• Information and Communications Magazine
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• v.31 no.12
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• pp.17-24
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• 2014

빅데이터, IoT, 클라우드 인프라 등 기술의 발달에 따라 일상생활 중에서도 개인과 환경의 변화에 대해 실시간 데이터 수집이 용이하게 되었다. 이를 활용하여 개인의 다양한 특성과 상황을 인지하고 다면적으로 의미를 분석할 수 있는 개인의 라이프스타일(lifestyle, 생활습관) 분석 기술이 중요하게 부각되고 있다. 이 라이프스타일 데이터는 개인의 질병이나 사회 심리적 문제의 원인 분석과 미래 트렌드의 변화예측을 할 수 있는 중요한 근거로 활용된다. 최근 이를 위한 연구로서 활동량, 스트레스, 위치, 수면 등의 라이프스타일 패턴을 추출하여 체계적인 프로세스로 삶의 질을 향상시키는 웰니스 (Wellness) 예측케어 서비스 연구와 서비스들이 활발히 진행되고 있다. 하지만 이러한 서비스를 제공하기에 앞서 개인의 복잡한 라이프스타일 패턴의 추출이 단편적으로만 이뤄지고 있어서, 패턴들 사이의 복잡한 관계를 분석하거나 연계 서비스로의 확장 및 라이프스타일 패턴의 재사용적인 측면에서의 문제가 어려운 이슈가 되고 있다. 이 때문에 웰니스 서비스의 신뢰도가 낮아 사용자가 단순히 재미로 느끼는 수준이거나 일회성에 그치는 모바일 어플리케이션 서비스를 제공받는 경우가 다반사이다. 본 논문에서는 IoT환경에서 다양한 스마트 디바이스에 의해 수집되는 라이프로그로 부터 라이프스타일 패턴 추출 및 모델링, 라이프스타일 패턴 분석으로부터 개인의 행동 추론 및 예측, 원인파악과 관련 지표를 정량적으로 설계하는 분석 엔진 개발 방안, 서비스 디자인을 통하여 실효적인 생활개선의 변화를 유도하는 기술, 개인의 심리적 특성까지 고려한 신뢰성 높은 케어 서비스 제공까지의 전반적인 웰니스 예측케어 서비스시스템 프로세스 및 플랫폼 설계 방안을 제시한다.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.259-265
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• 2021

This study is a study on the mobile gamification of PC games through analysis of the differences between PC games and mobile games, their respective advantages, and operation patterns, and attempts to extract important key elements of the mobile game process. The main methods of converting PC games to mobile games were analyzed and presented in four ways: complete transplantation, simplification, specialization and new genreization based on the standard of existing PC games. It is expected that PC games can play a role in providing guidelines for mobile gamification of PC games through the case analysis of existing games that have been converted to mobile games, as well as providing a path for the development and trends of game companies.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.87-94
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• 2020

Social reviews such as SNS feeds and blog articles have been widely used to extract keywords reflecting opinions and complaints from users' perspective, and often include proper nouns or new words reflecting recent trends. In general, these words are not included in a dictionary, so conventional morphological analyzers may not detect and extract those words from the reviews properly. In addition, due to their high processing time, it is inadequate to provide analysis results in a timely manner. This paper presents a method for efficient keyword extraction from social reviews based on the notion of cohesion scoring. Cohesion scores can be calculated based on word frequencies, so keyword extraction can be performed without a dictionary when using it. On the other hand, their accuracy can be degraded when input data with poor spacing is given. Regarding this, an algorithm is presented which improves the existing cohesion scoring mechanism using the structure of a word tree. Our experiment results show that it took only 0.008 seconds to extract keywords from 1,000 reviews in the proposed method while resulting in 15.5% error ratio which is better than the existing morphological analyzers.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.474-485
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• 2017

Sungnyemun Gate, Korea's National Treasure No.1, was destroyed by fire on February 10, 2008 and has been re-opened to the public again as of May 4, 2013 after a reconstruction work. Sungnyemun Gate become a national issue and draw public attention to be a major topic on news or research. In this research, text mining and association rule mining techniques were used on keyword of newspaper articles related to Sungnyemun Gate as a cultural heritage from 2002 to 2016 to find major keywords and keyword association rule. Next, we analyzed some typical and specific keywords that appear frequently and partially depending on before and after the fire and newpaper companies. Through this research, the trends and keywords of newspapers articles related to Sungnyemun Gate could be understood, and this research can be used as fundamental data about Sungnyemun Gate to information producer and consumer.

• The Journal of Society for e-Business Studies
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• v.25 no.1
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• pp.203-214
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• 2020

It is necessary to classify technical documents such as patents, R&D project reports in order to understand the trends of technology convergence and interdisciplinary joint research, technology development and so on. Text mining techniques have been mainly used to classify these technical documents. However, in the case of classifying technical documents by text mining algorithms, there is a disadvantage that the features representing technical documents must be directly extracted. In this study, we propose a BERT-based document classification model to automatically extract document features from text information of national R&D projects and to classify them. Then, we verify the applicability and performance of the proposed model for classifying documents.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.1
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• pp.31-36
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• 2014

Social network services(SNS) including Twitter and Facebook are good resources to extract various issues like public interest, trend and topic. This paper proposes a method to extract query-related issues by calculating relatedness between terms in Twitter. As a term that frequently appears near query terms should be semantically related to a query, we calculate term relatedness in retrieved documents by summing proximity that is proportional to term frequency and inversely proportional to distance between words. Then terms, relatedness of which is bigger than threshold, are extracted as query-related issues, and our system shows those issues with a connected network. By analyzing single transitions in a connected network, compound words are easily obtained.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.10
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• pp.251-258
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• 2018

Ransomware, a malicious software that requires a ransom by encrypting a file, is becoming more threatening with its rapid propagation and intelligence. Rapid detection and risk analysis are required, but real-time analysis and reporting are lacking. In this paper, we propose a ransomware infection detection system using social big data mining technology to enable real-time analysis. The system analyzes the twitter stream in real time and crawls tweets with keywords related to ransomware. It also extracts keywords related to ransomware by crawling the news server through the news feed parser and extracts news or statistical data on the servers of the security company or search engine. The collected data is analyzed by data mining algorithms. By comparing the number of related tweets, google trends (statistical information), and articles related wannacry and locky ransomware infection spreading in 2017, we show that our system has the possibility of ransomware infection detection using tweets. Moreover, the performance of proposed system is shown through entropy and chi-square analysis.

• Journal of Science Education
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• v.45 no.2
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• pp.156-171
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• 2021

This study aims at investigating the characteristics of trends of future education over time though the literature review and examining the accuracy of the framework for forecasting future education proposed by the previous studies by comparing the outcomes between the literature review and media articles. Thus, this study collects the articles dealing with future education searched from the Web of Science and categorized them into four periods during the new millennium. The new articles from media were selected to find out the present of education so that we can figure out the appropriateness of the proposed framework to predict the future of education. Research findings reveal that gradual tendencies of topics could not be found except teacher education and they are diverse from characteristics of agents (students and teachers) to the curriculum and pedagogical strategies. On the other hand, the results of analysis on the media articles focuses more on the projects launched by the government and the immediate responses to the COVID-19, as well as educational technologies related to big data and artificial intelligence. It is surprising that only a few key words are occupied in the latest articles from the literature review and many of them have not been discussed before. This indicates that the predictive framework is not effective to establish the long-term plan for education due to the uncertainty of educational environment, and thus this study will give some implications for developing the model to forecast the future of education.

• Proceedings of the Korean Society of Dyers and Finishers Conference
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• 2012.03a
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• pp.62-62
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• 2012

지구온난화 및 환경오염의 영향으로 선진국을 중심으로한 환경규제가 심해지면서, 홈 텍스타일 분야에서는 세계 패션 트렌드 및 소비자 선호에 부응한 친환경 섬유소재 개발, 웰빙 시대에 적합한 기능성 및 고감성 제품개발을 통한 차별화가 요구되고 있다. 최근의 섬유산업의 동향도 인체에 무해한 천연적인 섬유소재에 많은 관심이 증대됨에 따라 개인의 건강 뿐만 아니라 환경을 생각하는 생활패턴인 친환경섬유의 개발이 새로운 트렌드로 떠오르고 있는 실정이다. 헴프는 일년생 식물로서 학명은 Cannabis sativa L.이다. 헴프섬유의 장점으로 내구성 및 내수성, 항균성 등이 우수한 것으로 보고되고 있으나 양질의 원료 확보, 세섬도 추출 기술 및 combing 기술 등의 부족으로 100% 헴프 세 번수 방적사의 제조가 어려워 주로 면섬유와의 혼합소재로 제조되어 왔다. 최근 들어, 친환경 소재로서 박테리아 성장 억제 기능을 가진 재생섬유인 Tencel 소재를 이용하여 stiff한 Hemp의 성질에 유연성을 추가하여 촉감을 개선함과 동시에, Tencel과 Hemp를 혼용함으로써 soft touch부터 harsh touch까지 혼용율에 의한 다양한 감성을 느끼게 함으로써 용도의 다양화 추구가 시도되어 왔다. Hemp의 거친 느낌을 완화시키고 Tencel의 박테리아 억제 기능과 Hemp의 항균기능, 방충, 탈취기능이 상호 보완되어 친환경적이고 위생적인 다용도 홈 인테리어 및 가구용 직물 등의 제품으로 Hemp/Tencel 복합사가 많이 사용되고 있다. 본 연구는 Hemp와 Tencel의 혼용율의 변화에 따른 복합사의 물리적 특성을 확인하기 위하여 천연복합 태번수 방적사 최적 사설계 이론을 적용하여 Hemp 섬유 혼용율에 따른 사의 물성분석을 함으로써 Hemp/Tencel 방적사 최적 공정 조건을 결정하기 위한 사설계 이론 결과와 실험결과를 비교 분석하고자 한다. 최적 천연 Hemp복합방적사 사설계의 이론화 및 사 물성 DB화 그리고 태번수 Hemp사의 물성분석 및 이들을 DB화 함으로써 가구용 직물로 많이 사용되는 친환경 Hemp 소재사의 방적성 향상을 꾀하고자 한다. 이를 위해서 제조한 방적사의 Dry heat shrinkage와 Wet heat shrinkage를 측정하여 확인하였고 인장시험기를 이용하여 Tenacity, Initial Modulus, breaking strain을 측정 분석하였다. 방적사의 표면 특성은 영상 현미경 시스템을 사용하여 ${\times}40$ 배율로 측정하여 확인하였다.

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.253-263
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• 2016

We analyzed the social issue 'image of the elderly' using Big Data and Social Network Analysis. First, we analyzed the words extracted by the text mining technique by inputting the keyword 'elderly'. As a result of analysis, the image of the elderly viewed through media such as cafes, blogs, etc. Representing the trend of the public was using the word 'Senior' the most. The image of the elderly is expressed using the word having the highest frequency in the top 10, "The elderly are 'Senior' people who are respected by society, they are organized to earn money, to earn their qualifications, to health, and to 'Seniors' who desire to work healthy up to 100 years old". The purpose of this study is to differentiate from the existing analysis method by analyzing the macro-level image of the elderly including the social discourse by collecting vast amount of data and analyzing it with the social networking technique. When the image of the elderly that the public perceives is positively expressed as 'Senior', it can be said that the direction of the current elderly policy is evaluated as a desirable direction. On the other hand, it was able to feel the 'desire' of the public who wanted to be evaluated. Therefore, the policy direction of the elderly to be applied in the future should be the policy that enables the elderly to be perceived as 'Necessary existence' in society by taking on social roles. In addition, we proposed to implement the policy of the elderly that reflects priorities such as job creation, welfare, and alienation that can activity and maintain health.