• Journal of Digital Convergence
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• v.13 no.9
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• pp.49-56
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• 2015

Korea government has established clear cost estimation standard based on a survey of e-learning contents development cost and presented 'e-Learning Contents Development Cost Estimation Guidelines' that reflect the characteristics of the e-learning industry. However, if there is no institutional support, this guideline and system fails to achieve the purposes and objectives. And it is likely to be facing a dead document. Therefore, the policy foundation is required. This study suggested the following policy; stepwise activation of cost estimation standard, enact announcement and periodically adjustment of cost estimation standard, installation and operation of cost estimation standard operational committee, conjunction with the e-learning industry survey, cultural diffusion of co-owned copyright, systematic monitoring of the e-learning contents development process, research on activating policy of cost estimation standard, conjunction with the standard contract for enhancing policy effectiveness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.29-38
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• 2015

In this paper, scale efficiencies and relative efficiencies of R&D projects in Industrial Technology Program, sponsored by Ministry of Trade, Industry and Energy, Korea, are calculated and compared. For the process, various DEA (Data Envelopment Analysis) models are adopted as major techniques. For DEA, two stage input oriented models are utilized for calculating the efficiencies. Next, the calculated efficiencies are grouped according to their subprograms (Industrial Material, IT Fusion, Nano Fusion, Energy Resources, and Resources Technology) and recipient types (Public Enterprise, Large Enterprise, Medium Enterprise, Small Enterprise, Lab., Univ., and etc.) respectively. Then various subprograms and recipient types are compared in terms of scale efficiencies (CCR models) and relative efficiencies (BCC models). In addition, the correlation between the 1st stage relative efficiencies and the 2nd stage relative efficiencies is calculated, from which the causal relationship between them can be inferred. Statistical analysis shows that the amount of input, in general, should increase in order to be scale efficient (CCR models) regardless of the subprograms and recipient types, that the 1st and 2nd stage relative efficiencies are different in terms of the programs and recipient types (BCC models), and that there is no significant correlation between the 1st stage relative efficiencies and the 2nd stage relative efficiencies. However, the results should be used only as reference because the goal each and every subprogram has is different and the situation each and every recipient type faces is different. In addition, the causal link between the 1st stage relative efficiencies and the 2nd relative efficiencies is not considered, which, in turn, is the limitation of this paper.

• Journal of Information Technology and Architecture
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• v.10 no.4
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• pp.467-478
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• 2013

The necessity of research for EA service and performance is surfacing while nation-level and individual agency level performances utilizing Government-wide EA information. In this study, performance model for EA service has been developed categorizing characteristic elements of EA as service. And weight differences between quality items that constitute performance model have been calculated using AHP analysis method. To achieve the stated, SERVQUAL applied performance model for EA service has been developed working through logical reasoning and a broad range of theoretical studies concerning EA service. Moreover, relative weight differences between quality items that constitute the model have been calculated. The results of weight analysis find that importance differences between quality items in order of significance are as follows: EA administrator > EA information > EA education > EA policy > EA operating system. This study, as the nation's first research to graft the public-sector EA service onto SERVQUAL Model that is capturing remarkable attention, has considerable practical and theoretical implications.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.1-17
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• 2012

The Smartphone, one of essential mobile devices widely used recently, can be very effectively applied to capture knowledge on the spot by jointly applying the pervasive functionality of cloud computing. The process of knowledge capturing can be also effectively automated if the topic of knowledge is automatically identified. Therefore, this paper suggests an interdisciplinary approach to automatically acquire knowledge on the spot by combining technologies of text mining-based topic identification and cloud computing-based Smartphone. The Smartphone is used not only as the recorder to record knowledge possessor's dialogue which plays the role of the knowledge source, but also as the sensor to collect knowledge possessor's context data which characterize specific situations surrounding him or her. The support vector machine, one of well-known outperforming text mining algorithms, is applied to extract the topic of knowledge. By relating the topic and context data, a business rule can be formulated, and by aggregating the rule, the topic, context data, and the dictated dialogue, a set of knowledge is automatically acquired.

• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.25-34
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• 2013

In this paper, we propose an activity-performer bipartite matrix generation algorithm for analyzing workflow-supported human-resource affiliations in a workflow model. The workflow-supported human-resource means that all performers of the organization managed by a workflow management system have to be affiliated with a certain set of activities in enacting the corresponding workflow model. We define an activity-performer affiliation network model that is a special type of social networks representing affiliation relationships between a group of performers and a group of activities in workflow models. The algorithm proposed in this paper generates a bipartite matrix from the activity-performer affiliation network model(APANM). Eventually, the generated activity-performer bipartite matrix can be used to analyze social network properties such as, centrality, density, and correlation, and to enable the organization to obtain the workflow-supported human-resource affiliations knowledge.

• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.31-36
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• 2018

The tourism industry has a great influence on national economy activation. The development of IT technology has enabled the collection and analysis of personal profile information, location information and activity information based on the characteristics, behavior, purchase propensity and interest of tourists. In order to realize this, the implementation of convergence smart tourism information service platform is completed by developing business model, IoT & Big Data integration management system, big data algorithm development and analysis platform in three stages. The underlying technology of the platform and algorithm needs a process of adopting open source, expanding the service element on the basis of it, and then complementing the problem through the test-bed demonstration test that connects the area. Using this platform, it is possible to develop a smart tourism environment that can provide customized services for each tourist by analyzing various information in an integrated manner. Also, it will be possible to improve the life of tourist destination residents and contribute to regional revitalization and job creation through the creation of smart tourism ecosystem focused on the region.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.75-81
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• 2021

With the development of advanced information and communication technology, Generation Z, familiar with digital culture, is drawing keen attention as a major consumer of the fashion industry. In this study, casual wear for Generation Z, who is proficient in digital devices and prefers information acquisition and lifestyle over the Internet, was designed using 3D virtual simulation and developed into four looks: Gulish, Sportive, Easy and Contemporary. The use of simulation of 3D virtual clothing in costume design can build digitalization of future fashion industry through convergence with digital fashion design planning and production process in fandemic environment and strengthen online platform distribution. In a business environment that continues to innovate to enhance work efficiency by introducing an Untouch fashion production system, the use of 3D virtual clothing technology can increase the efficiency of sustainable management through 3D sample production, online fitting, modification, and final critic processes to reduce the time and cost of human and physical resources and review.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.65-76
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• 2022

Recently, not only traditional statistical techniques but also machine learning algorithms have been used to make more accurate bankruptcy predictions. But the insolvency rate of companies dealing with financial institutions is very low, resulting in a data imbalance problem. In particular, since data imbalance negatively affects the performance of artificial intelligence models, it is necessary to first perform the data imbalance process. In additional, as artificial intelligence algorithms are advanced for precise decision-making, regulatory pressure related to securing transparency of Artificial Intelligence models is gradually increasing, such as mandating the installation of explanation functions for Artificial Intelligence models. Therefore, this study aims to present guidelines for eXplainable Artificial Intelligence-based corporate bankruptcy prediction methodology applying SMOTE techniques and LIME algorithms to solve a data imbalance problem and model transparency problem in predicting corporate bankruptcy. The implications of this study are as follows. First, it was confirmed that SMOTE can effectively solve the data imbalance issue, a problem that can be easily overlooked in predicting corporate bankruptcy. Second, through the LIME algorithm, the basis for predicting bankruptcy of the machine learning model was visualized, and derive improvement priorities of financial variables that increase the possibility of bankruptcy of companies. Third, the scope of application of the algorithm in future research was expanded by confirming the possibility of using SMOTE and LIME through case application.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.815-821
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• 2021

The advantage of big data is to collect a large amount of data on the Internet and refine and use valuable data. That is, the unstructured data is processed so that the user can analyze and utilize it from a necessary point of view. This paper is a relatively small project and is based on unstructured data that can be closely applied to real life and used for marketing. The subjects of the experiment were modeled on lodging companies in the Seoul metropolitan area an hour away from Seoul, and analyzed for the increase in lodging rates before and after marketing using big data. As an experiment that shows the effects of increasing sales, reducing costs, and increasing returns by users, we propose a system to determine and filter whether data input in the process of analyzing big data such as social networks can be used as accommodation-related information.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.53-62
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• 2021

Distance learning (DL) has become a major issue in the educational field with the spread of COVID-19. In order to enhance the satisfaction of DL learners, efforts to cultivate learners' competencies, as well as investment to build IT infrastructure, and activities to support high-quality content provision should be comprehensively considered. Based on a survey of 221 college students, this study verified that digital literacy (knowledge, skill, and mind) and information systems success factors (system, information, and service quality) all positively affect DL satisfaction, in turn, which positively influences on DL continuance. This study is meaningful in that it comprehensively considered learner's ability and IT infrastructure and analyzed the effect on the satisfaction and intention of continuous use of DL. In the future, it is necessary to expand the target of not only college students but also elementary and secondary students and instructors, and to further consider interaction, which is a major factor in the distance learning process.