• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.195-202
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• 2023

This study is a comparative survey study conducted to explore the differences in learners' learning flow and learning satisfaction according to the non-face-to-face class operation methods implemented at universities. After implementing different class management methods for the same subject taught by the same instructor non-face-to-face for 15 weeks, each learning flow and learning satisfaction were compared and analyzed, and the collected data were analyzed with IBM SPSS 21.0. As a result of the study, learning flow was high in the order of lectures using real-time ZOOM and recorded lectures using self-studio(3.41±0.91, 3.28±1.01), and learning satisfaction was high in the order of lectures using real-time ZOOM and lectures using the automatic recording system of classes(3.40±0.80, 3.30±0.74). The item with the lowest score was the PPT audio recording lecture in both areas of learning flow and learning satisfaction(2.72±1.04, 1.73±1.04). Considering that system errors such as sound in the smart lecture environment operated for the first time in this study affected the research results, it is suggested that future research should be conducted by supplementing the corresponding part.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.173-180
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• 2018

This paper proposes a method to improve the performance of face recognition via super-resolution method using sparse representation and deep learning from low-resolution facial images. Recently, there have been many researches on ultra-high-resolution images using deep learning techniques, but studies are still under way in real-time face recognition. In this paper, we combine the sparse representation and deep learning to generate super-resolution images to improve the performance of face recognition. We have also improved the processing speed by designing in parallel structure when applying sparse representation. Finally, experimental results show that the proposed method is superior to conventional methods on various images.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.13-20
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• 2011

The face recognition has been used in a variety fields, such as identification and security. The procedure of the face recognition is as follows; extracting face features of face images, learning the extracted face features, and selecting some features among all extracted face features. The selected features have discrimination and are used for face recognition. However, there are numerous face features extracted from face images. If a face recognition system uses all extracted features, a high computing time is required for learning face features and the efficiency of computing resources decreases. To solve this problem, many researchers have proposed various Boosting methods, which improve the performance of learning algorithms. Mutual-Boost is the typical Boosting method and efficiently selects face features by using mutual information between two features. In this paper, we propose a GroupMutual-Boost method for improving Mutual-Boost. Our proposed method can shorten the time required for learning and recognizing face features and use computing resources more effectively since the method does not learn individual features but a feature group.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.39-44
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• 2023

With the development of e-learning teaching methods, the demand for blended learning, which combines face-to-face education and e-learning, is increasing, and it shows a learning effect that can replace the existing face-to-face class. Engineering subjects have various learning activities such as practice, so it is not easy to operate them with traditional blended learning. Therefore, a different teaching and learning design is required according to the learning activities required for the subject. In this paper, examples of teaching method design and operation for blended learning in engineering subjects were introduced, and their effects investigated and analyzed. Learning activities were subdivided into theoretical classes, practical classes, quizzes and Q&A, assignments and solutions, and teaching and learning methods such as online videos, LMS utilization, and face-to-face classes were applied according to learning activities. According to the results of the student satisfaction survey, blended learning showed higher satisfaction than pure online and face-to-face classes in engineering subjects, and showed differentiated satisfaction for each learning activity.

• The Journal of Korea Robotics Society
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• v.1 no.1
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• pp.66-72
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• 2006

In face recognition, learning speed of face is very important since the system should be trained again whenever the size of dataset increases. In existing methods, training time increases rapidly with the increase of data, which leads to the difficulty of training with a large dataset. To overcome this problem, we propose SVDD (Support Vector Domain Description)-based learning method that can learn a dataset of face rapidly and incrementally. In experimental results, we show that the training speed of the proposed method is much faster than those of other methods. Moreover, it is shown that our face recognition system can improve the accuracy gradually by learning faces incrementally at real environments with illumination changes.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.91-103
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• 2022

Purpose: After COVID-19, changes in the educational ecosystem take place and fire service academy education system have shifted from face-to-face into non fact-to-face. So, the educational effect of fire officials is decreased and the satisfaction level is also decreased. In this study, we want to examine the current status of non-face-to-face real-time remote education and supplement the problems to improve the educational methods, the educational environment, etc. Method: This study is an independent variable that affects non-face-to-face real-time remote education, consisting of education system environment, self-efficacy of computers, contents (education contents, structure, design, etc.), and proper interaction. A dependent variable was selected with satisfaction for non-face-to-face real-time remote education. In addition, it was selected and analyzed as an active property of learning motivation and learning attitude as control variables. Result: The better the content and the more active the learning motivation and the attitude toward learning, the higher the satisfaction of non-face-to-face real-time remote education, and the more active the learning motivation and the attitude toward learning, the more positive the computer self-efficacy and the satisfaction of learning Conclusion: In order to increase the satisfaction of non-face-to-face real-time education due to COVID-19, education designers or professors need to provide non-face-to-face education contents that can increase the aggressiveness of their learning motivation and learning attitude, and to increase the satisfaction of education for learners by increasing computer self-efficacy through pre-education of non-face-to-face education systems.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.96-99
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• 2020

According to brilliant development of smart devices, many related services are being devised. And, almost every service is designed to provide user-centric services based on personal information. In this situation, to prevent unintentional leakage of personal information is essential. Conventionally, ID and Password system is used for the user authentication. This is a convenient method, but it has a vulnerability that can cause problems due to information leakage. To overcome these problem, many methods related to face recognition is being researched. Through this paper, we investigated the trend of user authentication through biometrics and a representative model for face recognition techniques. One is DeepFace of FaceBook and another is FaceNet of Google. Each model is based on the concept of Deep Learning and Distance Metric Learning, respectively. And also, they are based on Convolutional Neural Network (CNN) model. In the future, further research is needed on the equipment configuration requirements for practical applications and ways to provide actual personalized services.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.848-855
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• 2016

Face recognition has received a great deal of attention for the wide range of applications in real-world scenario. In this scenario, mismatches (so called heterogeneity) in terms of resolution and illumination between gallery and test face images are inevitable due to the different capturing conditions. In order to deal with the mismatch problem, we propose a local feature learning method using deep canonical correlation analysis (DCCA) for heterogeneous face recognition. By the DCCA, we can effectively reduce the mismatch between the gallery and the test face images. Furthermore, the proposed local feature learned by the DCCA is able to enhance the discriminative power by using facial local structure information. Through the experiments on two different scenarios (i.e., matching near-infrared to visible face images and matching low-resolution to high-resolution face images), we could validate the effectiveness of the proposed method in terms of recognition accuracy using publicly available databases.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1310-1319
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• 2016

Face detection is the first step in a wide range of face applications. However, detecting faces in the wild is still a challenging task due to the wide range of variations in pose, scale, and occlusions. Recently, many deep learning methods have been proposed for face detection. However, further improvements are required in the wild. Another important issue to be considered in the face detection is the computational complexity. Current state-of-the-art deep learning methods require a large number of patches to deal with varying scales and the arbitrary image sizes, which result in an increased computational complexity. To reduce the complexity while achieving better detection accuracy, we propose a fully convolutional network-based face detection that can take arbitrarily-sized input and produce feature maps (heat maps) corresponding to the input image size. To deal with the various face scales, a multi-scale network architecture that utilizes the facial components when learning the feature maps is proposed. On top of it, we design multi-task learning technique to improve detection performance. Extensive experiments have been conducted on the FDDB dataset. The experimental results show that the proposed method outperforms state-of-the-art methods with the accuracy of 82.33% at 517 false alarms, while improving computational efficiency significantly.

• Knowledge Management Research
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• v.21 no.2
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• pp.119-135
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• 2020

Owing to pandemic (COVID-19), the traditional face-to-face education method has been changed to the non-face-to-face real-time online education methods. Using a real time-based video conference system, synchronous education can be adopted by face-to-face class easily. Specially, it is very important to minimize the difference in learning effects between face-to-face and non-face-to-face in Entrepreneurship education. In this study, in order to derive the factors that affect the satisfaction of learners in synchronous online education, authors collected data from learners taking a synchronous entrepreneurship course. Through previous research, learned the reality of education and the composition of lessons. Spatiotemporal effectiveness, mentor ability, and educational environment influence learning satisfaction. PLS-SEM results revealed that it was confirmed that only spatiotemporal effects affect learner satisfaction. However, the education environment (fluent operation and convenience of function use of real-time based online conference system) effect teaching presence, class structure, and spatiotemporal effects. Through this research, we hope to provide theoretical and practical support for developing effective teacher activities, proper lesson structure, convenient function of the conference system, and learner-centered online learning environment when developing synchronous online classes.