• Earthquakes and Structures
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• v.18 no.1
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• pp.73-82
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• 2020

With the coal mining depth increasing, both stress and gas pressure rapidly enhance, causing coal and gas outburst risk to become more complex and severe. The conventional method for prediction of coal and gas outburst adopts one prediction index and corresponding critical value to forecast and cannot reflect all the factors impacting coal and gas outburst, thus it is characteristic of false and missing forecasts and poor accuracy. For the reason, based on analyses of both the prediction indicators and the factors impacting coal and gas outburst at the test site, this work carefully selected 6 prediction indicators such as the index of gas desorption from drill cuttings Δh₂, the amount of drill cuttings S, gas content W, the gas initial diffusion velocity index ΔP, the intensity of electromagnetic radiation E and its number of pulse N, constructed the Bayes discriminant analysis (BDA) index system, studied the BDA-based multi-index comprehensive model for forecast of coal and gas outburst risk, and used the established discriminant model to conduct coal and gas outburst prediction. Results showed that the BDA - based multi-index comprehensive model for prediction of coal and gas outburst has an 100% of prediction accuracy, without wrong and omitted predictions, can also accurately forecast the outburst risk even for the low indicators outburst. The prediction method set up by this study has a broad application prospect in the prediction of coal and gas outburst risk.

• Korea Real Estate Review
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• v.27 no.3
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• pp.71-86
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• 2017

The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.176-183
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• 2019

The environmental changes in the Korean cosmetic medical service industry in the $21^{st}$ century are forming intense competition among medical institutions due to the quantitative expansion of its market. For stable growth of the cosmetic medical service industry, continuous quality improvement is necessary based on empirical research on the quality of cosmetic medical services rather than external expansion. The purpose of this study is to classify the quality attributes of cosmetic medical service using Kano model and to derive the degree of satisfaction and dissatisfaction of each quality attributes through Customer Satisfaction Coefficient (CSC). Through this, the study identified strategic priorities and suggested specific step-by-step approaches and quality improvement priorities that can increase customer satisfaction using the Potential Customer Satisfaction Improvement Index (PCSI Index). Based on SERVPERF, this study used measurement tools consisting of five dimensions : tangibles, reliability, responsiveness, assurance, and empathy. In addition, it was used of measurement items reconstructed into positive, negative, and satisfaction questions for Kano model analysis, CSC analysis, and PCSI Index analysis. A total of 300 medical consumers who experienced cosmetic medical services for the past one year in medical institutions such as plastic surgery and dermatology were collected with convenient sampling. As a result, urgent items for improving the quality of service using the PCSI Index, 'Consideration for customer benefits' in empathy category was followed by 'Immediate help' and 'Sincere response' in responsiveness category, and 'Understanding customer needs' in empathy category, respectively. That is, it is required to improve human service quality attributes such as empathy and responsiveness rather than physical service quality attributes. This study contributes practically in that it provides specific and discriminatory approaches to improve customer satisfaction on cosmetic medical service quality and suggests improvement priorities.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.2
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• pp.560-569
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• 2017

With a remarkable and rapid change of universities in Korea, the role of universities has been changed from a traditional education supplier to a demander. Both the remarkable decrease of the number of entering students and the advent of an open education market are enforcing the universities to be changed. Universities should provide a variety of educational services in accordance with the students' needs. This study is based on the systematical Kuh's model which the educational service quality provide a decision making of alternative plan. This research is purposed to empirically study the effects of educational service quality(circumstance index, input index, participate index, output index) provided by universities on student satisfaction and educational performance(Perceived usefulness, Education Achievement). The results of this research follows; first, circumstance index, input index and output index significantly effect student satisfaction, but participate index does not effect satisfaction. Second, student satisfaction significantly effects on the educational performance.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.219-234
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• 2010

In index investing according to KOSPI, we estimate Value at Risk(VaR) from the extreme losses of the daily returns which are obtained from KOSPI. To this end, we apply Block Maxima(BM) model which is one of the useful models in the extreme value theory. We also estimate the extremal index to consider the dependency in the occurrence of extreme losses. From the back-testing based on the failure rate method, we can see that the model is adaptable for the VaR estimation. We also compare this model with the GARCH model which is commonly used for the VaR estimation. Back-testing says that there is no meaningful difference between the two models if we assume that the conditional returns follow the t-distribution. However, the estimated VaR based on GARCH model is sensitive to the extreme losses occurred near the epoch of estimation, while that on BM model is not. Thus, estimating the VaR based on GARCH model is preferred for the short-term prediction. However, for the long-term prediction, BM model is better.

• Earthquakes and Structures
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• v.22 no.6
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• pp.609-623
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• 2022

To study the empirical seismic fragility of a reinforced concrete girder bridge, based on the theory of numerical analysis and probability modelling, a regression fragility method of a rapid fragility prediction model (Gaussian first-order regression probability model) considering empirical seismic damage is proposed. A total of 1,069 reinforced concrete girder bridges of 22 highways were used to verify the model, and the vulnerability function, plane, surface and curve model of reinforced concrete girder bridges (simple supported girder bridges and continuous girder bridges) considering the number of samples in multiple intensity regions were established. The new empirical seismic damage probability matrix and curve models of observation frequency and damage exceeding probability are developed in multiple intensity regions. A comparative vulnerability analysis between simple supported girder bridges and continuous girder bridges is provided. Depending on the theory of the regional mean seismic damage index matrix model, the empirical seismic damage prediction probability matrix is embedded in the multidimensional mean seismic damage index matrix model, and the regional rapid prediction matrix and curve of reinforced concrete girder bridges, simple supported girder bridges and continuous girder bridges in multiple intensity regions based on mean seismic damage index parameters are developed. The established multidimensional group bridge vulnerability model can be used to quantify and predict the fragility of bridges in multiple intensity regions and the fragility assessment of regional group reinforced concrete girder bridges in the future.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• The Journal of Information Systems
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• v.23 no.1
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• pp.161-184
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• 2014

Our study measures the Digital Life Index (DLI) of baby boomers by considering the utilization of digital devices in their everyday life. The study was conducted by implementing the following three-step approach: (1) development of survey questions and data collection; (2) build Digital Life Index (DLI) model and lastly; (3) empirical analysis using the Digital Life Index (DLI). In the first stage in order to develop the survey questions to measure the digital index, two surveys were conducted. For the first preliminary survey, it was done based on the existing literatures which enabled this investigation through FGI analysis involving real professionals. The second survey was conducted by commissioning a specialized external firm. In this survey, a total of 400 data was collected to verify the validity and objectivity of the data sample. The data gathered through the survey questions was used to develop the digital index. Firstly, the appropriate factors were extracted by conducting factor analysis. This factor analysis validates and verifies the factors which are appropriate in measuring Digital Life Index (DLI). The derived factors are broadly divided into five main factors. The first factor describes the possession, purchase and use of digital device (x1). Meanwhile, the second factor describes the digital device's software (x2) and the third factor describes the participation in utilizing digital device (x3). The fourth factor describes the utilization of digital device in human personal relationship (x4) and lastly, the fifth factor describes the effect of digital device in everyday life and work environment (x5). Secondly, the digital index model was developed. The variables to represent the Digital Life Index (DLI) are ${\chi}1t,{\chi}2t,{\chi}3t,{\chi}4t$ and ${\chi}5t$. Furthermore, as experience in using the digital index grows overtime, the growth can be represented by the "S" shape. Based on the results, Digital Life Index(DLI) is distributed with the highest point at 90.3 and the lowest point at 25.9.

• Korean Chemical Engineering Research
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• v.46 no.6
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• pp.1043-1051
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• 2008

In polyolefin processes melt index (MI) is the most important controlled variable indicating product quality. Because of the difficulty in the on-line measurement of MI, a lot of MI estimation and correlation methods have been proposed. In this work a new dynamic MI estimation scheme is developed based on system identification techniques. The empirical MI estimation equation proposed in the present study is derived from the $1^{st}$-order dynamic models. Effectiveness of the present estimation scheme was illustrated by numerical simulations based on plant operation data including grade change operations in high density polyethylene (HDPE) processes. From the comparisons with other estimation methods it was found that the proposed estimation scheme showed better performance in MI predictions. Using the model predictive control method based on the present dynamic MI estimation model, MI values are estimated and compared with those of MI setpoints. From the numerical simulation of the proposed control system, it was found that significant reduction of transition time and the amount of off-spec during grade changes were achieved.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.335-351
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• 2011

In order to resolve data scarcity problem related to crisis, Oh and Kim (2007) proposed to use stability oriented approach which focuses a base period of financial market, fits asymptotic stationary autoregressive model to the base period and then compares the fitted model with the current market situation. Based on such approach, they developed financial market instability index. However, since neural network, their major tool, depends on the base period too heavily, their instability index tends to suffer from inaccuracy. In this study, we consider linear asymptotic stationary autoregressive model and neural network to fit the base period and produce two instability indexes independently. Then the two indexes are combined into one integrated instability index via newly proposed combining method. It turns out that the combined instability performs reliably well.