• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1B
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• pp.1-10
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• 2007

We propose a new approach to accommodate bidirectional asymmetric traffic demands as well as unexpected dynamic internet traffic variation in the WDM mesh network by using optical networking technologies. In the proposed scheme, an intermediate node determines the optical path based on the switching statistics of IP router of the node which characterizes the Internet traffic variation, which in effect provides a dynamic and distributed traffic control over the network. It is expected to reduce the efficiency deterioration of RWA(Routing and Wavelength Assignment) due to the real-time variation of Internet traffic so that expandability and flexibility of the network can be enhanced. In this paper, we describe a methodology for traffic behavior analysis at a node, and the decision policy of the establishment/release of optical path. In addition, we evaluate the performance of the proposed scheme through the computer simulations.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.295-300
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• 2024

This paper discusses the incompleteness of the SNOMED CT and proposes a noble metric which evaluates similarity among sibling nodes as a method to address this incompleteness. SNOMED CT encompasses an extensive range of medical terms, but it faces issues of ontology incompleteness, such as missing concepts in the hierarchy. We propose a noble metric for evaluating similarity among nodes within a node group, composed of multiple sibling nodes, to identify missing concepts, and identify groups with low similarity. Analyzing the similarity of sibling node groups in the March 2023 international release of SNOMED CT, the average similarity of 29,199 sibling node groups, which are sub-concepts of the clinical finding concept and are consist of two or more sibling nodes, was found to be 0.81. The group with the lowest similarity was associated with child concepts of poisoning, with a similarity of 0.0036.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.791-800
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• 2022

CCN, which has emerged to overcome the limitations of existing network structures, enables efficient networking by changing the IP Address-based network method to the Content-based network method. At this time, the contents are stored on each node(router) rather than on the top server, and considering the limitation of storage capacity, it is very important to determine which contents to store and release through the cache policy, and there are several cache policies that have been studied so far. In this paper, two of the existing cache policies, producer popularity-based and distance-based, were mixed. In addition, the mixing ratio was set differently to experiment, and we proved which experiement was the most efficient one.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.23-29
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• 2024

In this paper, we propose a fast GPU-based method for representing pore flow, absorption, emission, and diffusion effects represented by cloth-liquid interactions using smoothed particle hydrodynamics (SPH), a particle-based fluid solver: 1) a unified framework for GPU-based representation of various physical effects represented by cloth-liquid interactions; 2) a method for efficiently calculating the saturation of a node based on SPH and transferring it to the surrounding porous particles; 3) a method for improving the stability based on Darcy's law to reliably calculate the direction of fluid absorption and release; 4) a method for controlling the amount of fluid absorbed by the porous particles according to the direction of flow; and finally, 5) a method for releasing the SPH particles without exceeding their maximum mass. The main advantage of the proposed method is that all computations are computed and run on the GPU, allowing us to quickly model porous materials, porous flows, absorption, reflection, diffusion, etc. represented by the interaction of cloth and fluid.

• Structural Engineering and Mechanics
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• v.58 no.5
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• pp.799-823
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• 2016

A finite element model for the non-linear dynamic analysis of a reinforced concrete (RC) containment shell of a nuclear power plant subjected to extreme loads such as impact and earthquake is presented in this work. The impact is modeled by using an uncoupled approach in which a load function is applied at the impact zone. The earthquake load is modeled by prescribing ground accelerations at the base of the structure. The nuclear containment is discretized spatially by using 20-node brick finite elements. The concrete in compression is modeled by using a modified $Dr{\ddot{u}}cker$-Prager elasto-plastic constitutive law where strain rate effects are considered. Cracking of concrete is modeled by using a smeared cracking approach where the tension-stiffening effect is included via a strain-softening rule. A model based on fracture mechanics, using the concept of constant fracture energy release, is used to relate the strain softening effect to the element size in order to guaranty mesh independency in the numerical prediction. The reinforcing bars are represented by incorporated membrane elements with a von Mises elasto-plastic law. Two benchmarks are used to verify the numerical implementation of the present model. Results are presented graphically in terms of displacement histories and cracking patterns. Finally, the influence of the shear transfer model used for cracked concrete as well as the effect due to a base slab incorporation in the numerical modeling are analyzed.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.471-476
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• 2024

With the spread of various smart devices and computing devices, big data generation is occurring widely. Machine learning is an algorithm that performs reasoning by learning data patterns. Among the various machine learning algorithms, the algorithm that attracts attention is deep learning based on neural networks. Deep learning is achieving rapid performance improvement with the release of various applications. Recently, among deep learning algorithms, attempts to analyze data using graph structures are increasing. In this study, we present a graph generation method for transferring to a deep learning network. This paper proposes a method of generalizing node properties and edge weights in the graph generation process and converting them into a structure for deep learning input by presenting a matricization We present a method of applying a linear transformation matrix that can preserve attribute and weight information in the graph generation process. Finally, we present a deep learning input structure of a general graph and present an approach for performance analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1567-1574
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• 2012

Since a migration method of the mobile agent is a factor that affects the overall performance of the entire distributed system, it is necessary to find efficient migration methods of the mobile agent within the sensor network and to collect and store data related to various components(server, sink and sensor node) of the sensor network, thereby providing consistent naming services. Accordingly, this paper presents design and implementation of MetaTable that is divided into MetaData where information on the sensor data server is stored and SubMetaData where various types of information on sink nodes and data on sensor nodes connected with the sink nodes is stored. Moreover, the paper also presented the implementation of forward migration of an active rule mobile agent applying the naming method based on RMI that used the meta_table and proposed the possibility of constructing efficient sensor network application environment. In this paper, for registration, release and retrieval methods suitable for new sensor network environment, we designed and implemented the naming agent by applying J2EE model based on RMI-IIOP(Internet Inter-ORB Protocol) technique.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12B
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• pp.1647-1658
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• 2011

This paper suggests the methods to improve the wireless backhaul link of the Relay FDD mode that 3GPP LTE-Advanced supports. New RRC message and Relay startsup procedure are introduced to apply Carrier Aggregation in the Relay wireless backhaul. Also, we design new reference signal that makes Type 1 Relay operated in full-duplexing, expecting to maximize the radio resource utilization. And, we propose the efficient configuration for the periodic uplink control information under the backhaul subframe allocation method in LTE-Advanced Relay. Hereafter, the standard activities in the 3GPP RAN is supposed to focus on the improvement of the LTE-Advanced features. Therefore, the suggestions in this paper are expected to be actively discussed in the LTE-Advanced future releases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.3
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• pp.223-233
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• 2014

As a broadcast message authentication method in wireless sensor networks, ${\mu}$TESLA enables sensor nodes efficiently authenticate message from base station (BS). However, if we use ${\mu}$TESLA that has very short length of key slot in unattended wireless sensor network (UWSN), sensors may calculate a huge amount of hashs at once in order to verify the revealed secret key. In contrast, if we set the length of ${\mu}$TESLA's key slot too long in order to reduce the amount of hashs to calculate, BS should wait out the long slot time to release key. In this paper, we suggest variable key slot ${\mu}$TESLA in order to mitigate the problem. As showing experiment results, we prove that our suggestion improve sensor node's response time and decrease of number of hash function calculation.