• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.885-890
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• 2023

As satellite technology progresses, a growing number of satellites-like CubeSat and radar satellites-are available with a higher spectral and spatial resolutions than previous. National initiatives used to be the main force behind satellite development, but current trendsindicate that private enterprises are also actively exploring and developing new satellite technologies. This special issue examines the recent research results and advanced technology in remote sensing approaches for Earth environment analysis. These results provide important information for the development of satellite sensors in the future and are of great interest to researchers working with artificial intelligence in thisfield. The special issue introduces the latest advances in remote sensing technology and highlights studies that make use of data to monitor and forecast Earth's environment. The objective is to provide direction for the future of remote sensing research.

• Journal of the Korean Chemical Society
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• v.54 no.5
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• pp.643-649
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• 2010

In this study, the relationships among high school students' epistemological views on theory and data, science process skills, the perceptions of the preferred laboratory learning environment and attitudes toward laboratory work were investigated. The results indicated that science process skills, all subcategories of the perceptions of the preferred laboratory learning environment (student cohesiveness, open-endedness, integration, rule clarity, and material environment) and attitudes toward laboratory work were significantly correlated with epistemological views on theory and data. The results of multiple regression analysis revealed that science process skills, open-endedness and material environment and attitudes toward laboratory work significantly predicted epistemological views on theory and data.

• Composites Research
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• v.19 no.3
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• pp.1-6
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• 2006

As the structures of the high performance electronic equipments and devices recently become more complex, the electromagnetic interference (EMI) and compatibility (EMC) have been very essential for commercial and military purposes. Thus, sensitive electrical devices and densely packed systems need to be protected from electromagnetic wave. In this research, glass fabric/epoxy composites containing conductive multi-walled carbon nanotube (MWNT) and carbon fiber/epoxy composites as electrical shielding materials were fabricated and electrical properties of the composites were measured. The concerning frequency band is from 300 MHz to 1 GHz. The performances of composite shielding enclosures were predicted using electromagnetic wave 3-D simulation tool, CST Microwave Studio. The shielding enclosure made of carbon fiber/epoxy composites were fabricated and the shielding effectiveness (SE) was measured in the anechoic chamber.

• Composites Research
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• v.19 no.3
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• pp.7-14
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• 2006

In this paper, an integrated probabilistic strength analysis was conducted to predict the reliability of a composite pressure vessel under inner pressure loading condition. As a probabilistic strength analysis, the probabilistic progressive failure model consisting of progressive failure model and Monte Carlo simulation was incorporated with a commercial FEA code, ABAQUS Standard, to perform the probabilistic failure analysis of composite structure which has a complex shape and boundary conditions. As design random variables, the laminar strengths of each direction were considered. Finally, from probabilistic strength analysis, the scattering of burst pressure could be explained and the reliability of composite pressure vessel could be obtained for each component. In case of composite structures in mass production, the effects of uncertainties in material and manufacturing on the performance of composite structures would apparently become larger. So, the probabilistic strength analysis is essential for the structural design of composite structures in mass production.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.9_10
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• pp.466-475
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• 2008

Owing to the improved wireless communication technologies, it is possible to provide streaming service of multimedia with PDAs and mobile phones in addition to desktop PCs. Since mobile client devices have low computing power and low network bandwidth due to wireless network, the transcoding technology to adapt media for mobile client devices considering their characteristics is necessary. Transcoding servers transcode the source media to the target media within corresponding grades and provide QoS in real-time. In particular, an effective load balancing policy for transcoding servers is inevitable to support QoS for large scale mobile users. In this paper, the transcoding load estimation algorithm is proposed for load balance on the distributed transcoding environments. The proposed algorithm estimates transcoding time from transcoding server information, movie information and target transcoding bit-rate. The estimated transcoding time is proved based on experiments.

• Journal of KIISE
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• v.43 no.7
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• pp.781-785
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• 2016

Learning from human behaviors in the real world is essential for human-aware intelligent systems such as smart assistants and autonomous robots. Most of research focuses on correlations between sensory patterns and a label for each activity. However, human activity is a combination of several event contexts and is a narrative story in and of itself. We propose a novel approach of human activity prediction based on event cognition. Egocentric multi-sensor data are collected from an individual's daily life by using a wearable device and smartphone. Event contexts about location, scene and activities are then recognized, and finally the users" daily activities are predicted from a decision rule based on the event contexts. The proposed method has been evaluated on a wearable sensor data collected from the real world over 2 weeks by 2 people. Experimental results showed improved recognition accuracies when using the proposed method comparing to results directly using sensory features.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.10
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• pp.387-396
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• 2018

Stock price prediction has been a difficult problem to solve. There have been many studies to predict stock price scientifically, but it is still impossible to predict the exact price. Recently, a variety of types of cryptocurrency has been developed, beginning with Bitcoin, which is technically implemented as the concept of distributed ledger. Various approaches have been attempted to predict the price of cryptocurrency. Especially, it is various from attempts to stock prediction techniques in traditional stock market, to attempts to apply deep learning and reinforcement learning. Since the market for cryptocurrency has many new features that are not present in the existing traditional stock market, there is a growing demand for new analytical techniques suitable for the cryptocurrency market. In this study, we first collect and process seven cryptocurrency price data through Bithumb's API. Then, we use the gradient boosting model, which is a data-driven learning based machine learning model, and let the model learn the price data change of cryptocurrency. We also find the most optimal model parameters in the verification step, and finally evaluate the prediction performance of the cryptocurrency price trends.

• Journal of Technology Innovation
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• v.22 no.2
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• pp.131-156
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• 2014

Nowadays, the R&D policy has gradually emphasized the creation of economic values added through technology transfer of R&D performance. Technology transfer plays an important role in making a profit on commercialization of technology, but the performance of technology transfer is a great difference between countries. Therefore, this study analyzes the effects of the science and technology environment on technology transfer from a national point of view. In this study, the science and technology environment largely consists of openness, ease of firms, protection of intellectual property, emphasis of science in schools. We select OECD 34 countries as the subject of this study and collect the data from IMD between 2002 and 2011. The results show that all hypotheses are supported. Openness and ease of firms are related to technology transfer as they help to improve the understanding and willingness about technology transfer nationally. Protection of intellectual property also influences the spread of R&D performance positively, and the environment that is emphasized science in school actively promotes both creation and technology transfer of R&D performance. Although prior studies have focused on technical capabilities to improve the performance of technology transfer, we consider the influence of the science and technology environment from an institutional perspective. This study will do much for formulate the R&D policy.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.278-286
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• 2019

Conventional methods for selecting jamming techniques in electronic warfare are based on libraries in which a list of jamming techniques for radar signals is recorded. However, the choice of jamming techniques by the library is limited when modified signals are received. In this paper, we propose a method to predict the jamming technique for radar signals by using deep learning methods. Long short-term memory(LSTM) is a deep running method which is effective for learning the time dependent relationship in sequential data. In order to determine the optimal LSTM model structure for jamming technique prediction, we test the learning parameter values that should be selected, such as the number of LSTM layers, the number of fully-connected layers, optimization methods, the size of the mini batch, and dropout ratio. Experimental results demonstrate the competent performance of the LSTM model in predicting the jamming technique for radar signals.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.215-217
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• 2010

IT융합의 중요성이 강조되고 있는 IT 지식정보화 사회에 지능형 로봇의 발전은 끝없이 연구되고 있다. 미래 인간사회에 로봇이 미치는 영향은 아주 클 것으로 예상하고 있으며, 인간의 실생활과 로봇의 가상공간 세계는 점점 일체화 되어 갈 것으로 예측된다. 따라서 인간은 로봇의 활동과 행동을 좀 더 구체적이고 자세하게 제어할 필요가 있다. 최근 현실세계를 가상공간의 세계로 표현하고자 하는 연구가 활발히 진행되고 있으며, 가상공간을 활용한 미래의 세상 환경은 점점 많아 질 것으로 예상한다. 따라서 본 논문에서는 지능로봇 시뮬레이션 맵 자동생성을 위한 제어 동기화 설계를 연구하였다. 현실세계의 실제 로봇과 가상공간의 시뮬레이션 로봇을 동기화하여 하드웨어 로봇의 움직임에 따라 가상공간의 로봇이 맵을 자동으로 생성시킬 수 있는 방법을 연구하고 설계하여 실험하였다. 실제 로봇의 움직임을 3차원 가상공간으로 자동 생성하게 함으로서, 로봇의 제어를 좀 더 지능화 시킬 수 있을 뿐아니라, 여러 다양한 시뮬레이션 테스트 환경으로 응용할 수 있는 큰 장점이 있다. 또한 로봇을 활용한 가상공간 세계의 시뮬레이션은 미래 인간과 로봇이 함께하는 공존세계에 큰 역할을 할 수 있을 것으로 기대한다.