• Journal of Preventive Medicine and Public Health
• /
• v.36 no.4
• /
• pp.339-348
• /
• 2003

Objective : To quantify the economic value of the Sirolimus fluting Stent ($CYPHER^{TM}$) in treating acute coronary heart disease (CMD), and to assist in determining an adequate level of reimbursement for $CYPHER^{TM}$ in Korea. Methods : A decision-analytical model, developed by the Belgium Health Economics Disease Management group, was used to investigate the incremental cost-effectiveness of $CYPHER^{TM}$ versus conventional stenting. The time horizon was five years. The probabilities for clinical events at each node of the decision model were obtained from the results of large, randomized, controlled clinical trials. The initial care and follow-up direct medical costs were analyzed. The initial costs consisted of those for the initial procedure and hospitalization, The follow-vp costs included those for routine follow-up treatments, adverse reactions, revascularization and death. Defending on the perspective of the analysis, the costs were defined as insurance covered or total medical costs (=sum of insurance covered and uncovered medical costs). The cost data were obtained from the administrative data of 449 patients that received conventional stenting from five participating Korean hospitals during June 2002. Sensitivity analyses were peformed for discount rates of 3, 5 and 7%. Since the major clinical advantage of $CYPHER^{TM}$ over conventional stenting was the reduction in the revascularization rates, the economic value of $CYPHER^{TM}$, in relation to the direct medical costs of revascularization, were evaluated. If the incremental cost of $CYPHER^{TM}$ per revascularization avoided, compared to conventional stenting, was no higher than that of a revascularization itself, $CYPHER^{TM}$ would be considered as being cost-effective. Therefore, the maximum acceptable level for the reimbursement price of $CYPHER^{TM}$ making the incremental cost-effectiveness ratio equal to the cost of a revascularization was identified. Results : The average weighted initial insurance covered and total medical costs of conventional stenting were about 6,275,000 and 8,058,000 Won, respectively. The average weighted sum of the initial and 5-year follow-up insurance covered and total medical costs of conventional stenting were about 13,659,000 and 17,353,000 Won, respectively. The estimated maximum level of reimbursement price of $CYPHER^{TM}$ from the perspectives of the insurer and society were $4,126,897{\sim}4,325,161$ and $4,939,939{\sim}5,078,181$ Won, respectively. Conclusion : By evaluating the economic value of $CYPHER^{TM}$, as an alternative to conventional stenting, the results of this study are expected to provide a scientific basis for determining the acceptable level of reimbursement for $CYPHER^{TM}$.

• Economic and Environmental Geology
• /
• v.54 no.1
• /
• pp.21-33
• /
• 2021

The purpose of this study is to determine the order of priority for the use of amendments, matching the optimal amendment to the specific site in Korea. This decision-making process must prioritize the stabilization and economic efficiency of amendment for heavy metals and metalloid based on domestic site contamination scenarios. For this study, total 5 domestic heavy metal contaminated sites were selected based on different pollution scenarios and 13 amendments, which were previously studied as the soil stabilizer. Batch extraction experiments were performed to quantify the stabilization efficiency for 8 heavy metals (including As and Hg) for 5 soil samples, representing 5 different pollution scenarios. For each amendment, the analyses using XRD and XRF to identify their properties, the toxicity characteristics leaching procedure (TCLP) test, and the synthetic precipitation leaching procedure (SPLP) test were also conducted to evaluate the leaching safety in applied site. From results of batch experiments, the amendments showing > 20% extraction lowering efficiency for each heavy metal (metalloid) was selected and the top 5 ranked amendments were determined at different amount of amendment and on different extraction time conditions. For each amendment, the total number of times ranked in the top 5 was counted, prioritizing the feasible amendment for specific domestic contaminated sites in Korea. Mine drainage treatment sludge, iron oxide, calcium oxide, calcium hydroxide, calcite, iron sulfide, biochar showed high extraction decreasing efficiency for heavy metals in descending order. When the economic efficiency for these amendments was analyzed, mine drainage treatment sludge, limestone, steel making slag, calcium oxide, calcium hydroxide were determined as the priority amendment for the Korean field application in descending order.

• Journal of Intelligence and Information Systems
• /
• v.22 no.4
• /
• pp.177-192
• /
• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• The Journal of Engineering Geology
• /
• v.29 no.2
• /
• pp.85-97
• /
• 2019

It can be observed that steep slopes ($65^{\circ}$ to $80^{\circ}$) consist of rock masses were kept stable for a long time. In rock-mass slopes with similar ground condition, steeper slopes than 1 : 0.5 ($63^{\circ}$) may be applied if the discontinuities of rock-mass slope are distributed in a direction favorable to the stability of the slope. In making a decision the angle of the slope, if the preliminary rock mass conditions applicable to steep slope are quantitatively setup, they may be used as guidance in design practice. In this study, the above rock mass was defined as a good continuum rock mass and the quantitative setup criterion range was proposed using RMR, SMR and GSI classifications for the purpose of providing engineering standard for good continuum rock mass conditions. The methods of study are as follows. The stable slope at steep slopes ($65^{\circ}$ to $80^{\circ}$) for each rock type was selected as the study area, and RMR, SMR and GSI were classified to reflect the face mapping results. The results were reviewed by applying the calculated shear strength to the stable analysis of the current state of rock mass slope using the Hoek-Brown failure criterion. It is intended to verify the validity of the preliminary criterion as a rock mass condition that remains stable on a steep slope. Based on the analysis and review by the above research method, it was analyzed that a good continuum rock mass slope can be set to Basic RMR ${\geq}50$ (45 in sedimentary rock), GSI and SMR ${\geq}45$. The safety factor of the LEM is between Fs = 14.08 and 67.50 (average 32.9), and the displacement of the FEM is 0.13 to 0.64 mm (average 0.27 mm). This can be seen as a result of quantitative representation and verification of the stability of a good continuum rock mass slope that has been maintained stable for a long period of time with steep slopes ($65^{\circ}$ to $80^{\circ}$). The setup guideline for a good continuum rock mass slope will be able to establish a more detailed setup standard when the data are accumulated, and it is also a further study project. If stable even on steep slopes of 1 : 0.1 to 0.3, the upper limit of steep slopes is 1 : 0.3 with reference to the overseas design standards and report, thus giving the benefit of ensuring economic and eco-friendlyness. Also, the development of excavation technology and plantation technology and various eco-friendly slope design techniques will help overcome psychological anxiety and rapid weathering and relaxation due to steep slope construction.

• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1095-1105
• /
• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.

• Journal of Intelligence and Information Systems
• /
• v.19 no.1
• /
• pp.35-55
• /
• 2013

Information technology is a critical resource necessary for any company hoping to support and realize its strategic goals, which contribute to growth promotion and sustainable development. The selection of information technology and its strategic use are imperative for the enhanced performance of every aspect of company management, leading a wide range of companies to have invested continuously in information technology. Despite researchers, managers, and policy makers' keen interest in how information technology contributes to organizational performance, there is uncertainty and debate about the result of information technology investment. In other words, researchers and managers cannot easily identify the independent factors that can impact the investment performance of information technology. This is mainly owing to the fact that many factors, ranging from the internal components of a company, strategies, and external customers, are interconnected with the investment performance of information technology. Using an agent-based simulation technique, this research extracts factors expected to affect investment performance on information technology, simplifies the analyses of their relationship with economic modeling, and examines the performance dependent on changes in the factors. In terms of economic modeling, I expand the model that highlights the way in which product quality moderates the relationship between information technology investments and economic performance (Thatcher and Pingry, 2004) by considering the cost of information technology investment and the demand creation resulting from product quality enhancement. For quality enhancement and its consequences for demand creation, I apply the concept of information quality and decision-maker quality (Raghunathan, 1999). This concept implies that the investment on information technology improves the quality of information, which, in turn, improves decision quality and performance, thus enhancing the level of product or service quality. Additionally, I consider the effect of word of mouth among consumers, which creates new demand for a product or service through the information diffusion effect. This demand creation is analyzed with an agent-based simulation model that is widely used for network analyses. Results show that the investment on information technology enhances the quality of a company's product or service, which indirectly affects the economic performance of that company, particularly with regard to factors such as consumer surplus, company profit, and company productivity. Specifically, when a company makes its initial investment in information technology, the resultant increase in the quality of a company's product or service immediately has a positive effect on consumer surplus, but the investment cost has a negative effect on company productivity and profit. As time goes by, the enhancement of the quality of that company's product or service creates new consumer demand through the information diffusion effect. Finally, the new demand positively affects the company's profit and productivity. In terms of the investment strategy for information technology, this study's results also reveal that the selection of information technology needs to be based on analysis of service and the network effect of customers, and demonstrate that information technology implementation should fit into the company's business strategy. Specifically, if a company seeks the short-term enhancement of company performance, it needs to have a one-shot strategy (making a large investment at one time). On the other hand, if a company seeks a long-term sustainable profit structure, it needs to have a split strategy (making several small investments at different times). The findings from this study make several contributions to the literature. In terms of methodology, the study integrates both economic modeling and simulation technique in order to overcome the limitations of each methodology. It also indicates the mediating effect of product quality on the relationship between information technology and the performance of a company. Finally, it analyzes the effect of information technology investment strategies and information diffusion among consumers on the investment performance of information technology.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.95-112
• /
• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of the East Asian Society of Dietary Life
• /
• v.13 no.1
• /
• pp.25-38
• /
• 2003

This study was conducted to establish the microbiological quality standards applying the HACCP system on sushi items of Japanese restaurant in Korea. The study evaluated hygienic conditions of kitchen and workers, pH time-temperature relationship, and microbial assessments during whole process of sushi making in 2001. Overall hygienic conditions were normal for both kitchen and for workers by 3 point scale, but hygienic controls against the cross-contamination were still needed. Each process of sushi making was performed under the risk of microbial contamination, since pH value of most of ingredients was over pH 4.6 and also production time(3.5~6 hrs) were long enough to cause problems. Microorganisms were high enough to cause foodborne illness ranged 8.0$\times$10$^2$~3.3$\times$10$^{6}$ CFU/g of TPC and 1.0$\times$10$^1$~1.6$\times$10$^3$CFU/g of coliforms, although TPC, coliforms and Staphylcoccus aureus were within the standard limits (TPC 10$^2$~10$^{6}$ CFU/g, coliforms 10$^3$CFU/g). However, Salmonella and Vibrio parahaemolyticus were not detected. High populations TPC and coliforms were also found in the cooks' hands and cooking utensils(TPC 10$^2$~10$^{6}$ CFU/100cm$^2$and Coliforms 10$^1$~10$^3$CFU/100cm$^2$). Based on the CCP decision tree analysis, the CCPs were the holding steps far six sushi production line except the tuna and the thawing step for tuna sushi. In conclusion, overall state of sushi production was fairly good but much improvement was still needed.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
• /
• v.38
• /
• pp.327-376
• /
• 2008

China is marking 9.4% annual growth rate in average since 1978. GDP reached $1090 in 2003 as the first time and China ranked at 4th with their economy size in 2006. One of the remarkable change in China is the extension of foreign open-door policy. China joined WTO in the end of 2001 and it strengthen the foundation of Chinese market economy structure and encouraged the inflow of foreign capital. While 400 of the 500 global corporations advanced into China, the economy trade has been rapidly increasing between Korea and China. The economy trade in both countries has been regularized since 1992 and the annual trade is tending upwards in last 15 years. Korean trade toward China reached 134,400 million which is increased 27 times compared with the total of 1982. In this period, Korean trade toward China marked 24.5% in Export increasing rate and 16.7% in import increasing rate. China became the 2nd biggest export country of Korea in 2001 and became the top in 2003. As the China foreign direct investment has been increasing rapidly, the number of Korean companies advanced into China has been remarkably increasing. By focusing on a thorough review of the nationally published documents of Korean-Chinese business management research during more than two decades (1981-2004), the present paper has been systematically classified and analyzed the current status of Korean-Chinese business management research. The paper raised some important issues regarding Korean-Chinese business management research and predominantly, its future prospects are outlined. In the paper, the documents which are registered in the Korean Academic Processing Foundation registration of journals and candidate registration of journals have been classified by: research purpose, main subject, research method and the results. Careful analysis among the research clarified the active and inactive business management research fields. This clarification enables us to get a better understanding of the current research of Korean-Chinese business management, and more importantly, it pointed out to the direction of future development of research. In addition, the systematic classification made by this paper may contribute to the decision making of subject index of Korean-Chinese business management research since there has been no classification standard of it until now.

• Korea Journal of Hospital Management
• /
• v.8 no.3
• /
• pp.143-160
• /
• 2003

Due to the dramatic and situational change in medical industry, it has became very important to keep existing patients and to attract new patients by monitoring the medical consumer's expectation and various needs and ensuring the patients' satisfaction. This study regards the patients' satisfaction as the final object of medical service. So the object of this study is to provide useful data for the decision making and medical service marketing by exploring the problems generated by the cognition difference for the medical service between inpatients and outpatients, by responding for the problems and by examining the relationship between the satisfaction with the medical service and revisiting. To achieve the object of this study, literature research and empirical analysis were used. I establish the research model based on the existing service marketing and some hypotheses were chosen for the empirical analysis. As a result of empirical analysis for the five hypotheses, two hypotheses were chosen. First, there was cognition difference about accessibility and convenience between inpatient and outpatient. I guess that the satisfaction degree of inpatient is higher than the outpatient because the inpatient has the reliability for the hospital and determines the hospitalization or emergent coming to hospital. Second, the fifth hypothesis, "the satisfaction of patient will influence the revisiting." was chosen. The hypothesis is not only coincident with existing scholars and studies but also it provides the meaningful points for medical service marketing. The result shows that the parties concerned with hospital management should endeavor for the patient satisfaction in medical service, and that hospital management should be medical consumer centered. To measure the quality of medical service, the cognition differences for accessibility, convenience, physical environment, and human service were evaluated and the result shows that the cognition difference for the accessibility and convenience was outstanding. The analysis shows that there was cognition difference in the four categories among six subcategories in the human service -- the attitude of medical technologist, the attitude of doctor, the length of time for doctor's diagnosis for the patient and doctor's explanation. Therefore, I think that further study is required for the cause analysis for service categories which have cognition difference between inpatient and outpatient. I think the result will be very useful. Through this study, the relationship between patient satisfaction with the medical service and revisiting was verified. And it suggests that, to face the changing medical environment actively and to improve the quality of medical service, marketing strategy should be focused not on medical service providers but on medical service consumers and that the further studies for the medical consumer should be continued.