• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.379-394
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• 2012

The ever-increasing demand for H.264 scalable video coding (H.264/SVC) distribution motivates researchers to devise ways to enhance the quality of video delivered on the Internet. Furthermore, researchers and practitioners in general depend on computer simulators to analyze or evaluate their designed network architecture or proposed protocols. Therefore, a complete toolset, which is called myEvalSVC, for evaluating the delivered quality of H.264/SVC transmissions in a simulated environment is proposed to help the network and video coding research communities. The toolset is based on the H.264 Scalable Video coding streaming Evaluation Framework (SVEF) and extended to connect to the NS2 simulator. With this combination, people who work on video coding can simulate the effects of a more realistic network on video sequences resulting from their coding schemes, while people who work on network technology can evaluate the impact of real video streams on the proposed network architecture or protocols. To demonstrate the usefulness of the proposed new toolset, examples of H.264/SVC transmissions over 802.11 and 802.11e are provided.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.642-646
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• 2006

The fuzzy TAM(Topographical Attentive Mapping) network is a supervised method of pattern analysis which is composed of input layer, category layer, and output layer. But if we don't know the target value of the pattern, the network can not be trained. In this case, the target value can be replaced by a result induced by using an unsupervised neural network as the SOM (Self-organizing Map). In this paper, we apply the results of SOM to fuzzy TAM network and show its usefulness through the case study.

• Journal of the Korea Society of Computer and Information
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• v.6 no.4
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• pp.131-137
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• 2001

This paper discussed to the ATM traffic measurement and analysis tool for analyzing the ATM traffic properties. This tool was applied at the ATM commercial network. The analysis result is verified effectivity to improve network resource from 20％ to 50％. Thus, this tool usefully can be used to network plan for the network expansion and new network building. Also, it can be used to the demand estimation of the ATM network traffic.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.2
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• pp.61-72
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• 2007

With the increasing growth of Internet and wireless IP networks, Multimedia systems need to be envisaged as information resources where users can access anywhere and anytime. However, efficient services in these multimedia systems are open and challenging research problem due to user mobility, limited resources in wireless devices and expensive radio bandwidth. To implement multimedia services over heterogeneous network, the IP header compression scheme can be used for saving bandwidth. In this paper, we present an efficient solution for header compression, which is modified form of ECRTP. It shows an architectural framework adopting modified ECRTP when IP tunneling network using GRE over IPSec is implemented. We have conducted simulations in order to analyze the effects of different header compression techniques while delivering real-time services to the wireless access network through secured IP Network. The impacts on performance have been investigated through a series of experiments.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.133-141
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• 2022

This paper introduces model compression algorithms which make a deep neural network smaller and faster for embedded systems. The model compression algorithms can be largely categorized into pruning, quantization and knowledge distillation. In this study, gradual pruning, quantization aware training, and knowledge distillation which learns the activation boundary in the hidden layer of the teacher neural network are integrated. As a large deep neural network is compressed and accelerated by these algorithms, embedded computing boards can run the deep neural network much faster with less memory usage while preserving the reasonable accuracy. To evaluate the performance of the compressed neural networks, we evaluate the size, latency and accuracy of the deep neural network, DenseNet201, for image classification with CIFAR-10 dataset on the NVIDIA Jetson Xavier.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.123-134
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• 2022

Existing network cryptography systems are threatened by recent developments in quantum computing. For example, the Shor algorithm, which can be run on a quantum computer, is capable of overriding public key-based network cryptography systems in a short time. Therefore, research on new cryptography systems is actively being conducted. The most powerful cryptography systems are quantum key distribution (QKD) and post quantum cryptograph (PQC) systems; in this study, a network based on both QKD and PQC is proposed, along with a quantum key management system (QKMS) and a Q-controller to efficiently operate the network. The proposed quantum cryptography communication network uses QKD as its backbone, and replaces QKD with PQC at the user end to overcome the shortcomings of QKD. This paper presents the functional requirements of QKMS and Q-Controller, which can be utilized to perform efficient network resource management.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.2015-2020
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• 2008

Real-time communication network is important for KTX high speed train system because small problem can make a huge accident. Communication network for KTX high speed train system consists of IEEE 802.4 token bus network and FM0 encoding. The network device is developed by using MC68824 TBC, MC68185 TPM and MC68020 MPU. The network device make available to analysis of the network protocol among vehicles for Kyung-Boo high-speed train system.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.2
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• pp.9-18
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• 1997

Abstract This paper presents a heuristic method for optimal design of water distribution system with multiple sources and potential links. In multiple source pipe network, supply rate at each source node affects the total cost of the system because supply rates are not uniquely determined. The Linear Minimum Cost Flow (LMCF) model may be used to a large scale pipe network with multiple sources to determine supply rate at each source node. In this study the heuristic method based on the LMCF is suggested to determine supply rate at each source node and then to optimize the given layout. The heuristic method in turn perturbs links in the longest path of the network to obtain the supply rates which make the optimal design of the pipe network. Once the best tree network is obtained, the frequency count of reconnecting links by considering link failure is in turn applied to form loop to enhance the reliability of the best tree network. A sample pipe network is employed to test the proposed method. The results show that the proposed method can yield a lower cost design than the LMCF alone and that the proposed method can be efficiently used to design irrigation systems or rural water distribution systems.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.203-206
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• 2003

In this paper we discuss an algorithm for a real time transmission of moving color images on the TCP/IP network using wavelet transform and neural network. The image frames received from the camera are two-level wavelet-transformed in the server, and are transmitted to the client on the network. Then, the client performs the inverse wavelet-fransform using only the received pieces of each image frame within the prescribed time limit to display the moving images. When the TCP/IP network is busy, only a fraction of each image frame will be delivered. When the line is free, the whole frame of each image will be transferred to the client. The receiver warns the sender of the condition of traffic congestion in the network by sending a special short frame for this specific purpose. The sender can respond to this condition of warning by simply reducing the data rate which is adjusted by a back-propagation neural network. In this way we can send a stream of moving images adaptively adjusting to the network traffic condition.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.179-190
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• 2018

This study is about the influence of authors' centrality on research outcomes in a large-scale collaborative research network. Using the social network analysis method, five types of centralities were derived. Six research outcomes of individual researchers were also derived through bibliographic information of the social science field for the last 10 years. A multivariate regression analysis was conducted to examine the causal relationship between the centrality and research outcome, and the effect of centrality on research outcomes was found to be statistically significant. The result of this study shows that the revised citation and H-index significantly influenced the authors' centrality. This result can imply that the centrality of the researcher can expect a considerable influence of the thesis as well as a certain level of productivity. The meaning of this study is to analyze the effect of centrality on the research outcomes of the large-scale collaborative research network in the past decade, and is carefully to suggest a guideline in order to support new research information services for active researchers and the advancement of collaborative research. This study has its limitation for interpreting the diverse academic fields of the social sciences in a uniform way. In future study, it is necessary to conduct studies using various weighted indices for network centrality in order to measure the influence of research.