• 한국항공우주학회지
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• 제31권4호
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• pp.35-43
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• 2003

본 논문에서는 새로 제안된 절점 활성화 기법을 실제 구현하기 위한 효율적 계산법을 소개하고 각종 수치실험을 수행한다. 포아송 방정식, 2차원 탄성문제, 3차원 탄성문제에 대하여 다양하게 수행된 수치실험을 통하여 절점활성화 이론의 타당성, 수렴성, 및 효율성을 고찰한다. 수렴성, 패치 테스트 등이 포함된 각종 수치실험 결과로부터 절점활성화 기법을 이용하면 정확도의 큰 손실 없이도 많은 수의 유한요소 절점 중 관심이 있는 일부 절점만을 선택, 활성화시켜 이들만을 미지수로 이용하여 효율적으로 문제를 해석할 수 있음을 입증한다.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2005년도 춘계 종합학술대회 논문집
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• pp.475-479
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• 2005

본 연구에서는 객체 지향 소스 코드의 검색과 재사용을 효율적으로 수행할 수 있는 의미망을 구축하였다. 이를 위하여 각 노드 간 객체지향 상속의 개념을 표현할 수 있도록 의미망의 초기 관련값을 시소러스로 구축하였다. 또한, 의미망의 노드와 간선을 활성화시키고 활성값을 전파 시키기 위해 사용되는 spreading activation 방법의 단점을 보완하여 spreading activation의 성능은 최대한 유지하면서 검색 속도를 향상 시킬 수 있는 방법을 제안하였다.

• 한국콘텐츠학회논문지
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• 제6권3호
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• pp.20-27
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• 2006

본 연구는 객체 지향 소스 코드의 검색과 재사용을 효율적으로 수행할 수 있는 의미망을 구축하였다. 이를 위하여 각 노드 간 객체지향 상속의 개념을 표현할 수 있도록 의미망의 초기 관련값을 시소러스로 구축하였다. 또한, 의미망의 노드와 간선을 활성화시키고 활성값을 전파시키기 위해 사용되는 스프레딩 엑티베이션 방법의 단점을 보완하여 스프레딩 엑티베이션의 성능은 최대한 유지하면서 검색 속도를 향상 시킬 수 있는 방법을 제안하였다.

• 한국항공우주학회지
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• 제31권4호
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• pp.26-34
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• 2003

본 논문에서는 이동최소자승 근사법 등의 무요소 근사법을 이용하여 유한요소모델 절점의 연결성과 무관하게 유한요소 절점을 자유로이 활성화시킬 수 있는 절점활성화 기법을 제안하고, 제안된 방법의 타당성을 고찰하기 위해 일관성 조건, 수치해의 유계성 등에 대한 이론적 고찰을 수행한다. 제안된 절점활성화 기법을 이용하면 많은 수의 유한요소 절점 중 관심이 있는 일부 절점만을 선택, 활성화시켜 이들만을 미지수로 이용하여 문제를 해석할 수 있기 때문에 설계 및 재해석을 효율적으로 수행할 수 있다.

• The Korean Journal of Physiology and Pharmacology
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• 제1권6호
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• pp.731-739
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• 1997

The aim of present study is to investigate the effects of cGMP on hyperpolarization activated inward current ($I_f$), pacemaker current of the heart, in rabbit sino-atrial node cells using the whole-cell patch clamp technique. When sodium nitroprusside (SNP, $80{\mu}M$), which is known to activate guanylyl cyclase, was added, $I_f$ amplitude was increased and its activation was accelerated. However, when $I_f$ was prestimulated by isopreterenol (ISO, $1{\mu}M$), SNP reversed the effect of ISO. In the absence of ISO, SNP shifted activation curve rightward. On the contrary in the presence of ISO, SNP shifted activation curve in opposite direction. $8Br-cGMP(100\;{\mu}M)$, more potent PKG activator and worse PDE activator than cGMP, also increased basal $I_f$ but did not reverse stimulatory effect of ISO. It was probable that PKG activation seemed to be involved in SNP-induced basal $I_f$ increase. The fact that SNP inhibited ISO-stimulated $I_f$ suggested cGMP antagonize cAMP action via the activation of PDE. This possibility was supported by experiment using 3-isobutyl-1-methylxanthine (IBMX), non-specific PDE inhibitor. SNP did not affect $I_f$ when $I_f$ was stimulated by $20{\mu}M$ IBMX. Therefore, cGMP reversed the stimulatory effect of cAMP via cAMP breakdown by activating cGMP-stimulated PDE. These results suggest that PKG and PDE are involved in the modulation of $I_f$ by cGMP: PKG may facilitate $I_f$ and cGMP-stimulated PDE can counteract the stimulatory action of cAMP.

• 한국BIM학회 논문집
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• 제12권2호
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• pp.40-48
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• 2022

It is very important to accurately predict construction costs in the early stages of the construction project. However, it is difficult to accurately predict construction costs with limited information from the initial stage. In recent years, with the development of machine learning technology, it has become possible to predict construction costs more accurately than before only with schematic construction characteristics. Based on machine learning technology, this study aims to analyze plans to more accurately predict construction costs by using only the factors influencing construction costs. To the end of this study, the effect of the error rate according to the activation function and the node configuration of the hidden layer was analyzed.

• Journal of Communications and Networks
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• 제15권1호
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• pp.45-53
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• 2013

Multi-hop cellular networks (MCNs), which reduce the transmit power, mitigate the inter-cell interference, and improve the system performance, have been widely studied nowadays. The relay selection scheme is a key technique that achieves these advantages, and inappropriate relay selection causes frequent relay switchings, which deteriorates the overall performance. In this study, we analyze the conditions for relay switching in MCNs and obtain the expressions for the relay switching rate and relay activation time. Two mobile-based relay selection schemes are proposed on the basis of this analysis. These schemes select the relay node with the longest relay activation time and minimal relay switching rate through mobility prediction of the mobile node requiring relay and available relay nodes. We compare the system performances via simulation and analyze the impact of various parameters on the system performance. The results show that the two proposed schemes can obtain a lower relay switching rate and longer relay activation time when there is no reduction in the system throughput as compared with the existing schemes.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2946-2948
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• 2000

In this paper, we proposed the Fuzzy Polynomial Neural Networks(FPNN) model with fuzzy activation node. The proposed FPNN structure is generated from the mutual combination of PNN(Polynomial Neural Networks) structure and fuzzy inference system. The premise of fuzzy inference rules defines by triangular and gaussian type membership function. The fuzzy inference method uses simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used. The structure of FPNN is not fixed like in conventional Neural Networks and can be generated. The design procedure to obtain an optimal model structure utilizing FPNN algorithm is shown in each stage. Gas furnace time series data used to evaluate the performance of our proposed model.

• Molecules and Cells
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• 제39권7호
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• pp.515-523
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• 2016

Long-term immunity to many viral and bacterial pathogens requires$ CD8^+$ memory T cell development, and the induction of long-lasting$ CD8^+$ memory T cells from a $na{\ddot{i}}ve$, undifferentiated state is a major goal of vaccine design. Formation of the memory$ CD8^+$ T cell compartment is highly dependent on the early activation cues received by $na{\ddot{i}ve}$ $CD8^+$ T cells during primary infection. This review aims to highlight the cellularity of various niches within the lymph node and emphasize recent evidence suggesting that distinct types of T cell activation and differentiation occur within different immune contexts in lymphoid organs.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권3호
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• pp.135-144
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• 2004

In this paper, we propose competitive fuzzy polynomial neurons-based advanced Self-Organizing Neural Networks(SONN) architecture for optimal model identification and discuss a comprehensive design methodology supporting its development. The proposed SONN dwells on the ideas of fuzzy rule-based computing and neural networks. And it consists of layers with activation nodes based on fuzzy inference rules and regression polynomial. Each activation node is presented as Fuzzy Polynomial Neuron(FPN) which includes either the simplified or regression polynomial fuzzy inference rules. As the form of the conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership (unction are studied and the number of the premise input variables used in the rules depends on that of the inputs of its node in each layer. We introduce two kinds of SONN architectures, that is, the basic and modified one with both the generic and the advanced type. Here the basic and modified architecture depend on the number of input variables and the order of polynomial in each layer. The number of the layers and the nodes in each layer of the SONN are not predetermined, unlike in the case of the popular multi-layer perceptron structure, but these are generated in a dynamic way. The superiority and effectiveness of the Proposed SONN architecture is demonstrated through two representative numerical examples.