• Proceedings of the Korean Institute of Building Construction Conference
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• 2008.11a
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• pp.159-165
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• 2008

For deciding the profitability and feasibility of the construction project, the schematic estimation has to not only link the design decision-making but also estimate the cost with reliability. The Object-based schematic estimation system was developed for easily linking with design-making and supports to evaluate the design alternatives in the design development stage but didn't consider the cost estimated by object supplementary and parameter work item. This research presents the Integrated Object-Parameter Schematic Estimation Model in the design development stage that can lead to more accurately estimate the cost through analyzing historical data from the high-storied office buildings. For the development of the proposed model for schematic estimation, after analyzing and classifying the work items from the Bills of Quantities(BOQs) and drawings of historical data, this research proposed the methods of estimating cost in accordance with attributes of each work item using regression analysis. In addition, a case study is performed for the effectiveness as comparing the proposed model with the previous estimating model.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.197-207
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• 2015

In baseball game, the hitting ability of batter is frequently assessed by a batting average, a run batted in, a home run, a run scored, an on-base percentage, etc. Recently, more comprehensive indicators such as OPS, ISO, SECA, TA, RC and XR are often used. But, these measures generally shows large deviations since they are calculated from the data for a certain period of time, and they are not an estimate of a population parameter, either. In this paper, we will presume the pure hitting ability of the korea professional baseball players as a parameter which is depend upon at bat. We will estimate the parameter by using the Bayesian method.

• Journal of Environmental Science International
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• v.4 no.5
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• pp.41-41
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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 0.07$m^3m^{-3}$ for sand, 0.l1$m^3m^{-3}$ for loam, 0.12 for clay, and 0.13$m^3m^{-3}$ 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.

• Quantitative Bio-Science
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• v.37 no.2
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• pp.133-141
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• 2018

Graphical lasso is one of the most popular methods to estimate a sparse precision matrix, which is an inverse of a covariance matrix. The objective function of graphical lasso imposes an ${\ell}_1$-penalty on the (vectorized) precision matrix, where a tuning parameter controls the strength of the penalization. The selection of the tuning parameter is practically and theoretically important since the performance of the estimation depends on an appropriate choice of tuning parameter. While information criteria (e.g. AIC, BIC, or extended BIC) have been widely used, they require an asymptotically unbiased estimator to select optimal tuning parameter. Thus, the biasedness of the ${\ell}_1$-regularized estimate in the graphical lasso may lead to a suboptimal tuning. In this paper, we propose a two-staged bias-correction procedure for the graphical lasso, where the first stage runs the usual graphical lasso and the second stage reruns the procedure with an additional constraint that zero estimates at the first stage remain zero. Our simulation and real data example show that the proposed bias correction improved on both edge recovery and estimation error compared to the single-staged graphical lasso.

• Nuclear Engineering and Technology
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• v.48 no.3
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• pp.684-701
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• 2016

The Gaussian process model (GPM) is a flexible surrogate model that can be used for nonparametric regression for multivariate problems. A unique feature of the GPM is that a prediction variance is automatically provided with the regression function. In this paper, we estimate the safety margin of a nuclear power plant by performing regression on the output of best-estimate simulations of a large-break loss-of-coolant accident with sampling of safety system configuration, sequence timing, technical specifications, and thermal hydraulic parameter uncertainties. The key aspect of our approach is that the GPM regression is only performed on the dominant input variables, the safety injection flow rate and the delay time for AC powered pumps to start representing sequence timing uncertainty, providing a predictive model for the peak clad temperature during a reflood phase. Other uncertainties are interpreted as contributors to the measurement noise of the code output and are implicitly treated in the GPM in the noise variance term, providing local uncertainty bounds for the peak clad temperature. We discuss the applicability of the foregoing method to reduce the use of conservative assumptions in best estimate plus uncertainty (BEPU) and Level 1 probabilistic safety assessment (PSA) success criteria definitions while dealing with a large number of uncertainties.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.3
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• pp.99-109
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• 2009

Negative binomial yield model for semiconductor manufacturing consists of two parameters which are the average number of defects per die and the clustering parameter. Estimating the clustering parameter is quite complex because the parameter has not clear closed form. In this paper, a Bayesian approach using Markov Chain Monte Carlo is proposed to estimate the clustering parameter. To find an appropriate estimation method for the clustering parameter, two typical estimators, the method of moments estimator and the maximum likelihood estimator, and the proposed Bayesian estimator are compared with respect to the mean absolute deviation between the real yield and the estimated yield. Experimental results show that both the proposed Bayesian estimator and the maximum likelihood estimator have excellent performance and the choice of method depends on the purpose of use.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.1
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• pp.48-55
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• 2018

The purpose of the present study is to compare the reduction rate of natural frequency and the increase rate of damping parameter with structural damage in system. For this purpose, experiment and numerical simulation analysis are performed for the 2-span H-Beam with lower natural frequency and higher damping parameter from free vibration in structure. The response signal by impact load before and after damage is analyzed at 14 locations. The response signals for all locations are performed fast fourier transform to estimate the natural frequency reduction rate and wavelet transform to estimate the damping parameter increase rate. The time domain function corresponding to each scale(frequency) is separated from the response signal by wavelet parameter. The estimation of damping parameter increase rate using wavelet transform is more sensitive than the estimation of natural frequency reduction rate in structure.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1257-1268
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• 2011

The thermal behavior of wood exposed to the outdoors is influenced by solar absorptivity and longwave emissivity. However, it is difficult to measure that properties directly. Hence we estimate the values of the parameter by using the least-square optimization technique. Finally we report the results for the computation of the values of the parameters.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.563-565
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• 2005

In this study, a algorithm to estimate Equivalent earth resistivity and Earth parameter using Artificial Neural Network(ANN) was proposed. Structures of the soil are grouped by using SOM algorithm before estimation. Earth parameter and Equivalent earth resistivity are obtained by using BP algorithm. The effectiveness of the proposed algorithm was verified. In the case study. afterwards, the algorithm proposed in this study will be used in more applications and gained more reliability.

• 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.