• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.20-25
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• 2019

Most multimedia signals contain image data, so the problem of efficient processing and transmitting the image data is an important task of the information society. This paper proposes a compression algorithm that reduces the color bits according to importance using YUV color space among the various methods of compressing image data. 4: 2: 2 subsampling is the standard in the field of video. Using the color information and the characteristics of the human retina, YUV color data was reduced by 4: 2: 2 subsampling. The YUV images and RGB images can be interconverted using the transformation matrix. The image data was converted into color space by YUV, and the relatively low U and V bits were subjected to a downscaling operation. The data was then compressed through 4: 2: 2 subsampling. The performance of the proposed algorithm was compared and analyzed by a comparison with existing methods. As a result of the analysis, it was possible to compress the image without reducing the information of the low importance color element and without significant deterioration in the quality compared to the original.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.35-45
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• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.106-113
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• 2019

The purpose of this paper is aimed to derive suggestions for convergence technology for effective management of distance education by analyzing a long-term case. The experiment was designed with notebook, smartphone or tablet based robot controlled by a remote instructor and a learner, who have experience of distance learning including robot assisted learning. The tablet based robot has the display system of feedback to speakers. During five months, three types of experiments were conducted randomly and a participant was interviewed thoroughly. The result, like the previous research, demonstrates that the task performance of the learner in telepresence robot-assisted learning was better than that in the notebook, and smartphone based. However, it is believed to be necessary to adjust the system for eye-contact and voice transmission for the remote instructor. The instructor required an additional sight by supplementing an extra camera and automatic direction control to source of sound.

• Journal of IKEEE
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• v.23 no.2
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• pp.508-516
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• 2019

Smart home with various IoT devices provides convenient and efficient services. However, security is important because sensitive information such as private video and audio can be collected and processed, as well as shared over the Internet. To manage such smart home IoT devices, we use blockchain technology that provides data integrity and secure management. In this paper, we utilize a PoS(Proof of Stake) method that verifies the block through the accumulated stake in the network rather than the computation power, out of the PoW(Proof of Work) block chain, in which the computation for the existing verification must be continuously performed. Among them, we propose a blockchain based system with DPoS(Delegated Proof of Stake) method to actively solve the scalability part, for security that is suitable for smart home IoT environment. We implement the proposed system with DPoS based EOSIO to show realization, and we show performance improvement in terms of transaction processing speed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.82-85
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• 2014

With high speed communication network and supplement of high quality multimedia devices, large demand of multimedia service stimulates digital signing industry to step forward to new opportunities. But in order to provide competitive quality services, Integrated management system is required. Digital signs use technologies such as LCD, LED and Projection to display content such as digital images, video, streaming media, and information. Digital signs rely on a variety of hardware to deliver the contents. The components of a typical digital sign installation include one or more display screens, one or more media players, and a content management server for each player. To improve the existing high cost and less efficient management system, this paper suggest cost effective Integrated Digital Signage Management System with the results of analysis of existing system. Also this paper presents an actual implementation on entertainment company to evaluate the suitability, to prove the result of superior performance of proposed system.

• Korean Journal of Applied Biomechanics
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• v.29 no.3
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• pp.129-135
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• 2019

Objective: The purpose of this study is to present the scientific and quantitative data by finding the common points and differences of the side-kick according to the height change through the difference of the side kick motion performance according to the three target height changes and the function of the lower limbs muscle in side kick motion of Taekwondo Poomsae. Method: For this, total 14 players were selected who were registered in Korea Taekwondo Association and skilled group 7 players who had a medal from national competition and 7 players who did not have Taekwondo experience from department of physics. 4 video cameras to the feature on side kick per target height, and the subjects' support foot was located on the ground reactor and the practice was conducted 3 times: waist, chest, and head as the target height. the basic materials were collected by using Kwon 3D XP program and the T-test was conducted to verify the statistic difference between groups (SPSS 24.0). At this time, the statistics significance level was set as .05 and the following conclusion was obtained. Results: The lower the proficiency and the higher the height, the more the joint coordination between the hip and the knee. Conclusion: Summary of the result shows a common point that the change of target's height makes the lower the proficiency and the higher the height, the more the joint coordination between the hip and the knee. Also, the higher the target's height became, the greater angular momentum of thighs, shanks, foot became in common.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.112-125
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• 2019

An acoustic signal-based, tunnel-incident detection system was developed and evaluated. The system was comprised of three components: algorithm, acoustic signal collector, and server system. The algorithm, which was based on nonnegative tensor factorization and a hidden Markov model, processes the acoustic signals to attenuate noise and detect incident-related signals. The acoustic signal collector gathers the tunnel sounds, digitalizes them, and transmits the digitalized acoustic signals to the center server. The server system issues an alert once the algorithm identifies an incident. The performance of the system was evaluated thoroughly in two steps: first, in a controlled tunnel environment using the recorded incident sounds, and second, in an uncontrolled tunnel environment using real-world incident sounds. As a result, the detection rates ranged from 80 to 95% at distances from 50 to 10 m in the controlled environment, and 94 % in the uncontrolled environment. The superiority of the developed system to the existing video image and loop detector-based systems lies in its instantaneous detection capability with less than 2 s.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1076-1081
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• 2019

In response to the recent development of IT technologies, there are more demands for visual devices such as display. However, noise is generated in the process of sending video data due to various reasons. Noise is the representative noise which is commonly found. While A-TMF, CWMF, and AMF are the typical ways for removing Salt and Pepper noise, the noise is not removed well in high-density noise environment. To remove the noise in the high-density noise environment, this study suggested an algorithm which identifies whether it's noise or not. If it's not a noise, matches the original pixel. If it's a noise, divide the $3{\times}3$ local mask into the area of the element treated and the area of the element to be processed. Then, algorithm proposes to apply different weights for each element to treat it as an average filter. To analyze the performance of the algorithm, this study compared PSNR to compare the algorithm with other existing methods.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.55-60
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• 2019

The web is one of the most intimate technologies in people's daily lives, and most of the time, people are sharing data on the web. Simple messenger, news, video, as well as various data are now spreading through the web. In addition, with the emergence of Web assembly technology, the programs that run in the existing native environment start to enter the domain of the Web, and the data shared by the Web is now getting wider and larger in terms of VR / AR contents and big data. Therefore, in this paper, we have studied how to effectively deliver web contentsto users who use Web service by using service worker that can operate independently without being dependent on browser and cache API that can effectively store data in web browser.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.41-68
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• 2019

Multi-view learning considers data from various viewpoints as well as attempts to integrate various information from data. Multi-view learning has been studied recently and has showed superior performance to a model learned from only a single view. With the introduction of deep learning techniques to a multi-view learning approach, it has showed good results in various fields such as image, text, voice, and video. In this study, we introduce how multi-view learning methods solve various problems faced in human behavior recognition, medical areas, information retrieval and facial expression recognition. In addition, we review data integration principles of multi-view learning methods by classifying traditional multi-view learning methods into data integration, classifiers integration, and representation integration. Finally, we examine how CNN, RNN, RBM, Autoencoder, and GAN, which are commonly used among various deep learning methods, are applied to multi-view learning algorithms. We categorize CNN and RNN-based learning methods as supervised learning, and RBM, Autoencoder, and GAN-based learning methods as unsupervised learning.