• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.21-22
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• 2021

Early academic warning is considered as an inherent problem in education data mining. Early and timely concern and guidance can save a student's university career. It is widely assumed as a multi-class classification system in view of machine learning. Therefore, An accurate and precise methodical solution is a complicated task to accomplish. For this issue, we present a hybrid model employing rough set theory with a back-propagation neural network to ameliorate the predictive capability of the system with an illustrative example. The experimental results show that it is an effective early academic warning model with an escalating improvement in predictive accuracy.

• Journal of Information Technology Applications and Management
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• v.28 no.5
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• pp.61-70
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• 2021

With the proliferation of smartphones and 5G networks, mobile social network service (SNS) has become an indispensable part of people's daily lives. However, with the use of SNS, fatigue and withdrawal behavior gradually emerged. Based on The Transactional Theory of Stress and Coping (TTSC), we explored the mechanism of SNS overload on users' discontinuous intention under the framework of "stressor-strain-outcome". And we also investigated the moderating effects of gender in this process. We hope that through our research, we can help SNS users to reduce unnecessary fatigue, and provide better suggestions for platform designers to adjust product design to improve user experience.

• International journal of advanced smart convergence
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• v.2 no.1
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• pp.6-11
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• 2013

Network Coding (NC) is a new paradigm for network communication. In network coding, intermediate nodes create new packets by algebraically combining ingress packets and send it to its neighbor node by broadcast manner. NC has rapidly emerged as a major research area in information theory due to its wide applicability to communication through real networks. Network coding is expected to improve throughput and channel efficiency in the wireless multi-hop network. Many researches have been carried out to employ network coding to wireless ad-hoc network. In this paper, we proposed a dynamic queue management to improve coding opportunistic to enhance efficiency of NC. In our design, intermediate nodes are buffering incoming packets to encode queue. We expect that the proposed algorithm shall improve encoding rate of network coded packet and also reduce end to end latency. From the simulation, the proposed algorithm achieved better performance in terms of coding gain and packet delivery rate than static queue management scheme.

• The Journal of Economics, Marketing and Management
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• v.6 no.3
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• pp.10-13
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• 2018

Purpose - On the basis of discussing the network economy concept and the commentary of the marginal revenue decreasing of traditional economic theory, The concept of network economy has just been put forward in recent years. The reason why such a concept appears is that the information technology, marked by computer network, plays an increasingly important role in economic activities. Some people define network economy as an economic form based on network technology and human capital. this paper points out network economy existing the marginal revenue increasing and analyzes the reasons that influencing the marginal revenue increasing. Research design, data, methodology - The network economy has fundamentally changed the traditional economic laws. The economic basis of industrial society is the law of incremental marginal cost, which reflects the socialization of high cost in industrial society. Results - As the number of network members increases, the value of the network increases explosively, and the value increases attract more members to join, resulting in more returns. Conclusion - In conclusion, network economy has changed many aspects of traditional economy, resulting in decreasing marginal cost, decreasing transaction cost in and out of enterprise organizations, and making the effect of increasing scale compensation more prominent. This is of great significance to the information construction in China.

• The KIPS Transactions:PartC
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• v.17C no.1
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• pp.119-128
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• 2010

In the shipbuilding industry, due to the global trends where the number of IT (Information Technology) devices of a smart ship have been increased rapidly, the need to develop a new shipboard backbone network has recently emerged for integrating and managing the IT devices of a smart ship efficiently. A shipboard backbone network requires high survivability because it is constructed in automatic and unmanned smart ships where a failure of the backbone network can cause critical problems. The purpose of this paper thus is to study SAN (Ship Area Network) as a efficient shipboard backbone network, considering particularity of shipboard environment and requirement of high survivability. In order to do so, we designed a dual network topology that all network nodes, including the IT devices installed in a smart ship, are connected each other through dual paths, and reuding tht IT devices pnstalles supporices network survivability as well as t Iffic efficiency for the dual network topology. And then, we verified the performance of the suggested SAN by theoretical and practical analysis including the graph theory, the probability theory, implemental specifications, and computer simulations.

• Journal of Power Electronics
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• v.11 no.6
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• pp.922-930
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• 2011

In this paper, two radial basis function neural networks (RBFNNs) are used to dynamically identify harmonics content in converter waveforms based on the p-q (real power-imaginary power) theory. The converter waveforms are analyzed and the types of harmonic content are identified over a wide operating range. Constant power and sinusoidal current compensation strategies are investigated in this paper. The RBFNN filtering training algorithm is based on a systematic and computationally efficient training method called the hybrid learning method. In this new methodology, the RBFNN is combined with the p-q theory to extract the harmonics content in converter waveforms. The small size and the robustness of the resulting network models reflect the effectiveness of the algorithm. The analysis is verified using MATLAB simulations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.206-211
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• 2006

The quantification analysis problem is that how the m entities that have n characteristics can be linked to p-dimension space to reflect the similarity of each entity In this paper, the optimization approach for the quantification analysis problem using neural networks is suggested, and the performance is analyzed The computation of average variation volume by mean field theory that is analytical approximated mobility of a molecule system and the annealed mean field neural network approach are applied in this paper for solving the quantification analysis problem. As a result, the suggested approach by a mean field annealing neural network can obtain more optimal solution than the eigen value analysis approach in processing costs.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.443-450
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• 2017

Nowadays, Power Line Communication (PLC) is gaining high attention from industry and electric supply companies for the services like demand response, demand side management and Advanced Metering Infrastructure (AMI). The reliable services to consumers using PLC can be provided by utilizing an efficient PLC channel for which sophisticated channel modeling is very important. This paper presents characterization of a Low Voltage (LV) access network for Narrowband Power Line Communications (NB-PLC) using transmission line (TL) theory and a Simulink model. The TL theory analysis not only includes the constant parameters but frequency selectivity is also introduced in these parameters such as resistance, conductance and impedances. However, the proposed Simulink channel model offers an analysis and characterization of capacitive coupler, network impedance and channel transfer function for NB-PLC. Analysis of analytical and simulated results shows a close agreement of the channel transfer function. In the absence of a standardized NBPLC channel model, this research work can prove significant in improving the efficiency and accuracy of NB-PLC communication transceivers for Smart Grid communications.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.4
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• pp.581-587
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• 2011

A wireless sensor network needs to consist of multi-sensors in order to infer a high level of information on circumstances. Data fusion, in turn, is required to utilize the data collected from multi-sensors for the inference of information on circumstances. The current paper, based on Dempster-Shafter's evidence theory, proposes data fusion in a wireless sensor network with different weights assigned to different sensors. The frequency of events per sensor is the crucial element in calculating different weights of the data of circumstances that each sensor collects. Data fusion utilizing these different weights turns out to show remarkable difference in reliability, which makes it much easier to infer information on circumstances.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.09a
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• pp.23-27
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• 2001

Making the safety situation from the various work system is very important in the industrial fields. The proposed neural network technique is the real titre computation method based theory of inter-node diffusion for searching the safety distances from the sudden appearance-objests during the work driving. The main steps of the distance computation using the theory of stereo vision like the eyes of man is following steps. One is the processing for finding the corresponding points of stereo images and the other is the interpolation processing of full image data from nonlinear image data of obejects. All of them request much memory space and titre. Therefore the most reliable neural-network algorithm is drived for real time recognition of obejects, which is composed of a dynamic programming algorithm based on sequence matching techniques. And the real time reconstruction of nonlinear image information is processed through several simulations. I-D LIPN hardware has been composed, and the real time reconstruction is verified through the various experiments.