• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권4호
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• pp.242-248
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• 2007

This paper analyzes on the chaos characteristics of the chaotic neural networks and presents the convergence condition. Although the transient chaos of neural network sould be beneficial to overcome the local minimum problem and speed up the learning, the permanent chaotic response gives adverse effect on optimization problems and makes neural network unstable in general. This paper investigates the dynamic characteristics of the chaotic neural networks with the chaotic dynamic neuron, and presents the convergence condition for stabilizing the chaotic neural networks.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.278-283
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• 2005

Cell cycle is regulated cooperatively by several genes. The dynamic regulatory mechanism of protein interaction network of cell cycle will be presented taking the budding yeast as a sample system. Based on the mathematical model developed by Chen et at. (MBC, 11,369), at first, the dynamic role of the feedback loops is investigated. Secondly, using a bifurcation diagram, dynamic analysis of the cell cycle regulation is illustrated. The bifurcation diagram is a kind of ‘dynamic road map’ with stable and unstable solutions. On the map, a stable solution denotes a ‘road’ attracting the state and an unstable solution ‘a repelling road’ The ‘START’ transition, the initiation of the cell cycle, occurs at the point where the dynamic road changes from a fixed point to an oscillatory solution. The 'FINISH' transition, the completion of a cell cycle, is returning back to the initial state. The bifurcation analysis for the mutants could be used uncovering the role of proteins in the cell cycle regulation network.

• 한국정보과학회논문지:소프트웨어및응용
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• 제36권11호
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• pp.955-959
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• 2009

본 논문에서는 휴먼 객체들의 이동 궤적 정보를 기반으로 휴먼 상호 행동을 인식하기 위한 새로운 모델을 제안한다. 복잡한 휴먼 상호 행동들은 의미있는 작은 단위로 분할될 수 있는데 이를 '부-상호행동'이라 하며, 이들을 표현하는 모델들의 순차적 연결 또는 네트워크로 상호 행동을 모델링한다. 제안하는 모델은 서로 다른 상호 행동들에 공통적으로 나타나는 부-상호 행동들을 공유하도록 함으로써 모델의 복잡도를 낮추어 매우 효율적이다. 상호 행동 네트워크 모델의 동작 분석 및 기존 방법과의 비교 실험을 통해 제안한 방법의 우수성을 확인할 수 있었다.

• 제어로봇시스템학회논문지
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• 제5권2호
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• pp.174-179
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• 1999

In this paper, we propose an optimal algorithm for finding the shortest connection of two kinds of parts to pairs. If total part numbers are of size N, then there are order 2ㆍ(N/2)$^{N}$ possible solutions, of which we want the one that minimizes the energy function. The appropriate dynamic rule and parameters used in network are proposed by a new energy function which is minimized when 3-constraints are satisfied. This dynamic nile has three important parameters, an enhancement variable connected to pairs, a normalized distance term and a time variable. The enhancement variable connected to pairs have to a perfect connection of two kinds of parts to pairs. The normalized distance term get rids of a unstable states caused by the change of total part numbers. And the time variable removes the un-optimal connection in the case of distance constraint and the wrong or not connection of two kinds of parts to pairs. First of all, we review the theoretical basis for Hopfield model and present a new energy function. Then, the connection matrix and the offset bias created by a new energy function and used in dynamic nile are shown. Finally, we show examples through computer simulation with 20, 30 and 40 parts and discuss the stability and feasibility of the resultant solutions for the proposed connection algorithm.m.

• Journal of Communications and Networks
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• 제17권4호
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• pp.406-418
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• 2015

Cognitive radio networks (CRNs) have emerged as a promising solution to the problem of spectrum under utilization and artificial radio spectrum scarcity. The paradigm of dynamic spectrum access allows a secondary network comprising of secondary users (SUs) to coexist with a primary network comprising of licensed primary users (PUs) subject to the condition that SUs do not cause any interference to the primary network. Since it is necessary for SUs to avoid any interference to the primary network, PU activity precludes attempts of SUs to access the licensed spectrum and forces frequent channel switching for SUs. This dynamic nature of CRNs, coupled with the possibility that an SU may not share a common channel with all its neighbors, makes the task of multicast routing especially challenging. In this work, we have proposed a novel multipath on-demand multicast routing protocol for CRNs. The approach of multipath routing, although commonly used in unicast routing, has not been explored for multicasting earlier. Motivated by the fact that CRNs have highly dynamic conditions, whose parameters are often unknown, the multicast routing problem is modeled in the reinforcement learning based framework of learning automata. Simulation results demonstrate that the approach of multipath multicasting is feasible, with our proposed protocol showing a superior performance to a baseline state-of-the-art CRN multicasting protocol.

• 한국지반공학회논문집
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• 제18권5호
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• pp.123-132
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• 2002

본 연구에서는 인간의 사고과정을 근거로 개발된 동적 인공신경망을 이용하여 이암풍화토의 전단거동을 예측하였다. 흙의 비선형거동을 예측함에 있어 피드백 과정에 의해 시간경과에 따른 패턴의 특성변화를 연속적으로 예측할 수 있는 동적신경망의 종류인 SNN모델과 RNN모델을 이용하였다. 인공신경망의 학습능력과 예측능력에 영향을 미치는 여러 변수등을 분석후 SNN모델에서는 학습율, 모멘텀 상수, 신경망구조가 0.5, 0.7, 8$\times$18$\times$2, RNN모델인 경우는 각각 0.3,0.9,8$\times$24$\times$2의 구조가 적합한 것으로 나타났다 예측결과는 두 네트워크 모두 정규압밀 상태의 전단거동을 잘 예측하였지만, 과압밀 상태의 전단거동 예측에서는 불규칙적인 입력패턴에 효과적인 RNN모델의 예측능력이 더욱 우수하였다.

• International Journal of Computer Science & Network Security
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• 제21권12호
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• pp.1-8
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• 2021

In Mobile Ad-Hoc Network (MANET) Application, routing protocol is essential to ensure successful data transmission to all nodes. Ad-hoc On-demand Distance Vector (AODV) Protocol is a reactive routing protocol that is mostly used in MANET applications. However, the protocol causes Route Request (RREQ) message flooding issue due to the broadcasting method at the route request stage to find a path to a particular destination, where the RREQ will be rebroadcast if no Request Response (RREP) message is received. A scalable neighbor-based routing (SNBR) protocol was then proposed to overcome the issue. In the SNBR protocol, the RREQ message is only rebroadcast if the number of neighbor nodes less than a certain fix number, known as drop factor. However, since a network always have a dynamic characteristic with a dynamic number of neighbor nodes, the fix drop factor in SNBR protocol could not provide an optimal flooding problem solution in a low dense network environment, where the RREQ message is continuously rebroadcast RREQ message until reach the fix drop factor. To overcome this problem, a new broadcasting method as Dynamic SNBR (DSNBR) is proposed, where the drop factor is determined based on current number of neighbor nodes. This method rebroadcast the extra RREQ messages based on the determined dynamic drop factor. The performance of the proposed DSNBR is evaluated using NS2 and compared with the performance of the existing protocol; AODV and SNBR. Simulation results show that the new routing protocol reduces the routing request overhead, energy consumption, MAC Collision and enhances end-to-end delay, network coverage ratio as a result of reducing the extra route request messages.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권5호
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• pp.949-966
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• 2013

Recently, Wireless Body Area Network (WBAN) has promise to revolutionize human daily life. The need for multiple sensors and constant monitoring lead these systems to be energy hungry and expensive with short operating lifetimes. In this paper, we offer a review of existing work of WBAN and focus on energy-aware management in it. We emphasize that nodes computation, wireless communication, topology deployment and energy scavenging are main domains for making a long-lived WBAN. We study the popular power management technique Dynamic Voltage and Frequency Scaling (DVFS) and identify the impact of slack time in Dynamic Power Management (DPM), and finally propose an enhanced dynamic power management method to schedule scaled jobs at slack time with the goal of saving energy and keeping system reliability. Theoretical and experimental evaluations exhibit the effectiveness and efficiency of the proposed method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.349-351
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• 2004

The classical dynamic backpropagation learning algorithm has the problems of learning speed and the determine of learning parameter. The Extend Kalman Filter(EKF) is used effectively for a state estimation method for a non linear dynamic system. This paper presents a learning algorithm using Dual Extended Kalman Filter(DEKF) for Fully Recurrent Neural Network(FRNN). This DEKF learning algorithm gives the minimum variance estimate of the weights and the hidden outputs. The proposed DEKF learning algorithm is applied to the system identification of a nonlinear SISO system and compared with dynamic backpropagation learning algorithm.

• Journal of Welding and Joining
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• 제18권3호
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• pp.114-121
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• 2000

Since resistance spot welding has become one of the most popular sheet metal fabrication processes, a strong emphasis is being put on the quality of the welds. Throughout the years many quality estimation systems have been developed by many researchers to ensure weld quality. In this study, the process variables, which were monitored in the primary circuit of the welding machine, are used to estimate the weld quality with Hopfield neural network. The primary dynamic resistance is vectorized and stored as five patterns in the network. As the welding is done, the dynamic resistance patterns are recognized and the quality is estimated with the proposed method. Due to the primary process variables, it is possible to utilize this algorithms as an in-process real time quality monitoring system.