• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.10-20
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• 2007

This paper describes a concept-based question analysis to analyze concept which is more important than keyword for the accurate answer extraction. Our idea is that we can extract correct answers from various paragraphs with different structures when we use well-defined concepts because concepts occurred in questions of same answer type are similar. That is, we will analyze the syntactic and semantic role of each word or phrase in a question in order to extract more relevant documents and more accurate answer in them. For each answer type, we define a concept frame which is composed of concepts commonly occurred in that type of questions and analyze user's question by filling a concept frame with a word or phrase. Empirical results show that our concept-based question analysis can extract more accurate answer than any other conventional approach. Also, concept-based approach has additional merits that it is language universal model, and can be combined with arbitrary conventional approaches.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.83-105
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• 2019

This study aims to suggest an effective method for the automatic classification of keywords with similar patterns by calculating pattern similarity of temporal data. For this, large scale news on the Web were collected and time series data composed of 120 time segments were built. To make training data set for the performance test of the proposed model, 440 representative keywords were manually classified according to 8 types of trend. This study introduces a Dynamic Time Warping(DTW) method which have been commonly used in the field of time series analytics, and proposes an application model, MA-DTW based on a Moving Average(MA) method which gives a good explanation on a tendency of trend curve. As a result of the automatic classification by a k-Nearest Neighbor(kNN) algorithm, Euclidean Distance(ED) and DTW showed 48.2% and 66.6% of maximum micro-averaged F1 score respectively, whereas the proposed model represented 74.3% of the best micro-averaged F1 score. In all respect of the comprehensive experiments, the suggested model outperformed the methods of ED and DTW.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.6
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• pp.1181-1190
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• 2013

The GOOSE(Generic Object Oriented Substation Event) is used as a network protocol to communicate between IEDs(Intelligent Electronic Devices) in international standard IEC 61850 of substation automation system. Nevertheless, the GOOSE protocol is facing many similar threats used in TCP/IP protocol due to ethernet-based operation. In this paper, we develop a IDS(Intrusion Detection System) for secure GOOSE Protocol using open software-based IDS Snort. In this IDS, two security functions for keyword search and DoS attack detection are implemented through improvement of decoding and preprocessing component modules. And we also implement the GOOSE IDS and verify its accuracy using GOOSE packet generation and communication experiment.

• The Research Journal of the Costume Culture
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• v.26 no.4
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• pp.485-502
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• 2018

In this research, we conducted an empirical study using the theory of sociologist Jean Baudrillard to examine the phenomenon of contemporary people in Korea acquiring tattoos. The researcher classified the consumption behavior of modern society, as described by Baudrillard in The Consumer Society, into three keyword phrases: consumption by personal taste, coded consumption, and recyclage of consumption. Using this as the premise of the study, 18 men and women in their 20s and 30s completed questionnaires and interviews, and the results supported labeling tattoo consumption as consumption by personal taste, tattoos as coded consumption, and recyclage of tattoo consumption, similar to the consumption pattern that Baudrillard sees. First, the younger generations have consumed tattoos according to their personal preferences. They express themselves by tattooing for self-complacency, self-marking, pursuing individuality, overcoming the appearance complex, and seeking pleasure. Second, they have consumed socially coded tattoos. They say that tattoos domestically act as negative codes and symbolize individuals. Although tattoos are a symbol of artists who are relatively free from social norms, they are still a symbol of social misfits created as such by negative perceptions. Third, the pattern of tattoo consumption is like that of contemporary consumption. Tattoos already have become part of popular culture in Korea, and there has been a changing trend in tattoo culture. This study has significance in that tattoos were regarded as a consumption behavior that deviated, from the perspective of deviance. That phenomenon of today's tattoo culture of today was confirmed through the empirical study.

• The Journal of Industrial Distribution & Business
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• v.11 no.5
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• pp.45-52
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• 2020

Purpose: In the 4th industrial revolution, services that incorporate various smart technologies in the tourism sector have begun to gain popularity. Accordingly, academic discussions on smart tourism have also started to become active in various fields. Despite recent research, the definition of smart tourism is still ambiguous, and it is not easy to differentiate its scope or characteristics from traditional tourism concepts. Thus, this study aims to analyze the perception of smart tourism exposed online to identify the current point of smart tourism in Korea and present the research direction for conceptualizing smart tourism suitable for the domestic situation. Research design, data, and methodology: This study analyzes the perception of smart tourism exposed online based on 20,198 news data from portal sites over the past six years. Data on words used with smart tourism were collected from the leading portal sites Naver, Daum, and Google. Text mining techniques were applied to identify the social awareness status of smart tourism. Network analysis was used to visualize the results between words related to smart tourism, and CONCOR analysis was conducted to derive clusters formed by words having similarity. Results: As a result of keyword analysis, the frequency of words related to the development and construction of smart tourism areas was high. The analysis of the centrality of the connection between words showed that the frequency of keywords was similar, and that the words "smartphones" and "China" had relatively high connection centrality. The results of network analysis and CONCOR indicated that words were formed into eight groups including related technologies, promotion, globalization, service introduction, innovation, regional society, activation, and utilization guide. The overall results of data analysis showed that the development of smart tourism cities was a noticeable issue. Conclusions: This study is meaningful in that it clearly reflects the differences in the perception of smart tourism between online and research trends despite various efforts to develop smart tourism in Korea. In addition, this study highlights the need to understand smart tourism concepts and enhance academic discussions. It is expected that such academic discussions will contribute to improving the competitiveness of smart tourism research in Korea.

• Journal of Korea Multimedia Society
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• v.8 no.2
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• pp.269-278
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• 2005

In order to process video data effectively, it is required that the content information of video data is loaded in database and semantics-based retrieval method can be available for various queries of users. In this paper, we propose a video retrieval system which support semantics retrieval of various users for massive video data by user's keywords and comparison area learning based on automatic agent. By user's fundamental query and selection of image for key frame that extracted from query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user becomes a query image and searches the most similar key frame through color histogram comparison and comparison area learning method that proposed. From experiment, the designed and implemented system showed high precision ratio in performance assessment more than 93 percents.

• Journal of Digital Convergence
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• v.15 no.7
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• pp.367-373
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• 2017

The study aims to suggests mobile application service that helps to reduce physical and mental suffering after surgery for breast cancer patients constantly increasing. At first, I analyze literature research to understand the breast cancer and similar services for achieving study purposes. After I finally design a mobile application that offers to customed one-to-one specialist coaching service focused on post-treatment management by using a double diamond design process model. In detail, first, I analyze the current program and comprehend user needs through interviews with the person such as breast patient, protector, nurse, salesperson in cancer center. Second, I specify the entire scenario, concept and keyword based on the persona and the customer journey map representing user. Third, I propose the mobile application for the ultimate goal by producing prototype to effectively communicate with specific functional descriptions. I hope this service will improve the quality of their lives.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.550-555
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• 2018

This study aims to identify and compare contents and keywords used in articles related to blockchain applications to various industries. The text mining and Semantic Network Analysis, as methods of keyword network analysis, were used to analyze articles including terms of 'finance' 'energy' and 'logistics', which media and government frequently mentioned as areas that can apply blockchain technologies. For this study, data were collected from 43,093 articles from January, 2017 through July, 2018. Data crawling was carried out by using Python BeautifulSoup and data cleaning was performed in order to eliminate mutual redundancies of the three terms. After that, text mining and semantic network analysis were performed using Textom and UCInet for network analysis between keywords. The results showed that all the three terms were similar in terms of 'technology', but there were differences in the contents of 'government policy' or 'industry' issues. In addition, there were differences in frequencies and centralities of these terms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.172-178
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• 2020

In order to recommend contents, the company generally uses collaborative filtering that takes into account both user preferences and video (item) similarities. Such services are primarily intended to facilitate user convenience by leveraging personal preferences such as user search keywords and viewing time. It will also be ranked around the keywords specified in the video. However, there is a limit to analyzing video similarities using limited keywords. In such cases, the problem becomes serious if the specified keyword does not properly reflect the item. In this paper, I would like to propose a system that identifies the characteristics of a video as it is by the system without human intervention, and analyzes and recommends similarities between videos. The proposed system analyzes similarities by taking into account all words (keywords) that have different meanings from training videos, and in such cases, the methods handled by big data clusters are applied because of the large scale of data and operations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.1-7
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• 2021

Investor mood from social media is gaining increasing attention for leading a price movement in stock market. Based on the behavioral finance theory, this study argues that sentiment extracted from social media using big data technique can predict a real-time (short-run) price momentum in Chinese stock market. Collecting Sina Weibo posts that related to COVID-19 using keyword method, a daily influential weighted sentiment factors is extracted from the sizable raw data of over 2 millions of posts. We examine one supervised and 4 unsupervised sentiment analysis model, and use the best performed word-frequency and BiLSTM mdoel. The test result shows a similar movement between stock price change and sentiment factor. It indicates that public mood extracted from social media can in some extent represent the investors' sentiment and make a difference in stock market fluctuation when people are concentrating on a special events that can cause effect on the stock market.