• 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 Technology Innovation
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• v.18 no.1
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• pp.153-175
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• 2010

Firms must focus on innovative activities via R&D investment in order to secure competitive advantage and sustainable growth. However, their innovative activities do not always result in successful outcomes and are often obstructed by uncertainty and non-appropriability of technology being developed and by insufficient internal resources and capabilities to tap into it. In this situation, collaboration with external partners can be a part of good alternative strategy to solve those problems. This paper aims to analyze what factors lead to Korean manufacturing companies’ decision to collaborate with external partners for technology innovation. For empirical analysis, we used the Korean Innovation Data compiled by STEPI, government-funded research institute in Korea. The research findings are: 1) firms tend to participate in external collaboration for product innovation with greater firm size, more past collaboration experiences and when they belong to high-tech industries 2) unlike our expectation, our chosen ‘innovation-impeding’ factors are found not to contribute to the enhancement of collaboration for product innovation.

• Journal of The Korean Association of Information Education
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• v.21 no.1
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• pp.57-66
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• 2017

Lately, academic interests in big data analysis and social network has been prominently raised. Various academic fields are involved in this social network based research trend, which is, social network has been actively used as the research topic in social science field as well as in natural science field. Accordingly, this paper focuses on the text analysis and the following social network analysis with the Master's and Doctor's dissertations. The result indicates that certain words had a high frequency throughout the entire period and some words had fluctuating frequencies in different period. In detail, the words with a high frequency had a higher betweenness centrality and each period seems to have a distinctive research flow. Therefore, it was found that the subjects of the Master's and Doctor's dissertations were changed sensitively to the development of IT technology and changes in information curriculum of elementary, middle and high school. It is predicted that researches related to smart, mobile, smartphone, SNS, application, storytelling, multicultural, and STEAM, which had an increased frequency in period 4, would be continuously conducted. Moreover, the topics of robots, programming, coding, algorithms, creativity, interaction, and privacy will also be studied steadily.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.1-6
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• 2014

Emotion recognition is defined as that machines automatically recognize human emotions. Because the human emotions is very subjective, it is impossible to measure objectively. Therefore, the goal of emotion recognition is to obtain a measure that is agreed by as many people as possible. Emotion recognition in a image is implemented as the method that matches human emotions to the various features of the image. In the paper, we propose an emotion recognition system using color features of clothing image based on the Kobayashi's image scale. The proposed system stores colors of image scale into a database. And extracted major colors from a input clothing image are compared with those in the database. The proposed system can obtain three emotions maximally. In order to evaluate the system performance 70 observers are tested. The test results shows that recognized emotions of the proposed system are very similar to the observers emotions.

• Journal of Digital Contents Society
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• v.13 no.3
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• pp.439-449
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• 2012

Recently, many interactive artworks produce through convergence of art and scientific technology. Artists or designers should cooperate with other areas for analysis in order to share database. According to these requirements, In this study, we analyze Kandinsky's works which are more emphasis on subject area than the emphasis to background area by using a number of colors, faces, and brightness levels. Due to this approach, it determines how to represent for stable screen composition attracting viewer's eyes. As a result, the subject area has more a number of colors, faces, and brightness levels than the background area. In addition, we produce the interactive artwork based on the analyzed data. According to the requirement of convergence and collaboration in the historical background, this study will be able to contribute to make the collaboration by sharing common information between artists and professionals in other areas. In addition, it is expected that other production makers who do not have the background of arts education will be able to configure the visual screen.

• Journal of KIISE
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• v.45 no.3
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• pp.311-320
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• 2018

Recently, the use of digital audio and video as proof in criminal and all kinds of litigation is increasing, and scientific investigation using digital forensic technique is developing. With the development of computing and file editing technologies, anyone can simply manipulate video files, and the number of cases of manipulating digital data is increasing. As a result, the integrity of the evidence and the reliability of the evidence Is required. In this paper, we propose a technique for extracting the Electrical Network Frequency (ENF) through a grid of power grids according to the geographical environment for power supply, and then performing signal processing for peak detection using QIFFT. Through the detection algorithm using the standard deviation, it was confirmed that the video file was falsified with 73% accuracy and the forgery point was found.

• Fire Science and Engineering
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• v.23 no.5
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• pp.9-16
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• 2009

Chemical products have had an favorable influence on our everyday life, and contributed very much to the development of human culture. According to the rapid change of industry and the development of scientific technique the using chemical products are increasing more and more. Chemical products can have any hazardous property such as flammability or explosiveness. There are occurring many accidents in the international trade due to the different classification and labelling of chemicals produced in various countries. The main purpose of this work is the development of global standard test methods for the chemicals, and the classification and labelling in building block approach by means of the basic technical data. Oxidizing solids, combustible solids, spontaneously combustible materials, water-prohibitive materials, flammable liquids, self-reactive materials and oxidizing liquids have been classification The first Experiment have tested Oxidizing solids of third five. The results have been classified according to the hazard material safety regulation and the UN regulation, and summarized in a data-base.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06b
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• pp.231-235
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• 2010

요즘 들어 컴퓨터 처리 능력의 향상에 따라 사이버인프라스트럭처(Cyberinfrastructure)를 이용하는 계산과학이 주목을 받고 있다. 그 중에서도 대용량 데이터의 복잡한 계산과 시뮬레이션을 동반하는 계산화학 연구 분야에서의 컴퓨터 활용은 매우 중요하다. 계산화학을 간단하게 설명하자면 컴퓨터를 이용한 계산을 통하여 이론 화학의 문제를 다루는 화학의 한 분야라고 말할 수 있다. 계산화학 분야의 연구를 위하여 고성능 컴퓨터와 데이터를 처리, 분석하는 계산화학 도구는 이론연구자 및 실험연구자 모두에게 있어 필수적인 요소이다. 더불어 계산화학 연구자간의 협업과 원격지에 있는 사이버인프라스트럭처 자원의 활용을 위해 e-Science 환경에서의 연구 및 교육 환경이 개발되어야 한다. 이에, 본 논문에서는 한국과학기술정보연구원(KISTI)이 보유 및 운영하고 있는 사이버인프라스트럭처(고성능 컴퓨터, 초고속 네트워크)를 기반으로 컴퓨터에 익숙하지 않은 계산화학 관련 연구자 및 전공자들이 인터넷 상에서 계산화학 분야 교육을 받을 수 있는 e-Science 기반 계산화학 교육을 위한 환경을 설계하고자 한다. 이를 위해 1) 세계적으로 유명한 GridChem, CICC, NBCR 웹사이트를 이용하여 발표된 논문을 분석하였으며, 2) 분석된 결과를 가지고 주로 사용되는 계산화학 도구의 통계를 산출하여, 3) 이를 바탕으로 KISTI 사이버인프라스트럭처를 활용한 e-Science 기반 계산화학 교육 환경(e-Chem)을 설계하였다.

• Proceedings of Korean Society of Archives and Records Management
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• 2019.05a
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• pp.17-23
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• 2019

People who usually receive weather information are now becoming agents providing such information through crowdsourcing based on the Internet. As the archival academia recognizes the significance of information management including data, it is necessary to focus on the change and the current state of the meteorological information. Therefore, this dissertation has confirmed the current state and the problem of the meteorological network built by the information provided by the agent. In addition, it has analyzed the collection, use, and possibility of meteorological information by participating in the forecast process through crowdsourcing to identify how to gather information in the field of meteorology. Furthermore, it suggests a future development prospect of meteorological application through crowdsourcing.

• The Journal of Bigdata
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• v.7 no.1
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• pp.1-14
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• 2022

The proof-of-work consensus algorithm used by most blockchains is causing a massive waste of computing resources in the form of mining. A useful proof-of-work consensus algorithm has been studied to reduce the waste of computing resources in proof-of-work, but there are still resource waste and mining centralization problems when creating blocks. In this paper, the problem of resource waste in block generation was solved by replacing the relatively inefficient computation process for block generation with distributed artificial intelligence model learning. In addition, by providing fair rewards to nodes participating in the learning process, nodes with weak computing power were motivated to participate, and performance similar to the existing centralized AI learning method was maintained. To show the validity of the proposed methodology, we implemented a blockchain network capable of distributed AI learning and experimented with reward distribution through resource verification, and compared the results of the existing centralized learning method and the blockchain distributed AI learning method. In addition, as a future study, the thesis was concluded by suggesting problems and development directions that may occur when expanding the blockchain main network and artificial intelligence model.