• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.29 no.12
• /
• pp.88-96
• /
• 2015

This paper describes a new method for enhancing the accuracy of network analysis applications in energy management systems. Topology processing, state estimation, power flow analysis, and contingency analysis play a key factor in the stable and reliable operation of power systems. In this respect, the aim of topology processing is to provide the electrical buses and the electrical islands with the actual state of the power system as input data. The results of topology processing is used to input of other applications. New method, which includes the topology error analysis based on inconsistency check, coherency check, bus mismatch check, and outaged device check is proposed to enhance the accuracy of network analysis. The proposed methodology is conducted by energy management systems and the Korean power systems have been utilized for the test systems.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2004.11a
• /
• pp.361-368
• /
• 2004

The purpose of the study was to explore the potential of the Social Network Analysis as an analytical tool for scientific investigation of learner-learner, or learner-tutor interaction within an Computer Supported Corporative Learning (CSCL) environment. Theoretical and methodological implication of the Social Network Analysis had been discussed. Following theoretical analysis, an exploratory empirical study was conducted to test statistical correlation between traditional performance measures such as achievement and team contribution index, and the centrality measure, one of the many quantitative measures the Social Network Analysis provides. Results indicate the centrality measure was correlated with the higher order learning performance and the peer-evaluated contribution indices. An interpretation of the results and their implication to instructional design theory and practices were provided along with some suggestions for future research.

• Proceedings of the KSME Conference
• /
• 2004.04a
• /
• pp.864-869
• /
• 2004

In this paper, a blackbox approach is carried out to model the nonlinear dynamic bushing model. One-axis durability test is performed to describe the mechanical behavior of typical vehicle elastomeric components. The results of the tests are used to develop an empirical bushing model with an artificial neural network. The back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra's algorithm of 'NARMAX' form is employed in the neural network bushing module. A numerical example is carried out to verify the developed bushing model.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.10
• /
• pp.181-188
• /
• 2018

In this paper, we tried to investigate whether the influence of 'I' in a heterogeneous network of physical network and virtual network can be quantitatively measurable. To do this, we used Networked Community(NC) methodology to devise a concrete model of influence assessment in heterogeneous network. In order to test the model, we conducted an experiment with Donald J. Trump and his surroundings to evaluate the effectiveness of this influence assessment model. Experimentation included the measurement of impacts on the physical and virtual networks, and the impact on the networked community. Using Trump's case, we found that analyzing only one of the two networks can not accurately analyze the impact on others.

• The Journal of Information Systems
• /
• v.14 no.3
• /
• pp.97-104
• /
• 2005

The paper proposes an implementation architecture of DiffServ-over-MPLS traffic engineering (TE) on Intel IXP2400 network processor using Intel IXA SDK 4.0 Framework. Program architecture and functions are described. Also fast and scalable range-match classification scheme is proposed for DiffServ-over-MPLS TE that has been integrated with functional blocks from Intel Microblocks library. Performance test shows that application can process packets at approximate data rate of 3.5 Gbps. The proposed implementation architecture of DiffServ-over-MPLS TE on Network processor can provide guaranteed QoS on high-speed next generation Internet, while being flexible and easily modifiable.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.54 no.9
• /
• pp.567-569
• /
• 2005

NARX(Nonlinear AutoRegressive with eXogenous input) neural network was used for prediction of nuclear reactor behavior which was influenced by control rods in short-term period and also by the concentration of xenon and boron in long-term period in load following operations. The developed model was designed to predict reactor power, xenon worth and axial offset with different burnup states when control rods and boron were adjusted in load following operations. Data of the Korea Next Generation PWR were collected by ONED94 code. The test results presented exhibit the capability of the NARX neural network model to capture the long term and short term dynamics of the reactor core and the developed model seems to be utilized as a handy tool for the use of a plant simulation.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2000.07a
• /
• pp.645-650
• /
• 2000

The discrete wavelet transform is utilized as processing of neural network(NN) to identifying aging state of internal partial discharge in transformer. The discrete wavelet transform is used to produce wavelet coefficients which are used for classification. The mean values of the wavelet coefficients are input into an back-propagation neural network. The networks, after training, can decide if the test signals is aging early state or aging last state, or normal state.

• Transactions on Electrical and Electronic Materials
• /
• v.7 no.4
• /
• pp.210-214
• /
• 2006

In this paper, two sampling methods which are Latin hypercube sampling (LHS) and simple random sampling were. compared to improve the modeling speed of neural network model. Sampling method was used to generate initial weights and bias set. Electrical characteristic data for $HfO_2$ thin film was used as modeling data. 10 initial parameter sets which are initial weights and bias sets were generated using LHS and simple random sampling, respectively. Modeling was performed with generated initial parameters and measured epoch number. The other network parameters were fixed. The iterative 20 minimum epoch numbers for LHS and simple random sampling were analyzed by nonparametric method because of their nonnormality.

• Computers and Concrete
• /
• v.11 no.3
• /
• pp.237-252
• /
• 2013

This paper presents the application of artificial neural network (ANN) to predict deep beam deflection using experimental data from eight high-strength-self-compacting-concrete (HSSCC) deep beams. The optimized network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of ten and four neurons in first and second hidden layers using TRAINLM training function predicted highly accurate and more precise load-deflection diagrams compared to classical linear regression (LR). The ANN's MSE values are 40 times smaller than the LR's. The test data R value from ANN is 0.9931; thus indicating a high confidence level.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.22 no.12
• /
• pp.2626-2634
• /
• 1997

The purpose of multicasting is to reduce the network costs for transmitting the same traffic to multiple destinations. In this paper, an efficient delay-bounded multicasting routing algorithm is proposed, which satistifies the network conditions of cost minimization and can adjust the dynamic events, such as 'leave and/or join ones' from the multicast group. Also, our algorithm is designed for various network requirements such as the efficiet dynamic group support, high-quality data distribution, and adaptability to variable situation. After the delay tolerance and the maximum group size are determined according to network state and requirements for delay and cost, the dynamic delay-bounded multicast tree is constructed using partial multicast routing. We evaluate the performance of the proposed algorithm by running simulations on randomly generated test networks using a Sun Sparc 20 workstation. We were able to obtain good simulation resutls, which means solutions that lies between the minimum cost solution and the minimum delay one.