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

• Korean Journal of Construction Engineering and Management
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• v.10 no.4
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• pp.26-37
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• 2009

Ministry of Land, Transportation and Marine Affairs(MLTM) has implemented Construction CALS project for improving productivity of construction industry and for making construction project management efficient by responding to informatization and knowledge base society in 21st century. CALS has beeb applied successfully to projects form MLTM, however, the outcomes of Construction CALS hasn't been recognized. In addition, there is no way to find how high the level of Construction CALS is when it is compared with other SOC informatization projects. This study found out the informatiziation level of Construction CALS by using the evaluation index proposed in the former study, and by comparing with other sectors. The evaluation on the level was implemented in the three parts such as informatization infrastructure(network, hardware, standardization, data, informatization, informatization security), informatization utilization(information usage, IT performance), and informatization support(informatization goal, organization of informatization, informatization investment, informatization education), and then, this evaluation was compared with "Assessment for level of industry information system", "Assessment for level of small and midium sized industry information system", and "IICI(Informatizaion Index for the Construction Industry)". With the result from the comparison, this study produced superior factors and inferior factors for each sector. These results are expected to be useful for prioritizing budget allocation by finding out the informatization level of Construction CALS.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.97-107
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• 2015

Up until recently, Korea has ranked the first place in UN e-Government Survey for three consecutive years. In keeping with such accomplishment, the size of budget execution has been consistently growing in accordance with Korea's Government 3.0 policy and vision, leading to increase in big-sized informatization projects in the business. Especially in mission critical public sector's infrastructure where it affects many people, growing demand for establishing high-quality information system with new technologies being brought to attention in order to meet the complex needs of citizens. National defense information system, being one of representative domains examples in the concerned area, established high military competency by applying breakthrough technology. Network-oriented national defense knowledge informatization was set as the vision in order to implement core roles in making efficient national defense management; and effort has been made to materialize the vision by making advancement in national defense's information system and its informatization implementation system. This research studies new quality index relevant to test and evaluation (T&E)of informatization business in national defense which is the representative example of mission critical public sector's infrastructure. We studied international standards and guidelines, analyzed actual T&E cases, and applied them to the inspection items that are currently in use, complying with the e-government law (Act No. 12346, Official Announcement Date 2014. 1.28., Enforcement Date 2014. 7.29.) As a result of productivity analysis, based on hypothesis in which suggested model was applied to T&E of the national defense informatization business, we confirmed the possibility of enhancement in the T&E productivity by assessing reliability, expertise, and safety as evaluation factors.

• Journal of Technology Innovation
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• v.24 no.4
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• pp.1-26
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• 2016

The period after the PhD has a huge impact on the careers of researcher from a researcher lifecycle perspective. This is a turning point which student receives guidance from professor and become an independent researcher. Furthermore, they learn to develop ideas for independent research, apply for grants and manage a project; they also form expert networks in related filed and publish papers to share their findings. This study focuses on the period between earning doctoral degree and being employed as a stable position in university. This study starts from a research questions that asks which factors of postdoctoral experience affect research output. In this study, the paper performance, especially co-authorship of paper, of postdoctoral researchers was investigated. The cumulative advantage theory and Matthew effect were employed to shed a light on this research question. The empirical work is based on the Survey & Analysis of National R&D program in Korea conducted by Korea Institute of S&T Evaluation and Planning (KISTEP). The correlations between the research output and characteristics of postdoctoral experience were verified. These results are expected to contribute as new empirical evidences on investigating knowledge transfer activities of new PhDs.

• Information Systems Review
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• v.10 no.2
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• pp.137-158
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• 2008

Rapid development of various information technologies creates new opportunities in online and offline markets. In this changing market environment, customers have various demands on new products and services. Therefore, their power and influence on the markets grow stronger each year. Companies have paid great attention to customer relationship management. Especially, personalized product recommendation systems, which recommend products and services based on customer's private information or purchasing behaviors in stores, is an important asset to most companies. CRM is one of the important business processes where reliable information is mined from customer database. Data mining techniques such as artificial intelligence are popular tools used to extract useful information and knowledge from these customer databases. In this research, we propose a recommendation system that predicts customer's purchase intention. Then, customer's purchasing intention of specific product is predicted by using data mining techniques using receipt data set. The performance of this suggested method is compared with that of other data mining technologies.

• Journal of Korean Tunnelling and Underground Space Association
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• v.4 no.4
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• pp.277-286
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• 2002

Most of the problems in dealing with the tunnel construction are the uncertainties and complexities of the stress conditions and rock strengths in ahead of the tunnel excavation. The limitations on the investigation technology, inaccessibility of borehole test in mountain area and public hatred also restrict our knowledge on the geologic conditions on the mountainous tunneling area. Nevertheless an extensive and superior geophysical exploration data is possibly acquired deep within the mountain area, with up to the tunnel locations in the case of alternative design or turn-key base projects. An appealing claim in the use of artificial neural networks (ANN) is that they give a more trustworthy results on our data based on identifying relevant input variables such as a little geotechnical information and biological learning principles. In this study, error back-propagation algorithm that is one of the teaching techniques of ANN is applied to presupposition on Rock Mass Ratings (RMR) for unknown tunnel area. In order to verify the applicability of this model, a 4km railway tunnel's field data are verified and used as input parameters for the prediction of RMR, with the learned pattern by error back propagation logics. ANN is one of basic methods in solving the geotechnical uncertainties and helpful in solving the problems with data consistency, but needs some modification on the technical problems and we hope our study to be developed in the future design work.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.699-708
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• 2017

The service industry, as a new growth engine, has become more important in response to changes in the global economy and the industrial environment. Developed countries have promoted the competitiveness of the service industry and have enhanced economic added value. Developing new services requires extensive resources. Therefore, cooperation and network building capabilities with customers, suppliers, and various knowledge creation agencies are critical sources of competitiveness. This study classified the Korean service industry by industrial type in order to enhance innovation competence.The Korean service industry lags behind that of developed countries, and this study analyzed the differences of innovation results according to collaboration partners by the classified industry. By adopting a method that applies industrial classification by Dialogic's innovation pattern, this study showed external cooperation results were different by industrial type. Analysis results revealed that companies cooperate with customers and competitors in many cases; however, product innovation was higher for companies that collaborated with private service companies. In the 'Innovation in services' industry, industry cooperation with universities showed organizational innovation achievements. In the 'Innovation through services' industry, cooperation with customers positively affected marketing innovation achievements. Consequently, the need to foster consulting firms and universities that can professionally collaborate with companies is implied in order to enhance the Korean service industry.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.2
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• pp.45-60
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• 2014

In recent year's we have seen that due to education and social position there has been a significant increase in the number of women engaging in entrepreneurial activities. Concurrently research into the activities of women currently engaged in entrepreneurial activities has increased. This research has focused on the individuals themselves, their motivations and the performance of their ventures but there is not enough systematic research on the subject. Therefore it is necessary expand the criteria of the current research to examine every aspect of how these emergent entrepreneurs operate and to provide others with the same goals the appropriate skills and knowledge needed to succeed. This study wants to identify and evaluate the psychological, environmental and personal characteristics that these women possess and to assess the impact these factors have on the level of their success. This study will focus on those women who were empirical in their methods as this lends itself better to a detailed systematic analysis. It will examine the concept of entrepreneurial self-efficacy with regard to how they searched, planned, marshaled and implemented steps to influence the environmental and personal factors that affected them. In addition the searching phase of entrepreneurial self-efficacy directly influences entrepreneurial motivation and thus performance. The results of this study will suggest that as Korean women's interest in entrepreneurship grows it will have wide reaching theoretical implications and affect the direction of the policy that supports it.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.3
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• pp.235-245
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• 2005

The rapid development of computer network technology has brought the Internet as the major infrastructure to our society. But the rapid increase in malicious computer intrusions using such technology causes urgent problems of protecting our information society. The recent trends of the intrusions reflect that the intruders do not break into victim host directly and do some malicious behaviors. Rather, they tend to use some automated intrusion tools to penetrate systems. Most of the unknown types of the intrusions are caused by using such tools, with some minor modifications. These tools are mostly similar to the Previous ones, and the results of using such tools remain the same as in common patterns. In this paper, we are describing design and implementation of attacker-traceback system, which traces the intruder based on the multi-agent architecture. The system first applied SOM to classify the unknown types of the intrusion into previous similar intrusion classes. And during the intrusion analysis stage, we formalized the patterns of the tools as a knowledge base. Based on the patterns, the agent system gets activated, and the automatic tracing of the intrusion routes begins through the previous attacked host, by finding some intrusion evidences on the attacked system.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.14-25
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• 2018

The operation of high-speed rail (HSR) has an effect on the agglomeration economies, and the impact is shown as a relocation of individual firm and worker to where business activity can be maximized. The proximity to the HSR station could be considered as a core district to maximize the industrial benefit through the HSR network. From this perspective, this study considers the agglomeration effect of HSR within the HSR station-area and analyzed the agglomerated spatial pattern through hotspot analysis by service industry in the cases of Korea and Japan using GIS. This study analyzed the service industry within 1km distance from 8 HSR stations of Korea and 4 Kyushu Shinkansen stations of Japan. The results suggest that the hotspot patterns are observed in the service industry within 1km distance from the HSR station of Korea and Japan, except for two HSR stations of Gupo station and Kagoshima-Chuo station. Leisure, amusement, association, and other specific service industries could be affected by HSR passengers and knowledge-spillovers through HSR station. Therefore, the observed hotspot districts near the HSR station-area could explain an agglomeration pattern of the service industry through a closeness to the HSR station. Further, we could expect that the impact of HSR affects the service industry, and the impact could attract business activities of the service-area to maximize their benefit from HSR travelers. With the result, it is required to build up a supportive policy to maximize the HSR's impact on the service industry when considering the HSR station-area development.