• Journal of Engineering Education Research
• /
• v.17 no.3
• /
• pp.34-41
• /
• 2014

In distance education, use of proper software tools can greatly enhance student's attention and learning efficiency. In such software tools, offering diverse multimedia information is one of the most critical factors. However, integration and synchronization of the various media types has been relatively difficult parts of implementation. This paper proposes a prototype system that uses a metafile and event handling mechanism for the uniform treatment of various media types. This event-level integration and synchronization of multimedia makes the implementation relatively simple. With this approach, instructor's behaviors are automatically recorded, and the instructors can freely choose and show any type of multimedia contents while lecturing. Current commercial or non-commercial lecture management systems could incorporate this approach, so that the distance education market could be expanded with richer multimedia contents.

• Smart Media Journal
• /
• v.11 no.11
• /
• pp.63-74
• /
• 2022

Nowadays, lithium-ion battery has become more popular around the world. Knowing when batteries reach their end of life (EOL) is crucial. Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is needed for battery health management systems and to avoid unexpected accidents. It gives information about the battery status and when we should replace the battery. With the rapid growth of machine learning and deep learning, data-driven approaches are proposed to address this problem. Extracting aging information from battery charge/discharge records, including voltage, current, and temperature, can determine the battery state and predict battery RUL. In this work, we first outlined the charging and discharging processes of lithium-ion batteries. We then summarize the proposed techniques and achievements in all published data-driven RUL prediction studies. From that, we give a discussion about the accomplishments and remaining works with the corresponding challenges in order to provide a direction for further research in this area.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2018.06a
• /
• pp.63-66
• /
• 2018

Recently, a convolutional neural network (CNN) models at single image super-resolution have been very successful. Residual learning improves training stability and network performance in CNN. In this paper, we compare four convolutional neural network models for super-resolution (SR) to learn nonlinear mapping from low-resolution (LR) input image to high-resolution (HR) target image. Four models include general CNN model, global residual learning CNN model, local residual learning CNN model, and the CNN model with global and local residual learning. Experiment results show that the results are greatly affected by how skip connections are connected at the basic CNN network, and network trained with only global residual learning generates highest performance among four models at objective and subjective evaluations.

• Journal of the Korean School Mathematics Society
• /
• v.2 no.1
• /
• pp.231-242
• /
• 1999

The focus of this development program is to input multimedia materials into learning according to the trend of recent social changes and to maximize the learning effect for improving problem-solving by offering familiar teaching materials. The expecting effects of this study are as follows: 1. This program helps students acquire mathematical concepts and principles about circular equation through concrete examples using a variety of media - text, voice, sound, and animation and so on - , makes it possible individual learning which was difficult for students to expect at the existing multitude class as progressing learning each unit on the screen and the perfect learning by offering FEED BACK 2. This program varied the difficulty of learning contents to learn according to learning abilities of learners by using animation and making the most of merits of computer and was able to improve learning effect by studying in a mutual way with managing learning procedure nonsuccessively. 3. Class using CAI program about developed circular equation unit has a positive effect on improving problem-solving by becoming from teacher centered class to student centered one. 4. This program makes students understand the contents of auxiliary learning in multimedia computer more efficiently, and cultivate abilities to adopt in accordance with changes in the future society by forming familiar computer mind.

• 한국HCI학회:학술대회논문집
• /
• 2009.02a
• /
• pp.926-932
• /
• 2009

Recently many research reports on effective use of mobile devices for museums to provide information on displayed artifacts providing individualized learning space, collaborative learning, and discovery learning, Such devices have many possibilities to support learning as a participatory media and social network. Mobile devices are used, however, limited for its usability and lack in providing expected learning experiences. It offers one-way interaction and they are often limited in providing customized services for different patrons to experience learning and entertainment. In this research, we have adopted user centered design approach to identify the needs and possible usage of PDA system in the museum. Research methods include contextual observation and inquiry with symbolic interactionism for qualitative research and its epistemology. We have developed conceptual model with scenario and storyboard method, and developed vertical prototype with Flash.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.95-117
• /
• 2022

With the emergence of various media types due to the development of Internet technology, advertisers have difficulty choosing media suitable for corporate advertising strategies. There are challenging to effectively reflect consumer contextual information when advertising media is selected based on traditional marketing strategies. Thus, a recommender system is needed to analyze consumers' past data and provide advertisers with personalized media based on the information consumers needs. Since the traditional recommender system provides recommendation services based on quantitative preference information, there is difficult to reflect various contextual information. This study proposes a methodology that uses deep learning to recommend personalized media to advertisers using consumer contextual information such as consumers' media viewing time, residence area, age, and gender. This study builds a recommender system using media & consumer research data provided by the Korea Broadcasting Advertising Promotion Corporation. Additionally, we evaluate the recommendation performance compared with several benchmark models. As a result of the experiment, we confirmed that the recommendation model reflecting the consumer's contextual information showed higher accuracy than the benchmark model. We expect to contribute to helping advertisers make effective decisions when selecting customized media based on various contextual information of consumers.

• Smart Media Journal
• /
• v.9 no.3
• /
• pp.25-30
• /
• 2020

The purpose of this paper is to implement a deep learning-based real-time video analysis algorithm that monitors safety of workers in industrial facilities. The worker's clothes were divided into six classes according to whether workers are wearing a helmet, safety vest, and safety belt, and a total of 5,307 images were used as learning data. The experiment was performed by comparing the mAP when weight was applied according to the number of learning iterations for 645 images, using YOLO v4. It was confirmed that the mAP was the highest with 60.13% when the number of learning iterations was 6,000, and the AP with the most test sets was the highest. In the future, we plan to improve accuracy and speed by optimizing datasets and object detection model.

• Journal of Digital Contents Society
• /
• v.14 no.2
• /
• pp.255-262
• /
• 2013

Useful applications for English education enable immediate conversion of mobile devices into remote wireless systems for classroom computers. Once the free software has been installed in the main computers in the classroom, using powerpoint, students can operate the computers through their mobile devices by installing Air mouse on them. By using this, the students can draw or write on the "board" to manipulate the educational resources from where they are/from their seats. The study of English language encompasses not only academic study but also language training. Until recently, the issue of the English language learning has been ridden with certain problems-instead of being a tool that facilitates communication, its main purpose has been for school grades, TOEIC, and TOEFL. This study suggests English language learning methodology using various applications such as mobile, VOD English language content, and movie scripts in implementing easy and fun English language learning activities that can be studied regularly. This is operationalized by setting a specific limit on learning and by using various media such as podcast, Apps, to increase interest, motivation, and self-directed learning in a passive learning environment.

• Smart Media Journal
• /
• v.11 no.5
• /
• pp.48-55
• /
• 2022

Tomato crops are easy to expose to disease and spread in a short period of time, so late measures against disease are directly related to production and sales, which can cause damage. Therefore, there is a need for a service that enables early prevention by simply and accurately diagnosing tomato diseases in the field. In this paper, we construct a system that applies a deep learning-based model in which ImageNet transition is learned in advance to classify and serve nine classes of tomatoes for disease and normal cases. We use the input of MobileNet, ResNet, with a deep learning-based CNN structure that builds a lighter neural network using a composite product for the image set of leaves classifying tomato disease and normal from the Plant Village dataset. Through the learning of two proposed models, it is possible to provide fast and convenient services using MobileNet with high accuracy and learning speed.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.115-127
• /
• 2023

Video stabilization is one of the camera technologies that the importance is gradually increasing as the personal media market has recently become huge. For deep learning-based video stabilization, existing methods collect pairs of video datas before and after stabilization, but it takes a lot of time and effort to create synchronized datas. Recently, to solve this problem, unsupervised learning method using only unstable video data has been proposed. In this paper, we propose a network structure that learns the stabilized trajectory only with the unstable video image without the pair of unstable and stable video pair using the Convolutional Auto Encoder structure, one of the unsupervised learning methods. Optical flow data is used as network input and output, and optical flow data was mapped into grid units to simplify the network and minimize noise. In addition, to generate a stabilized trajectory with an unsupervised learning method, we define the loss function that smoothing the input optical flow data. And through comparison of the results, we confirmed that the network is learned as intended by the loss function.