• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.750-757
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• 2020

One of the basic tasks for robots to interact with humans is to quickly and accurately grasp human behavior. Therefore, it is necessary to increase the accuracy of human pose recognition when the robot is estimating the human pose and to recognize it as quickly as possible. However, when the human pose is estimated using deep learning, which is a representative method of artificial intelligence technology, recognition accuracy and speed are not satisfied at the same time. Therefore, it is common to select one of a top-down method that has high inference accuracy or a bottom-up method that has high processing speed. In this paper, we propose two methods that complement the disadvantages while including both the advantages of the two methods mentioned above. The first is to perform parallel inference on the server using multi GPU, and the second is to mix bottom-up and One-class Classification. As a result of the experiment, both of the methods presented in this paper showed improvement in speed. If these two methods are applied to the entertainment robot, it is expected that a highly reliable interaction with the audience can be performed.

• Korean Journal of Comparative Education
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• v.27 no.1
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• pp.247-284
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• 2017

The purpose of this study is to analyze firm's motivation of participating and investing in apprenticeship in Germany and Korea, and to investigate institutional factors influencing firm's motivation. By comparing institutional factors of the two countries, it aims to drawing out policy implications for improving Korean apprenticeship. The main method for data collection was comprehensive literature review on international organizations, each countries' government and research institutes' policy materials, statistical data, research outputs and media resources related to each countries' apprenticeship. Considering whether firm's motivation for participating and investing in apprenticeship is production-oriented or investment-oriented, Germany is more inclined to investment motivation with firm's covering net cost during apprenticeship period. On the other hand, Korea is more inclined toward production orientation with firm's expectation of gaining net profit during the training period. Why is firm's training motivation different in these two countries? The author tried to find the reason from the difference of institutional factors of the countries by dividing institutional factors into 4 categories: context(tripartite relations, legal framework), input (flexibility of the system, government incentive), process(training contents, training duration, quality assurance), and output(completion/retention rate, apprentice's productivity). The key implication from the comparative analysis of institutional factors is that it is necessary to enforce companies to have "accountability" on the minimum critical elements, but also to ensure them to have "autonomy" on the rest of the elements.

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

• Journal of The Korean Association For Science Education
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• v.44 no.2
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• pp.167-178
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• 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 Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of Korea Entertainment Industry Association
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• v.13 no.3
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• pp.193-202
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• 2019

This study focuses on what changes are required as to a curriculum of dance department in the university in the age of the 4th industrial revolution. By comparing and analyzing the curricula of dance department in the five universities in Seoul, five academic subjects as to curricula of dance department, which covers what to learn for dance education in the age of the 4th industrial revolution, are presented. First, dance integrative education, the integration of creativity and science education, can be referred to as a subject that stimulates ideas and creativity and raises artistic sensitivity based on STEAM. Second, the curriculum characterized by prediction of the future prospect through Big Data can be utilized well in dealing with dance performance, career path of dance-majoring people, and job creation by analyzing public opinion, evaluation, and feelings. Third, video education. Seeing the images as modern major media tends to occupy most of the expressive area of art, dance by dint of video enables existing dance work to be created as new form of art, expanding dance boundaries in academic and performing art viewpoint. Fourth, VR and AR are essential techniques in the era of smart media. Whether upcoming dance studies are in the form of performance or education or industry, for VR and AR to be digitally applied into every relevant field, keeping with the time, learning about VR and AR is indispensable. Last, the 4th industrial revolution and the curriculum of dance art are needed to foresee the changes in the 4th industrial revolution and to educate changes, development and seeking in dance curriculum.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 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 The Korean Association For Science Education
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• v.21 no.3
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• pp.473-486
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• 2001

The study aimed to evaluate an activity-oriented extracurricular science program as informal science education through the assessment of opinions of student participants and lead-students and lead-teachers who organized the program. An 'Exciting Science Fair' was designed by science teachers and students and provided for 857 students for two days in early 1998. Students chose a course of science activities designed by different levels of student knowledge and interests. During their own science activity courses, the participating students were grouped as pair of two students and guided and facilitated by lead-students. A survey instrument was developed by researchers and asked respondents' opinions of 121 participating students, 72 lead-students, and 19 lead-teachers to the significance of program goals, degree of goal achievement, and program planning and management system before and after the program. It was found that most student participants, lead-students and lead-teachers satisfied with the efficiency of the program. However, it was recommended that the program should place more emphases on engaging student participants in science activities, strengthening scientific inquiry through activities, and increasing science content related to student daily life. It was also suggested that advertizement of the program be publicized in advance through media, an effect teaching-learning strategy for lead-students be developed, and collaboration among lead-students and lead-teachers be improved.

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

• The Journal of the Korea Contents Association
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• v.15 no.1
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• pp.506-518
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• 2015

Recently there are growing needs and demand to enhance 'Unity of knowledge' as the concept of "Creating new value through integration and convergence" is developing rapidly in many different areas in the society. This also has significant implication to education. Especially, it requires paradigm shift in terms of required capabilities and qualifications for the students with science major. To accommodate this trend, Natural Sciences and Engineering's College has been increasing convergence education which focus on cultivating creative and cooperative learning capabilities as well as acquiring fundamental knowledge of individual majors. However, convergence education developed and implemented by Sciences college or liberal education so far has been mechanical combination of knowledge from different academic fields - not effectively integrated and interdisciplinary education. Given this situation, this research is to develop and propose a "convergence education program based on the development of scientific and cultural contents" as an education tool to enhance capabilities to apply and re-create integrated knowledge as well as acquire and learn existing knowledge. Education program developed in this research aims to achieve two different and sequential capabilities. First is to understand 'Science and Technology' and 'Cultural Archetype' which would be essential and useful to create cultural contents. Second is to develop capabilities to convert this understanding into cultural contents - a storytelling capability. This education program is differentiated in that it defines cultural contents as a medium to converge and integrate science and technology and humanities. By leveraging the concept of cultural content and storytelling, this education program would be able to overcome restrictions of existing interdisciplinary approach. Also, this program would encourage students to try in-depth research and new applications, and develop logical and creative thinking.