• Journal of Digital Convergence
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• v.14 no.11
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• pp.45-51
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• 2016

Although the problem of personal data leakage is reported to be serious, there has been no research that tries to excavate out that real cause of the leakage in scientific prospective. Although this topic is considered to be crucial, there have been no literatures relevant to the topic, and the reason for this limitation is that scientific approach to this problem was not feasible. In this respect, in this paper a model for such scientific analysis and a methodology of analysis have been devised. Results show that the degree of rigidity turns out be the determinant that vindicates the degree of leakage. The notion of data rigidity is revealed to be very strongly correlated to the number of hacking incidents in each country. The notion of resident data freedom was then deployed in this paper to determine the world-wide ranking for a slew of different countries. The United Kingdom and the Republic of Korea turned out to be the two extreme countries that lie in the spectrum of the scale, with UK the most flexible and ROK one of the most rigid.

• Journal of Korea Technology Innovation Society
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• v.21 no.2
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• pp.818-837
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• 2018

Technological innovation has inherent difficulties, largely due to the uncertainties of technology. Thus, the forecasting methodology to reduce the risk of uncertainty in the innovation process has been presented both in quantitative and qualitative fields. On the other hand, big data and artificial intelligence have attracted great interest recently, and deep learning, which is one of the algorithms of AlphaGo, is showing excellent performance. In this study, deep learning methodology was applied to the prediction of innovation performance. To make the prediction model, we used KIS 2016 data. The input factors were importance of information source and innovation objectives and the output factor was innovation performance index, which was calculated for this study. As a result of the analysis, it can be confirmed that the accuracy of prediction is improved compared with the previous studies. As learning progressed, the degree of freedom of prediction also improved.

• Journal of the Korean Society for information Management
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• v.35 no.3
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• pp.101-121
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• 2018

This study proposes the analysis method in sentence semantics that can be automatically identified and processed as appropriate items in the system according to the composition of the sentences contained in the data corresponding to the logical semantic structure metadata of the research papers. In order to achieve the purpose, the structure of sentences corresponding to 'Research Objectives' and 'Research Outcomes' among the semantic structure metadata was analyzed based on the number of words, the link word types, the role of many-appeared words in sentences, and the end types of a word. As a result of this study, the number of words in the sentences was 38 in 'Research Objectives' and 212 in 'Research Outcomes'. The link word types in 'Research Objectives' were occurred in the order such as Causality, Sequence, Equivalence, In-other-word/Summary relation, and the link word types in 'Research Outcomes' were appeared in the order such as Causality, Equivalence, Sequence, In-other-word/Summary relation. Analysis target words like '역할(Role)', '요인(Factor)' and '관계(Relation)' played a similar role in both purpose and result part, but the role of '연구(Study)' was little different. Finally, the verb endings in sentences were appeared many times such as '~고자', '~였다' in 'Research Objectives', and '~었다', '~있다', '~였다' in 'Research Outcomes'. This study is significant as a fundamental research that can be utilized to automatically identify and input the metadata element reflecting the common logical semantics of research papers in order to support researchers' scholarly sensemaking.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.65-77
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• 2016

As respecting one's privacy becomes an important issue in mobile device data analysis, on-device analysis is getting attention, in which the data analysis is conducted inside a mobile device without sending data from the device to outside. One possible application of the on-device analysis is gender prediction using text data in mobile devices, such as text messages, search keyword, website bookmarks, and contact, which are highly private, and the limited computing power of mobile devices can be addressed by utilizing the word comparison method, where words are selected beforehand and delivered to a mobile device of a user to determine the user's gender by matching mobile text data and the selected words. Moreover, it is known that performing prediction after filtering instances using definite evidences increases accuracy and reduces computational complexity. In this regard, we propose a two-phase approach to on-device gender prediction, where both discriminability and popularity of a word are sequentially considered. The proposed method performs predictions using a few highly discriminative words for all instances and popular words for unclassified instances from the previous prediction. From the experiments conducted on real-world dataset, the proposed method outperformed the compared methods.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.81-87
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• 2016

It is important to analyze the exact damage information for fast recovery when natural disasters cause damage on river-side facilities such as dams, bridges, embankments etc. In this study, we shows the method to effectively damage analysis plan using UAV(Unmanned aerial vehicle) images and accuracy assessment of it. The UAV images are captured on area near the river-side facilities and the core methodology for damage analysis are image matching and change detection algorithm. The result(point cloud) from image matching is to construct 3-dimensional data using by 2-dimensional images, it extracts damage areas by comparing the height values on same area with reference data. The results are tested absolute locational precision compared by post-processed aerial LiDAR data named reference data. The assessment analysis test shows our matching results 10-20 centimeter level precision if external orientation parameters are very accurate. This study shows suggested method is very useful for damage analysis in a large size structure like river-side facilities. But the complexity building can't apply this method, it need to the other method for damage analysis.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.1
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• pp.161-182
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• 2023

In the recent cataloging environment, the function of authority data is becoming more important. In Korea, a pilot project for sharing and joint use of the national authority data has been promoted since 2019, and a plan to automatically construct retrospective authority data using national authorities is in progress. This study aims to (1) analyze the results of the pilot project for retrospective authority data jointly promoted by the National Library of Korea and KERIS in 2019, (2) explore methodological limitations and problems in the process, and (3) derive policy proposals for constructing authority data more efficiently. To this end, a university library participating in the pilot project was investigated as a case study. Based on it, the automatic generation rate and accuracy of authority data, and errors and omissions cases and causes were analyzed in detail. As a results of the study, it was found that the quality of union catalogs and bibliographic records had a great influence on automatically constructing authority data, and supplementary methods were needed to minimize these limitations.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.351-355
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• 2023

The need for Advanced LOSA arises from the limitations and drawbacks of traditional LOSA. Amended LOSA aims to address some of the shortcomings of the original methodology and make it more effective and relevant to current aviation safety needs. Some of the key reasons for developing Advanced LOSA include Enhancing the scope, Improving data collection and analysis, Providing more targeted safety recommendations. First, Traditional LOSA mainly focuses on flight deck operations, but Advanced LOSA expands the scope to include other operational areas such as cabin operations, ground handling, and maintenance. Second, Advanced LOSA can build a Forecasting System that can predict the future through data collection and data analysis. Third, Advanced LOSA aims to provide more specific and targeted safety recommendations based on the Aviation data collection and Aviation data analysis. Overall, Advanced LOSA seeks to improve aviation safety by addressing the limitations of traditional LOSA and providing a more comprehensive and effective methodology for identifying and mitigating safety risks in aviation operations.

• Journal of Aerospace System Engineering
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• v.13 no.6
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• pp.80-89
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• 2019

Prospecting future social environmental changes and improvement research on future technologies is required for prospecting promising technology, as it would be useful for institution·company to set up technical planning. This study aims at providing a methodology for retaining international technology competitiveness, marketable industry, and sustainable promising technology in a field of new growth engine industry such as national unmanned aerial vehicle industry. We draw a result by analysing with tools such as KrKwic, Excel, NetMiner, presenting methods of a Social Network Analysis, sub-group analysis, and cognitive map analysis based on patent data in a field of unmanned aerial vehicle industry. Therefore, this study explored the technology evolution of UAV and to prospect promising technology. As a result, some future promising technologies are prospected as what worths concentrated investment, such as 'system integration tech', 'assessment/airworthiness certification tech', 'avionics', 'pilot control tech', 'identification of friend or foe', 'flight control tech', 'supportive equipment'.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.11
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• pp.419-426
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• 2018

Due to the FMD(foot-and-mouth disease), the domestic animal husbandry and related industries suffer enormous damage every year. Although various academic researches related to FMD are ongoing, engineering studies on the social effects of FMD are very limited. In this study, we propose a systematic methodology to analyze emotional responses of regular citizens on FMD using text mining techniques. The proposed system first collects data related to FMD from the tweets posted on Twitter, and then performs a polarity classification process using a deep-learning technique. Second, keywords are extracted from the tweet using LDA, which is one of the typical techniques of topic modeling, and a keyword network is constructed from the extracted keywords. Finally, we analyze the various social effects of regular citizens on FMD through keyword network. As a case study, we performed the emotional analysis experiment of regular citizens about FMD from July 2010 to December 2011 in Korea.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.15-26
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• 2014

This study focus on a economic value of the Big Data technologies by real options model using big data technology company's stock price to determine the price of the economic value of incremental assessed value. For estimating stochastic process of company's stock price by big data technology to extract the incremental shares, Generalized Moments Method (GMM) are used. Option value for Black-Scholes partial differential equation was derived, in which finite difference numerical methods to obtain the Big Data technology was introduced to estimate the economic value. As a result, a option value of big data technology investment is 38.5 billion under assumption which investment cost is 50 million won and time value is a about 1 million, respectively. Thus, introduction of big data technology to create a substantial effect on corporate profits, is valuable and there are an effects on the additional time value. Sensitivity analysis of lower underlying asset value appear decreased options value and the lower investment cost showed increased options value. A volatility are not sensitive on the option value due to the big data technological characteristics which are low stock volatility and introduction periods.