• KSCE Journal of Civil and Environmental Engineering Research
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• v.8 no.2
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• pp.109-116
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• 1988

The strength and load of structure are varied with the Passage of time, and become a process of probability. It is possible to estimate the reliability from the relation between strength and load. Considering the safety, It is reasonable to estimate allowable stress from the safety factor based on the reliability. In this study, the method to estimate probability of fracture, which is index of reliability for rail subjeict to long term fatigue load were examined. In estimation of reliablity it is meaningless to evaluate numerical value especially this difficult case estimating parameter of random variable. To overcome this problem, conventional design method estimating relative reliability were proposed. In this study the Cornell method were examined. The uncertainity of random variale, ie coefficient of variation which is the index of variation of strength and load were considered. The effect of uncertainity related to probability of fracture, and safety factor based on reliability were examined. The results of this study are followed. The reliability of weld metal were influenced by variation of strength more then load, and base metal were influenced by load. It is confirmed that the allowable stress range calculating with factor of safety based an reliability is conservative.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.2
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• pp.37-45
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• 2022

A simplified model using the lifetime distribution has been presented to estimate the Mean Residual Life (MRL) of rubble-mound breakwaters, which is not like a stochastic process model based on time-dependent history data to the cumulative damage progress of rubble-mound breakwaters. The parameters involved in the lifetime distribution can be easily estimated by using the upper and lower limits of lifetime and their likelihood that made a judgement by several experts taking account of the initial design lifetime, the past sequences of loads, and others. The simplified model presented in this paper has been applied to the rubble-mound breakwater with TTP armor layer. Wiener Process (WP)-based stochastic model also has been applied together with Monte-Carlo Simulation (MCS) technique to the breakwater of the same condition having time-dependent cumulative damage to TTP armor layer. From the comparison of lifetime distribution obtained from each models including Mean Time To Failure (MTTF), it has found that the lifetime distributions of rubble-mound breakwater can be very satisfactorily fitted by log-normal distribution for all types of cumulative damage progresses, such as exponential, linear, and logarithmic deterioration which are feasible in the real situations. Finally, the MRL of rubble-mound breakwaters estimated by the simplified model presented in this paper have been compared with those by WP stochastic process. It can be shown that results of the presented simplified model have been identical with those of WP stochastic process until any ages in the range of MTT F regardless of the deterioration types. However, a little of differences have been seen at the ages in the neighborhood of MTTF, specially, for the linear and logarithmic deterioration of cumulative damages. For the accurate estimation of MRL of harbor structures, it may be desirable that the stochastic processes should be used to consider properly time-dependent uncertainties of damage deterioration. Nevertheless, the simplified model presented in this paper can be useful in the building of the MRL-based preventive maintenance planning for several kinds of harbor structures, because of which is not needed time-dependent history data about the damage deterioration of structures as mentioned above.

• Journal of the Korea Society of Computer and Information
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• v.9 no.4 s.32
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• pp.203-214
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• 2004

This paper presents a practical application of possibilistic programming in a information system projects planning model. The estimate of the parameters of the model is often a problematic task for the decision maker(DM), Who has imprecise information or express his considerations subjectively. In this case. possibilistic decision making models can provide an important aspect in handling practical decision making problems. We suggest CPM(Critical Path Method) applying possibilistic programming. CPM is an approach to planning and coordinating large projects by directing managerial focus to the project's most critical aspects and providing completion time of project and beginning time of each activity. This model is an aid in the control of considering aspiration levels by the DM, the fuzziness of decision making, and computational efficiency. The problem is solved by using GINO computer package and the best compromised solution is found.

• Journal of Labour Economics
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• v.43 no.3
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• pp.33-61
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• 2020

The purpose of this study is to analyze the disparity in on-the-job training between regular and non-regular workers and to compare the wage effects of on-the-job training. Using the Korean micro data from the Programme for the International Assessment of Adult Competencies(PIAAC) published by OECD, Propensity Score Method(PSM) is applied to overcome the endogeneity problem. The average treatment effect(ATT) on the training participation is analyzed, using non-regular workers as treatment group and regular workers as comparison group. Odds ratios of non-regular employees' training participation compared to regular employees shows 0.67 times after constructing matching sample and this means that non-regular workers are facing a disadvantageous training opportunity compared to regular workers. In order to estimate the wage effect of on-the-job training, the average treatment effect(ATT) of on-the-job training on average wages for regular workers and non-regular workers is estimated respectively. I find insignificant wage effect from on-the-job training among regular workers and significant positive effect on non-regular worker's wage from on-the-job training.

• Journal of the Korean Society of Propulsion Engineers
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• v.16 no.4
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• pp.33-41
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• 2012

The aim of the study is to investigate the method to estimate a shelf life of KM6 single base propellant by stochastic gamma process model. The state failure level is assumed that the degradation content of stabilizer is below 0.8%. The constant of time dependent shape function and the scale parameter of stationary gamma process are estimated by moment method. The state distribution at each storage time can be shown from probability density function of deterioration. It is estimated that the $B_{10}$ life, a time at which the cumulative failure probability is 10%, is 25 years and the $B_{50}$ life is 36 years from cumulative failure distribution function curve. The $B_{50}$ life can be treated as the average shelf life from the practical viewpoint and the lifetime can be expressed as distribution curve by using stochastic process theory.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.311-320
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• 2007

This paper presents vision-based 3D facial expression animation technique and system which provide the robust 3D head pose estimation and real-time facial expression control. Many researches of 3D face animation have been done for the facial expression control itself rather than focusing on 3D head motion tracking. However, the head motion tracking is one of critical issues to be solved for developing realistic facial animation. In this research, we developed an integrated animation system that includes 3D head motion tracking and facial expression control at the same time. The proposed system consists of three major phases: face detection, 3D head motion tracking, and facial expression control. For face detection, with the non-parametric HT skin color model and template matching, we can detect the facial region efficiently from video frame. For 3D head motion tracking, we exploit the cylindrical head model that is projected to the initial head motion template. Given an initial reference template of the face image and the corresponding head motion, the cylindrical head model is created and the foil head motion is traced based on the optical flow method. For the facial expression cloning we utilize the feature-based method, The major facial feature points are detected by the geometry of information of the face with template matching and traced by optical flow. Since the locations of varying feature points are composed of head motion and facial expression information, the animation parameters which describe the variation of the facial features are acquired from geometrically transformed frontal head pose image. Finally, the facial expression cloning is done by two fitting process. The control points of the 3D model are varied applying the animation parameters to the face model, and the non-feature points around the control points are changed by use of Radial Basis Function(RBF). From the experiment, we can prove that the developed vision-based animation system can create realistic facial animation with robust head pose estimation and facial variation from input video image.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.539-553
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• 2017

We propose a multi-state model to analyze semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the three states of the illness-death model: healthy, disease, and dead. The 'diseased' state can be considered as the intermediate event. Two more states are added into the illness-death model to incorporate the missing events, which are caused by a loss of follow-up before the end of a study. One of them is a state of the lost-to-follow-up (LTF), and the other is an unobservable state that represents an intermediate event experienced after the occurrence of LTF. Given covariates, we employ the Lin and Ying additive hazards model with log-normal frailty and construct a conditional likelihood to estimate transition intensities between states in the multi-state model. A marginalization of the full likelihood is completed using adaptive importance sampling, and the optimal solution of the regression parameters is achieved through an iterative quasi-Newton algorithm. Simulation studies are performed to investigate the finite-sample performance of the proposed estimation method in terms of empirical coverage probability of true regression parameters. Our proposed method is also illustrated with a dataset adapted from Helmer et al. (2001).

• Journal of Korean Society of Forest Science
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• v.71 no.1
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• pp.90-98
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• 1985

Inheritance of four isozymes, GOT, ACP, MDH and ADH, in Populus alba ${\times}$ P. glandulosa was investigated with starch gel electrophoresis. All four isozymes showed bands. For GOT, six or seven loci were postulated and observed segregation of hybrids at five variable loci was in agreement with expected segregation. Two loci were postulated in ACP; one locus showed no variation but the other locus showed variation. As one additional band was found in P. alba ${\times}$ P. alba (italy), hybrids from P. alba ${\times}$ P. alba ${\times}$ P. glandulosa showed more variation than hybrids from P. alba ${\times}$ P. glandulosa. One monomorphic locus and two variable loci were postulated in MDH. For ADH, both parents were turned out as homozygotes but for different alleles and thus all progenies were heterozygotes. ADH in hybrids seems to be a dieter enzyme as it showed on additional band between two parental bands. There were no variation in band betweens of four enzymes among the clones of P alba and P. glandulosa, respectively.

• KDI Journal of Economic Policy
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• v.13 no.4
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• pp.117-140
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• 1991

The purpose of this paper is primarily to introduce a nonparametric statistical tool developed by Baek and Brock to detect a unidirectional causal ordering between two economic variables and apply it to interesting macroeconomic relationships among money, production and prices. It can be applied to any other causal structure, for instance, defense spending and economic performance, stock market index and market interest rates etc. A key building block of the test for nonlinear Granger causality used in this paper is the correlation. The main emphasis is put on nonlinear causal structure rather than a linear one because the conventional F-test provides high power against the linear causal relationship. Based on asymptotic normality of our test statistic, the nonlinear causality test is finally derived. Size of the test is reported for some parameters. When it is applied to a money, production and prices model, some evidences of nonlinear causality are found by the corrected size of the test. For instance, nonlinear causal relationships between production and prices are demonstrated in both directions, however, these results were ignored by the conventional F-test. A similar results between money and prices are obtained at high lag variables.