• Journal of Broadcast Engineering
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• v.17 no.1
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• pp.1-16
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• 2012

In order to improve the workflow of 3D video production, this paper analyzes the visual fatigue caused from distortions in 3D video production stage through a set of subjective visual assessment tests. To establish a set of objective indicators for subjective visual tests, various distortions in production stage are investigated to be categorized into 7 representative visual-fatigue-producing factors, and to conduct visual assessment tests for each selected category, 4 test video clips are produced by combining the extent of camera movement as well as the object(s) movement in the scene. Each produced test video is distorted to reflect each of the selected 7 visual-fatigue-producing factors, and we set 7 levels of distortion for each factor, resulting in 196 5-second-long video clips for testing. Based on these test materials and the recommendation of ITU-R BT.1438, subjective visual assessment tests are conducted by 101 applicants. The test results provide a relative importance and the tolerance limit of each visual-fatigue-producing factor, which corresponds to various distortions in 3D video production field.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1024-1034
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• 2019

Preference for music is determined by a variety of factors, and identifying characteristics that reflect specific factors is important for music recommendations. In this paper, we propose a method to extract the singing voice related music features reflecting various musical characteristics by using a model learned for singer identification. The model can be trained using a music source containing a background accompaniment, but it may provide degraded singer identification performance. In order to mitigate this problem, this study performs a preliminary work to separate the background accompaniment, and creates a data set composed of separated vocals by using the proven model structure that appeared in SiSEC, Signal Separation and Evaluation Campaign. Finally, we use the separated vocals to discover the singing voice related music features that reflect the singer's voice. We compare the effects of source separation against existing methods that use music source without source separation.

• KIISE Transactions on Computing Practices
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• v.22 no.5
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• pp.246-251
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• 2016

Recently, the influence of SNS and online media is rapidly growing with a consequent increase in the interest of marketing using these tools. Blog marketing can increase the ripple effect and information delivery in marketing at low cost by prioritizing keyword search results of influential portal sites. However, because of the tough competition to gain top ranking of search results of specific keywords, long-term and proactive efforts are needed. Therefore, we propose a new method that recommends associated keyword groups with the possibility of higher exposure of the blog. The proposed method first collects the documents of blog including search results of target keyword, and extracts and filters keyword with higher association considering the frequency and location information of the word. Next, each associated keyword is compared to target keyword, and then associated keyword group with the possibility of higher exposure is recommended considering the information such as their association, search amount of associated keyword per month, the number of blogs including in search result, and average writhing date of blogs. The experiment result shows that the proposed method recommends keyword group with higher association.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.616-621
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• 2006

Recently many cars are equipping a navigation system. The main purpose of the early system guides a user through the route. A navigation system includes various abilities by development of various technologies and it has given more convenience to user. It can play various records on the tape and announces which are useful information about each road. Also it can use various multi-media contents by DMB device during driving. However, guide function of basic and important road in the navigation system has not grown greatly yet. In this paper, we proposed recommendation method of human-friendly road considering user's condition through various information of outside environment, user's velocity intention, a driver's emotion and a preference of the road. Modules consists of hierarchical structure that can easily correct and add each algorithm and those use fuzzy logic algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1567-1573
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• 2020

The development of Internet technology has brought the era of one-man media. An individual produces content on user own and uploads it to related online services, and many users watch the content of online services using devices that allow them to use the Internet. Currently, most users find and watch content they want through search functions provided by existing online services. These features are provided based on information entered by the user who uploaded the content. In an environment where content needs to be retrieved based on these limited word data, user unwanted information is presented to users in the search results. To solve this problem, in this paper, the system actively analyzes the video in the online service, and presents a way to extract and reflect the characteristics held by the video. The research was conducted to extract morphemes based on the story content based on the voice data of a video and analyze them with big data technology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1321-1329
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• 2019

With the development of social information networks, the marketing methods are also changing in various ways. Unlike successful marketing methods based on existing celebrities and financial support, Influencer-based marketing is a big trend and very famous. In this paper, we first extract influencer features from more than 54 YouTube channels using the multi-dimensional qualitative analysis based on the meta information and comment data analysis of YouTube, model representative themes to maximize a personalized video satisfaction. Plus, the purpose of this study is to provide supplementary means for the successful promotion and marketing by creating and distributing videos of new items by referring to the existing Influencer features. For that we assume all comments of various videos for each channel as each document, TF-IDF (Term Frequency and Inverse Document Frequency) and LDA (Latent Dirichlet Allocation) algorithms are applied to maximize performance of the proposed scheme. Based on the performance evaluation, we proved the proposed scheme is better than other schemes.

• Journal of Information Technology Services
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• v.20 no.5
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• pp.11-28
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• 2021

As untact travel has become a new trend in leisure culture due to the spread of COVID-19, car camping market is rapidly increasing. The sales of car camping-related goods increased by up to 600 percent, and the sales of SUV in Korea also increased by about four times. Despite the growth of the car camping market, there is a lack of research on the actual condition of the car camping market or research on the user's perspective. Therefore, in this study, a survey of actual camping users was conducted to derive factors that they consider important in camping, and through this, a car camping map was produced. As a result, two types of maps were produced: a map about the car camping site and convenience facilities closest to the car camping site in Gangwon-do, and a hash tag themed map based on keywords for each car camping site. We gathered data on portal sites and social media to obtain information related to camping sites and proceeded with analysis using text mining. In addition, we extracted keywords using network analysis techniques and selected key themes that represent them. This allows the user to choose a car camping site by selecting keywords that suit their taste. We hope that this research will help car camping researchers as a prior study and provide a foundation for leading a clean camping culture through clean camping campaign. Also, we hope that car camping users will be able to do quality trip.

• Safety and Health at Work
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• v.13 no.1
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• pp.23-31
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• 2022

Background: Potato is the main crop of Ardabil Plain (accounting for one-fifth of potato production in Iran). Its health hazard risk to farmers is rising due to the increasing rate of pesticide use. The present study analyzes potato farmers' health hazard risk in the use of chemical pesticides. Methods: The rate of pesticide use by farmers (n = 370) was first compared with the recommended dosage (on pesticide label). Then, a composite index was employed to estimate the health hazard risk of farmers during pesticide use, and the variables accounting for pesticide overuse and nonoveruse were analyzed. Safety behavior was examined in four steps, namely of pesticide purchase and storage, preparation, application, and postapplication. Results: It was found that 74.6 percent of potato farmers used pesticides in higher concentrations than the recommended dosage. The higher average rate of pesticide use versus recommendation (label instruction) was related to Chlorpyrifos and Trifluralin, and the highest average health hazard risk among farmers was related to the use of Chlorpyrifos and Metribuzin. Farmers with a higher risk of health hazard displayed much lower safety behavior than the other farmers at all steps of pesticide use. Conclusion: The most important variables discriminating the health hazard risk of farmers' overuse included health behavior identity, attitude, knowledge and awareness, and cues to action. Therefore, using social media, holding local exhibitions, and engaging local leaders and skilled farmers in the region to improve farmers' attitudes and health behavior identity toward the dangers of chemical pesticides can play a significant role in motivating farmers' display of overuse preventive behaviors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.240-243
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• 2021

Recently, there have been research results of applying Big data and AI technologies to the evaluation and individual learning for education. It is information technology innovations that collect dynamic and complex data, including student personal records, physiological data, learning logs and activities, learning outcomes and outcomes from social media, MOOCs, intelligent tutoring systems, LMSs, sensors, and mobile devices. In addition, e-learning was generated a large amount of learning data in the COVID-19 environment. It is expected that learning analysis and AI technology will be applied to extract meaningful patterns and discover knowledge from this data. On the learner's perspective, it is necessary to identify student learning and emotional behavior patterns and profiles, improve evaluation and evaluation methods, predict individual student learning outcomes or dropout, and research on adaptive systems for personalized support. This study aims to contribute to research in the field of education by researching and classifying machine learning technologies used in anomaly detection and recommendation systems for educational data.

• Journal of Fashion Business
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• v.27 no.3
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• pp.132-149
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• 2023

This study aims to analyze Instagram hashtags based on big data to investigate changes in consumer trends and perceptions of vegan fashion, and to derive strategies for revitalizing vegan fashion brands based on derived results. Among social media, Instagram was selected as a collection channel, and Instagram hashtags for 'Vegan Fashion' were collected from July 1, 2021 to December 31, 2021. After conducting semantic network analysis with the Ucinet 6 program based on the collected data, the CONCOR analysis on vegan fashion showed the following four clusters: 'Veganism practiced with fashion', 'Bag type of vegan fashion brand', 'Sharing vegan fashion', and 'Diversification of eco-friendly products'. Analysis results showed that the Instagram hashtag for vegan fashion confirmed the MZ generation's increased interest in vegan fashion and their thoughts to recommend and share frequently used items or brand products to people around them. CONCOR analysis of vegan fashion brands showed the following four groups: 'Differentiating the material of vegan bags', 'Eco-friendly products of vegan fashion brands', 'Interest in vegan shoes', and 'Donation campaign of vegan fashion brands'. CONCOR analysis on Meaningout showed the following four clusters: 'MZ Generation's Meaningout Start-up', 'Recommendation Platform for Skin Products', 'Value Consumption Trend for Eco-friendly Clothing', and 'Interest in Eco-friendly Packaging'. The results of this study on vegan fashion, a practical eco-friendly movement that can require changes in social responsibility and perception as issues that directly affect animals, the environment, and humans, are expected to provide basic data to help domestic vegan fashion brands develop marketing strategies.