• 한국수학교육학회지시리즈A:수학교육
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• 제53권1호
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• pp.25-40
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• 2014

Mathematical concept exists in the structural form, not in the independent form. The purpose of this study is to consider the network which students actually have for the mathematical concept structure related to the properties of derivatives. First, we analyzed the properties of derivatives in 'Mathematics II' and showed the mathematical concept structure of the relations among derivatives, functions, and primitive functions as a network. Also, we investigated the understanding of high school students for the mathematical concept structure between derivatives and functions, and the structure between functions and second order derivatives when the functional formula is not given, and only the graph is given. The results showed that students mainly focus on the relation of 'function-derivatives', the thinking process for direction of derivative and the thinking style for algebra. On this basis, we suggest the educational implication that is necessary for students to build the network properly.

• Design & Manufacturing
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• 제16권3호
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• pp.1-8
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• 2022

In this study, an artificial neural network(ANN) was constructed to establish the relationship between process condition prameters and the qualities of the injection-molded product in the injection molding process. Six process parmeters were set as input parameter for ANN: melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time. As output parameters, the mass, nominal diameter, and height of the injection-molded product were set. Two learning structures were applied to the ANN. The single-task learning, in which all output parameters are learned in correlation with each other, and the multi-task learning structure in which each output parameters is individually learned according to the characteristics, were constructed. As a result of constructing an artificial neural network with two learning structures and evaluating the prediction performance, it was confirmed that the predicted value of the ANN to which the multi-task learning structure was applied had a low RMSE compared with the single-task learning structure. In addition, when comparing the quality specifications of injection molded products with the prediction values of the ANN, it was confirmed that the ANN of the multi-task learning structure satisfies the quality specifications for all of the mass, diameter, and height.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.631-634
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• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, in the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.

• 한국비블리아학회지
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• 제23권2호
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• pp.109-132
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• 2012

본 연구는 저자 동시인용 데이터를 이용하여 커뮤니케이션 분야의 학자들간의 네트워크를 연구하였다. 저자 동시인용 분석이란 두 저자가 제 3의 다른 저자에 의해 동시에 인용되는 경우를 말하는데, 본 연구에서는 International Communication Association의 가장 대표적인 학술지인 Journal of Communication의 편집위원회를 그 대상으로 하였다. 저자동시인용 데이터는 좌우대칭의 매트릭스에 입력되고, 그 행렬에서 얻어지는 저자들의 위치도(network map)를 통해 각 저자들의 전문분야들이 위치도 안에서 어떻게 구분되는지, 또한 네트워크 상에서 어떤 저자들이 다른 저자들에 비해 중심적인 지 보여주는 데 이용된다. 기본적인 저자동시인용분석 외에도 두 매트릭스의 연관성을 비교하는 QAP 분석을 통해 어떠한 요인들이 커뮤니케이션 분야의 지식구조에 영향을 미치는 지 조사하였는데, 저자들의 교육적 배경이나 현재 소속된 기관보다는 각자의 전문분야가 더 많은 영향을 미치는 것으로 나타났다. 저자동시인용분석에 필요한 데이터는 Social Science Citation Index (SSCI) 데이터베이스를 통해 수집되었고, 저자들의 네트워크 지도는 UCInet이라는 프로그램을 이용하여 만들어졌다.

• Journal of information and communication convergence engineering
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• 제6권4호
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• pp.470-474
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• 2008

A well-designed computer network technology produces benefits on several fields within the organization, between the organizations(suborganizations) or among different organizations(suborganizations). Network technology streamlines business processes, decision process. Graphs are useful data structures capable of efficiently representing a variety of networks in the various fields. Metagraph is a like graph theoretic construct introduced recently by Basu and Blanning in which there is set to set mapping in place of node to node as in a conventional graph structure. Metagraph is thus a new type of data structure occupying its popularity among the computer scientists very fast. Every graph is special case of Metagraph. In this paper the authors introduce the notion of Meta-Networking as a new network technological representation, which is having all the capabilities of crisp network as well as few additional capabilities. It is expected that the notion of meta-networking will have huge applications in due course. This paper will play the role of introducing this new concept to the network technologists and scientists.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1716-1722
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes bacteria foraging algorithm based optimal learning fuzzy-neural network (BA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by bacteria foraging algorithm. The learning algorithm of the BA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, bacteria foraging algorithm is used for tuning of membership functions of the proposed model.

• 대한전기학회논문지
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• 제41권4호
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• pp.331-338
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• 1992

The application of Connectionist expert system using neural network to fault diagnosis of power system is presented and compared with rule-based expert system. Also, the merits of Connectionist model using neural network is presented. In this paper, the neural network for fault diagnosis is hierarchically composed by 3 neural network classes. The whole power system is divided into subsystems, the neural networks (Class II) which take charge of each subsystem and the neural network (Class III) which connects subsystems are composed. Every section of power system is classified into one of the typical sections which can be applied with same diagnosis rules, as line-section, bus-section, transformer-section. For each typical section, only one neural network (Class I) is composed. As the proposed model has hierarchical structure, the great reduction of learning structure is achieved. With parallel distributed processing, we show the possibility of on-line fault diagnosis.

• E2M - 전기 전자와 첨단 소재
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• 제10권7호
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• pp.651-658
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• 1997

In this paper, effect of interpenetrating polymer network(IPN) introduction on the dielectric properties, heat proof properties, internal structure and defects of the Epoxy/SiO$_2$composite materials, were investigated. we reported a relation between network structures and electrical properties, especially dielectric characteristics with variation of network structures for epoxy composite materials. According to experimental results, the specimens which have single network structures have lower dielectric constant than interpenetrating polymer network(IPN) specimens, but have relatively larger dependency to variation of temperature and frequency. It was confirmed that change of structures is attained by introducing of IPN to insulating materials. Therefore it is counted that introduction of multiple structure including IPN is necessary to improve heat proof and electrical properties.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2414-2416
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• 2003

In this raper, we present a Wavelet Neural Network(WNN) approach to the solution of the tracking problem for mobile robots that possess complexity, nonlinearity and uncertainty. The neural network is constructed by the wavelet orthogonal decomposition to form a wavelet neural network that can overcome the problems caused by local minima of optimization and various uncertainties. This network structure is helpful to determine the number of the hidden nodes and the initial value of weights with compact structure. In our control method, the control signals are directly obtained by minimizing the difference between the reference track and the pose of a mobile robot that is controlled through a wavelet neural network. The control process is a dynamic on-line process that uses the wavelet neural network trained by the gradient-descent method. Through computer simulations, we demonstrate the effectiveness and feasibility of the proposed control method.

• ETRI Journal
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• 제41권4호
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• pp.452-464
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• 2019

The use of wireless body area networks (WBANs) in healthcare applications has made it convenient to monitor both health personnel and patient status continuously in real time through wearable wireless sensor nodes. However, the heterogeneous and complex network structure of WBANs has some disadvantages in terms of control and management. The software-defined network (SDN) approach is a promising technology that defines a new design and management approach for network communications. In order to create more flexible and dynamic network structures in WBANs, this study uses the SDN approach. For this, a WBAN architecture based on the SDN approach with a new energy-aware routing algorithm for healthcare architecture is proposed. To develop a more flexible architecture, a controller that manages all HUBs is designed. The proposed architecture is modeled using the Riverbed Modeler software for performance analysis. The simulation results show that the SDN-based structure meets the service quality requirements and shows superior performance in terms of energy consumption, throughput, successful transmission rate, and delay parameters according to the traditional routing approach.