• Journal of The Korean Association of Information Education
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• v.16 no.3
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• pp.327-336
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• 2012

The Korea government has recently announced "A Master Plan for Smart Education", including application of digital textbooks and composition of education system using cloud computing. Our education system in future circumstance, over the conventional e-learning methods, needs the smart education solutions which enable students to study and communicate on various types of devices. The ongoing government project related with the digital textbook has been performed as mid- and long-term goals, whereas PDF-based e-textbook project, similar to e-book model and, has been already completed for the short-term goal. For the purpose of improved future smart education circumstance, however, a specific strategy is required in the following areas: flexibility of format conversion and independency of original text sources among the multiple device platforms. Therefore, in this paper, we propose a standardization scheme for e-textbook format based on e-book structure. To do this, we survey trends in e-book technologies, and research on standardization of e-book format for digitalization of textbooks, based on the analysis of existing textbooks. Moreover, we produce an example e-book content using our proposed standard method. As a result, our approach can be applied to the future smart education circumstance, and we may say that it will be efficiently applicable to the long-term digital textbook project.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.802-810
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• 2006

For a thorough train control, the precise train position detection is necessarily required. The widely used current way for train position detection is the one of using track circuits. The track circuit has a simple structure, and has a high level of reliability. However trains can be detected only on track circuits, which have to be installed on all ground sections, and much amount of cost for its installation and maintenance is needed. In addition, for the track circuit, only discontinuous position detection is possible because of the features of the closed circuit loop configuration. As the recent advances in telecommunication technologies and high-tech vehicle-based control equipments, for the train position detection, the method to detect positions directly from on trains is being studied. Vehicle-based position detection method is to estimate train positions, speed, timing data continuously, and to use them as the control information. In this paper, the features of GPS navigation and DR navigation are analyzed, and the navigation filters are designed by constructing vehicle-based train position detection method by combining GPS navigation and DR navigation for their complementary cooperation, and by using kalman filter. The position estimation performance of the proposed method is also confirmed by simulations.

• The KIPS Transactions:PartC
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• v.16C no.3
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• pp.285-298
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• 2009

According to the development of ubiquitous technologies, sensor networks is used in various area. In particular, medical field is one of the significant application areas using sensor networks, and recently it has come to be more important according to standardization of the body sensor networks technology. There are special characteristics of their own for medical sensor networks, which are different from the one of sensor networks for general application or environment. In this paper, we propose a hierarchical medical sensor networks structure considering own properties of medical applications, and also introduce transmission mechanism based on hierarchical structure. Our mechanism uses the priority and threshold value for medical sensor nodes considering patient's needs and health condition. Through this way Cluster head can transmit emergency data to the Base station rapidly. We also present the new key establishment mechanism based on key management mechanism which is proposed by L. Eschenauer and V. Gligor for our proposed structure and transmission mechanism. We use key provisioning for emergency nodes that have high priority based on patients' health condition. This mechanism guarantees the emergency nodes to establish the key and transmit the urgent message to the new cluster head more rapidly through preparing key establishment with key provisioning. We analyze the efficiency of our mechanism through comparing the amount of traffic and energy consumption with analysis and simulation with QualNet simulator. We also implemented our key management mechanism on TmoteSKY sensor board using TinyOS 2.0 and through this experiments we proved that the new mechanism could be actually utilized in network design.

• Korean Journal of Construction Engineering and Management
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• v.20 no.1
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• pp.124-132
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• 2019

Notwithstanding the crucial contribution of international construction industry in the national economy, previous studies on international construction contracts had mainly focused either on trend investigation or market share analysis at a point of time. Fundamentally, the international construction industry is fragmented due to its project-based nature, is heterogeneous that has to involve different firms from diverse fields, and tends to be dynamic according to macroeconomic conditions. Therefore, the combination of static and dynamic analyses is necessary to understand its underlying structure. This study analyzes the market structure of international construction contracts using the data of 9,173 projects awarded by Korean construction firms from 2000 to 2017. Industry-level performance data is analyzed both in static (market concentration) and dynamic (market mobility and instability) methods, and detailed methodology is also provided. Consequently, the static analysis result shows that the competition among Korean construction firms has been more intensified, and the dynamic analysis result indicates that market positions of Korean construction firms are unstable and vulnerable in most of the regions and the sectors. The combination of static and dynamic indices is found to be helpful to understand the underlying aspects of market structure and can be utilized as an effective strategic reference in the highly competitive market.

• Journal of Korean Home Economics Education Association
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• v.35 no.1
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• pp.53-72
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• 2023

This study aimed to develop educational material on the proper use of plant-based milk alternatives in the form of a card news, based on a focus group interview(FGI) with adult consumers. The FGI participants were individuals who had directly purchased cow's milk, soy milk, or other plant-based milk alternatives within the past three months and consumed them at least once a month. The study consisted of 17 adults between the ages of 19 and 63 years who met these criteria(9 males and 8 females). It was identified what participants were curious about, interested in, and needed information regarding the proper use of plant-based milk alternatives through FGI. A 10-page card news was developed that was highly usable, taking into account the FGI results and the latest literature. In conclusion, this study developed a card news aimed at promoting the proper use of plant-based milk alternatives, which can be easily disseminated online in line with the trends in food consumption and digitization. The results of this study suggest that the continuous development and distribution of educational materials that reflect food consumption trends and maximize their usability should be provided for dietary life education, such as school subjects or out-of-school programs.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.11
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• pp.11-20
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• 2018

The Korean Statistical Information Service (KOSIS), classifies and summarizes 1,062 cases that were found when searching [An Investigation on Multicultural Families in Korea] on the website. These years are 2009, 2012, and 2015. The suggestion of this study is as follows. First, it is a comparison between the migrant group and the permanent resident group of 'children' in the research data. Identifying the relative position of a married migrant family to a permanent resident can provide a very important clue. Also, for other date, comparison of the settled and migrant groups is as important as the 'children'. Second, in the 'medical care' category, we need to add more depth than the surface content. For example, the inclusion of details about cancer diagnosis will determine the incidence of marriage migrants. Such efforts can provide more practical assistance to married migrant families. KOSIS is a very useful site that provides useful site that provides us with a lot of information. In the future, they should be able to use migrant group information more efficiently and be more helpful to them. This is the beginning of a sustainable society.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.236-256
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• 2011

Forest carbon stocks change due to land use change is an important data required by UNFCCC(United Nations framework convention on climate change). Spatially explicit estimation of forest carbon stocks based on IPCC GPG(intergovernmental panel on climate change good practice guidance) tier 3 gives high reliability. But a current estimation which was aggregated from NFI data doesn't have detail forest carbon stocks by polygon or cell. In order to improve an estimation remote sensing and GIS have been used especially in Europe and North America. We divided research trends in main countries into 4 categories such as remote sensing, GIS, geostatistics and environmental modeling considering spatial heterogeneity. The easiest way to apply is combination NFI data with forest type map based on GIS. Considering especially complicated forest structure of Korea, geostatistics is useful to estimate local variation of forest carbon. In addition, fine scale image is good for verification of forest carbon stocks and determination of CDM site. Related domestic researches are still on initial status and forest carbon stocks are mainly estimated using k-nearest neighbor(k-NN). In order to select suitable method for forest in Korea, an applicability of diverse spatial data and algorithm must be considered. Also the comparison between methods is required.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.