• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.31 no.4
• /
• pp.35-43
• /
• 2003

In this paper, an efficient computational algorithm for the implementation of the newly proposed node activation technique is presented, and its computational aspects are thoroughly investigated. To verify the validity, convergence, and efficiency of the node activation technique, various numerical examples are worked out including the problems of Poisson equation, 2D elasticity problems, and 3D elasticity problems. From the numerical tests, it is verified that one can arbitrarily activate and handle the nodal points of interest in finite element model with very little loss of the numerical accuracy.

• Proceedings of the Korea Contents Association Conference
• /
• 2005.05a
• /
• pp.475-479
• /
• 2005

In this paper we constructed semantic net that can efficiently conform retrieval and reuse of object-oriented source code. In odor that initial relevance of semantic net was constructed using thesaurus to represent concept of object-oriented inheritance between each node. Also we made up for the weak points in spreading activation method that use to activate node and line of semantic net and to impulse activation value. Therefore we proposed the method to enhance retrieval time and to keep the quality of spreading activation.

• The Journal of the Korea Contents Association
• /
• v.6 no.3
• /
• pp.20-27
• /
• 2006

In this paper we constructed semantic net that can efficiently conform retrieval and reuse of object-oriented source code. In order that initial relevance of semantic net was constructed using thesaurus to represent concept of object-oriented inheritance between each node. Also we made up for the weak points in spreading activation method that use to activate node and line of semantic net and to impulse activation value. Therefore we proposed the method to enhance retrieval time and to keep the quality of spreading activation.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.31 no.4
• /
• pp.26-34
• /
• 2003

In this paper, a novel technique is proposed to arbitrarily activate the nodal points in finite element model through the meshless approximation methods such as MLS(moving least squares method), and theoretical investigations are carried out including the consistency and boundeness of numerical solution to prove the validity of the proposed method. By using the proposed node activation technique, one can activate and handle only the concerned nodes as unknown variables among the large number of nodal points in the finite element model. Therefore, the proposed technique has a great potential in design and reanalysis procedure.

• The Korean Journal of Physiology and Pharmacology
• /
• v.1 no.6
• /
• pp.731-739
• /
• 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.

• Journal of KIBIM
• /
• v.12 no.2
• /
• pp.40-48
• /
• 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
• /
• v.15 no.1
• /
• pp.45-53
• /
• 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.

• Proceedings of the KIEE Conference
• /
• 2000.07d
• /
• pp.2946-2948
• /
• 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
• /
• v.39 no.7
• /
• pp.515-523
• /
• 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.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.3
• /
• pp.135-144
• /
• 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.