• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.287-290
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• 2010

오늘날 모바일 시장을 중심으로 다양한 플랫폼이 등장하면서 모바일 어플리케이션 개발 시 여러 플랫폼을 고려해야 하는 부담이 증대되고 있다. 이러한 상황에서 Model-Driven Development(MDD) 는 멀티플랫폼에 대응하는 어플리케이션 개발의 효율성을 높여줄 수 있다. 하지만 이 기법을 이용하는 대다수의 연구 결과들은 해당 방법론을 통해 생성된 결과물의 질을 객관적으로 평가할 수 없고, 이 때문에 해당 방법론의 성능 평가가 힘들다. 본 연구에서는 대상 플랫폼들이 제공하는 API 를 분석한 결과에 근거하여 공통 요소를 추출하고 이를 이용하여 MDD 기반으로 개발을 진행할 수 있는 개발 프로세스를 소개하고, 이를 통해 생성된 소스 코드의 기능적 유사도 및 코드 생성률과 기능적 유사도를 평가할 수 있는 방법을 제안한다. 이 방법은 코드를 AST 로 바꾸고 API 맵핑 테이블에 근거하여 동일한 키워드로 변환하고 유사도를 측정하여 설계 시 의도한 기능이 얼마나 잘 코드로 생성되었는 지 평가할 수 있는 방법이다. 본 연구에서는 이 방법을 이용하여 생성된 코드의 기능적 유사도와 코드 생성률을 측정하였다.

• Spatial Information Research
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• v.23 no.5
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• pp.1-7
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• 2015

Twitter has the fast propagation and diffusion of information compare to other SNS. Therefore, many researches about detecting real-time event using twitter are progressing. Twitter real-time event detecting system assumes every twitter user as a sensor and analyzes their written tweet in order to detect the event. Researches that are related to this twitter have already obtained good results but confronted the limits because of some problems. Especially, many existing researches are using the method that can trace an event location by using GPS coordinate. However, it can be suggested a definite limitation through the present user's skeptical responses about making personal location information public. Therefore, this paper suggests the method that traces the location information in tweet contents text without using the provided location information from twitter. Associated words were grouped by using the keyword that extracted in tweet contents text. The place that the events have occurred and whether the events have surely occurred are detected by this experiment using this algorithm. Furthermore, this experiment demonstrated the necessity of the suggested methods by showing faster detection compare to the other existing media.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.30 no.3
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• pp.149-170
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• 2019

This study aimed at analyzing the 6 foreign reformed theological journals and index terms, authors to find out the intellectual structures and the characteristics of the journals, the authors. First, analyzing the index terms, we analyzed the main keywords and the increasing trend by period, second, analyzing the journals, we analyzed the main subject of them and the subjects of distinguishing features by the journals, third we analyzed the authors' profiling. The index terms were clustered four big clusters and 23 small clusters. The journals were clustered the two clusters, and we also analyzed the index terms to distinguish a journal from other journals. The authors were clustered the six clusters, the index terms the clusters of the authors shared were similar to the results of the two analyses. The biblical teachings of The Old Testament and the New Testament and reformed theology were core subject terms and it was consistent in the results of these three analyses. This study mainly aimed at analyzing the 6 foreign reformed theological journals, and we found out that the parts of the results of this study were similar to the results of analyzing the domestic reformed theological journals. Therefore, there should be more researches needed to figure out what the results of this study mean to Korean reformed theology.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.116-126
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• 2022

Many studies using trademark information have suggested that trademark information is good data to monitor business trends. This study intends to analyze the trend change before and after COVID-19 using trademark information. Changes before and after COVID-19 were analyzed by using goods & service classification, similar group code, and designated goods information as trademark information. Among the trademark information, it was statistically significant that the change in trends before and after COVID-19 using designated goods names. To verify the results, the changes in keywords using designated goods names before and after COVID-19 were compared with the frequency of keywords in Google Trends. Among the top 8 keywords extracted from designated goods names, the frequency of Google trend searches for 'online, antibacterial, prevention of epidemics, meal kit, virtual' is on the rise, and 'mask, droplet' is not on the rise, but it increased rapidly at the time of COVID-19, and even after COVID-19, it showed a higher level than before. The frequency of 'unmanned' does not differ much before and after COVID-19, but it has been maintained at a consistently high level, and related businesses have been active since before COVID-19, and it can be interpreted as a keyword with high public interest. This study has academic achievements in that it specifically identified information that could be used in business trends by using three types of trademark information.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.8
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• pp.3677-3688
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• 2011

The patent information retrieval system can serve a variety of purposes. In general, the patent information is retrieved using limited key words. To identify earlier technology and priority rights repeated effort is needed. This study proposes a method of content-based retrieval using text mining. Using the proposed algorithm, each of the documents is invested with characteristic value. The characteristic values are used to compare similarities between query documents and database documents. Text analysis is composed of 3 steps: stop-word, keyword analysis and weighted value calculation. In the test results, the general retrieval and the proposed algorithm were compared by using accuracy measurements. As the study arranges the result documents as similarities of the query documents, the surfer can improve the efficiency by reviewing the similar documents first. Also because of being able to input the full-text of patent documents, the users unacquainted with surfing can use it easily and quickly. It can reduce the amount of displayed missing data through the use of content based retrieval instead of keyword based retrieval for extending the scope of the search.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1418-1432
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• 1997

Most general content_based image retrieval techniques use color and texture as retrieval indices. In color techniques, color histogram and color pair based color retrieval techniques suffer from a lack of spatial information and text. And This paper describes the design and implementation of content_based image retrieval system using color space and keywords. The preprocessor for image retrieval has used the coordinate system of the existing HSI(Hue, Saturation, Intensity) and preformed to split One image into chromatic region and achromatic region respectively, It is necessary to normalize the size of image for 200*N or N*200 and to convert true colors into 256 color. Two color histograms for background and object are used in order to decide on color selection in the color space. Spatial information is obtained using a maximum entropy discretization. It is possible to choose the class, color, shape, location and size of image by using keyword. An input color is limited by 15 kinds keyword of chromatic and achromatic colors of the Korea Industrial Standards. Image retrieval method is used as the key of retrieval properties in the similarity. The weight values of color space ${\alpha}(%)and\;keyword\;{\beta}(%)$ can be chosen by the user in inputting the query words, controlling the values according to the properties of image_contents. The result of retrieval in the test using extracted feature such as color space and keyword to the query image are lower that those of weight value. In the case of weight value, the average of te measuring parameters shows approximate Precision(0.858), Recall(0.936), RT(1), MT(0). The above results have proved higher retrieval effects than the content_based image retrieval by using color space of keywords.

• Journal of KIISE:Databases
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• v.34 no.3
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• pp.193-205
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• 2007

Increasing amount of digital images requires more accurate and faster way of image retrieval. So far, image retrieval method includes content-based retrieval and keyword based retrieval, the former utilizing visual features such as color and brightness and the latter utilizing keywords which describe the image. However, the effectiveness of these methods as to providing the exact images the user wanted has been under question. Hence, many researchers have been working on relevance feedback, a process in which responses from the user are given as a feedback during the retrieval session in order to define user’s need and provide improved result. Yet, the methods which have employed relevance feedback also have drawbacks since several feedbacks are necessary to have appropriate result and the feedback information can not be reused. In this paper, a novel retrieval model has been proposed which annotates an image with a keyword and modifies the confidence level of the keyword in response to the user’s feedback. In the proposed model, not only the images which have received positive feedback but also the other images with the visual features similar to the features used to distinguish the positive image are subjected to confidence modification. This enables modifying large amount of images with only a few feedbacks ultimately leading to faster and more accurate retrieval result. An experiment has been performed to verify the effectiveness of the proposed model and the result has demonstrated rapid increase in recall and precision while receiving the same number of feedbacks.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.05a
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• pp.559-570
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• 2017

우리나라는 과거부터 현재까지 미래 성장동력 육성을 목표로 정부주도하에 국가 R&D 투자를 점진적으로 늘려왔다. 그 결과, 최근에는 GDP 대비 연구개발비 비중이 세계 최고 수준에 이르렀다. 이렇게 연구개발 예산의 양적인 확대와 함께 연구개발 예산의 효율적 활용은 더욱 중요한 과학기술 분야의 정책적 이슈로 부각되고 있다. 연구개발 예산의 효율적인 집행을 위해서는 R&D 사업의 유사 중복성의 검토가 필수적이지만, 대부분의 유사 중복성 검토는 전문가의 직관적인 판단에 근거하여 이루어져왔다. 하지만, 전문가의 직관에만 의지한 판단은 때로는 불명확하거나 잘못된 결과를 가져올 수도 있다. 따라서, 본 연구에서는 네트워크 분석을 통해 정부 R&D 사업의 유사 중복성을 체계적으로 검토하기 위한 데이터기반의 방법론을 제안하여 전문가의 직관에 의한 유사 중복성 검토를 보완할 수 있는 가능성을 모색하고자 한다. 먼저, 본 연구에서는 정부 R&D사업 유사영역의 전체적인 구조 및 형태와 국가과학기술연구회 소속 25개 정부출연연구기관 R&D사업의 유사영역의 전반적인 형태를 시각화하여 유사영역을 파악하고 직관적인 판단과 선택을 할 수 있는 의사결정 정보를 제공하는데 초점을 두었다. 이를 위해, NTIS의 2015년 데이터를 사용하여 과제 키워드 기반으로 동시단어출현 분석을 수행하였다. 본 분석을 통해 25개 기관의 세부적인 유사연구영역 형태를 제시하였으며, 국내의 과학기술정책적 또는 과학기술학적인 현상들을 시각화하였다. 그 결과, 국내 출연연 R&D사업이 기관별 고유영역이 확고히 보이는 Mode 1적인 형태와 사회경제적인 맥락과 필요 및 유망성을 따르고, 다학제적, 적용중심적이며 과제별로 다양한 과제수행기관들이 과제들을 동시에 수행하는 Mode 2적인 형태가 출연연의 R&D사업 내에 공존하고 있음을 확인하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.916-918
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• 2005

표절이란 원본 자료의 출처를 밝히지 않고 사용하는 행위인데, 프로그램 표절은 표절로 의심되는 유사 소스코드를 말한다. 프로그램 표절을 검출하는 방법은 소스코드의 토큰 요약 후 비교하거나 단어와 키워드의 개수를 측정해 통계적으로 비교, 계산하거나 또는 프로그램의 구문구조를 비교하는 등 여러 접근 방법이 있다. 본 조사 보고서에서는 프로그램 표절 검출 방법 및 응용 도구에 대한 최근 연구 동향을 조사하여 정리하고 향후 발전 방향을 토론한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.401-404
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• 2004

본 연구에서는 다양한 뉴스그룹들 중에서 사용자의 취향과 유사한 뉴스그룹들을 코호넨 신경망을 이용하여 추천해주는 방법을 제시한다. 신경망을 학습시키기 위한 뉴스 문서의 키워드들을 선택하기 위해 여러 문서들로부터 후보 용어들을 추출하고 퍼지 추론을 적용하여 대표 용어들을 선택한다. 하지만 신경망의 학습패턴을 관찰해 보면, 맡은 부분이 비어있는 희소성 문제를 발견할 수 있다. 이에 본 연구에서는 통계적인 결정계수를 도입하여 불필요한 차원을 제거한 후 신경망을 학습시키는 새로운 방법을 제안한다. 제안된 방법은 모든 차원을 활용할 때 보다 클러스터내 거리와 클러스터간 거리의 척도를 이용한 클러스터 중첩도 면에서 우수한 분류 성능을 보여줌을 확인하였다.