• Journal of Korea Multimedia Society
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• v.8 no.1
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• pp.119-127
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• 2005

Each university in Korea has connected its campus network to outer network with a variety methods since every year KREN adopted an open bid in selecting a network service company. In particular many universities connected two or more outer networks have faced more complex decision problems about their network operations due to the intricacy of the networks. So, those university needs the system which helps the managers to select the optimum operating method for their campus networks. In this paper, campus network traffic efficiency based on utilization was analyzed using the manager's answers to the questions for traffic management. And, by the result of analysis, the link strategy to outer networks was suggested for the universities running simultaneously two outer networks.

• Journal of Communications and Networks
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• v.3 no.4
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• pp.317-323
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• 2001

Traditional configuration management involves complex labor-intensive processes performed by experts. The configuration tasks such as installing or reconfiguring a system, provisioning network services and allocating resources typically involve a large number of activities involving multiple network elements. The network elements may be associated with proprietary configuration management instrumentation and may also be spread across heterogeneous network domains thereby increasing the complexity of configuration management. This paper introduces an architecture for the self-configuration of networks (SELFCON). The proposed architecture involves a directory server, which is uses to maintain configuration information. The configuration information stared in the directory server is modeled using the standard DEN specification thereby allowing effective exchange of network, system and configuration management data among heterogeneous management domains. SELFCON associates configuration intelligence with the components of the network, rather than limit it to a centralized management station. The network elements are notified about related changes in configuration policies, based upon which, they perform self-configuration. SELFCON is able to provide automation of configuration management and also an effective unifying framework for enterprise management.

• Water for future
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• v.27 no.2
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• pp.139-146
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• 1994

Simplified sewer networks have been used to simulate runoff hydrographs for urban watersheds since configurations of sewer networks in urban area are commonly so complex that it is too cumbersome to simulate them as what they are. If they were to be simulated without any simplification, it is not likely that satisfactory results are obtained due to accumulation of numerous little errors. Even for the well-known models widely used in everyday practicesit is not appropriate to simulate everything in the watershed as what they are. In resolving these problems, it is common practice to simplify network configurations so as to be fitted to the models for runoff hydrograph simulation. In case of netwrok simplication, hydraulic and hydrologic characteristics of the watersheds should be carefully taken into consideration to derive meaningful results. On the bases of these considerations, this study analyzes simulation outputs using simplified networks and compares them, as well as inestigates the methods to make hydraulically sound simplification of sewer networks.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2699-2701
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• 2001

The study is concerned with an approach to the design of a new category of fuzzy neural networks. The proposed Fuzzy Polynomial Neural Networks(FPNN) with hybrid multi-layer inference architecture is based on fuzzy neural networks(FNN) and polynomial neural networks(PNN) for model identification of complex and nonlinear systems. The one and the other are considered as premise and consequence part of FPNN respectively. We introduce two kinds of FPNN architectures, namely the generic and advanced types depending on the connection points (nodes) of the layer of FNN. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process and to get output performance with superb predictive ability. The availability and feasibility of the FPNN is discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed FPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.216-239
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• 2021

The present work addresses the challenging problem of coordinating power allocation with interference management in multi-robot networks by applying the promising expansion capabilities of multiple-input multiple-output (MIMO) and full duplex systems, which achieves it for maximizing the throughput of networks under the impacts of Doppler frequency shifts and external jamming. The proposed power allocation with interference coordination formulation accounts for three types of the interference, including cross-tier, co-tier, and mixed-tier interference signals with cluster head nodes operating in different full-duplex modes, and their signal-to-noise-ratios are respectively derived under the impacts of Doppler frequency shifts and external jamming. In addition, various optimization algorithms, including two centralized iterative optimization algorithms and three decentralized optimization algorithms, are applied for solving the complex and non-convex combinatorial optimization problem associated with the power allocation and interference coordination. Simulation results demonstrate that the overall network throughput increases gradually to some degree with increasing numbers of MIMO antennas. In addition, increasing the number of clusters to a certain extent increases the overall network throughput, although internal interference becomes a severe problem for further increases in the number of clusters. Accordingly, applications of multi-robot networks require that a balance should be preserved between robot deployment density and communication capacity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.334-349
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• 2022

The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters. Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consistent performance for the increasing number of stations. Yet, it is still the default when accessing channels for single-users of 802.11 transmissions. Recently, there has been a spike in attempts to enhance network performance using a machine learning (ML) technique known as reinforcement learning (RL). Its advantage is interacting with the surrounding environment and making decisions based on its own experience. Deep RL (DRL) uses deep neural networks (DNN) to deal with more complex environments (such as continuous state spaces or actions spaces) and to get optimum rewards. As a result, we present a new approach of CW control mechanism, which is termed as contention window threshold (CW_Threshold). It uses the DRL principle to define the threshold value and learn optimal settings under various network scenarios. We demonstrate our proposed method, known as a smart exponential-threshold-linear backoff algorithm with a deep Q-learning network (SETL-DQN). The simulation results show that our proposed SETL-DQN algorithm can effectively improve the throughput and reduce the collision rates.

• Journal of the Korean Society of Safety
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• v.39 no.1
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• pp.70-81
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• 2024

With heavy rainfall due to extreme weather causing increasing damage, the importance of urban flood forecasting continues to grow. To forecast urban flooding accurately and promptly, a sewer network and surface grid with appropriate detail are necessary. However, for urban areas with complex storm sewer networks and terrain structures, high-resolution grids and detailed networks can significantly prolong the analysis. Therefore, determining an appropriate level of network simplification and a suitable surface grid resolution is essential to secure the golden time for urban flood forecasting. In this study, InfoWorks ICM, a software program capable of 1D-2D coupled simulation, was used to examine urban flood forecasting performance for storm sewer networks with various levels of simplification and different surface grid resolutions. The inundation depth, inundation area, and simulation time were analyzed for each simplification level. Based on the analysis, the simulation time was reduced by up to 65% upon simplifying the storm sewer networks and by up to 96% depending on the surface grid resolution; further, the inundation area was overestimated as the grid resolution increased. This study provides insights into optimizing the simplification level and surface grid resolution for storm sewer networks to ensure efficient and accurate urban flood forecasting.

• Journal of the Korean Society for information Management
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• v.31 no.3
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• pp.153-179
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• 2014

This study explores the characteristics of centrality measures for analyzing researchers' impact and structural positions in research collaboration networks. We investigate four binary network centrality measures (degree centrality, closeness centrality, betweenness centrality, and PageRank), and seven existing weighted network centrality measures (triangle betweenness centrality, mean association, weighted PageRank, collaboration h-index, collaboration hs-index, complex degree centrality, and c-index) for research collaboration networks. And we propose SSR, which is a new weighted centrality measure for collaboration networks. Using research collaboration data from three different research domains including architecture, library and information science, and marketing, the above twelve centrality measures are calculated and compared each other. Results indicate that the weighted network centrality measures are needed to consider collaboration strength as well as collaboration range in research collaboration networks. We also recommend that when considering both collaboration strength and range, it is appropriate to apply triangle betweenness centrality and SSR to investigate global centrality and local centrality in collaboration networks.

• Journal of Contemporary Eastern Asia
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• v.17 no.2
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• pp.176-198
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• 2018

Environmental issues are among the most critical issues nowadays. These issues are no longer confined to individual countries, and international society has been progressing in building global dialogues since the early 1970s. Within these international efforts, Germany and Japan have played essential roles in global environmental governance. However, there are major differences in nation-level environmental policies in both countries. Governance based on network structure is more efficient than that based on hierarchy for solving complex problems. The network structure is formed through horizontal cooperation among various autonomous actors, and the relationship intensity among actors is one of the key concepts in the governance. Using social network analysis as a framework to explain complicated societal structures explains how interaction among actors creates networks, and these networks further affect their interactions. The purpose of this study is to investigate the structure of environmental policy governance as collaborative governance in Germany and Japan. To address this goal, this paper analyzes the relationship between the informational dimension of governance networks and its complement resource-sharing networks in both countries. The results show that the information-sharing networks have lower-level network influence on the resource-sharing networks as higher-level networks even if not all of the information factors have singular influences. The results suggest that the information-sharing networks may be one of the pieces of the puzzle for explaining this phenomenon in environmental governance in Germany and Japan.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.119-125
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• 2015

In this paper, we introduce a Hadamard matrix interstream transmission based blind channel estimation for multi-cells multiple-input and multiple-output (MIMO) networks. The proposed scheme is based on a network with mobile stations (MS) which are deployed with multi cells. We assume that the MS have the signals from both cells. The signal from near cell are considered as desired signal and the signals from the other cells are interference signal. Since the channel is blind, so that we transmit Hadamard matrix pattern pilot stream to estimate the channel; that gives easier and fast channel estimation for large scale MIMO channel. The computation of Hadamard based system takes only complex additions, and thus the complexity of which is much lower than the scheme with Fourier transform since complex multiplications are not needed. The numerical analysis will give perfection of proposed channel estimation.