• 한국의상디자인학회지
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• 제22권3호
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• pp.49-61
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• 2020

This study analyzed the social media accounts and performed a Big Data analysis of Z-generation fashion using Textom Text Mining Techniques program and Ucinet Big Data analysis program. The research results are as follows: First, as a result of keyword analysis on 67.646 Z-generation fashion social media posts over the last 5 years, 220,211 keywords were extracted. Among them, 67 major keywords were selected based on the frequency of co-occurrence being greater than more than 250 times. As the top keywords appearing over 1000 times, were the most influential as the number of nodes connected to 'Z generation' (29595 times) are overwhelmingly, and was followed by 'millennials'(18536 times), 'fashion'(17836 times), and 'generation'(13055 times), 'brand'(8325 times) and 'trend'(7310 times) Second, as a result of the analysis of Network Degree Centrality between the key keywords for the Z-generation, the number of nodes connected to the "Z-generation" (29595 times) is overwhelmingly large. Next, many 'millennial'(18536 times), 'fashion'(17836 times), 'generation'(13055 times), 'brand'(8325 times), 'trend'(7310 times), etc. appear. These texts are considered to be important factors in exploring the reaction of social media to the Z-generation. Third, through the analysis of CONCOR, text with the structural equivalence between major keywords for Gen Z fashion was rearranged and clustered. In addition, four clusters were derived by grouping through network semantic network visualization. Group 1 is 54 texts, 'Diverse Characteristics of Z-Generation Fashion Consumers', Group 2 is 7 Texts, 'Z-Generation's teenagers Fashion Powers', Group 3 is 8 Texts, 'Z-Generation's Celebrity Fashions' Interest and Fashion', Group 4 named 'Gucci', the most popular luxury fashion of the Z-generation as one text.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.799-802
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• 2009

We have been studying the next generation of video creation solution based on TVML (TV program Making Language) technology. TVML is a well-known scripting language for computer animation and a TVML Player interprets the script to create video content using real-time 3DCG and synthesized voices. TVML has a long history proposed back in 1996 by NHK, however, the only available Player has been the one made by NHK for years. We have developed a new TVML Player from scratch and named it T2V (Text-To-Vision) Player. Due to the development from scratch, the code is compact, light and fast, and extendable and portable. Moreover, the new T2V Player performs not only a playback of TVML script but also a Text-To-Vision conversion from input written in XML format or just a mere plane text to videos by using 'Text-filter' that can be added as a plug-in of the Player. We plan to make it public as freeware from early 2009 in order to stimulate User-Generated-Content and a various kinds of services running on the Internet and media industry. We think that our T2V Player would be a key technology for upcoming new movement.

• Journal of Information Technology Applications and Management
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• 제17권3호
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• pp.83-97
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• 2010

The purpose of this study is to examine the impacts of media symbol variety on group performance in virtual teams. Symbol variety is defined as the number of ways in which information can be communicated and includes Daft and Lengel [1986]'s multiplicity of cues and language variety. According to media richness theory and media synchronicity theory, the use of media with high symbol variety is assumed to facilitate and promote communications among virtual team members. Therefore, it is expected that the media symbol variety is positively associated with group performance in virtual teams. Furthermore, online relationship building is expected to mediate the impacts of symbol variety on the performance. To confirm the suppositions, a controlled lab experiment was conducted with 60 undergraduate students as subjects. In the experimental virtual teams, subjects were allowed to communicate with other members using text-based messenger with emoticons. Subjects in the control virtual teams were allowed to communicate using only text-based messenger. The direct impact of symbol variety on group performance in virtual teams was found insignificant. However, the online relationship was found to completely mediate the positive impact of symbol variety on group performance. The implications and limitations of this study are also discussed for future research.

• 한국통신학회논문지
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• 제19권8호
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• pp.1612-1627
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• 1994

기존 OSI 세션계층 동기기능은 문자 위주의 단일 미디어 동기만을 다루고 있기 때문에 audio, video, graphic 등의 멀티미디어 정보통신 서비스를 위해서는 새로운 동기 방식과 프로토콜이 필요하다. 본 논문은 이러한 멀티미디어 동기 서비스를 위해서 개념적인 동기층 환경을 설정하고 이 계층에서 사용하는 '멀티채널, 기준미디어 동기' 기법의 동기층 프리미티브와 동기층 프로토콜을 설계, 제안 하였다. 본 멀티미디어 동기층(MS layer)은 상대편 동기층과의 연결을 설정한 후 미디어별로 별도의 채널을 관리하여 미디어별 특성을 효과적으로 이용할 수 있게 하고, 미디어 프레임 번호를 time stamp처럼 이용함으로써 특정 동기점의 삽입 없이도 손쉽게 동기점을 찾아내고 동기 서비스를 제공한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.170-191
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• 2024

The transmission of confidential data using cover media is called steganography. The three requirements of any effective steganography system are high embedding capacity, security, and imperceptibility. The text file's structure, which makes syntax and grammar more visually obvious than in other media, contributes to its poor imperceptibility. Text steganography is regarded as the most challenging carrier to hide secret data because of its insufficient redundant data compared to other digital objects. Unicode characters, especially non-printing or invisible, are employed for hiding data by mapping a specific amount of secret data bits in each character and inserting the character into cover text spaces. These characters are known with limited spaces to embed secret data. Current studies that used Unicode characters in text steganography focused on increasing the data hiding capacity with insufficient redundant data in a text file. A sequential embedding pattern is often selected and included in all available positions in the cover text. This embedding pattern negatively affects the text steganography system's imperceptibility and security. Thus, this study attempts to solve these limitations using the Part-of-speech (POS) tagging technique combined with the randomization concept in data hiding. Combining these two techniques allows inserting the Unicode characters in randomized patterns with specific positions in the cover text to increase data hiding capacity with minimum effects on imperceptibility and security. Format-preserving encryption (FPE) is also used to encrypt a secret message without changing its size before the embedding processes. By comparing the proposed technique to already existing ones, the results demonstrate that it fulfils the cover file's capacity, imperceptibility, and security requirements.

• Communications for Statistical Applications and Methods
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• 제11권2호
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• pp.329-343
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• 2004

With advancement of computer and network, world wide web(WWW) as a medium of information communication is generalized in many fields. In educational aspect, applications of WWW as alternative media for class teachings or printed matters are increasing. In this article, we demonstrate a web-based electronic text on the 'probability and statistics' which is one of six fields of mathematics in the 7th curriculum. This text places importance on comprehension of concepts of probability and statistics as an applied science.

• 디지털융복합연구
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• 제18권2호
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• pp.277-284
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• 2020

텍스트마이닝은 비정형, 대용량의 텍스트 자료로부터 유의미한 정보를 추출하는 빅데이터 분석의 대표적인 방법이다. 트위터와 같은 SNS는 1초에서 수십만건의 데이터를 생성해내며 대중들의 의견이나 생각 등을 즉각적이며 직접적으로 보여주는 1인 미디어로의 역할을 하고 있다. 기성 미디어인 언론은 정보전달, 사회비판, 여론형성의 기능을 수행하고 있다. 본 논문에서는 미디어로의 SNS와 언론을 비교해 보고자 한다. 이를 위해 2019년 하반기 국내의 이슈 중의 하나인 "한일군사정보보호협정(GSOMIA) 종료"에 대한 SNS의 반응과 언론의 반응을 비교 분석한다. 수집된 데이터는 총 201,728개의 트윗과 20,698개의 신문 기사를 대상으로 감성분석, 연관분석, 군집분석을 수행하였다. 그 결과로 SNS의 경우 이슈에 대해 긍정적 반응이 높았고 언론의 경우는 부정적 반응이 높았다. 연관검색의 경우는 SNS의 경우 "파기, 결정, 우리" 등 국내적 이슈에 대한 지지가 높았고 언론의 경우 "실망, 유감, 우려" 등으로 대외적 이슈에 대한 부정적 견해를 보여주는 차이를 보여주었다. SNS는 정보전달의 기능보다는 사회 비판 및 여론의 추이를 살피거나 형성하는데 언론보다 빠르고 강하게 나타내고 있어 언론이 대중의 인식을 반영해주는 역할을 보완할 수 있다.

• 스마트미디어저널
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• 제12권7호
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• pp.76-83
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• 2023

본 연구에서는 웹툰 작가의 웹툰 제작 과정을 보조하기 위해 사전학습된 Text-to-Image 모델을 미세조정하여 텍스트에서 웹툰을 생성하는 방법을 제안한다. 제안하는 방법은 웹툰 화풍으로 변환된 웹툰 데이터셋을 사용하여 사전학습된 Stable Diffusion 모델에 LoRA 기법을 활용하여 미세조정한다. 실험 결과 3만 스텝의 학습으로 약 4시간 반이 소요되어 빠르게 학습하는 것을 확인하였으며, 생성된 이미지에서는 입력한 텍스트에 표현된 형상이나 배경이 반영되어 웹툰 이미지가 생성되는 것을 확인하였다. 또한, Inception score를 통해 정량적인 평가를 수행하였을 때, DCGAN 기반의 Text-to-Image 모델보다 더 높은 성능을 나타냈다. 본 연구에서 제안된 웹툰 생성을 위한 Text-to-Image 모델을 웹툰 작가가 사용한다면, 웹툰 저작에 시간을 단축시킬 수 있을 것으로 기대된다.

• 한국의류학회지
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• 제44권2호
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• pp.295-309
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• 2020

In 2019, Japan announced trade regulations against Korean products; consequently, the sales of Japanese products in Korea dropped due to a Korean consumers' boycott. This study measured the Korean consumers' political consumption behavior toward Japanese fashion products. Unstructured text data from online media sources and consumer posted sources such as blog and SNS were collected. Text mining techniques and semantic network analysis were used to process unstructured data. This study used text mining techniques and semantic network analysis to process data. The results identified boycotting Japanese fashion products and buycotting alternative products and Korean brands due to consumers' political consumption. Two brand cases were investigated in detail. Online text data before and after the political action were compared and significant changes in consumption as well as emotional expressions were identified. Product related industry sectors were identified in terms of the political consumption of fashion: liquor, automobile and tourism industry sectors were closely linked to the fashion sector in terms of boycotting. More "boycott" and "buycott" fashion brands (reflected in consumer attitudes and feelings) were detected in consumer driven texts than in media driven sources.

• 스마트미디어저널
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• 제7권4호
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• pp.24-29
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• 2018

딥러닝에서 자연어 처리를 위한 텍스트 분석 기법은 워드 임베딩을 통해 단어를 벡터 형태로 표현한다. 본 논문에서는 워드 임베딩 기법과 딥러닝 기법을 이용하여 SMS 문자 메시지를 문서 벡터로 구성하고 이를 스팸 문자 메시지와 정상적인 문자 메시지로 분류하는 방법을 제안하였다. 유사한 문맥을 가진 단어들은 벡터 공간에서 인접한 벡터 공간에 표현되도록 하기 위해 전처리 과정으로 자동 띄어쓰기를 적용하고 스팸 문자 메시지로 차단되는 것을 피하기 위한 목적으로 음절의 자모를 특수기호로 왜곡하여 맞춤법이 파괴된 상태로 단어 벡터와 문장 벡터를 생성하였다. 또한 문장 벡터 생성 시 CBOW와 skip gram이라는 두 가지 워드 임베딩 알고리즘을 적용하여 문장 벡터를 표현하였으며, 딥러닝을 이용한 스팸 문자 메시지 필터링의 성능 평가를 위해 SVM Light와 정확도를 비교 측정하였다.