• Structural Engineering and Mechanics
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• v.34 no.1
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• pp.109-133
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• 2010

This paper addresses the modified Dynamic Relaxation algorithm, called mdDR by minimizing displacement error between two successive iterations. In the mdDR method, new relationships for fictitious mass and damping are presented. The results obtained from linear and nonlinear structural analysis, either by finite element or finite difference techniques; demonstrate the potential ability of the proposed scheme compared to the conventional DR algorithm. It is shown that the mdDR improves the convergence rate of Dynamic Relaxation method without any additional calculations, so that, the cost and computational time are decreased. Simplicity, high efficiency and automatic operations are the main merits of the proposed technique.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.113-117
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• 1998

We propose a way to show the inherent learning complexity for the multilayer perceptron. We display the solution space and the error surfaces on the input space of a single neuron with two inputs. The evolution of its weights will follow one of the two error surfaces. We observe that when we use the back-propagation(BP) learning algorithm (1), the wight cam not jump to the lower error surface due to the implicit continuity constraint on the changes of weight. The self-relaxation approach is to explicity find out the best combination of all neurons' two error surfaces. The time complexity of training a multilayer perceptron by self-relaxationis exponential to the number of neurons.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.228-230
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• 1993

In this paper, to investigate the activation energy, dipole moment, relaxation time, and so on related to charged particles more completely, it was applied numerical method of asymptotic estimation to separate single relaxation from TSC spectra with a complex relaxation. As a result, we could calculated the error of physical factors related to charged particles of specimen, more accurately.

• Journal of Korean Academy of Nursing
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• v.30 no.2
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• pp.282-292
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• 2000

A meta-analysis of 14 quasi-experimental studies was conducted to compare the effect of size on various relaxation therapies applied to patients and health volunteer students. These studies were selected from theses, dissertations and papers that have been done between 1982 to 1993. Also They have a randomized or nonequivalent control group in a pre test-post test design. The studies were evaluated in different ways; 1) types of relaxation therapy, 2) total amount of time of relaxation therapy, and 3) types of outcome variables. For a group of homogenious studies, the weighted mean effect size and standard error were estimated. Some findings are summarized as follows : 1. Jacobson relaxation therapy had a larger effect on systolic and diastolic blood pressures than on state anxiety. 2. For the total time of relaxation therapy, (longer than 60 minutes) had a much larger effect in decreasing systolic and diastolic blood pressures than in the case of a time period shorter than 60 minutes. 3. Relaxation therapy applied to surgery patients also had a larger effect in decreasing state anxiety than when applied to other patients.

• Atmosphere
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• v.20 no.3
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• pp.287-306
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• 2010

An ensemble Kalman filter (EnKF) with Weather Research and Forecasting (WRF) Model is applied for Typhoon Wukong (200610) to investigate the performance of ensemble forecasts depending on experimental configurations of the EnKF. In addition, the ensemble sensitivity analysis is applied to the forecast and analysis ensembles generated in EnKF, to investigate the possibility of using the ensemble sensitivity analysis as the adaptive observation guidance. Various experimental configurations are tested by changing model error, ensemble size, assimilation time window, covariance relaxation, and covariance localization in EnKF. First of all, experiments using different physical parameterization scheme for each ensemble member show less root mean square error compared to those using single physics for all the forecast ensemble members, which implies that considering the model error is beneficial to get better forecasts. A larger number of ensembles are also beneficial than a smaller number of ensembles. For the assimilation time window, the experiment using less frequent window shows better results than that using more frequent window, which is associated with the availability of observational data in this study. Therefore, incorporating model error, larger ensemble size, and less frequent assimilation window into the EnKF is beneficial to get better prediction of Typhoon Wukong (200610). The covariance relaxation and localization are relatively less beneficial to the forecasts compared to those factors mentioned above. The ensemble sensitivity analysis shows that the sensitive regions for adaptive observations can be determined by the sensitivity of the forecast measure of interest to the initial ensembles. In addition, the sensitivities calculated by the ensemble sensitivity analysis can be explained by dynamical relationships established among wind, temperature, and pressure.

• Journal of Korea Multimedia Society
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• v.6 no.5
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• pp.788-796
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• 2003

This paper proposes using dynamic momentum for squential learning method. Using The dynamic momentum improves convergence speed and performance by the variable momentum, also can identify it in the RMSE(root mean squared error). The proposed method is reflected using variable momentum according to current state. While static momentum is equally influenced on the whole, dynamic momentum algorithm can control the convergence rate and performance. According to the variable change of momentum by training. Unlike former classification and regression problems, this paper confirms both performance and regression rate of the dynamic momentum. Using RMSE(root mean square error ), which is one of the regression methods. The proposed dynamic momentum has been applied to the kernel adatron and kernel relaxation as the new sequential learning method of support vector machine presented recently. In order to show the efficiency of the proposed algorithm, SONAR data, the neural network classifier standard evaluation data, are used. The simulation result using the dynamic momentum has a better convergence rate, performance and RMSE than those using the static moment, respectively.

• Journal of Welding and Joining
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• v.17 no.6
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• pp.84-89
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• 1999

Most of ferrous b.c.c weld materials may experience martensitic transformation during rapid cooling after welding. It is well known that volume expansion due to the phase transformation could influence on the relaxation of welding residual stress. To apply this effect practically, it is a prerequisite to establish a numerical model which is able to estimate the effect of phase transformation on residual stress relaxation quantitatively. For this purpose, the analysis is carried out in two regions. i.e., heating and cooling, because the variation of material properties following a phase transformation in cooling is different in comparison with the case in heating, even at the same temperature. The variation of material properties following phase transformation is considered by the adjustment of specific heat and thermal expansion coefficient, and the distribution of residual stress in analysis is compared with that of experiment by previous study. consequently, in this study, simplified numerical procedures considering phase transformation, which based on a commercial finite element package was established through comparing with the experimental data of residual stress distribution by other researcher. To consider the phase transformation effect on residual stress relaxation, the transition of mechanical and thermal property such as thermal expansion coefficient and specific heat capacity was found by try and error method in this analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12C
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• pp.1082-1087
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• 2008

Maximum Likelihood (ML) detection is well known to exhibit better bit-error-rate (BER) than many other detectors for multiple-input multiple-output (MIMO) channel. However, ML detection has been shown a difficult problem due to its NP-hard problem. It means that there is no known algorithm which can find the optimal solution in polynomial-time. In this paper, Semi-Definite relaxation (SDR) is iteratively applied to ML detection problem. The probability distribution can be obtained by survival eigenvector out of the dominant eigenvalue term of the optimal solution. The probability distribution which is yielded by SDR is recurred to the received signal. Our approach can reach to nearly ML performance.

• Transactions of Materials Processing
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• v.13 no.6 s.70
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• pp.509-514
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• 2004

An analysis using FEM simulation was conducted to predict residual stresses and birefringence in simple compressed cylindrical glass as a preliminary part of the optimum design determination of optical lenses. The FEM simulation with the Maxwell viscoelastic constitutive model was used to predict thermal induced residual stresses and birefringence during the compression test considering stress relaxation. Also the linear photoelastic theory was introduced to calculate birefringence from the residual stress state. The error of simulation results between experimental results in the birefringence value at the center of glass specimen is $4.2\%$, and the error in the maximum radius of deformed glass specimen is $1.2\%$. The simulation results were in good agreement with deformation and birefringence distribution in the existing experimental result.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.151-156
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• 2002

There are two methods in evaluation student s achievement. The two evaluation methods are absolute evaluation and relative evaluation. They have much advantages respectively, but also have some limitations such as being too stereotyped or causing overcompetition among learners. This paper suggests a new evaluation method which evaluates student s achievements by considering the score distribution and the frequency The proposed method classifies the scores into several clusters considering the goodness. This approach calculates the goodness by applying the RE(relaxation error), and grades the achievement scores based on the goodness. The suggested method can avoid the problem of grading caused by the narrow gap of scores because it sets a standard for grading by the calculated goodness considering the score distribution and frequency of occurrence. The method can differentiate achievements of a school from those of others, and that it is useful for selecting advanced students and dull ones, and for evaluation of classes based on student s achievement.