• Journal of the Korean Society of Safety
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• v.10 no.2
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• pp.10-16
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• 1995

In estimating the fatigue data, only the uniform safety rate has been applied so far However, since more reasonable design concepts such as machine structures or subsidiary materials will be required in the future, the importance of a statistical estimation method for fatigue data is being highlighted. With such basic conception in mind, this study was aimed at critically discussing the interval estimation method which has been applied using the classical statistics thus far It was conceived that this conventional method would result in the estimation of the unstable side from the viewpoint of the likelihood Interval estimation method. In this regard, this study aimed at estimating the fatigue strength through the likelihood interval estimation method comparing it with the conventional interval estimation method would result in the estimation of the unstable side from the viewpoint of the likelihood interval estimation method. One of the methods using the likelihood for estimation data is the Bayes method. Based on this theory, statistical estimations were positivly applied, and thereupon, the fatigue data were estimated.

• Journal of The Korean Digital Architecture Interior Association
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• v.10 no.3
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• pp.11-18
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• 2010

Cost estimation through fast and correct quantity take offs are crucial in the process of construction project. The existing methods for cost estimation are mainly based on 2D-based drawings and the estimation result tends to be different according to the estimator's experience, the quality and quantity of used information and estimation time. To solve these problems, the domestic construction industry have recently tried to use the data extracted from 3D data modeling based on BIM(Building Information Modeling) in order to achieve more accurate and objective cost estimation. However it tends to increase dramatically the quantity of information that can be used in cost estimation by estimators. Therefore in order to achieve quality information data from 3D data modeling, the characteristics of the project should be reflected on the 3D model and it is most important to extract information only for cost estimation from the whole 3D model fast and accurately. Thus this study aims to propose the 3D modeling method through Data Normalization which maximizes the usability of 3D Data modeling in cost estimation process.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.449-451
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• 2003

The estimation of time-series data is fundamental process in many data analysis cases. However, the unwanted measurement error is usually added to true data, so that the exact estimation depends on efficient method to eliminate the error components. The wavelet transform method nowadays is expected to improve the accuracy of estimation, because it is able to decompose and analyze the data in various resolutions. Therefore, the wavelet based Kalman filter method for the estimation of time-series data is proposed in this paper. The wavelet transform separates the data in accordance with frequency bandwidth, and the detail wavelet coefficient reflects the stochastic process of error components. This property makes it possible to obtain the covariance of measurement error. We attempt the estimation of true data through recursive Kalman filtering algorithm with the obtained covariance value. The procedure is verified with the fundamental example of Brownian walk process.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.14-17
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• 2005

Global latent heat flux data sets are crucial for many studies such as those related to air-sea interaction and climate variation. Currently, various global latent heat flux data sets are constructed using satellite data. Japanese Ocean Flux data sets with Use of Remote sensing Observations (J-OFURO) includes one of the satellite-derived global latent heat flux data (Kubota et aI., 2000). In this study, we review future development of J-OFURO global latent heat flux data set. In particular, we investigate usage of multi-satellite data for estimating accurate global latent heat flux. Accurate estimation of surface wind speeds over the global ocean is one of key factors for the improved estimation of global latent heat flux. First, we demonstrate improvement of daily wind speed estimation using multi-satellites data from microwave radiometers and scatterometers such as DMSP/SSMI, ERS/AMI, QuikSCAT/SeaWinds, AqualAMSR-E, ADEOS2/AMSR etc. Next, we demonstrate improvement of global latent heat flux estimation using the wind speed data derived from multi-satellite data.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.537-547
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• 2011

In this article, we propose an estimation procedure for the treatment eect for the right censored data. We apply the least square method for deriving the estimation equation and obtain an explicit formula for an estimation. Then we consider some asymptotic properties with derivation of the asymptotic normality for the estimate. Finally we illustrate our procedure with an example and discuss some interesting aspects for the estimation procedure.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.6
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• pp.982-989
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• 2010

This paper shows accuracy comparison results of reliability estimation methods for one-shot systems such as ammunitions. Quantal-response data, following a binomial distribution at each sampling time, characterizes lifetimes of one-shot systems. Various quantal-response data of different sample sizes are simulated using lifetime data randomly sampled from assumed weibull distributions with different shape parameters but the identical scale parameter in this paper. Then, reliability estimation methods in open literature are applied to the simulated quantal-response data to estimate true reliability over time. Rankings in estimation accuracy for different sample sizes are determined using t-test of SSE. Furthermore, MSE at each time, including both bias and variance of estimated reliability metrics for each method are analyzed to investigate how much both bias and variance contribute the SSE. From the MSE analysis, MSE provides reliability estimation trend for each method. Parametric estimation method provides more accurate reliability estimation results than the other methods for most of sample sizes.

• Plant Journal
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• v.8 no.1
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• pp.50-59
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• 2012

The current dual-watchdog estimation system has individually calculated the construction, the engineering and the procurement cost. The dual-watchdog estimation system is inefficient and prolonged estimation period because of the lack of the interoperability and the difference of material unit cost and construction unit cost. In order to resolve this problem, new estimation software was developed. The estimation software is developed by making up for the weak points in existing estimation method. The cost data with the same standard is the key point. And this software enhanced accuracy and speed of the data search in stylized estimation standard. A summary of the construction, the engineering and the procurement cost was generated in this estimation software. The unit rate about the labor cost, equipment and expense through a sheet was handled. The developed estimation software has five categories on engineering cost, procurement cost, construction cost and subcontractor management sheet. In this study, the estimation software to supplement the faults of the existing estimation method was developed. And estimation software on petrochemical projects increases an efficiency of the estimation work.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.4
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• pp.212-218
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• 2010

This paper shows accuracy comparison results of reliability estimation methods for one-shot systems with respect to sample sizes. To compare accuracy in reliability estimation methods, quantal-response data, characterizing one-shot systems, were simulated using failure times of LED obtained through the accelerated life test, and then the true reliability over time was evaluated using the failure times. The simulated quantal-response data were used to estimate the true reliability through applying reliability estimation methods in open literature. Accuracy of each reliability estimation method was compared in terms of both SSE (Sum of Squared Error) and MSE (Mean Squared Error), and then estimation trend for each method is found. Feasible bounds which true reliability would exist within were estimated through applying the found trends to quantal-response data set of a real weapon system.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.93-107
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• 2002

Random effects generalised linear models are useful for analysing clustered count data in which responses are usually correlated. We propose a Bayesian approach to parameter estimation and variable selection in random effects generalised linear models for count data. A simple Gibbs sampling algorithm for parameter estimation is presented and a simple and efficient variable selection is done by using the Gibbs outputs. An illustrative example is provided.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.47-55
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• 2001

Usually estimating the means is used for statistical inference. However depending the purpose of survey, sometimes totals will give the better and more meaningful in statistical inference than the means. Here in this study, we dealt with the unemployment population of small areas with using 4 different small area estimation methods: Direct, Synthetic, Composite, Bayes estimation. For all the estimates considered in this study, the average of absolute bias and men square error were obtained in the Monte Carlo Study which was simulated using data from 1998 Economic Active Population Survey in Korea.