• Journal of the Korean Society for information Management
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• v.35 no.4
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• pp.141-164
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• 2018

Recently, as the amount of academic literature has increased rapidly and complex researches have been actively conducted, researchers have difficulty in analyzing trends in previous research. In order to solve this problem, it is necessary to classify information in units of academic papers. However, in Korea, there is no academic database in which such information is provided. In this paper, we propose an automatic classification system that can classify domestic academic literature into multiple classes. To this end, first, academic documents in the technical science field described in Korean were collected and mapped according to class 600 of the DDC by using K-Means clustering technique to construct a learning set capable of multiple classification. As a result of the construction of the training set, 63,915 documents in the Korean technical science field were established except for the values in which metadata does not exist. Using this training set, we implemented and learned the automatic classification engine of academic documents based on deep learning. Experimental results obtained by hand-built experimental set-up showed 78.32% accuracy and 72.45% F1 performance for multiple classification.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.325-336
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• 2018

This study was conducted to explore the latest trends in domestic research related to career development for nurses and present a theoretical approach and practical direction of career related papers in the field of nursing and health care. The subject of the study was articles regarding career development of nurses published in domestic journals from 2008 to 2017. The final 29 papers were found after searching the RISS and KISS databases for 'nursing', 'nurse', 'career', 'career development', 'career development program', and 'career management.' To analyze the overall trends, the analysis was reviewed by year, source, research design, and subjects, and content analysis was used to identify trends by topic. The results of content analysis were divided into career development and career development results as the upper categories. The subcategories of career development were divided into organizational career management, personal career management, and career performance. The subcategories of personal career management consisted of career exploration, career planning, career plateau, and career interruption. Organizational career management consisted of CLS development and application, CLS awareness survey, and career development model building. Career performance consisted of career commitment, organizational commitment, turnover intention, change after CLS application, career satisfaction, and career withdrawal intention. The most frequently discussed topics were career commitment among career performance. Therefore, the direction of nursing research related to career development requires integrated research that meets personal and organizational needs, and diversity in subject, research design and research theme is required.

• Journal of Digital Convergence
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• v.13 no.2
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• pp.193-204
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• 2015

An array DBMS has been expected widely from scientists because it is convenient to store and analyze the data of array type. In this paper, we describe how to handle satellite remote-sensing images in the array DBMS. However, previous works in their visualization have two problems as follows. First, the images are visualized as a state of distorted by the curvature of the earth. Second, it is difficult to apply the results of visualization by pre-written queries to other analyses. Therefore, this paper proposes a three dimensional visualization method of satellite remote-sensing images, not traditional 2D visualization. Our research contents are as follows. First, we describe how to store, process, and analyze the satellite remote-sensing images in the array DBMS. Second, we propose a three-dimensional visualization method for their processed outputs. Lastly, our contributions can be summarized that we propose a method of handling satellite remote-sensing images in the array DBMS and their 3D visualization techniques. It is also expected that their use be available widely in many industrial areas.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.187-194
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• 2013

Recently, there have been many trials to analyze characteristics of research trends through a structural analysis of keyword networks in various fields. However, most previous studies have mainly focused on structural analysis harbored in some static networks and there is a lack of research on changes of such networks structure with time. In this paper, we constructed annual keyword networks by using a database of papers published in the international computer engineering-field journals from 2002 through 2011, and examined the changes of them. As a result, it was shown that most keywords in a network are preserved in the network of the next year, and their degree of connectivity and the average weight of the connections were higher and smaller, respectively, than those of the keywords which are not preserved. In addition, when a keyword network shifted to one of the next year, the connections between keywords were more likely to be removed than preserved, and the average weight of the removal connections was higher than that of the preserved ones. These results imply that the keywords are not changed over time but their connections are very likely to be changed; and there is apparent differences between the preserved and removal groups of keywords/connections with respect to degree and weights of connections. All these results are consistently observed over the ten-year datasets and they can be important principles in understanding the structural changes of the keyword networks.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.35-44
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• 2023

In recent, there are many research cases that analyze trends or research trends with text analysis. When collecting documents by searching for keywords in abbreviations for data analysis, it is necessary to disambiguate abbreviations. In many studies, documents are classified by hand-work reading the data one by one to find the data necessary for the study. Most of the studies to disambiguate abbreviations are studies that clarify the meaning of words and use supervised learning. The previous method to disambiguate abbreviation is not suitable for classification studies of documents looking for research data from abbreviation search documents, and related studies are also insufficient. This paper proposes a method of semi-automatically classifying documents collected by abbreviations by going topic modeling with Non-Negative Matrix Factorization, an unsupervised learning method, in the data pre-processing step. To verify the proposed method, papers were collected from academic DB with the abbreviation 'MSA'. The proposed method found 316 papers related to Micro Services Architecture in 1,401 papers. The document classification accuracy of the proposed method was measured at 92.36%. It is expected that the proposed method can reduce the researcher's time and cost due to hand work.

• 한국체육학회지인문사회과학편
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• v.55 no.5
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• pp.249-262
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• 2016

The policy regarding school sport club participation that was implemented in 2007 to mitigate reductions in fitness has been reported to be satisfactory at improving the physical, mental, and social health of elementary, middle, and high school students by the students as well as by their teachers and parents. The purpose of this systematic review is to investigate the association between participation in school sport club and character-building among adolescents in Korea, and to provide evidence to inform physical education policies in the future. Using "School Sport Club" as the key word, the Korean electronic databases were searched, and this systematic review included a total of 13 articles. School sport club participation among school students is closely correlated to character-building, prevention of school violence, attitude towards life and attitude toward education. The findings of this study support the Ministry of Education's new policy (2015) to extend and expand school sport club participation and highlights the importance of physical activity.

• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.283-308
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• 2008

In this study, we testify that network structural attributes of a citation network can explain other aspects of journal citation behaviors and the importances of journals. And we also testify various citation impact indicators of journals including JIF and h-index to verify the difference among them especially focused on their ability to explain an institution's local features of citation behaviors. An institutional citation network is derived using the articles published in 2006-2007 by biotechnology faculties of Y University. And various journal citation impact indicators including JIF, SJR, h-index, EigenFactor, JII are gathered from different service sites such as Web of Science, SCImago, EigenFactor.com, Journal-Ranking.com. As a results, we can explain the institution's 5 research domains with inter-citation network. And we find that the co-citation network structural features can show explanations on the patterns of institutional journal citation behavior different from the simple cited frequency of the institution or patterns based on general citation indicators. Also We find that journal ranks with various citation indicators have differences and it implies that total-based indices, average-based indices, and hybrid index(h-index) explain different aspects of journal citation pattern. We also reveal that the coverage of citation DB doesn't be a matter in the journal ranking. Analyzing the citation networks derived from an institution's research outputs can be a useful and effective method in developing several library services.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.286-289
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• 2005

기존의 물리적 인증도구를 이용한 방식이나 패스워드 인증 방식은 분실, 도난, 해킹 등에 취약점을 가지고 있다. 따라서 지문, 서명, 홍채, 음성, 얼굴 등을 이용한 생체 인식기술을 보안 기술로 적용하려는 연구가 진행중이며 일부는 실용화도 되고 있다. 본 논문에서는 최근 널리 보급되어 있는 임베디드 시스템중의 하나인 PDA에 음성 기술을 이용한 내장형 화자 인증기를 구현하였다. 화자 인증기는 음성기술에서 널리 사용되고 있는 벡터 양자화 기술과 은닉 마코프 모델 기술을 사용하였으며, PDA의 하드웨어적인 제약 사항을 고려하여 사용되는 벡터 코드북을 두 가지로 다르게 하여 각각 구현하였다. 처음은 코드북을 화자 등록시에 발성음만을 이용하여 생성하고 화자인증 시에 이용하는 방법이며, 다른 하나는 대용량의 음성 데이터베이스를 이용하여 코드북을 사전에 생성하여 이를 화자 인증시에 이용하는 방법이다. 화자인증기의 성능평가는 5명의 화자가 10번씩 5개의 단어에 대하여 실험하여, 각각 화자종속 코득북을 이용한 인증기는 88.8%, 99.5%, 화자독립 코드북을 이용한 인증기는 85.6%, 95.5%의 인증율과 거절율을 보였으며, 93.5%와 90.0%의 평균 확률을 보였다.. 실험을 통하여 화자독립 인증기의 경우가 화자종속 인증기의 경우보다 낮은 인식율을 보였지만, 화자종속 인증기에서 나타나는 코드북 훈련시에 발생하는 메모리 문제를 해결 할 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.249-251
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• 2012

Augmented Reality (AR) enables the enhanced realism and interaction by providing the overlaid digital information on the user's view of the physical world. In this paper, we propose an AR-based ship design visualization system for presenting ship 3D model in smart phones or table PCs. The proposed system compute corner points and feature points by contour finding method and harris corner detector, and build a ship-design drawing database. By using SURF algorithm, key feature points are extracted from ship-design drawing image which is obtained by mobile camera. Then ship-design drawing image is recognized by matching the feature points stored in DB and extracted key feature points. 3D ship structures are visualized by overlaying the ship-design drawing image on the smart phone or table PC's screen. Compared to conventional 2D ship-design, proposed system helps to easily understand the structures of the ship and reduce the business design period. Thus, Enhanced competitiveness of business is expected.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.210-214
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• 2008

It is actually impossible to model and store all objects which exist in real home environment into robot's database in advance. To resolve this problem, this paper proposes new object modeling method that can be available for robot self-modeling, which is capable of estimating whole model's shape from partial surface data using Generic Model. And this whole produce is conducted to cylindrical objects like cup, bottles and cans which can be easily found at indoor environment. The detailed process is firstly we obtain cylinder's initial principle axis using points coordinates and normal vectors from object's surface after we separate cylindrical object from 3D image. This 3D image is obtained from 3D sensor. And second, we compensate errors in the principle axis repeatedly. Then finally, we do modeling whole cylindrical object using cross sectional principal axis and its radius To show the feasibility of the algorithm, We implemented it and evaluated its accuracy.