• Journal of the Korean Society for Library and Information Science
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• v.34 no.1
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• pp.71-92
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• 2000

This study aims to content analysis of Library Classification System in the North Korean. Also This paper is analyze and overview to conceptual framework. Notational system. Principle of hierarchy in the North Korean's Library Classification System. Libraries usually arrange their collections according to the systematic structure of the library classification. A decimal point follows the third digit. After which division by ten continues to the specific degree of classification needed. This system is based on the social and communism thought. The libraries in the South and the North has different concepts, goals, information resources, classification system and the different ways of using them. Considering the practical aspects of the libraries and the reasons for their existence, they must structure the mutual cooperative system so as to minimize the shock when confronting the social changes, so-cold the national unification.

• Journal of The Korean Association For Science Education
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• v.16 no.3
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• pp.270-277
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• 1996

The purpose of this study is to classify and state of evaluating objectives using three-dimensional assessment framework of science inquiry. The first, as an attempt to provide a theoretical base for developing an assessment framework taxonomies and classificatory schemes of educational objectives were analyzed Bloom's taxonomy, Klopfer's specification, NAEP(National Assessment of Educational Progress), and APU(Assessment of Performance Unit) framework. The second, three-dimensional assessment framework use in this study has formed a clear definition of three-dimensional matrix. These three dimensions consists of content, context and process. The third, the model of three-dimensional taxonomy of science inquiry developed in this study is presented. In addition, an example of classification and statement of evaluating objectives based on the model is presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4090-4102
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• 2018

After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers' sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.89-95
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• 2023

In recent decades, human action recognition (HAR) has demonstrated potential applications in sports analysis, human-robot interaction, and large-scale signage content. In this paper, spatial temporal attention graph convolutional network (STAGCN)-based HAR system is proposed. Spatioal-temmporal features of skeleton sequences are assigned different weights by STAGCN, enabling the consideration of key joints and viewpoints. From simulation results, it has been shown that the performance of the proposed model can be improved in terms of classification accuracy in the NTU RGB+D dataset.

• The Journal of Korea Robotics Society
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• v.1 no.1
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• pp.1-8
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• 2006

In this paper, we propose a methodology for classifying types of lower limb disability and their mechanical structure, based on extensive survey of previous developments. We also propose a task-oriented design with human-friendly and energy-efficient assistive system. The result can be used for optimal design of wearable walking-assistive robot considering the type of disability and the content of task.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.99-105
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• 2021

In addition to the OTT video production service represented by Nexflix and YouTube, a personalized recommendation system for content with artificial intelligence has become common. YouTube's personalized recommendation service system consists of two neural networks, one neural network consisting of a recommendation candidate generation model and the other consisting of a ranking network. Netflix's video recommendation system consists of two data classification systems, divided into content-based filtering and collaborative filtering. As the online platform-led content production is activated by the Corona Pandemic, the field of virtual influencers using artificial intelligence is emerging. Virtual influencers are produced with GAN (Generative Adversarial Networks) artificial intelligence, and are unsupervised learning algorithms in which two opposing systems compete with each other. This study also researched the possibility of developing AI platform based on individual recommendation and virtual influencer (metabus) as a core content of OTT in the future.

• Journal of KIISE:Software and Applications
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• v.35 no.11
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• pp.692-700
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• 2008

In some previous works on musical genre classification, human experts specify segments of a song for extracting musical features. Although this approach might contribute to performance enhancement, it requires manual intervention and thus can not be easily applied to new incoming songs. To extract musical features without the manual intervention, most of recent researches on music genre classification extract features from a pre-determined part of a song (for example, 30 seconds after initial 30 seconds), which may cause loss of accuracy. In this paper, in order to alleviate the accuracy problem, we propose a new method, which extracts features from representative segments (or main theme part) identified by structure analysis of music piece. The proposed method detects segments with repeated melody in a song and selects representative ones among them by considering their positions and energies. Experimental results show that the proposed method significantly improve the accuracy compared to the approach using a pre-determined part.

• Journal of Digital Contents Society
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• v.14 no.1
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• pp.97-109
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• 2013

Recently the value and importance of contents industry is rapidly increasing. In particular, consumption on contents convergence integrating contents and new technologies is being increased as smart devices become popular. Next generation contents convergence industry is being recognized as a new engine for national economic development. However there is no concrete framework defining next generation contents convergence industry. This fact brings about serious problems in defining the scope of the industry. To develop effectively new industry, there is a strong need to define types of contents convergence based on dominant factors leading content convergence trend while shedding light on contents, technologies and industry. To define classification of next generation contents convergence indudstry, this study analyzes attributes of contents convergence and carries out conjoint analysis. Based on the attributes found from conjoint analysis, the study suggests new classification scheme of contents convergence industry.

• Journal of agricultural medicine and community health
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• v.47 no.2
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• pp.61-66
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• 2022

Objectives: In the epidemiology of communicable diseases, the term epidemic period, also referred to as "wave" is often used in the general and academic milieu. A wave refers to a natural pattern of increase in the number of sick individuals, a defined peak, and then a decline in the number of cases. It implies a pattern of peaks and valleys after a particular peak is taken. The idea of epidemic waves is a useful tool for predicting the course as well as helping to accurately describe an epidemic. However, in many domestic and foreign news as well as in various research results in Korea, most of the reports either had no standard, were inaccurate, had a questionable classification of the period of the epidemic, or the basis for classification of a given wave was not presented. Methods: The author reviewed and organized related literature with epidemic wave. The author made several suggestions of an epidemic wave as follows. Results: To start with, it should be based on the number of incident cases in consideration of the size of the outbreak, then the period from the bottom to the peak and then reaching the next bottom; also, the period over a certain scale based on the number of incident cases; and the period according to the change in the major infection type (mutation-dominant species). In addition, according to the period of change in the vaccination rate (formation of herd immunity), as well as the content and duration of the intervention, that is, classification according to the applied quarantine stage. Furthermore, the classification of epidemic periods by the time-dependent reproduction number or time-varying reproduction number (Rt), and lastly the application of mathematical methodology. Conclusions: Therefore, classifying the epidemic period into generally known and accepted time frames is considered to be a very important task for future research analysis and development of intervention strategies.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.528-535
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• 2022

Although the domestic aviation industry has made rapid progress with the development of aircraft manufacturing and transportation technologies, aviation safety accidents continue to occur. The supervisory agency classifies hazards and risks based on risk-based aviation safety data, identifies safety trends for each air transportation operator, and conducts pre-inspections to prevent event and accidents. However, the human classification of data described in natural language format results in different results depending on knowledge, experience, and propensity, and it takes a considerable amount of time to understand and classify the meaning of the content. Therefore, in this journal, the fine-tuned KoBERT model was machine-learned over 5,000 data to predict the classification value of new data, showing 79.2% accuracy. In addition, some of the same result prediction and failed data for similar events were errors caused by human.