• The Journal of Information Systems
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• v.14 no.2
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• pp.75-99
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• 2005

Today, many universities are confronted with the changing education paradigm such as e-learning, Distance Education, Virtual University, This IT-based teaming paradigm shift is certainly a new opportunity or a threat to our universities. To overcome this problem the universities should think e-Learning as strategic weapon, such as many firms created competitive weapons from the information systems at the 1980s. So, e-Learning system can be a SIS(Strategic Information System) which supports university's future education strategies. To build a e-Learning system, not only many H/W and S/W resources but also expert personnels are required. An organization such as local university who is week at financial status can't himself plan the system. The Local University Community e-Learning Centers that support the demand of e-learning for their community are recommended. In order to operate these centers efficiently, the strategic roles of the e-Learning center should first be defined. To define the strategic roles, We classified the strategic roles of the e-Learning center into four dimensions, (1) to improve management efficiency, (2) to enhance educational service, (3) to acquire competitive advantages, (4) to build new education infrastructure, and each dimension has 5 or 6 measurement items. As result, to enhance the educational service was considered as the most significant factor among the four dimensions of strategic roles, and the infrastructure building was the next. We also tried to find the difference for each factor by the characteristics of responsor. The data showed that there was litter difference between the groups in evaluating the significance of strategic roles of e-learning centers. Through the strategic roles definition and analysis of expected role ratings, we could have recommended the direction and operation policies of the e-Loaming centers.

• The Journal of Society for e-Business Studies
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• v.11 no.4
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• pp.87-106
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• 2006

Today as the scale of e-commerce constantly expands, the number and the amount of the consumer frauds are also increasing very rapidly, without sufficient levels of systematic support to prevent them. Internet Escrow service is one of the promising payment mechanisms, which guarantees secure electronic trades and payments. Especially, if the real-time product delivery information is available via RFID-based track-and-trace environment, the security and efficiency of the Internet Escrow services would be improved a lot. In this research, proposed a novel approach to integrate EPCglobal Network, which is a de-facto standard for RFID-based information network model, with Internet Escrow services. The proposed service model was implemented in the form of "Integrated Financial Platform", which supports the contracts among trading partners and the payment via Escrow services by being fully integrated with bank systems. Using the implemented EPCglobal Network-based Escrow service system, we would be able not only to shorten the money-flow cycle and to develop new kinds of loan services, but also to overcome the problems of existing Escrow services including the lack of product-related information and the delay of purchasing decisions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5872-5878
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• 2013

This study provides two channel structure and two factor authentication. First, a purchasing request by Internet and then user certification and a settlement approval process by mobile communication. Second, it support that proposal protocol utilize a partial factor value of stored in users smartcard, smart phone and password of certificate. Third, storage stability is improved because certificate store in smartcard. Finally, proposal protocol satisfy confidentiality, integrity, authentication, and non- repudiation on required E-commerce guideline. In comparative analysis, Efficiency of the proposal protocol with the existing system was not significantly different. But, In terms of safety for a variety of threats to prove more secure than the existing system was confirmed.

• Environmental and Resource Economics Review
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• v.15 no.1
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• pp.95-133
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• 2006

This research first reviewed and analysed the current domestic situation of the voluntary agreement implementation and then it developed the policy implementation scenarios which will be applied to the model, KORTEM_ V.2. The model, consisted with 83 industries and commodities, examined the economic and environmental impacts of this policy instrument. Depending on the efforts of participating sectors and agents for fuel substitution and energy efficiency improvement, it has been evaluated that the voluntary agreement could be the "no-regret" policy. In other words, if the participating sectors and agents can achieve the voluntary energy conservation and emission reduction target without the negative impact on output level, the reduction of national emission will be achieved by creating the economic benefit, simultaneously. Therefore, for the successful implementation of voluntary agreement, this study emphasized the importance of expansion and strengthening of the current financial and institutional support for participating sectors and agents.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.34-41
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• 2020

There has not been any scientific analysis on appropriate size of workforce and salary for civilian workers in military so far. Thus, this paper conducted analysis on propriety in employment size of military doctrine researchers using system dynamic methodology based on annual military doctrine workload. Vensim software was mainly used to measure complement of the research group based on data from job analysis. Secondly, a multiple regression analysis was performed to study an appropriate wage for researchers based on their expertise and working condition. The data from twenty public research institutions and twenty eight job positions that are performing similar duty with military doctrine researchers was obtained and utilized to create a salary-estimation regression equation in the analysis. Finally, with cost-benefit analysis method this paper studied financial effectiveness of hiring military doctrine researchers. Contingent valuation method, which has been recognized as one of the most effective methodologies in cost-benefit analysis on intangible value, was utilized to measure benefit of hiring the researchers. The methodology presented in this paper can be applied to measure and improve the efficiency of military organization not only in military doctrine research area but also in several military functional area (military training, logistics, administration, combat development, and combat support).

• Journal of Digital Convergence
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• v.13 no.2
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• pp.23-34
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• 2015

The purpose of this study is to analyze comprehensively Jeju Self-Governing School Policy (JSSP) by using the four dimensional views of policy theory frame and to suggest its improvements. The results are as follows: First, JSSP should improve differentiation of curriculum and locality centered on local basis, wide application of the special laws, and professional accountability. Second, JSSP should improve the policy structure of educational governance and differentiated standard of students achievement assessment, resolve equity issue, and secure the self-finance of the schools. Third, JSSP should improve localization of educational administration, administrative and financial support for teachers, parent's empowerment, and students understanding of the policy, and expand principal invitation system, Fourth, JSSP should improve public relations, the roles of the Council and professionalism of assessors, and adopt efficiency approach into the assessment system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.119-124
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• 2017

In this paper, 18 railway stations in Gyunggi-do are selected as metropolitan transfer centers out of 203 stations based on three elements. They are the number of passengers, the level of connection transport, and the level of relevant plans. For 18 stations the level of service (LOS) is analyzed for connection transport system. As a result, half of them are found to be below LOS "D". In order to maximize the use of those railway stations, a method is proposed to upgrade the level of service to "C" above. Finally, the improvement plans are suggested for two acts. In the Special Act on Metropolitan Traffic Management of the Metropolitan Region, the central government financial support ratio is suggested from 30% to 50%, from "necessary costs" to "total costs." In the Act on National Integrated Transport System Efficiency, 50% for connection road and 70% for connection raiway are suggested.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2278-2291
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• 2023

Imported ornamental fish should be quarantined because they can have dangerous diseases depending on their habitat. The quarantine requires a lot of time because quarantine officers collect various information on the imported ornamental fish. Inefficient quarantine processes reduce its work efficiency and accuracy. Also, long-time quarantine causes the death of environmentally sensitive ornamental fish and huge financial losses. To improve existing quarantine systems, information on ornamental fish was collected and structured, and a server was established to develop quarantine performance support software equipped with a text search engine. However, the long names of ornamental fish in general can cause many typos and time bottlenecks when we type search words for the target fish information. Therefore, we need a technique that can correct typos. Typical typo character calibration compares input text with all characters in a calibrated candidate text dictionary. However, this approach requires computational power proportional to the number of typos, resulting in slow processing time and low calibration accuracy performance. Therefore, to improve the calibration accuracy of characters, we propose a fusion system of simple Artificial Neural Network (ANN) models and character preprocessing methods that accelerate the process by minimizing the computation of the models. We also propose a typo character generation method used for training the ANN models. Simulation results show that the proposed typo character correction system is about 6 times faster than the conventional method and has 10% higher accuracy.

• The Korean Journal of Financial Management
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• v.7 no.1
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• pp.1-20
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• 1990

In most of studies on market efficiency, the stability of risk measures and the normality of residuals unexplained by the pricing model are presumed. This paper re-examines stock splits, taking the possible violation of two assumptions into accounts. The results does not change the previous studies. But, the size of excess returns during the 2-week period before announcements decreases by 43%. The results also support that betas change around announcements and the serial autocorrelation of residuals is caused by events. Based on the results, the existing excess returns are most likely explained as a compensation to old shareholders for unwanted risk increases in their portfolio, or by uses of incorrect betas in testing models. In addition, the model suggested in the paper provides a measure for the speed of adjustment of the market to the new information arrival and the intensity of information contents.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.