• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.255-267
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• 2021

The purpose of this study is to present basic data for systematic and effective basic medical education by developing a flipped learning class design model using smart tools and verifying its validity. To this end, in this study, a model proposal was developed based on literature review, and its validity was verified through expert review and field application. In this study, as a flipped learning class design model using smart tools, RECIPE(R: Ready, E: Establish a Plan, C: Create and Connect Media, I: Into the Classroom, P: Process-focused Assessment, E: Evaluation) model was developed. This model is a model that enhances the learning effect by applying an appropriate smart tool at each stage of designing flipped learning. As a result of applying this model to the development of'Anatomy'and'Neuroscience'lectures in the first semester of 2019, students' interest and satisfaction are high, and it is proposed as a specialized model in the field of basic medicine. Therefore, the RECIPE model developed in this study can be applied to various basic medicine-related classes, and it is expected that students will be able to understand basic medicine through the design of the flipped learning class based on this.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.2
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• pp.379-408
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• 2023

This case study reports on the redevelopment of a course, Local Culture Information Theory offered by the Department of Library and Information Science at C University, into a capstone design course using a project-based learning approach. In collaboration with a local community youth organization, the redesigned course provided an opportunity for LIS students to develop and implement a digital literacy program that enabled high school students to use a variety of digital multimedia technologies to complete a project of digital Human Library featuring video, audio, and digital are such as webtoons. Through semi-structured interviews with 5 students and 3 staff from partner organizations, this study reports on course development process, the establishment of local partnerships, project outcome, as well as suggestions for improvements. In addition, a qualitative analysis of the participating students' interview responses using the Framework for 21st Century Learning (P21) found they developed and improved 11 skills across three core areas: life and career skills including self-direction, project management, collaboration with diverse teams, flexibility, responsibility, leadership; learning and innovation skills including communication and collaboration, problem-solving, creativity, and critical thinking; and information, media, and technology skills through media creation. Lessons learned and recommendations from this case study may be useful for other LIS programs and faculty interested in implementing project-based learning or developing capstone design courses.

• Smart Media Journal
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• v.13 no.2
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• pp.32-43
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• 2024

Game agents which are behavioral agent for game playing are a crucial component of game satisfaction. However it takes a lot of time and effort to create game agents for various game levels, environments, and players. In addition, when the game environment changes such as adding contents or updating characters, new game agents need to be developed and the development difficulty gradually increases. And it is important to have a game agent that can be customized for different levels of players. This is because a game agent that can play games of various levels is more useful and can increase the satisfaction of more players than a high-level game agent. In this paper, we propose a method for learning and controlling the level of play of game agents that can be rapidly developed and fine-tuned for various game environments and changes. At this time, reinforcement learning applies a policy-based distributed reinforcement learning method IMPALA for flexible processing and fast learning of various behavioral structures. Once reinforcement learning is complete, we choose actions by sampling based on Softmax-Temperature method. From this result, we show that the game agent's play level decreases as the Temperature value increases. This shows that it is possible to easily control the play level.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.293-298
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• 2016

Document summarization is an important task in various areas where the goal is to select a few the most descriptive sentences from a given document as a succinct summary. Even without training data of human labeled summaries, there has been several interesting existing work in the literature that yields reasonable performance. In this paper, within the same unsupervised learning setup, we propose a more principled learning framework for the document summarization task. Specifically we formulate an optimization problem that expresses the requirements of both faithful preservation of the document contents and the summary length constraint. We circumvent the difficult integer programming originating from binary sentence selection via continuous relaxation and the low entropy penalization. We also suggest an efficient convex-concave optimization solver algorithm that guarantees to improve the original objective at every iteration. For several document datasets, we demonstrate that the proposed learning algorithm significantly outperforms the existing approaches.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.718-721
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• 2018

This paper presents an inter-prediction technique using deep learning, where a virtual reference frame of the current frame is synthesized by using the reconstructed frames to improve coding efficiency. Experimental results demonstrate that the proposed algorithm provides 1.9% BD-rate reduction on average as compared to HEVC reference software in the Random Access condition.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.11a
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• pp.139-141
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• 2017

화재는 다른 재난보다 확산 속도가 빠르기 때문에 신속하고 정확한 감지와 지속적인 감시가 요구된다. 최근, 신속하고 정확한 화재 감지를 위해, CCTV(Closed-Circuit TeleVision)으로 획득한 이미지를 기계학습(Machine Learning)을 이용해 화재 발생 여부를 감지하는 화재 감지 시스템이 주목받고 있다. 본 논문에서는 기계학습의 기술 중 정확도가 가장 높은 딥러닝(Deep Learning)기반의 CCTV 화재 감지 시스템을 제안한다. 본 논문의 시스템은 딥러닝 기술 적용뿐만이 아니라, CCTV 이미지 전처리 과정을 보완함으로써 딥러닝에서의 미지 데이터(unseen data)의 낮은 분류 정확도 문제인 과적합(overfitting)문제를 해결하였다. 본 논문의 시스템은 약 80,000 개의 CCTV 이미지 데이터를 학습하여, 90% 이상의 화재 이미지 분류 정확도의 성능을 보여주었다.

• Journal of Industrial Technology
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• v.21 no.A
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• pp.135-139
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• 2001

There has been a study about the effective multimedia education using a computer following the appearance of a virtual space. Also, there has been an effort to connect the information & communication technology with education. The popular on-line lecture systems are mostly on English lecture sites. However, they just offer the VOD(Video On Demand) services ignoring students' convenience. To improve these week points, we design and implement the multimedia leaching system focusing on an efficient repeat-effect in order that students can control the Media Player by clicking a sentence on a web page. This paper presents the Editor and Player considering students' interest and the effective learning fruits. So users can easily make multimedia materials and use them to improve their English listening skill.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.254-257
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• 2009

In this paper, we propose a learning-based super-resolution algorithm. In the proposed algorithm, a multi-resolution wavelet approach is adopted to perform the synthesis of local high-frequency features. To obtain a high-resolution image, wavelet coefficients of two dominant LH- and HL-bands are estimated based on wavelet frames. In order to prepare more efficient training sets, the proposed algorithm utilizes the LH-band and transposed HL-band. The training sets are then used for the estimation of wavelet coefficients for both LH- and HL-bands. Using the estimated high frequency bands, a high resolution image is reconstructed via the wavelet transform. Experimental results demonstrate that the proposed scheme can synthesize high-quality images.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.241-248
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• 2006

Web is greatly contributing in providing a variety of information. Especially, as media for the purpose of development and education of human resources, the role of web is important. Furthermore, E-Learning through web plays an important role for each enterprise and an educational institution. Also, above all, fast and various searches are required in order to manage and search a great number of educational contents in web. Therefore, most of present information is composed in HTML, so there are lots of restrictions. As a solution to such restriction, XML a standard of Web document, and its various search functions is being extended and studied variously. This paper proposes a search system able to search XML in E-Learning or var ious contents of non-XML using mobile device PDA.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.162-170
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• 2014

Appearance-based subspace models such as eigenfaces have been widely recognized as one of the most successful approaches to face recognition and tracking. The success of eigenfaces mainly has its origins in the benefits offered by principal component analysis (PCA), the representational power of the underlying generative process for high-dimensional noisy facial image data. The sparse extension of PCA (SPCA) has recently received significant attention in the research community. SPCA functions by imposing sparseness constraints on the eigenvectors, a technique that has been shown to yield more robust solutions in many applications. However, when SPCA is applied to facial images, the time and space complexity of PCA learning becomes a critical issue (e.g., real-time tracking). In this paper, we propose a very fast and scalable greedy forward selection algorithm for SPCA. Unlike a recent semidefinite program-relaxation method that suffers from complex optimization, our approach can process several thousands of data dimensions in reasonable time with little accuracy loss. The effectiveness of our proposed method was demonstrated on real-world face recognition and tracking datasets.