• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.419-427
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• 2015

Despite of correlation between climate changes and food-related information, it is still not easy for many users to get access to the information with interest. This study investigated how much climate change and food-related information are correlated with each other and how often they are exposed through frequency and correlation analysis on news articles on the internet portals. Through analysis on the frequency of climate change and food-related news articles, this study was able to figure out how often they are exposed at the same time by the internet news portals. In addition, a total of 59 correlation rules regarding the climate change and food-related vocabularies were derived from these news articles using the climate change and food-related glossaries. Then, a correlation between certain climate change-related and food-related words was analyzed in order to package the related words.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.509-515
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• 2023

Despite the continued development of alternative energies, fuel consumption is increasing. In particular, the price of gasoline fluctuates greatly according to fluctuations in international oil prices. Gas stations adjust their gasoline inventory to respond to gasoline price fluctuations. In this study, news datasets is used to analyze the gasoline consumption patterns through fluctuations of the gasoline inventory. First, collecting news datasets with web crawling. Second, summarizing news datasets using KoBART, which summarizes the Korean text datasets. Finally, preprocessing and deriving the fluctuations factors through N-Gram Language Model and TF-IDF. Through this study, it is possible to analyze and predict gasoline consumption patterns.

• Information Systems Review
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• v.18 no.1
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• pp.141-155
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• 2016

This study identified terminologies related to the proximity and frequency of disaster by social network analysis (SNA) and text mining, and then expressed the outcome into a mind map. The termdocument matrix of text mining was utilized for the terminology proximity analysis, and the SNA closeness centrality was calculated to organically express the relationship of the terminologies through a mind map. By analyzing terminology proximity and selecting disaster response-related terminologies, this study identified the closest field among all the disaster response fields to disaster response and the core terms in each disaster response field. This disaster response terminology analysis could be utilized in future core term-based terminology standardization, disaster-related knowledge accumulation and research, and application of various response scenario compositions, among others.

• Journal of Korean Society of Archives and Records Management
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• v.23 no.4
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• pp.73-89
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• 2023

This study aims to analyze the frequency of keywords used in Korean abstracts, which are unstructured text data in the domestic record management research field, using text mining techniques to identify domestic record management research trends through distance analysis between keywords. To this end, 1,157 keywords of 77,578 journals were visualized by extracting 1,157 articles from 7 journal types (28 types) searched by major category (complex study) and middle category (literature informatics) from the institutional statistics (registered site, candidate site) of the Korean Citation Index (KCI). Analysis of t-Distributed Stochastic Neighbor Embedding (t-SNE) and Scattertext using Word2vec was performed. As a result of the analysis, first, it was confirmed that keywords such as "record management" (889 times), "analysis" (888 times), "archive" (742 times), "record" (562 times), and "utilization" (449 times) were treated as significant topics by researchers. Second, Word2vec analysis generated vector representations between keywords, and similarity distances were investigated and visualized using t-SNE and Scattertext. In the visualization results, the research area for record management was divided into two groups, with keywords such as "archiving," "national record management," "standardization," "official documents," and "record management systems" occurring frequently in the first group (past). On the other hand, keywords such as "community," "data," "record information service," "online," and "digital archives" in the second group (current) were garnering substantial focus.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.349-352
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• 2003

A personal low-frequency stimulator is a portable device to relax muscle pains of a person. The stimulator generates combined low-frequency pulses to be applied to pads attached to painful muscles. This paper reports a development of such device with its characteristic analyses. The major components of our stimulator are MCU, high-voltage generating circuit part, high-voltage switching circuit part, input switch part and display unit. High-voltage generating circuit is designed by using a boost converter circuit and allows user control of the output voltage. High-voltage switching circuit, controlled by MCU, generates output voltage to be applied to pads. Input switch part is composed of power supply, intensity selection, mode selection and memory. Display unit adopts a text LCD module to display modes, Intensity, output frequency and user set-up time. Our designed safety circuit, to protect human body from possible electric shock, slowly increases the output voltage to the selected output intensity. It continuously checks the output pulse shape and disable the output when dangerous pulses are detected. This paper also shows some experimental results.

• Fashion & Textile Research Journal
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• v.25 no.5
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• pp.549-559
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• 2023

Along with the transition to the fourth industrial revolution, the possibility of metaverse-based innovation in the fashion field has been confirmed, and various applications are being sought. Therefore, this study performs meaning structure analysis and discusses the prospects of meta fashion using big data. From 2020 to 2022, data including the keyword "metaverse + fashion design" were collected from portal sites (Naver, Daum, and Google), and the results of keyword frequency, N-gram, and TF-IDF analyses were derived using text mining. Furthermore, network visualization and CONCOR analysis were performed using Ucinet 6 to understand the interconnected structure between keywords and their essential meanings. The results were as follows: The main keywords appeared in the following order: fashion, metaverse, design, 3D, platform, apparel, and virtual. In the N-gram analysis, the density between fashion and metaverse words was high, and in the TF-IDF analysis results, the importance of content- and technology-related words such as 3D, apparel, platform, NFT, education, AI, avatar, MCM, and meta-fashion was confirmed. Through network visualization and CONCOR analysis using Ucinet 6, three cluster results were derived from the top emerging words: "metaverse fashion design and industry," "metaverse fashion design and education," and "metaverse fashion design platform." CONCOR analysis was also used to derive differentiated analysis results for middle and lower words. The results of this study provide useful information to strengthen competitiveness in the field of metaverse fashion design.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.527-528
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• 2022

빠르게 변하는 환경에 맞춰 평생 교육이 일반화되고 개인에게 요구되는 학습량은 많아지고 있으며 높아진 학습량에 맞게 학습 시간 단축과 효율적인 학습을 위한 학습 방법을 선택하는 것이 중요해지고 있다. 본 논문에서는 학습 정리를 위해 작성한 문서를 분석하여 해당 문서와 관련된 문서를 제안하고 본 문서와 엮어 학습을 위한 문서 묶음을 만들 수 있는 시스템을 제안한다. 문서의 유사도, 중요도를 구할 수 있는 TF-IDF를 이용하여 문서를 분석해 키워드를 추출한 다음 그와 관련된 문서를 제안하고 문서 묶음을 만들어 조회할 수 있도록 한다. 이 시스템은 학습 정리 시 관련 문서를 함께 볼 수 있도록 하고, 필요하다면 묶음으로 만들어 효과적인 학습을 위한 도구로 이용할 수 있다.

• Asia Pacific Journal of Corpus Research
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• v.4 no.2
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• pp.57-74
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• 2023

Having a firm grasp of technical terms is essential for learners of Japanese for Specific Purposes (JSP). This research aims to analyze Japanese nursing care vocabulary based on objective corpus-based frequency and subjectively rated word familiarity. For this purpose, we constructed a text corpus centered on the National Examination for Certified Care Workers to extract nursing care keywords. The Log-Likelihood Ratio (LLR) was used as the statistical criterion for keyword identification, giving a list of 300 keywords as target words for a further word recognition survey. The survey involved 115 participants of whom 51 were certified care workers (CW group) and 64 were individuals from the general public (GP group). These participants rated the familiarity of the target keywords through crowdsourcing. Given the limited sample size, Bayesian linear mixed models were utilized to determine word familiarity rates. Our study conducted a comparative analysis of word familiarity between the CW group and the GP group, revealing key terms that are crucial for professionals but potentially unfamiliar to the general public. By focusing on these terms, instructors can bridge the knowledge gap more efficiently.

• Structural Engineering and Mechanics
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• v.88 no.2
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• pp.109-115
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• 2023

In this paper, frequencies of zigzag structure of carbon nanotubes isinvestigated based on Donnell shell theory. These tubes are wrapped with the ring supports in the axial direction. The fundamental frequency curves displayed in article show the dependence of vibrations attributes to zigzag single walled carbon nanotubes. Various zigzag indices are introduced against the variation of length to predict the vibration. Also, the influence of ring supports is sketched with proposed structure for frequency analysis. The frequencies of zigzag tube decreases as the length increases. It is observed that the frequencies decreases with ring support and have higher frequencies without ring. The problem is formulated using Partial Differential Equation. Three expressions of modal deformation displacement functions is used for the elimination of temporal variation to form the solution in the eigen from. For the stability of present study the results are compared with experimentally and numerically in the open text.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.3
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• pp.47-56
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• 1999

Various phonological alteration occurs when we pronounce continuously in korean. This phonological alteration is one of the major reasons which make the speech recognition of korean difficult. This paper presents a rule-based system which converts a speech recognition character string to a text-based character string. The recovery results are morphologically analyzed and only a correct text string is generated. Recovery is executed according to four kinds of rules, i.e., a syllable boundary final-consonant initial-consonant recovery rule, a vowel-process recovery rule, a last syllable final-consonant recovery rule and a monosyllable process rule. We use a x-clustering information for an efficient recovery and use a postfix-syllable frequency information for restricting recovery candidates to enter morphological analyzer. Because this system is a rule-based system, it doesn't necessitate a large pronouncing dictionary or a phoneme dictionary and the advantage of this system is that we can use the being text based morphological analyzer.