• Proceedings of the Korea Information Processing Society Conference
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• 2003.05b
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• pp.1305-1308
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• 2003

Mobile Network은 일정한 규모 이상의 이동성을 지원하는 네트워치를 말한다. Mobile IP는 표준 IP에 이동성을 지원하기 위한 기법이다. Mobile IPv4에서는 Mobile Network을 가상적인 하나의 Mobile Node처럼 취급하여 지원이 가능하다. 하지만, Mobile IPv6에서는 몇몇의 문제점으로 인하여 Mobile IPv4에서와 같은 접근이 어렵다. 이 논문에서는 Mobile IPv6환경에서 Mobile Network을 지원할 수 있는 기법을 제안하고 분석하였다.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.3
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• pp.65-77
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• 2001

Survivability of a network is one of the most important issues in designing present-day communication networks. the k-edge survivability of a given network is defined as the percentage of total traffic surviving the worst case failure of k edges. Although several researches calculated k-edge survivability on small networks by enumeration, prior research has considered how to calculate k-edge survivability on large networks. In this paper, we develop an efficient procedure to obtain lower and upper bounds on the k-edge survivability of a network.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.21-29
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• 2004

In this paper, we define an availability of repairable network system, when the states of links are modeled by alternating renewal processes. The actual availabilities of some well-known network systems are obtained. The expected numbers of failures and repairs of the network systems are also calculated.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.186-189
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• 1996

In most of the recent research literature, network performance is expressed in terms of network engineering measures such as delay or loss. These performance measures are important to network owners and operators, but it is believed that user preferences should be the primary consideration which drives the resource allocation scheme. A network is only as valuable as its users perceive it to be. Therefore, it is advocated that the users themselves determine relative traffic priorities. This paper describes the role of feedback in network resource allocation, which could be part of a user-oriented framework for network operation and control. Feedback mechanism can also be used to improve the two types of efficiency in the network; network efficiency and economic efficiency.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.1
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• pp.15-26
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• 2000

We consider an open tandem queueing network with population constraint and constant service times. The total number of customers that may be present in the network can not exceed a given value k. Customers arriving at the queueing network when there are more than k customers are forced to walt in an external queue. The arrival process to the queueing network is assumed to be arbitrary. It is known that the queueing network with population constrant and constant service times can be transformed into a simple network involving only two nodes. In this paper, the departure process from the queueing network is examined using this simple network. An approximation can be calculated with accuracy. Finally, validations against simulation data establish the tightness of these.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.159-168
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• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10c
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• pp.49-51
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• 2003

Developers of network games have used several prediction techniques for hiding transmission delay to support the real­time requirement of network games. Nowadays many researches that are related with network game are in progress to solve delay problems more radically, such as to propose new routers architecture and transport protocols suitable to characteristics of network game traffic. So for these advanced researches the tasks to grasp the traffic characteristics of a network game are needed. In this paper we aimed to capture the traffic of MMORPG and present the statistical analysis of measured data. The measurement and the analysis were accomplished with the server of 'Lineage' that regarded as the most successful MMORPG. Next, we have implemented a traffic generator that reflects the characteristics of MMORPG and shown that the trace generated by MMORPG traffic generator had identical characteristics with actual traffic using statistical testing method. We expect that this traffic generator can be used in many researches related with a network game.

• Journal of Communications and Networks
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• v.12 no.3
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• pp.266-274
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• 2010

The efficient network resource management is one of the important topics in a real-time system. In this paper, we present a practical network resource management framework, control-theoretic packet scheduler (CPS) system. Using our framework, an operating system can schedule both input and output streams accurately and efficiently. Our framework adopts very portable feedback control theory for efficiency and accuracy. The CPS system is able to operate independent of the internal network protocol state, and it is designed to schedule packet streams in fine-grained time intervals to meet the resource requirement. This approach simplifies the design of the CPS system, and leads us to obtain the intended output bandwidth. We implemented our prototype system in Linux, and measured the performance of the network resource management system under various network QoS constraints. The distinctive features of our principles are as follows: It is robust and accurate, and its operation is independent of internal network protocols.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3991-4007
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• 2022

In the public healthcare, a computational system that can automatically and efficiently detect and classify falls from a video sequence has significant potential. With the advancement of deep learning, which can extract temporal and spatial information, has become more widespread. However, traditional 3D CNNs that usually adopt shallow networks cannot obtain higher recognition accuracy than deeper networks. Additionally, some experiences of neural network show that the problem of gradient explosions occurs with increasing the network layers. As a result, an enhanced three-dimensional ResNet-based method for fall detection (3D-ERes-FD) is proposed to directly extract spatio-temporal features to address these issues. In our method, a 50-layer 3D residual network is used to deepen the network for improving fall recognition accuracy. Furthermore, enhanced residual units with four convolutional layers are developed to efficiently reduce the number of parameters and increase the depth of the network. According to the experimental results, the proposed method outperformed several state-of-the-art methods.

• Current Optics and Photonics
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• v.7 no.1
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• pp.47-53
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• 2023

We propose a centralized passive-optical-network monitoring scheme using the resonance-spectrum properties of a Fabry-Perot cavity based on fiber Bragg gratings. Each cavity consists of two identical uniform fiber Bragg gratings and a varying cavity length or grating length, which can produce a unique single-mode resonance spectrum for the drop-fiber link. The output spectral properties of each cavity can be easily adjusted by the cavity length or the grating length. The resonance spectrum for each cavity is calculated by the transfer-matrix method. To obtain the peak wavelength of the resonance spectrum more accurately, the effective cavity length is introduced. Each drop fiber with a specific resonance spectrum distinguishes between the peak wavelength or linewidth. We also investigate parameters such as reflectivity and bandwidth, which determine the basic performance of the fiber Bragg grating used, and thus the output-spectrum properties of the Fabry-Perot cavity. The feasibility of the proposed scheme is verified using the Optisystem software for a simplified 1 × 8 passive optical network. The proposed scheme provides a simple, effective solution for passive-optical-network monitoring, especially for a high-density network with small end-user distance difference.