• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.647-663
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• 2017

Weibull distribution is a popular distribution for modeling lifetimes because it reflects the characteristics of failure adequately and it models either increasing or decreasing failure rates simply. It is a standard method of the lifetimes test to wait until all samples failed; however, censoring can occur due to some realistic limitations. In this paper, we propose a generalized likelihood ratio (GLR) chart to monitor changes in the scale parameter for type I right-censored Weibull lifetime data. We also compare the performance of the proposed GLR chart with two CUSUM charts proposed earlier using average run length (ARL). Simulation results show that the Weibull GLR chart is effective to detect a wide range of shift sizes when the shape parameter and sample size are large and the censoring rate is not too high.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.4
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• pp.383-389
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• 2011

In this paper, a statistical calibration method is proposed in order to identify the variability of complex modulus for a rubber material due to operational temperature and experimental/model errors. To describe temperature- and frequency-dependent material properties, a fractional derivative model and a shift factor relationship are used. A likelihood function is defined as a product of the probability density functions where experimental values lie on the model. The variation of the fractional derivative model parameters is obtained by maximizing the likelihood function. Using the proposed method, the variability of a synthetic rubber material is estimated and applied to a rubber mount problem. The dynamic characteristics of the rubber mount are calculated using a finite element model of which material properties are sampled from Monte Carlo simulation. The calculated dynamic stiffnesses show very large variation.

• Korean Journal of Cognitive Science
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• v.17 no.4
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• pp.303-321
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• 2006

According to Harris'(1966) distributional hypothesis, understanding the meaning of a word is thought to be dependent on its context. Under this hypothesis about human language ability, this paper proposes a computational model for native speaker's language processing mechanism concerning word sense disambiguation, based on two sets of experiments. Among the three computational models discussed in this paper, namely, the logic model, the probabilistic model, and the probabilistic inference model, the experiment shows that the logic model is first applied fer semantic disambiguation of the key word. Nexr, if the logic model fails to apply, then the probabilistic model becomes most relevant. The three models were also compared with the test results in terms of Pearson correlation coefficient value. It turns out that the logic model best explains the human decision behaviour on the ambiguous words, and the probabilistic inference model tomes next. The experiment consists of two pans; one involves 30 sentences extracted from 1 million graphic-word corpus, and the result shows the agreement rate anong native speakers is at 98% in terms of word sense disambiguation. The other pm of the experiment, which was designed to exclude the logic model effect, is composed of 50 cleft sentences.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6C
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• pp.259-266
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• 2011

Conducting statistical analysis of foundation resistance using sufficient number of well-performed load test results is prerequisite for the calibration of reliable resistance factors for foundation LRFD. In this study, a rational analysis method is proposed so that the proof pile load test results can be reflected in update of resistance statistical characteristics based on Bayesian theory. Then, resistance factors for driven steel pipe piles compatible with Korea foundation practices are updated by implementing this rational analysis method. To accomplish the resistance factor updates, (1) prior pile resistance distribution is constructed based on the results of pile load tests, which loads are imposed at least up to their ultimate limit loads. (2) likelihood function is obtained from the results of proof pile load tests, and (3) posterior pile resistance distribution is updated by combining these prior pile resistance distribution and likelihood function. The resistance factors are updated using the posterior pile resistance following the first-order reliability method (FORM). From the possible results of five consecutive proof pile load tests, the updated resistance factors vary within ranges of 0.27-0.96 and 0.19-0.68 for target reliability indices of 2.33 and 3.0, respectively. Consequently, it was found that the Bayesian theory-implemented method enables the updates of resistance factors in an efficient way when reliable resistance factors are not available due to the lack of well-performed pile load test results.

• Journal of Korean Society of Transportation
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• v.21 no.3
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• pp.15-22
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• 2003

In general, the linear regression model has been used to estimate trip generation in the travel demand forecasting procedure. However, the model suffers from several methodological limitations. First, trips as a dependent variable with non-negative integer show discrete distribution but the model assumes that the dependent variable is continuously distributed between -$\infty$ and +$\infty$. Second, the model may produce negative estimates. Third, even if estimated trips are within the valid range, the model offers only forecasted trips without discrete probability distribution of them. To overcome these limitations, a poisson model with a assumption of equidispersion has frequently been used to analyze count data such as trip frequencies. However, if the variance of data is greater than the mean. the poisson model tends to underestimate errors, resulting in unreliable estimates. Using overdispersion test, this study proved that the poisson model is not appropriate and by using Vuong test, zero inflated negative binomial model is optimal. Model reliability was checked by likelihood test and the accuracy of model by Theil inequality coefficient as well. Finally, marginal effect of the change of socio-demographic characteristics of households on trips was analyzed.

• Tuberculosis and Respiratory Diseases
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• v.57 no.5
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• pp.419-424
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• 2004

Background : Although bronchodilator reversibility testing is widely performed to diagnose asthma or COPD, there is debate upon its usefulness and methods to differentiate asthma from COPD. The purpose of this study is to elucidate the role of bronchodilator reversibility testing in differentiating asthma from COPD and to confirm which method is better at evaluating bronchodilator reversibility. Methods : 26 asthma patients and 31 COPD patients were reviewed retrospectively. Spirometry was performed before and after bronchodilator inhalation to get $FEV_1$, FVC. To evaluate bronchodilator reversibility, the increase in $FEV_1$ or FVC was expressed as three methods, 'percentage of the baseline value', 'percentage of the predicted value', or 'absolute value'. Area under the ROC curve was measured to compare the three methods. In addition, the criteria of American Thoracic Society (ATS) for bronchodilator reversibility were compared to those of European Respiratory Society (ERS). Results : 1. In differentiating asthma from COPD, 'percentage of the predicted value', or 'absolute value' method was useful but 'percentage of the baseline value' was not. However, the ability to differentiate was weak because areas under the ROC curves by all methods were less than 0.75. 2. The criteria of ERS were superior to those of ATS for bronchodilator reversibility to differentiate asthma from COPD because likelihood ratio (LR) of a positive test by ERS criteria was greater than ATS criteria and because LR of a negative test by ERS criteria was less than ATS criteria. Conclusion : In differentiating asthma from COPD, bronchodilator reversibility testing has a weak role and should be considered as an adjunctive test.

• Journal of Animal Science and Technology
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• v.46 no.3
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• pp.427-436
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• 2004

The current study was conducted to determine the effect of suspension method on satisfaction level of Korean consumers and objective meat quality traits in Hanwoo longissimus dorsi(LD), triceps brachii(TB) and semimembranosus(SM) muscles. Eighteen Hanwoo steers were slaughtered and alternative sides were hung either by pelvic bone(TS) or Achilles tendon(Al). Sensory characteristics, WB-shear force, sarcomere length, water-holding capacity, saroomere length and cooking loss were determined after a 7-d chiller ageing. Higher carcass quality grade received significantly(p < 0.05) greater eating quality for LD, but the grade did not affect eating quality for both TB and SM. TS did not influence objective and subjective meat quality for TB, but that significantly(P < 0.05) improved eating quality for LD and SM. The most noticeable result was that when SM was tenderstretehed, eating quality was equivalent to that of nonna1ly hung LD. In relationship between objective and subjective meat quality assessments, eating qualty for LD had a significant(P < 0.05) relationship with intramuscular fat content, while that for SM was greatly(P < 0.05) related to saroomere length. The current study indicated that pelvic hanging was an effective way to improve eating quality both LD and SM, and carcass quality grades did not greatly reflect eating quality of SM and TB. The data also implied that instnunental measurements poorly estimated the satisfaction level of Korean conswners.

• 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.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.9
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• pp.735-742
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• 2015

In this paper, we compare and verify the prediction accuracy and feasibility for wind resources on a wind farm using the Weather Research and Forecasting (WRF) model, which is a numerical weather-prediction model. This model is not only able to simulate local weather phenomena, but also does not require automatic weather station (AWS), satellite, or meteorological mast data. To verify the feasibility of WRF to predict the wind resources required from a wind farm pre-feasibility study, we compare and verify measured wind data and the results predicted by WAsP. To do this, we use the Pyeongdae and Udo sites, which are located on the northeastern part of Jeju island. Together with the measured data, we use the results of annual and monthly mean wind speed, the Weibull distribution, the annual energy production (AEP), and a wind rose. The WRF results are shown to have a higher accuracy than the WAsP results. We therefore confirmed that WRF wind resources can be used in wind farm pre-feasibility studies.

• Journal of the Korean Geophysical Society
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• v.3 no.3
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• pp.161-174
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• 2000

Bayesian inversion is a stable approach to infer the subsurface structure with the limited data from geophysical explorations. In geophysical inverse process, due to the finite and discrete characteristics of field data and modeling process, some uncertainties are inherent and therefore probabilistic approach to the geophysical inversion is required. Bayesian framework provides theoretical base for the confidency and uncertainty analysis for the inference. However, most of the Bayesian inversion require the integration process of high dimension, so massive calculations like a Monte Carlo integration is demanded to solve it. This method, though, seemed suitable to apply to the geophysical problems which have the characteristics of highly non-linearity, we are faced to meet the promptness and convenience in field process. In this study, by the Gaussian approximation for the observed data and a priori information, fast Bayesian inversion scheme is developed and applied to the model problem with electric well logging and dipole-dipole resistivity data. Each covariance matrices are induced by geostatistical method and optimization technique resulted in maximum a posteriori information. Especially a priori information is evaluated by the cross-validation technique. And the uncertainty analysis was performed to interpret the resistivity structure by simulation of a posteriori covariance matrix.