• Journal of Embryo Transfer
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• v.29 no.3
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• pp.289-296
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• 2014

The pedigree information and race records of 1,000 m finishing time of Jeju race horses at KRA were used to study the effect of amount of pedigree information and parental misidentification on the estimates of genetic parameters. The modified data sets were made at the range of 2.5 to 25% parental misidentifications or loss of parental information of individuals with an increment of 2.5 percent. For each incremental level, 20 randomly replicated data sets were obtained and analyzed by single-trait analysis with a DF-REML(AI) algorithm. As the rate of misidentification increased or the amount of pedigree information decreased, the estimates of fraction of additive genetics variance component gradually decreased almost linearly (p<0.05), while the estimated fractions of error variance and permanent environmental variance components gradually increased for the finishing time. Regression coefficients of the percentage amount of both parents' information loss and incorrect pedigree information on additive genetic variances were -0.079 and -0.114, respectively (p<0.01). The estimate of heritability decreased by 0.92% for one percent loss of both parents' information and 1.39% for one percent increase of both parental misidentifications of progeny (p<0.01). For the consideration of probable incorrect and missing parent information of progeny in this early population of Jeju horses, the estimates of additive genetic parameters would be biased downward about ten percent. This results indicate that the amount of pedigree information loss and misidentification of progeny would severely affect estimates of genetic parameters and would reduce genetic gains for selection in Jeju horse population.

• Geophysics and Geophysical Exploration
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• v.10 no.3
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• pp.191-202
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• 2007

To identify the detailed attenuation structure in the southern Korean Peninsula, a numerical test was conducted for the Q tomography inversion to be applied to the accumulated dataset until 2005. In particular, the stochastic pointsource ground-motion model (STGM model; Boore, 2003) was adopted for the 2D Q tomography inversion for direct application to simulating the strong ground-motion. Simultaneous inversion of the STGM model parameters with a regional single Q model was performed to evaluate the source and site effects which were necessary to generate an artificial dataset for the numerical test. The artificial dataset consists of simulated Fourier spectra that resemble the real data in the magnitude-distance-frequency-error distribution except replacement of the regional single Q model with a checkerboard type of high and low values of laterally varying Q models. The total number of Q blocks used for the checkerboard test was 75 (grid size of $35{\times}44km^2$ for Q blocks); Q functional form of $Q_0f^{\eta}$ ($Q_0$=100 or 500, 0.0 < ${\eta}$ < 1.0) was assigned to each Q block for the checkerboard test. The checkerboard test has been implemented in three steps. At the first step, the initial values of Q-values for 75 blocks were estimated. At the second step, the site amplification function was estimated by using the initial guess of A(f) which is the mean site amplification functions (Yun and Suh, 2007) for the site class. The last step is to invert the tomographic Q-values of 75 blocks based on the results of the first and second steps. As a result of the checkerboard test, it was demonstrated that Q-values could be robustly estimated by using the 2D Q tomography inversion method even in the presence of perturbed source and site effects from the true input model.

• Geotechnical Engineering
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• v.12 no.6
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• pp.87-100
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• 1996

This paper verifies the accuracy and efficiency of the implicit stress integration algorithm for an anisotropic hardening constitutive model developed in a companion paper[Oh & Lee (1996)3. Simulation of undrained triaxial test results shows the accuracy of the method through an error estimation, and analyses of accuracy and convergence were performed for a numerical excavation problem. As a result, the stress was accurately integrated by the algorithm and the nonlinear solution was converged to be asymptotically quadratic. Furthermore nonlinear FE analysis of a real excavation problem was by performed considering the initial soil conditions and the in-situ construction sequences. The displacements of wall induced by excavation were more accurately estimated by the anisotropic hardening model than by the Cam-clay model.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.23 no.1
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• pp.51-54
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• 1978

3∼6m long plot with 3-4 replications will be practical for yield trials in the early hybrid generations. The C. V. values with 9m long plot was about 6.6％ in variety Yudal and 13.9％ in 12m plot. These results indicate that 9-12m plot with 3-4 replications could be employed in securate yield test in the advanced generations.

• Aerospace Engineering and Technology
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• v.11 no.2
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• pp.80-86
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• 2012

A fast maneuvering LEO satellite producing high resolution images was developed by Korea Aerospace Research Institute and launched successfully. To achieve accurate pointing and stringent pointing stability, the attitude orbit control subsystem implements high performance star trackers and gyroscopes. In addition, series of on-orbit calibration need to be performed to compensate mainly misalignment errors due to launch shock and on-orbit thermal environment. In this paper, the on-orbit calibration approach is described with the performance enhancement result through flight data analysis.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.649-653
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• 2005

To estimate for an error signal with sigmoid nonlinearity what reduced constellation applies closed eye pattern in the initial equalization, there can be improves problems of previous soft decision-directed algorithm that increasing estimate complexity and decreasing of convergence speed when substitute high-order constellation. The characteristic of sigmoid function is adjusted by a mean and a variance parameter, so it depends on adjustment of variance that what reduced constellation $values(\gamma)$ can have ranges between + $\gamma$ and - $\gamma$. In this paper, we proposed Variable Modulus Algorithm (VMA) that can be improving a performance of steady-state by adjustment of variance when equalization works normally and each cluster of constellation decrease.

• Journal of Korea Water Resources Association
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• v.35 no.5
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• pp.541-552
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• 2002

Two global optimization methods, the SCE-UA method and the Annealing-Simplex(A-S) method for calibrating a daily rainfall-runoff model, a Tank model, was compared with that of the Downhill Simplex method. In synthetic data study, 100% success rates for all objective functions were obtained from the A-S method, and the SCE-UA method was also consistently able to obtain good estimates. The Downhill Simplex method was converged to the true values only when the initial guess was close to the true values. In the historical data study, the A-S method and the SCE-UA method showed consistently good results regardless of objective function. An objective function was developed, which puts more weight on the low flows.

• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.9-19
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• 2013

This paper deals with fuzzy $H_{\infty}$ filtering problem of discrete-time nonlinear Markovian jump systems with state and output time delays. The purpose is to design fuzzy $H_{\infty}$ filter such that the corresponding estimation error system with time delays and initial state uncertainties is stochastically stable and satisfies an $H_{\infty}$ performance level. A sufficient condition for the existence of fuzzy $H_{\infty}$ filter is given in terms of matrix inequalities. In order to relax conservatism, a stochastic mode dependent fuzzy Lyapunov function is employed. The Lyapunov function not only is dependent on the operation modes of system, but also includes the fuzzy membership functions. An illustrative example is finally given to show the applicability and effectiveness of the proposed method.

• International Area Studies Review
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• v.16 no.1
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• pp.275-301
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• 2012

This study analyzes the regional disparity in India between 24 states over the period 1980 to 2009. The traditional regressive and spatial autoregressive models are used that includes measures of spatial effects. The results provide no evidence that convergence is valid in India. However, the results indicate that spatial interaction is an important element of state growth in India. The result of spatial analysis excluded two outliner states reveals more strong relationship between the weighted spatial income level and the state growth rates. Moreover, the results find that the coefficients of spatial lag of initial per capital and error terms are significantly negative. The coefficient of variation measures that the distribution of state income level has diverged over time. Therefore, this study concludes that the growth of regional state income does not have a tendency to converge rater than diverge. The results is rational because as the Indian economy is growing rapidly, some states grow faster than the others while initial poor states become the poorest ones, which increases regional disparity in India.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.847-856
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• 2013

We propose a modified learning process for generalized neural network using a learning algorithm by Liu et al. (2001). We consider the effect of initial weights, training results and learning errors using a modified learning process. We employ an incremental training procedure where training patterns are learned systematically. Our algorithm starts with a single training pattern and a single hidden layer neuron. During the course of neural network training, we try to escape from the local minimum by using a weight scaling technique. We allow the network to grow by adding a hidden layer neuron only after several consecutive failed attempts to escape from a local minimum. Our optimization procedure tends to make the network reach the error tolerance with no or little training after the addition of a hidden layer neuron. Simulation results with suitable initial weights indicate that the present constructive algorithm can obtain neural networks very close to minimal structures and that convergence to a solution in neural network training can be guaranteed. We tested these algorithms extensively with small training sets.