• 한국IT서비스학회지
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• 제11권sup호
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• pp.17-26
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• 2012

Information technology convergence has been recognized one of the key drivers in the industry perspective. Korea government established IT convergence policy in 2008 and has been implementing it to the core industry such as automotive, shipbuilding and defense industries. This research analyzes various IT convergence issues based on an operation of defense IT convergence center, one of the industry IT convergence centers. Defense IT convergence issues are as follows : the methods for introducing rapid changing IT to military area, rapid deployment procedures of verified commercial technologies and products, regulations for using of domestic software promotion and so on. We define the concept of defense IT convergence and propose the framework and processes for applying IT to our defense sector as one of industries. Also, we establish various business models in the military perspective using defense IT convergence framework. In this paper, we focus the development of defense IT convergence through the alignment of national IT convergence policy and propose various business models established through operating a defense IT convergence center.

• 마이크로전자및패키징학회지
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• 제27권4호
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• pp.39-45
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• 2020

The Cu/Cu2O/CuO photoelectrode has been successfully fabricated by Rapid Thermal Annealing technique. The structural characterization of fabricated photoelectrode was performed using X-Ray diffraction, while elemental composition of the prepared material has been checked with X-Ray Photoelectron Spectroscopy. The synthesis parameters are optimized on the basis of photoelectrochemical performance. The best photoelectrochemical performance has been observed for the Cu/Cu2O/CuO photoelectrode fabricated at 550 ℃ oxidation temperature and oxidation time of 50 seconds with highest photocurrent density of -3 mA/㎠ at -0.13 V vs. RHE.

• ALGAE
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• 제31권4호
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• pp.391-401
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• 2016

To develop an easy and rapid method of quantifying lipid contents of marine dinoflagellates, we quantified lipid contents of common dinoflagellate species using a colorimetric method based on the sulpho-phospho-vanillin reaction. In this method, the optical density measured using a spectrophotometer was significantly positively correlated with the known lipid content of a standard oil (Canola oil). When using this method, the lipid content of each of the dinoflagellates Alexandrium minutum, Prorocentrum micans, P. minimum, and Lingulodinium polyedrum was also significantly positively correlated with the optical density and equivalent intensity of color. Thus, when comparing the color intensity or the optical density of a sample of a microalgal species with known color intensities or optical density, the lipid content of the target species could be rapidly quantified. Furthermore, the results of the sensitivity tests showed that only $1-3{\times}10^5cells$ of P. minimum and A. minutum, $10^4cells$ of P. micans, and $10^3cells$ of L. polyedrum (approximately 1-5 mL of dense cultures) were needed to determine the lipid content per cell. When the lipid content per cell of 9 dinoflagellates, a diatom, and a chlorophyte was analyzed using this method, the lipid content per cell of these microalgae, with the exception of the diatom, were significantly positively correlated with cell size, however, volume specific lipid content per cell was negatively correlated with cell size. Thus, this sulpho-phospho-vanillin method is an easy and rapid method of quantifying the lipid content of autotrophic, mixotrophic, and heterotrophic dinoflagellate species.

• 융합보안논문지
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• 제9권2호
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• pp.59-69
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• 2009

본 논문에서 RAPID를 이용한 통합개발환경을 위하여 가상 프로토타이핑 기반의 통합개발환경 연동 인터페이스를 설계 및 구현하였다. 연동 인터페이스를 통하여 서로 다른 가상 및 실물 임베디드시스템 프로토타입들간의 통합이 가능함을 제시하였다. 특히 구현 결과의 시험을 통하여 연동 인터페이스의 우수함을 입증하였다.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2009년도 춘계학술대회
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• pp.219-222
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• 2009

Rapid object recognition is one of the main stream research themes focusing to reveal how human recognizes object and interacts with environment in natural world. This field of study is of consequence in that it is highly important in evolutionary perspective to quickly see the external objects and judge their characteristics to plan future reactions. In this study, we investigated how human detect natural scene objects and categorize them in a limited time frame. We applied Magnetoencepahlogram (MEG) while participants were performing detection (e.g. object vs. texture) or basic-level categorization (e.g. cars vs. dogs) tasks to track the dynamic interaction in human brain for rapid object recognition process. The results revealed that detection and categorization involves different temporal and functional connections that correlated for the successful recognition process as a whole. These results imply that dynamics in the brain are important for our interaction with environment. The implication from this study can be further extended to investigate the effect of subconscious emotional factors on the dynamics of brain interactions during the rapid recognition process.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.805-807
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• 2021

In-hospital cardiac arrest is a significant problem for medical systems. Although the traditional early warning systems have been widely applied, they still contain many drawbacks, such as the high false warning rate and low sensitivity. This paper proposed a strategy that involves a deep learning approach based on a novel interpretable deep tabular data learning architecture, named TabNet, for the Rapid Response System. This study has been processed and validated on a dataset collected from two hospitals of Chonnam National University, Korea, in over 10 years. The learning metrics used for the experiment are the area under the receiver operating characteristic curve score (AUROC) and the area under the precision-recall curve score (AUPRC). The experiment on a large real-time dataset shows that our method improves compared to other machine learning-based approaches.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 추계 학술대회 학술발표 논문집
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• pp.95-98
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• 2003

In this paper, an adaptive fuzzy neural control of unknown nonlinear systems based on the rapid learning algorithm is proposed for optimal parameterization. We combine the advantages of fuzzy control and neural network techniques to develop an adaptive fuzzy control system for updating nonlinear parameters of controller. The Fuzzy Neural Network(FNN), which is constructed by an equivalent four-layer connectionist network, is able to learn to control a process by updating the membership functions. The free parameters of the AFN controller are adjusted on-line according to the control law and adaptive law for the purpose of controlling the plant track a given trajectory and it's initial values are off-line preprocessing, In order to improve the convergence of the learning process, we propose a rapid learning algorithm which combines the error back-propagation algorithm with Aitken's $\delta$$\^$2/ algorithm. The heart of this approach ls to reduce the computational burden during the FNN learning process and to improve convergence speed. The simulation results for nonlinear plant demonstrate the control effectiveness of the proposed system for optimal parameterization.

• Rapid Communication in Photoscience
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• 제1권2호
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• pp.52-52
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• 2012

• 한국정밀공학회지
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• 제29권1호
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• pp.33-40
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• 2012

The scaffold serves as 3D substrate for the cells adhesion and mechanical support for the newly grown tissue by maintaining the 3D structure for the regeneration of tissue and organ. In this paper, we proposed integrated scaffold fabrication system using multi-axis rapid prototyping (RP) technology. It can fabricate various types of scaffolds: arbitrary sculptured shape, primitive shape, and tube shape scaffolds by layered dispensing biocompatible/ biodegradable polymer strands in designated patterns. In order to fabricate the 3D scaffold, we need to generate the plotting path way for the scaffold fabrication system. We design a data processing program - scaffold plotting software, which can convert the 3D STL file, primitive and tube model images into the NC code for the system. Finally, we fabricated the customized 3D scaffolds with high accuracy using the plotting software and the fabrication system.

• 대한전기학회논문지
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• 제39권8호
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• pp.856-867
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• 1990

In this paper, the convergence and the learning accuracy of the back-propagation (BP) method in neural network are investigated by 1) analyzing the reason for decelerating the convergence of BP method and examining the rapid deceleration of the convergence when the learning is executed on the part of sigmoid activation function with the very small first derivative and 2) proposing the modified logistic activation function by defining, the convergence factor based on the analysis. Learning on the output patterns of binary as well as analog forms are tested by the proposed method. In binary output patter, the test results show that the convergence is accelerated and the learning accuracy is improved, and the weights and thresholds are converged so that the stability of neural network can be enhanced. In analog output patter, the results show that with extensive initial transient phenomena the learning error is decreased according to the convergence factor, subsequently the learning accuracy is enhanced.