• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.453-455
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• 2022

Metadata is data that explains attributes and features to the data as structured data. Among them, video metadata refers to data extracted from information constituting the video for accurate content-based search. Recently, as the number of users using video content increases, the number of OTT providers is also increasing, and the role of metadata is becoming more important for OTT providers to recommend a large amount of video content to individual users or to search appropriately. In this paper, a study was conducted on a method of automatically extracting metadata for mood attributes through sound effects of images. In order to classify the sound effect of the video and generate metadata about the attributes of the mood, I would like to propose a method of establishing a terminology dictionary for the mood and extracting information through supervised learning.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.1
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• pp.39-46
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• 2020

Accident prevention is more important than follow-up, which is based on Heinrich's law. The marine incident system is a very meaningful system that can prevent similar accidents, and was introduced in 2010 in Korea in accordance with the enforcement of the Code for the Investigation of Marine Casualties and Incidents (CI Code). Based on the CI Code, ship owners or ship operators are required to notify the Central Chief Inspector using the designated notification form in the event of a marine incident, but the number of voluntary notifications is still small. In this regard, this study intends to provide a direction for improvement by conducting an in-depth analysis focusing on the lack of notification procedures and forms of the marine incident system. To this end, we analyzed related regulations, cases of excellent overseas shipping countries such as the United Kingdom and Singapore, cases of similar domestic transportation systems such as aviation and railways, and marine incident notification procedures and forms of leading shipping companies. Major improvements in the notification process include the transition of the marine incidents to voluntary reporting, the expansion of the reporting subjects, and the identification of the security of the informer's identity. The main contents of the notification form revision include the use of the term "reporting" instead of "notification," the content of the identity guarantee in the notification form, and the increase in statistical value through the expansion of optional entries.

• Journal of Science Education
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• v.33 no.1
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• pp.87-99
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• 2009

The purpose of this study was to evaluate the quality of biology contents of cyber home learning systems which provided by 16 metropolitan and provincial offices of education. The contents were evaluated by the 9 categories: needs assessment, instruction design, learning contents, teaching & learning strategy, interaction, supporting system, evaluation, ethicality and copy right. The result showed that the contents have advantage in detailed learning goal, useful learning environment, learning activities by level, and various learning parts material, but lack in evaluation method tool for personal learning by level and the latest learning material. Based on this results, it is expected that the barrier of the efficient learning contents should be searched for the complement, as well as the development high-quality educational contents and the management of cyber home learning system.

• Knowledge Management Research
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• v.22 no.2
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• pp.1-17
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• 2021

In this study, we predicted the bitcoin prices of Bithum and Coinbase, a leading exchange in Korea and USA, using ARIMA and Recurrent Neural Networks(RNNs). And we used news articles from each country to suggest a separated RNN model. The suggested model identifies the datasets based on the changing trend of prices in the training data, and then applies time series prediction technique(RNNs) to create multiple models. Then we used daily news data to create a term-based dictionary for each trend change point. We explored trend change points in the test data using the daily news keyword data of testset and term-based dictionary, and apply a matching model to produce prediction results. With this approach we obtained higher accuracy than the model which predicted price by applying just time series prediction technique. This study presents that the limitations of the time series prediction techniques could be overcome by exploring trend change points using news data and various time series prediction techniques with text mining techniques could be applied to improve the performance of the model in the further research.

• Cartoon and Animation Studies
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• s.31
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• pp.153-176
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• 2013

In the early 20th century, the some of futurist painter was attempted to represents the 'fast-paced' and 'dynamism' on a two-dimensional picture. The expression of fast-paced and dynamic for look like move image in the painting have evolved as a variety of visual symbol. Visual symbols that represent these movements were settled as the line of the most movement expression in the comic. The of Toriyama Akira gained worldwide popularity is emphasized speed and dynamism as the action genre in cartoon, is nice a data to research the line of the movement expression of cartoon. There is three terms as the action line, The speed line, the effect line on the movement expression in The glossary of in the dictionary of , but it not easy to Separate them by means similar. This study is willing to says the semantically problem of previous lines on the movement expression and to present a new alternative in order to study the line on the movement expression of . This study Separate the line on movement expression from symbolic the perspective and try to newly define by using, was classified lines of four kinds by add the afterimage line on existing the speed line, the motion line, the effect line. First, the speed line was defined as 'The line expressing the movement expression of a moving target as the concept of speed'. It on the way of expression was subdivided the direct as speed line when it alter the shape of the target and the indirect speed line when it alter the background of the target. Second, the motion line was defined as 'The line simplified the moving form or the moving path of moving target'. Third, the effect line was defined as 'the line emphasizing the movement expression of a moving target by Sensory expression or emotional expression. Fourth, the afterimage lines was defined as 'The line expressing slowly moving or swaying the movement expression of target to the afterimage effect. The terminology presented in this study will be able to help the understanding of the line on the movement expression .

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.49-67
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• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.

• The Journal of Society for e-Business Studies
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• v.20 no.4
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• pp.209-225
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• 2015

Research and development projects in railroad domain are different from those in other domains in terms of their close relationship with laws. Some cases are reported that new technologies from R&D projects could not be industrialized because of relevant laws restricting them. This problem comes from the fact that researchers don't know exactly what laws can affect the result of R&D projects. To deal with this problem, we suggest a model for law retrieval system that can be used by researchers of railroad R&D projects to find related legislation. Input of this system is a research plan describing the main contents of projects. After laws related to the R&D project is provided with their rankings, which are assigned by scores we developed. A ranking of a law means its order of priority to be checked. By using this system, researchers can search the laws that may affect R&D projects throughout all the stages of project cycle. So, using our system model, researchers can get a list of laws to be considered before the project they participate ends. As a result, they can adjust their project direction by checking the law list, avoiding their elaborate projects being useless.

• Journal of Science Education
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• v.32 no.2
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• pp.87-102
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• 2008

The purposes of this study were to analyze vocabularies used the section of 'Living Earth' in 11-grade Earth science textbooks with the Science Word Analysis (SWA) program and to investigate the vocabularies selected by the 11th grade students as difficult ones. For the purpose, we extracted the Earth science vocabularies from six textbooks, and classified into the scientific and non-scientific vocabularies with SWA program based on the standard Korean language dictionary. Also, we investigated the difficulty of each vocabulary by using questionnaire to three hundred sixty students. From the results analyzed with the program, it was found that the frequency of the scientific vocabularies out of the level was the largest any other level in all textbooks. And from the survey, most of the vocabularies selected by students as difficult to understand were classified into out of the level. From these results, it were suggested that the students' cognitive level should be considered in developing science textbooks and difficult vocabularies should be replaced to easy ones within the limits of changeless in the meanings.

• Journal of the Korea Institute of Building Construction
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• v.22 no.3
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• pp.281-291
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• 2022

This study attempts to use big data to determine the indicators necessary for a fire risk assessment of buildings. Because most of the causes affecting the fire risk of buildings are fixed as indicators considering only the building itself, previously only limited and subjective assessment has been performed. Therefore, if various internal and external indicators can be considered using big data, effective measures can be taken to reduce the fire risk of buildings. To collect the data necessary to determine indicators, a query language was first selected, and professional literature was collected in the form of unstructured data using a web crawling technique. To collect the words in the literature, pre-processing was performed such as user dictionary registration, duplicate literature, and stopwords. Then, through a review of previous research, words were classified into four components, and representative keywords related to risk were selected from each component. Risk-related indicators were collected through analysis of related words of representative keywords. By examining the indicators according to their selection criteria, 20 indicators could be determined. This research methodology indicates the applicability of big data analysis for establishing measures to reduce fire risk in buildings, and the determined risk indicators can be used as reference materials for assessment.

• Journal of Korean Society of societal Security
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• v.4 no.2
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• pp.29-40
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• 2011

The objective of this paper is to develop an intelligent decision support system that is able to advise disaster countermeasures and degree of incidents on the basis of the collected and analyzed signs of disasters. The concepts derived from ontology, text mining and case-based reasoning are adapted to design the system. The functions of this system include term-document matrix, frequency normalization, confidency, association rules, and criteria for judgment. The collected qualitative data from signs of new incidents are processed by those functions and are finally compared and reasoned to past similar disaster cases. The system provides the varying degrees of how dangerous the new signs of disasters are and the few countermeasures to the disaster for the manager of disaster management. The system will be helpful for the decision-maker to make a judgment about how much dangerous the signs of disaster are and to carry out specific kinds of countermeasures on the disaster in advance. As a result, the disaster will be prevented.