• Tunnel and Underground Space
• /
• v.16 no.1 s.60
• /
• pp.26-37
• /
• 2006

This study addresses the applicability of box fractal dimension, $D_B$, as an index of statistical homogeneity of fractured rock mass. The box-count method's capability in quantifying the combined effect of fracture density and size distribution is examined systematically. Total of 129 two-dimensional fracture configurations were generated based on different combinations of fracture size distribution and fracture density. $D_B$was calculated for the generated fracture network systems using the box-counting method. It was found that was standard deviation of trace length and fracture orientation have no effect on calculated $D_B$. The estimated $D_B$ was found to increase with increasing total density and/or mean trace length. To explore the field applicability of this study, the statistical homogeneity of fractured rock mass was investigated at the rock slope and the underground facility using the box-counting method as well as conventional contingency table analysis. The results obtained in this study clearly show that the methodologies given in this paper have the capability of determining the statistical homogeneity of fractured rock mass.

• Food Science and Industry
• /
• v.52 no.1
• /
• pp.40-51
• /
• 2019

For better understanding the relationship between consumers' perception and sensory characteristics of products, diverse types of rapid sensory profiling technique have been suggested as alternatives to conventional descriptive analysis. Among these, check-all-that-apply (CATA) method has gained popularity for studying consumers' perception and intuitive responses to products due to their simplicity, speed, and ease of use. CATA method has been used to gather consumers' perception derived from sensory characteristics of products as well as consumers' emotion responses to products in recent years. Moreover, many researchers reported that CATA method can be used to provide valuable information for product optimization by applying a penalty analysis and collecting responses to ideal product. Thus, this article reviews recent research using CATA in the field of sensory and consumer science and introduces practical applications to achieve various business objectives in food industry.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.107-122
• /
• 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) Korean Journal of Educational Psychology
• /
• v.23 no.3
• /
• pp.645-663
• /
• 2009

The purpose of this study is to develop and verify a Korean Diagnostic Scale of Multiple Intelligence(MI), which will be an alternative test to avoid problems with former Shearer's MI test and to adopt H. Gardner's suggestions to develop MI assessment. The test is developed 5 types; kindergartner, elementary lower grader, elementary upper grader, middle schooler, high schooler test. A form of test is diversified with 3 types; multiple-choice items for accomplishment, true or false items for ability, and self-reported items with likert scale for interest and ability. According to H. Gardner's suggestions, we have tried to reanalyze key component of MI, analyze an overlapping or hierarchical relationship between intelligences, develop intelligences-fair items, diversify form of item. We have developed a final standardized test through a primary, secondary preliminary-test analysis, and sampled 5,585 students by age, gender, and regional groups. As a result of this sampling test, we can get a norm score and compare individuals with other's score relatively. To verify this test, we analyzed behavior observation, mean, standard deviation, a percentage of correct answers, reliability of each test type, correlation between intelligence scales, Kruskal-Wallis test of mean rank of career choice by intelligences. As a result of correlation analysis between sub-intelligence scales, we can conclude that this MI test is satisfied with intelligence independent assumption. Besides, as non-parametric statistics test(Kruskal-Wallis) of career choice by intelligences, we can identify that MI is related with domain of career choice. This test is not a linguistic and logical-mathematical biased test but a intelligences-fair test. It makes us compare individual's potential with a norm score. Besides, it could be useful as a means of educational prescription or counsel in comparison with ability, interest, and accomplishment of individual. But this test is limited to do factor or correlation analysis between types of sub-test, because items are minimized for a time-constraint and a heavy burden of test receiver. But if it could be tested with increased items by two sessions, further research could be expected to get over this constraints and do a further validation analysis.