• KOREAN POULTRY JOURNAL
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• v.49 no.8
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• pp.192-195
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• 2017

CJ제일제당 바이오 사업부문은 지난 6월 12일 북경(China World Hotel)에서 제 3회 AAFAN(Amino Acid Forum in Animal Nutrition)을 개최하였다. 이번 포럼은 '사료의 조단백질 저감을 통한 축산의 발전 및 환경개선'이라는 주제로 진행되었다. AFFAN은 CJ가 단독으로 개최하는 국제 학회이며, 처음으로 한국이 아닌 해외에서 개최함으로서 그 명성이 더 높아지게 되었다. 이번 포럼에서는 중국을 포함한 각국의 주요 사료회사 연구개발 담당자 및 정부기관 담당자들이 100명 이상 참석하였으며, 활발한 토의를 통하여 사료의 조단백 저감에 대한 공감대를 형성하게 되었다. 본고는 본 심포지움의 주요내용을 정리한 것이다.

• 한국IT서비스학회:학술대회논문집
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• 2006.11a
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• pp.512-512
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• 2006

1999년부터 2004년까지 세계 주요국가의 정보통신산업의 투자현황과 경제지표를 기반으로, 정보통신산업의 투자가 어떻게 국가경제 발전에 영향을 미치는지를 연구한다. 이 주제의 다른 연구들에 비하여 방법론적인 차이점은, 첫째 인터넷 거품 현상의 와중과 그 이후의 기간을 모두 고려했다는 점, 둘째, 정보통신 투자의 영향이 발현되는데 다소 시간의 경과가 필요하므로 Malmquist 방법을 이용한 통시적인 접근을 사용했다는 점, 셋째, 각 국가의 고유 현상이 고려된 모형하에서 분석되어야 한다는 점 등이다. 분석방법을 이끄는 주요 이론적 기반으로는, 정보기술의 경제적 가치를 인프라와 응용기술로 구분하여 분석하는 Barua, Kriebel & Mukhopadhyay (1995) 의 Two way approach를 채택하고자 한다.

• Tunnel and Underground Space
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• v.9 no.4
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• pp.283-284
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• 1999

지하에서의 안전성과 광체의 경제적 회수가 광업계의 주요관심분야이다. 지하구조물과 주변 응려의 상호작용으로 채굴로 인한 지진활동(seismicity)이 지하안정성과 생산성에 결정적인 영향을 미치며, 암석절단, 천공, 발파기술 등을 적절하게 사용하는 것이 주요관심의 대상이 된다. 이 들에 대한 기초적인 이해는 석유 생산이나 천공장비 개발 등에도 적용될 수 있다.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.278-291
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• 2016

This study examined research trends via network analyses of keywords appeared in academic research articles about news reports in South Korea during the last 10 years from 2006 to 2015. Keyword network analyses of 4410 keywords from 1108 articles suggested that framing, agenda setting, third-person effect, selective exposure, and uses and gratification were main theories but most studies used framing theory. Research areas included news reports on politics, economics, science, world issues, or tour. However, research on news reports covering culture, sports or daily life were not identified. In terms of media, research on both traditional and emerging media were ample. Research on broadcasting new, online news, and social media were frequently observed.

• Journal of the Korean Society for Library and Information Science
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• v.42 no.1
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• pp.253-272
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• 2008

While relevant studies emphasize the significance of user-oriented indexing associated with fulfilling both topical and non-topical needs of individual users, a great number of operational retrieval systems supports only those searches related to subject attributes of the users' needs. Retrieval systems for presidential image collections are not an exception for such a restriction. Upon this reality. this study seeks diversification of access points for presidential images based on descriptors directly presented by potential user groups. Improvements of subject-based descriptors are suggested based on those descriptors identified.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.275-277
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• 2019

The purpose of this study is to classify the topics related to the suicide papers published so far and to identify the proporations of the main topics and the trends of the topics over the past 20 years. For this purpose, a text mining technique used in big data analysis was used as a data base of the Korean Journal of Citation Index (KCI), where information sharing about the papers is most active. This study, which grasps the trends of suicide related research according to the changes of the times, will become a basic data for establishing a strategy to adapt the academic direction related to suicide in the future.

• Journal of KIISE
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• v.44 no.2
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• pp.139-147
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• 2017

Most of the information prevailing in the Internet space consists of textual information. So one of the main topics regarding the huge document analyses that are required in the "big data" era is the development of an automated understanding system for textual data; accordingly, the automation of the keyword extraction for text summarization and abstraction is a typical research problem. But the simple listing of a few keywords is insufficient to reveal the complex semantic structures of the general texts. In this paper, a text-visualization method that constructs a graph by computing the related degrees from the selected keywords of the target text is developed; therefore, two construction models that provide the edge relation are proposed for the computing of the relation degree among keywords, as follows: influence-interval model and word- distance model. The finally visualized graph from the keyword-derived edge relation is more flexible and useful for the display of the meaning structure of the target text; furthermore, this abstract graph enables a fast and easy understanding of the target text. The authors' experiment showed that the proposed abstract-graph model is superior to the keyword list for the attainment of a semantic and comparitive understanding of text.

• The Journal of Korean Association of Computer Education
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• v.19 no.2
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• pp.21-29
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• 2016

The introduction of educational programming language has changed programming learning environment to learn programming through various learning activities. We need to analyze how effective these learning activities could be in programming learning. We performed a meta analysis of the programming learning effects according to 8 types of learning activities. The 44 studies were collected from 1993 to 2015 for the meta analysis. The study data of 77 were extracted among 44 studies through several steps. The major results were as follows. The effect size of cognitive domain was shown to be mid-level with .595 and the effect size of affective domain was shown to be mid-level with .594. We analysed according to learning activities. The effect size were no significant difference between learning activities in the cognitive domain. But simulation, animation and mathematical activities was shown to be more consistent results and mid-level effect size. Although the effect size were no significant difference, the homogeneity was shown to be high in the affective domain. The implications were suggested from research findings. First, it is desirable that learners learn programming according to various learning activity themes. Second, instructors should pay attention to simulation, animation and mathmatics activities. Third, researchers need research to find another factors for effective learning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.68-73
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• 2016

This study was conducted to identify trends and knowledge structures associated with recent trends in Korean tourism from 2010 to 2015 using keyword data. To accomplish this, we constructed a network using keywords extracted from KCI journals. We then made a matrix describing the relationships between rows as papers and columns as keywords. A keyword network showed the connectivity of papers that have included one or more of the same keywords. Major keywords were then extracted using the cosine similarity between co-occurring keywords and components were analyzed to understand research trends and knowledge structure. The results revealed that subjects of tourism research have changed rapidly and variously. A few topics related to 'organization-employee' were major trends for several years, but intrinsic and extrinsic factors have been further subdivided and employees of specific fields have been targeted as subjects of research. Component analysis is useful for analyzing concrete research topics and the relationships between them. The results of this study will be useful for researchers attempting to identify new topics.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.47-55
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• 2021

Analysis of research trends in specific subject areas is performed by examining related topics and subject changes by using topic modeling techniques through keyword extraction for most of the literature information (paper, patents, etc.). Unlike existing research methods, this paper extracts topics related to the research topic using the LDA topic modeling technique for the project information of national R&D projects provided by the National Science and Technology Knowledge Information Service (NTIS) in the field of artificial intelligence. By analyzing these topics, this study aims to analyze research topics and investment directions for national R&D projects. NTIS provides a vast amount of national R&D information, from information on tasks carried out through national R&D projects to research results (thesis, patents, etc.) generated through research. In this paper, the search results were confirmed by performing artificial intelligence keywords and related classification searches in NTIS integrated search, and basic data was constructed by downloading the latest three-year project information. Using the LDA topic modeling library provided by Python, related topics and keywords were extracted and analyzed for basic data (research goals, research content, expected effects, keywords, etc.) to derive insights on the direction of research investment.