400, the improvement continues but becomes ineffective ; and 3) plots of the evaluation measure have a similar plateau pattern beyond the sample size of 400. A case study with real datasets presents for verifying the experimental results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.11
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• pp.1758-1764
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• 2012

A large number of wire rope has been used in various inderstiries as Cranes and Elevators from expanding the scale of the industrial market. But now, the management of wire rope is used as manually operated by rope replacement from over time or after the accident.It is caused to major accidents as well as economic losses and personal injury. Therefore its time to need periodic fault diagnosis of wire rope or supply of real-time monitoring system. Currently, there are several methods has been reported for fault diagnosis method of the wire rope, to find out the feature point from extracting method is becoming more common compared to time wave and model-based system. This method has implemented a deterministic modeling like the observer and neural network through considering the state of the system as a deterministic signal. However, the out-put of real system has probability characteristics, and if it is used as a current method on this system, the performance will be decreased at the real time. And if the random noise is occurred from unstable measure/experiment environment in wire rope system, diagnostic criterion becomes unclear and accuracy of diagnosis becomes blurred. Thus, more sophisticated techniques are required rather than deterministic fault diagnosis algorithm. In this paper, we developed the fault diagnosis of the wire rope using probability density estimation techniques algorithm. At first, The steady-state wire rope fault signal detection is defined as the probability model through probability distribution estimate. Wire rope defects signal is detected by a hall sensor in real-time, it is estimated by proposed probability estimation algorithm. we judge whether wire rope has defection or not using the error value from comparing two probability distribution.