• Korean Journal of Logic
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• v.8 no.2
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• pp.33-58
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• 2005

The old evidence problem raises a profound problem to Bayesian theory of confirmation that evidence known prior to a hypothesis explaining it cannot give any empirical support to the hypothesis. The old evidence problem has resisted to a lot of trials to solve it. The purpose of the paper is to solve the old evidence problem by showing that the problem originated from a serious misunderstanding about the Bayesian concept of confirmation. First, I shall make a brief analysis of the problem, and examine critically two typical Bayesian strategies to solve it. Second, I shah point out a misunderstanding commonly found among Bayesian discussions about the old evidence problem, the ignorance of the asymmetry of confirmation in the context of explanation and prediction. Lastly, 1 shall suggest two different concepts of confirmations by using the asymmetry and argue that the concept of confirmation presupposed in the old evidence problem is not a genuine Bayesian concept of confirmation.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.169-169
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• 1999

최근 산업 플랜트의 공정제어 시스템은 복잡하고 대규모화되어 고장 발생시 경제적 손실과 위험성이 증폭되어 규정된 안정서와 신뢰성 확보가 필수적이라 할 수 있다. 고장검출 및 진단기법은 시스템의 신뢰성을 높이기 위한 효과적인 방안을 연구하는 것으로 현대에 들어서 많은 학자들의 관심을 끌고 있으며 실제 계통에 점차적으로 응용되고 있다. 현재까지 개발된 고장검출 및 진단기법은 사용된 프로세스 모델의 형태, 고장검출 진단 알고리즘에 따라 다양하게 분류 될 수 있으며 일반적으로 사용된 모델에 따라 크게 1) 정량적 모델에 근거한 해석적 기법, 2) 정성적 모델에 근거한 기법, 3) 지식기반 진단 기법으로 구분 할 수 있다. 이중 정량적 모델 기법은 대상계통의 수학적 모델에 근거하여 운전 데이터를 분석함으로서 고장검출 진단을 수행하는 해석적 기법으로서 근본적으로 계통의 정확한 수학적 모델을 요구하므로 불확실성을 포함한 계통 및 비선형성이 강한 계통등에는 적용이 곤란하다. 정성적 모델 및 지식기반 기법은 정량적 진단 기법과는 달리 대상 프로세스에 대한 수학적 모델 대신에 운전자의 경험과 프로세스 변수간의 상호 작용 및 고장의 전파과정, 고장원인과 증상과의 직접적인 관계에 대한 구조적 지식에 근거한 것으로 고장원인에 대한 계통의 동작을 추론 할 수 있으며, 상황 변화에 따른 영향을 예측할 수 있다. 본 논문에서는 정성적 모델 및 지식기반 기법에 근거한 고장검출 및 진단 기술을 화력 발전소 보일로 프로세스에 적용하여 정성적 시뮬레이션에 의한 설비의 고장을 조기에 발견하여 고장 파급으로 인한 발전 정지 및 설비의 손상 확대를 방지하고 고장 발생시 신속한 원인 규명 및 후속 조치관련 정보들을 운전원에게 제공할 목적으로 현재 전력원에서 개발중인 지능형 경보시스템에 대하여 기술하고자 한다.음과 같이 설명하였다. 서로 상반되는 것들이 다음과 같이 설명하였다. 서로 상반되는 것들이 부딛힘이 없이 공존하고 일상의 논리가 무시된다. 부정, 의심이 없고 확실한 것이 없다. 한 대상에 가졌던 생각이 다른 대상에 옮겨간다(displacement). 한 대상이 여러 대상이 갖고 있는 의미를 함축하고 있다(condensation). 시각적인 순서가 무시된다. 마음속의 생각과 외부의 실제적인 일을 구분하지 못한다. 시간 상의 순서가 있다가 없다가 한다. 차례로 일어나야 할 일이 동시에 한꺼번에 일어난다. 대상들이 서로 비슷해지고 동시에 있을 수 없는 대상들이 함께 나타난다. 사고의 정상적인 구조가 와해된다. Matte-Blance는 무의식에서는 여러 독립된 대상들간의 구분을 없애며, 주체와 객체를 하나로 보려는 대칭화(symmetrization)의 경향이 있기 때문에 이런 변화가 생긴다고 하였다. 또 대칭화가 진행되면 무한대의 느낌을 갖게 되어, 전지(moniscience), 전능(omnipotence), 무력감(impotence), 이상화(idealization)가 나타난다. 그러나 무의식에 대칭화만 있는 것은 아니며, 의식의 사고양식인 비대칭도 어느 정도 나타나며, 대칭화의 정도에 따라, 대상들이 잘 구분되어 있는 단계, 의식수준의 감정단계, 집단 내에서의 대칭화 단계, 집단간에서의 대칭화 단계, 구분이 없어지는 단계로 구분하였다.systems. We believe that this taxonomy is a significant contribution because it adds clarity, completeness, and "global perspective" to workflow architectural discussions. The vocabulary suggested here

• Journal of the Korean Chemical Society
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• v.42 no.6
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• pp.646-651
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• 1998

The molecular structure of sulfur compounds and the retention relationship are studied by gas chromatography. Analyzed sulfur compounds are, hydrogen sulfide, sulfur dioxide, carbon disulfide, ethyl mercaptan, dimethyl sulfide, iso-propyl mercaptan, normal propyl mercaptan, ethyl methyl sulfide, tert-butyl mercaptan, tetrahydrothiophene, thiophene, and 2-chlorothiophene. Multiple linear regression explains the retention relationship of molecular descriptors. In GC the temperature program is 30$^{\circ}C$ held for 10.5 min, and then increased to 150$^{\circ}C$ at a rate 15$^{\circ}C$/min. Predicted equation for relative retention time (RRT) using SAS program is as follows; $RRT=0.121bp+14.39dp-8.94dp^2+0.0741sqmw-35.78\; (N=8,\; R^2=0.989, \;Variance=0.175,\;F=66.21)$. RRTs are function of boiling point, the square root of molecular weight, molecular dipole moment, and boiling point effects mostly on RRT. The RRT is maximized at the molecular dipole moment of 0.805D, when using nonpolar columns. The planar and highly symmetric compounds are eluted slowly. The square, of correlation coefficient $(R^2)$ using SAS program, is 0.989, and the variance is 0.175 in training sets. For three sulfur compounds, the variance between observed RRTs and predicted RRTs is 0.432 in testing sets.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.39-48
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• 2024

In this paper, we introduce an extended GARCH model designed to address asymmetric leverage effects. The variance in the standard GARCH model is composed of past conditional variances and past squared residuals. However, it is not possible to model asymmetric leverage effects with squared residuals alone, so in this paper, we propose a new extended GARCH model to explain the leverage effects using the Yeo-Johnson transformation which adjusts transformation parameter to make asymmetric data more normal or symmetric. We utilize the reverse properties of Yeo-Johnson transformation to model asymmetric volatility. We investigate the characteristics of the proposed model and parameter estimation. We also explore how to derive forecasts and forecast intervals in the proposed model. We compare it with standard GARCH and other extended GARCH models that model asymmetric leverage effects through empirical data analysis.

• 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.