• Journal of Korean Home Economics Education Association
• /
• v.26 no.2
• /
• pp.15-30
• /
• 2014

The purpose of this study is to provide basic information on clothing preference to help teachers suggest clothes that help students adapt better to school life, and to get basic data specific to Home Economics curricula and how teenagers' attitude toward clothes correlate with their conformity to school as a whole. The survey of 587 students was conducted between December 17-24, 2012 in Gwangju. The data was analyzed using the SPSS 18.0 for window program: frequency, means, factor analysis, cluster analysis, Chronbach's ${\alpha}$, t-test, and ANOVA. There were significant differences in the many adolescents's adapted to school life based on the clothing behavior conformity and the attitude toward the name-brand. The results showed that, if it is not extreme, adolescents had the more the clothing behavior conformity on peer & mass media than less, they did well adapt on school life. And the group of more preference for the name-brand clothing result in low school discipline adaptation but high peer and teacher relationship. In adaptation of learning, they were not different significantly based on attitudes toward the name brand.

• Journal of Broadcast Engineering
• /
• v.24 no.1
• /
• pp.132-141
• /
• 2019

The necessity of transmitting video data over a narrow-bandwidth exists steadily despite that video service over broadband is common. In this paper, we propose a scalable video coding framework for low-resolution video transmission over a very narrow-bandwidth network by super-resolution of decoded frames of a base layer using a convolutional neural network based super resolution technique to improve the coding efficiency by using it as a prediction for the enhancement layer. In contrast to the conventional scalable high efficiency video coding (SHVC) standard, in which upscaling is performed with a fixed filter, we propose a scalable video coding framework that replaces the existing fixed up-scaling filter by using the trained convolutional neural network for super-resolution. For this, we proposed a neural network structure with skip connection and residual learning technique and trained it according to the application scenario of the video coding framework. For the application scenario where a video whose resolution is $352{\times}288$ and frame rate is 8fps is encoded at 110kbps, the quality of the proposed scalable video coding framework is higher than that of the SHVC framework.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.6
• /
• pp.801-806
• /
• 2022

In the modern era, the necessity of color capability in the digital era is the demand of the era, and research on improving color practice on the subdivided digital four areas that are not in the existing practice is needed. For digital majors who are difficult to solve in existing paint color practice, classes in digital color practice in four more specialized areas are needed, and the use of efficient artificial intelligence was studied for classes in digitized color and color sense. In this paper, we tried to show the expansion of the color practice area by suggesting digital color practice and color matching method based on Photoshop artificial intelligence and big data technology that existing color and color matching were practice that only CMYK could do. In addition, based on the color quantification data of individual users provided by the latest Adobe Sceney program artificial intelligence, the purpose of the practice was to improve learners' predictions of actual color combinations and random colors using filter effects. In conclusion, it is a study on the use of programs that eliminate ambiguity in the mixing process of existing paint practice, secure digital color details, and propose a practical method that can provide effective learning methods for beginners and intermediates to develop their senses through artificial intelligence support. The Adobe program practice method necessary for coloration and main color through theoretical consideration and improvement of teaching skills that are better than existing paint practice were presented.

• Journal of Broadcast Engineering
• /
• v.12 no.5
• /
• pp.460-479
• /
• 2007

When culture took large part in industrial area, every country has tried to utilize own cultural contents for educational or commercial purpose and the various cultures and histories are recognized as a main concept or subject so that a number of scholars who study history increase. In video game field, special characteristics of interface that audiences participate in the game to complete story-telling is considered as efficient material for learning process. As observed above, it is important to analyze the games that every country makes and export to the world in which the video games is understood as a play of human in general. This Paper has firstly analyzed the most favorite historical games developed in Korea, the USA, Japan, Taiwan and Germany from 1980 to 2005 and secondly, compared that wars and historical origin appears in game scenario, a world view and background story and finally after point out the preferable era and genre of the countries then propose the promising way of design for historical video games. In the process of analysis of a view and heroes in historical games, we compared the real persons, the real historical events and novel in which 11.8% only employed the real persons in 8 out of 68 games. Also the real history and background story are appeared in 37 games which is 54.4% of them. We discovered that the main material that is popular for each country is the historical backing rather than real persons where the favorite historical background is chosen at which they are proud of; 3-Throne era with strong ancient Gogurye for Korea, the 1st and 2nd World Wars and the Independence War for the USA, the tide of war around Middle age for Japan, ancient history of Europe for Germany. The favorite age for video games is Ancient times with 37 games for 54.4%, Middle Age with 7 games fer 10.3%, the prehistoric age with 5 games for 7.35%, remote age with 1 for 1.47%, while current historical games favor Ancient or Modern Age.

• Journal of Intelligence and Information Systems
• /
• v.26 no.3
• /
• pp.149-169
• /
• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.119-142
• /
• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Cartoon and Animation Studies
• /
• s.29
• /
• pp.129-149
• /
• 2012

The purpose of this study was to investigate the clustering and Big Five Personality domains in discriminating groups by level of school-related adjustment, as experienced by Social Sims game users. Social Games are based on web that has simple rules to play in fictional time and space background. This paper is to analyze the relationships between social networks and user behaviors through the social games . In general, characteristics of social games are simple, fun and easy to play, popular to the public, and based on personal connections in reality. These features of social games make themselves different from video games with one player or MMORPG with many unspecific players. Especially Social Game show a noticeable characteristic related to social learning. The object of this research is to provide a possibility that game that its social perspective can be strengthened in social game environment and analyze whether it actually influences on problem solving of real life problems, therefore suggesting its direction of alternative play means and positive simulation game. Data was collected by administering 4 questionnaires (the short version of BFI, Satisfaction with life, Career Decision-.Making Self-.Efficacy, Depression) to the participants who were 20 people in Seoul and Daejeon. For the purposes of the data analysis, both Stepwise Discriminant analysis and Cluster analysis was employed. Neuroticism, Openness, Conscientiousness within the Big Five Personality domains were seen to be significant variables when it came to discriminating the groups. These findings indicated that the short version of the BFI may be useful in understanding for game user behaviors When it comes to cultural research, digital game takes up a significant role. We can see that from the fact that game, which has only been considered as a leisure activity or commercial means, is being actively research for its methodological, social role and function. Among digital game's several meanings, one of the most noticeable ones is the research on its critical, social participating function. According to Jame Paul gee, the most important merit of game is 'projected identity'. This means that experiences from various perspectives is possible.[1] In his recent autobiography , he described gamer as an active problem solver. In addition, Gonzalo Francesca also suggested an alternative game developing method through 'game that conveys critical messages by strengthening critical reasons'. [2] They all provided evidences showing game can be a strong academic tool. Not only does a genre called social game exist in the field of media and Social Network Game, but there are also some efforts to positively evaluate its value Through these kinds of researches, we can study how game can give positive influence along with the change in its general perception, which would eventually lead to spreading healthy game culture and enabling fresh life experience. This would better bring out the educative side of the game and become a social communicative tool. The object of this game is to provide a possibility that the social aspect can be strengthened within the game environment and analyze whether it actually influences the problem solving of real life problems. Therefore suggesting it's direction of alternative play means positive game simulation.

• Journal of The Korean Association For Science Education
• /
• v.44 no.2
• /
• pp.167-178
• /
• 2024

In this study, we investigated the perceptions of science-related careers and career education among middle school science teachers. Sixty-four science teachers experienced in teaching unit 7 in the first year of middle school participated. The results of the study revealed that not only careers in science but also careers with science were found to be quite high when teachers were asked to provide examples of science-related careers. Jobs related to research/engineering, which are careers in science, comprised the highest proportion of teachers' answers, followed by jobs related to education/law/social welfare/police/firefighting/military, and health/medical, which are careers with science. However, the proportion of jobs mentioned related to installation/maintenance/production was extremely low. The skills required for science-related careers were mainly perceived to consist of tools for working and ways of working. The number of skills classified under living in the world was perceived to be extremely low across most careers, irrespective of career type. Most teachers only taught unit 7 for two to four sessions and devoted little time to science-related career education, even in general science classes. In the free semester system, a significant number of teachers responded that they provide science-related career education for more than 8 hours. Teachers mainly utilize lecture, discussion/debate, and self-study activities. Meanwhile, in the free semester system, the resource-based learning method was utilized at a high proportion compared to other class situations. Teachers generally made much use of media materials, with the use of textbooks and teacher guides found to be lower than expected. There were also cases of using materials supported by science museums or the Ministry of Education. Teachers preferred to implementing student-centered classes and utilizing various teaching and learning methods. Based on the above research results, discussions were proposed to improve teachers' perceptions of science-related careers and career education.

• Journal of Broadcast Engineering
• /
• v.19 no.5
• /
• pp.578-589
• /
• 2014

Window image is displayed through a monitor screen when we execute the application programs on the computer. This includes webpage, video player and a number of applications. The webpage delivers a variety of information by various types in comparison with other application. Unlike a natural image captured from a camera, the window image like a webpage includes diverse components such as text, logo, icon, subimage and so on. Each component delivers various types of information to users. However, the components with different characteristic need to be divided locally, because text and image are served by various type. In this paper, we divide window images into many sub blocks, and classify each divided region into background, text and subimage. The detected subimages can be applied into 2D-to-3D conversion, image retrieval, image browsing and so forth. There are many subimage classification methods. In this paper, we utilize AdaBoost for verifying that the machine learning-based algorithm can be efficient for subimage detection. In the experiment, we showed that the subimage detection ratio is 93.4 % and false alarm is 13 %.

• Culinary science and hospitality research
• /
• v.19 no.1
• /
• pp.230-243
• /
• 2013

The purpose of this study is to offer the basic materials for the development of nutrition education programs for youth and help domestic science teachers in schools implement effective dietary education by examining youth's purchase behavior of processed foods. As a result of figuring out youth's purchasing behavior of processed food and the difference in accordance with social, demographic variables, they considered taste and price mainly when choosing foods. The results showed that what they consider important when checking food display information was shelf life and price. It was observed that 56% of them check additives display information in food when purchasing processed food. In terms of demographic factors, the more likely they are a girl student, the lower grader they are, and the lower price they purchase processed food at, the better they used the nutritional knowledge learned in school. Based upon these results, it is necessary to offer the consumer's level of education and training for their demands by accurately figuring out youth's purchasing behavior of processed foods. For this, home economics education must allow youth to lead healthy diet by implementing a systematic and professional training on food additives on a basis of the research and utilization of a variety of educational media and teaching and learning methods.