• Journal of Satellite, Information and Communications
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• v.4 no.2
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• pp.5-11
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• 2009

In this paper, we propose our design of adaptive resource allocation in the cognitive radio network assisted by satellite to improve the performance of Cognitive Radio user. Most of today’s telecommunication network operates in a fixed, licensed frequency band using a specific spectrum access network. However, the spectrum is not always used all the time, all the band. It causes the inefficiency in the spectrum usage. Thus, cognitive radio network is proposed to solve these spectrum inefficiency problems. The cognitive radio users (secondary users) are coexistent with primary users (PUs) who are licensed. That cognitive radio network is considered as lower priority comparing with primary user. So, the operation of the cognitive radio network is limited to interference constraints. Especially, when the number of secondary users increases, CCI among SUs will increase as well as interference to PU. That motivates our objective to improve the performance even if cognitive radio users increase. To solve this problem, we suggest an adaptive resource allocation scheme to improve the performance of cognitive radio network assisted by satellite. Through this algorithm, we can improve the cognitive radio network performance. And the simulation results confirm the effectiveness of our proposed algorithm.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.1
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• pp.78-89
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• 2006

In development of network-enabled consumer electronics, much of the time and effort is spent analyzing and solving network performance problems. In this paper, we define an instance of such problems discovered while developing a commercial home network gateway. We then analyze its cause and propose a solution mechanism. Our home network gateway uses art asymmetric link (ADSL) and suffers from an undesirable phenomenon where downlink traffic interferes with upload speed. We call this phenomenon the network performance interference problem. While this problem can easily be confused with receive livelock caused by packet contention at the input queue, we and that this is not the case. By performing extensive experiments and analysis, we reveal that our problem is caused by packet contention at the output queue and certain intrinsic characteristics of TCP. We devise an ACK-separation queuing mechanism for this problem and implement it in the home network gateway Our experiments show that it effectively solves the problem.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.549-551
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• 2004

This paper used OPNET to simulate video streaming service a test IP network and MPLS network with the traffic shaping that have with CQ_ LLQ algorithm, LSP of fixed bandwidth, policy of limitation users and measures parameters such as delay, throughput, packet loss. To verify the performance of video streaming service in IP network and MPLS network, two scenario that have same topology and traffic source. One is the simulation for best-effort service in pure IP network. The other is the simulation for QoS-enabled service in MPLS Network. Based on simulation result, the MPLS network with CQ_ LLQ algorithm and fixed LSP show advantage of the video streaming service QoS, specially delay and packet loss

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1357-1368
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• 2017

Sensor networks are used in various fields such as marine, defense, and smart home etc. Among the components of the sensor network, the sensor node collects sensor data, as one of the representative sensor network roles, and the sensor node makes a greate influence on the overall performance of the sensor network. Therefore, how to design the sensor node is an important issue in the sensor network field. However, the research on the sensor network architecture suitable for the sensor network installation environment has been made more important than the research on how to configure the sensor node. In this paper, we propose to identify elements to be considered for designing a sensor node that makes a large influence on the performance of the sensor network, and to easily implement the sensor node through the state transition model based on these elements.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.334-337
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• 2005

This paper describes the implementation of DCS communication network that provides high bandwidth and reliability. The network for DCS in this paper adopts the Reflective Memory (RM) architecture and Fast Ethernet physical media that have 100Mbps bandwidth. Also, this network uses Ring Enhancement Device (RED) which was invented to reduce the time delay of each node. The DCS network that is introduced in this paper is named as ERCNet(Ethernet based Real-time Control Network). This paper describes the architecture and working algorithms of ERCNet and performs numerical analysis. In addition, the performance of ERCNet is evaluated by experiment using the developed ERCNet network.

• Journal of Communications and Networks
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• v.14 no.4
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• pp.461-466
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• 2012

Any future heterogeneous overlay network system must be able to support ubiquitous access across multiple wireless networks. To coordinate these diverse network environments, one challenging task is a call admission decision among different types of network. In this paper, we propose a new call admission control scheme to provide quality of service (QoS) while ensuring system efficiency. Based on the interplay between network structure and dynamics, we estimate the network's QoS level and adjust the service price adaptively with the aim of maximizing the network performance. A simulation shows that the proposed scheme can approximate an optimized solution while ensuring a well-balanced network performance in widely different network environments.

• ETRI Journal
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• v.39 no.6
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• pp.841-850
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• 2017

We investigate neural network image reconstruction for magnetic particle imaging. The network performance strongly depends on the convolution effects of the spectrum input data. The larger convolution effect appearing at a relatively smaller nanoparticle size obstructs the network training. The trained single-layer network reveals the weighting matrix consisting of a basis vector in the form of Chebyshev polynomials of the second kind. The weighting matrix corresponds to an inverse system matrix, where an incoherency of basis vectors due to low convolution effects, as well as a nonlinear activation function, plays a key role in retrieving the matrix elements. Test images are well reconstructed through trained networks having an inverse kernel matrix. We also confirm that a multi-layer network with one hidden layer improves the performance. Based on the results, a neural network architecture overcoming the low incoherence of the inverse kernel through the classification property is expected to become a better tool for image reconstruction.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.631-634
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• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, in the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.

• Convergence Security Journal
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• v.16 no.7
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• pp.3-11
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• 2016

With repid development of optical networking technology, Software-Defined Network (SDN) and Network Function Virtualization (NFV), high performance networking service, collaboration platform that enables collaborative research globally, drastically National Research Network (NRN) including Internet Service has changed. Therefore we compared and analyzed several world-class NRNs and took a view of future development strategy of the NRN. Also we suggest high speed security environment in super high bandwidth network with 40Gbps and 100Gbps optical transmission technology, network separation of NRN with Science DMZ to support high performance network transmission for science big data, building security environment for last-mile in campus network that supports programmability of IDS using BRO framework.

• Computers and Concrete
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• v.14 no.6
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• pp.727-743
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• 2014

The management of existing concrete bridges has become a major social concern in many developed countries due to the large number of bridges exhibiting signs of significant deterioration. This problem has increased the demand for effective maintenance and renewal planning. In order to implement an appropriate management procedure for a structure, a wide array of corrective strategies must be evaluated with respect to not only the condition state of each defect but also safety, economy and sustainability. This paper describes a new performance evaluation system for existing concrete bridges. The system evaluates performance based on load carrying capability and durability from the results of a visual inspection and specification data, and describes the necessity of maintenance. It categorizes all girders and slabs as either unsafe, severe deterioration, moderate deterioration, mild deterioration, or safe. The technique employs an expert system with an appropriate knowledge base in the evaluation. A characteristic feature of the system is the use of neural networks to evaluate the performance and facilitate refinement of the knowledge base. The neural network proposed in the present study has the capability to prevent an inference process and knowledge base from becoming a black box. It is very important that the system is capable of detailing how the performance is calculated since the road network represents a huge investment. The effectiveness of the neural network and machine learning method is verified by comparing diagnostic results by bridge experts.