• Journal of the military operations research society of Korea
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• v.36 no.1
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• pp.39-49
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• 2010

This paper tries to identify emerging defense technology using S&T Text Mining. As a national agenda, there has been much effort to build S&T information systems including NTIS and DTiMS that enable researchers, policy makers, or field users to analyze technological changes and promote the best policy practices for efficient workflow, knowledge sharing, strategy development, or institutional competitiveness. In this paper, the S&T Text Mining application to unmanned combat technology using INSPEC DB is empirically illustrated and shows that it is a feasible approach to identify emerging defense technology as well as the structure of knowledge network of the future technology candidates.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.717-734
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• 2022

Purpose: The purpose of this study is to effectively detect violations that occur simultaneously against Good Manufacturing Practice, which were concealed by drug manufacturers. Methods: In this study, we present an analysis framework for analyzing regulatory violation patterns using Association Rule Mining (ARM), Text Mining, and t-distributed Stochastic Neighbor Embedding (t-SNE) to increase the effectiveness of on-site inspection. Results: A number of simultaneous violation patterns was discovered by applying Association Rule Mining to FDA's inspection data collected from October 2008 to February 2022. Among them there were 'concurrent violation patterns' derived from similar regulatory ranges of two or more regulations. These patterns do not help to predict violations that simultaneously appear but belong to different regulations. Those unnecessary patterns were excluded by applying t-SNE based on text-mining. Conclusion: Our proposed approach enables the recognition of simultaneous violation patterns during the on-site inspection. It is expected to decrease the detection time by increasing the likelihood of finding intentionally concealed violations.

• Journal of Korea Trade
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• v.24 no.5
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• pp.71-86
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• 2020

Purpose - This study investigates research trends by searching for English keywords and abstracts in 1,511 Korean journal articles in the Korea Citation Index from the 2002-2019 period using the term "Port." The study aims to lay the foundation for a more balanced development of port research. Design/methodology - Using abstract and keyword data, we perform frequency analysis and word embedding (Word2vec). A t-SNE plot shows the main keywords extracted using the TextRank algorithm. To analyze which words were used in what context in our two nine-year subperiods (2002-2010 and 2010-2019), we use Scattertext and scaled F-scores. Findings - First, during the 18-year study period, port research has developed through the convergence of diverse academic fields, covering 102 subject areas and 219 journals. Second, our frequency analysis of 4,431 keywords in 1,511 papers shows that the words "Port" (60 times), "Port Competitiveness" (33 times), and "Port Authority" (29 times), among others, are attractive to most researchers. Third, a word embedding analysis identifies the words highly correlated with the top eight keywords and visually shows four different subject clusters in a t-SNE plot. Fourth, we use Scattertext to compare words used in the two research sub-periods. Originality/value - This study is the first to apply abstract and keyword analysis and various text mining techniques to Korean journal articles in port research and thus has important implications. Further in-depth studies should collect a greater variety of textual data and analyze and compare port studies from different countries.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.9-21
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• 2021

The study aims to identify convergence and trends in technology-based patent data for the biohealth sector in IP5 countries (KR, EP, JP, US, CN) and present the direction of development in that industry. We used patent co-classification analysis-based network analysis and TF-IDF-based text mining as the principal methodology to understand the current state of technology convergence. As a result, the technology convergence cluster in the biohealth industry was derived in three forms: (A) Medical device for treatment, (B) Medical data processing, and (C) Medical device for biometrics. Besides, as a result of trend analysis based on technology convergence results, it is analyzed that Korea is likely to dominate the market with patents with high commercial value in the future as it is derived as a market leader in (B) medical data processing. In particular, the field is expected to require technology convergence activation policies and R&D support strategies for the technology as the possibility of medical data utilization by domestic bio-health companies expands, along with the policy conversion of the "Data 3 Act" passed by the National Assembly in January 2019.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.99-106
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• 2014

Generation and analysis methods have been proposed in recent years, such as using a natural language and formal language processing, artificial intelligence algorithms based knowledge model is effective meaning. its semantic based knowledge model has been used effective decision making tree and problem solving about specific context. and it was based on static generation and regression analysis, trend analysis with behavioral model, simulation support for macroeconomic forecasting mode on especially in a variety of complex systems and social network analysis. In this study, in this sense, integrating knowledge-based models, This paper propose a text mining derived from the inter-Topic model Integrated formal methods and Algorithms. First, a method for converting automatically knowledge map is derived from text mining keyword map and integrate it into the semantic knowledge model for this purpose. This paper propose an algorithm to derive a method of projecting a significant topic map from the map and the keyword semantically equivalent model. Integrated semantic-based knowledge model is available.

• Korea Trade Review
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• v.45 no.5
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• pp.37-53
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• 2020

This study aims to provide the implications of research development by comparing domestic and international studies conducted on the subject of FTA. To this end, among the papers written during the period from 2000 to July 23, 2020, papers whose title is searched by FTA (Free Trade Agreement) were selected as research data. In the case of domestic research, 1,944 searches from the Korean Citation Index (KCI) and 970 from the Web of Science and SCOPUS were selected for international research, and the research trend was analyzed through keywords and abstracts. Frequency analysis and word embedding (Word2vec) were used to analyze the data and visualized using t-SNE and Scattertext. The results of the analysis are as follows. First, in the top 30 keywords of domestic and international research, 16 out of 30 were found to be the same. In domestic research, many studies have been conducted to analyze the outcomes or expected effects of countries that have concluded or discussed FTAs with Korea, on the other hand there are diverse range of study subjects in international research. Second, in the word embedding analysis, t-SNE was used to visually represent the research connection of the top 60 keywords. Finally, Scattertext was used to visually indicate which keywords were frequently used in studies from 2000 to 2010, and from 2011 to 2020. This study is the first to draw implications for academic development through abstract and keyword analysis by applying various text mining approaches to the FTA related research papers. Further in-depth research is needed, including collecting a variety of FTA related text data, comparing and analyzing FTA studies in different countries.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.223-228
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• 2020

The stock price reflects people's psychology, and factors affecting the entire stock market include economic growth rate, economic rate, interest rate, trade balance, exchange rate, and currency. The domestic stock market is heavily influenced by the stock index of the United States and neighboring countries on the previous day, and the representative stock indexes are the Dow index, NASDAQ, and S & P500. Recently, research on stock price analysis using stock news has been actively conducted, and research is underway to predict the future based on past time series data through artificial intelligence-based analysis. However, even if the stock market is hit for a short period of time by the forecasting system, the market will no longer move according to the short-term strategy, and it will have to change anew. Therefore, this model monitored Samsung Electronics' stock data and news information through text mining, and presented a predictable model by showing the analyzed results.

• Economic and Environmental Geology
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• v.31 no.6
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• pp.547-555
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• 1998

A new trigonometrical method for calculating total slip (T) of faulting is presented. The parameters for the calculations are used rake of fault striation, strike and dip of fault and of index planar structure such as bedding plane. The faults are groupped into three types. The direction of plunging of fault striation is out of a range ${\pm}90^{\circ}$ to the bedding dip direction in $360^{\circ}$ system, which is groupped into the type I. Meanwhile, the case of the direction lies in the above range can be separated into two different types, type II and type III, according to relative largeness of the angles rake of fault striation and i (see text). The type II has smaller rake than angle i and the type III has larger rake than angle i. Here I propose a few equations for calculating not only total slip (T) but strike slip (L) or dip slip (S) of the faulting. The equations are adapted selectively to the types of fault mentioned before. The limitation of the method is that the equations do not fit to polyphase faulting.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.11a
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• pp.134-136
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• 2016

최근 TV시청은 다양한 매체를 통해서 이루어지고 있으며, 특히 스마트폰을 통한 시청률이 늘고 있는 상황이다. 광고시장에서도 TV시청 중에 스마트기기를 함께 이용하는 멀티태스킹 사용자가 급증하고 있으며 특히 10~30대의 사용이 적극적이다. TV시청 중 스마트 기기의 사용분야는 메신저, 정보검색, SNS 순이며 스마트 기기사용 내용 중 69%는 시청하던 TV 시청과 관련된 것이었다. 이 중에 75%는 TV에 등장한 제품, 브랜드, 장소에 관한 것이다[1]. TV를 시청하는 상황에 스마트기기의 소셜 활동의 문자를 분석하는 것은 사용자 의도를 파악할 수 있는 의미가 있으며, 시청자의 현재 위치를 파악함으로써 시청자의 의도에 반영되는 상황을 파악할 수 있다. T-Commerce 구매 의도는 사용자의 현재 상황에 대한 순간 의도를 파악하는것이 중요하며, 이와 같은 구매의도를 파악하기 위해서 본 연구에서는 GPS와, Wi-Fi 기반 Fingerprinting 측위기법을 사용하여 특별한 도구나 장비의 설치 없이 현재위치와 멀티태스킹 데이터를 분석하여 구매의도를 파악한다. T-Commerce 소비환경 패턴이 바뀜에 따라, 다양한 소비 환경 데이터 분석은 효율적인 광고 제공과 만족도를 높일 것으로 기대된다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.137-147
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• 2022

Diagnosis and management of skin condition is a very basic and important function in performing its role for workers in the beauty industry and cosmetics industry. For accurate skin condition diagnosis and management, it is necessary to understand the skin condition and needs of customers. In this paper, we developed SCIS, a big data-based skin care information system that supports skin condition diagnosis and management using social media big data for skin condition diagnosis and management. By using the developed system, it is possible to analyze and extract core information for skin condition diagnosis and management based on text information. The skin care information system SCIS developed in this paper consists of big data collection stage, text preprocessing stage, image preprocessing stage, and text word analysis stage. SCIS collected big data necessary for skin diagnosis and management, and extracted key words and topics from text information through simple frequency analysis, relative frequency analysis, co-occurrence analysis, and correlation analysis of key words. In addition, by analyzing the extracted key words and information and performing various visualization processes such as scatter plot, NetworkX, t-SNE, and clustering, it can be used efficiently in diagnosing and managing skin conditions.