• Journal of Information Processing Systems
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• v.7 no.1
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• pp.75-84
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• 2011

The enhanced pyramid graph was recently proposed as an interconnection network model in parallel processing for maximizing regularity in pyramid networks. We prove that there are two edge-disjoint Hamiltonian cycles in the enhanced pyramid networks. This investigation demonstrates its superior property in edge fault tolerance. This result is optimal in the sense that the minimum degree of the graph is only four.

• Electronics and Telecommunications Trends
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• v.34 no.2
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• pp.51-59
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• 2019

Edge computing, which is computing on the edge of the network, is becoming a market value as a means of overcoming the fear of communication disconnection and delay reduction, which are the technical weaknesses of cloud computing. Edge computing is continuously expanding applications in various applications such as safety industry, smart factories, autonomous vehicles, mobile communications, and AR/VR. Looking at edge computing trends from Microsoft, IBM, HPE, and Dell EMC, current edge computing must be understood as an integral binding technology and not as a simple complement to the cloud. This paper examines market trends in edge computing and analyzes the impact of edge computing on major related industries.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.65-73
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• 2006

Network based VPN(Virtual Private Network) using MPLS(Multiprotocol Label Switching) technology, called PE(Provider Edge router) based MPLS VPN, is regarded as a good solution for intranets or ext3nets because of the low cost and the flexibility of the service provision. In this paper, we describe a mechanism that allows the VPN users to move from one site to another site of the VPN network based on the BGP-E MPLS technology. This mechanism is designed for PE(Provider Edge) routers of the backbone network. PE routers connected to the VPN sites establish a new MPLS path to the mobile node after they detect movement of the mobile VPN node. The new location may belong to the same VPN or to different VPN. We desisted VPN management and control functions of the PE routers in order to interface with the Mobile IP protocol and support the QoS mechanism. The pilot implementation and performance measurement were carried out on a simulation using COVERS tool.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.25-34
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• 1999

In this paper, an algorithm is presented to recognize lane and obstacles based on highway road image. The road images obtained by a video camera undergoes a pre-processing that includes filtering, edge detection, and identification of lanes. After this pre-processing, a part of image is grouped into 27 sub-windows and fed into a three-layer feed-forward neural network. The neural network is trained to indicate the road direction and the presence of absence of an obstacle. The proposed algorithm has been tested with the images different from the training images, and demonstrated its efficacy for recognizing lane and obstacles. Based on the test results, it can be said that the algorithm successfully combines the traditional image processing and the neural network principles towards a simpler and more efficient driver warning of assistance system

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.345-350
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• 2002

In this study, researchers classifying the artificial flaws in semiconductor packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method fur entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.

• ETRI Journal
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• v.35 no.2
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• pp.207-217
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• 2013

This paper introduces the modular cellular neural network (CNN), which is a new CNN structure constructed from nine one-layer modules with intercellular interactions between different modules. The new network is suitable for implementing many image processing operations. Inputting an image into the modules results in nine outputs. The topographic characteristic of the cell interactions allows the outputs to introduce new properties for image processing tasks. The stability of the system is proven and the performance is evaluated in several image processing applications. Experiment results on texture segmentation show the power of the proposed structure. The performance of the structure in a real edge detection application using the Berkeley dataset BSDS300 is also evaluated.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.721-726
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• 2005

In this study, researchers classifying the artificial flaws in semiconductor. packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method for entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.896-914
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• 2020

With the rapid growth of network traffic, a large number of connected devices, and higher application services, the traditional network is facing several challenges. In addition to improving the current network architecture and hardware specifications, effective resource management means the development trend of 5G. Although many existing potential technologies have been proposed to solve the some of 5G challenges, such as multiple-input multiple-output (MIMO), software-defined networking (SDN), network functions virtualization (NFV), edge computing, millimeter-wave, etc., research studies in 5G continue to enrich its function and move toward B5G mobile networks. In this paper, focusing on the resource allocation issues of 5G core networks and radio access networks, we address the latest technological developments and discuss the current challenges for resource management in 5G.

• Journal of IKEEE
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• v.22 no.4
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• pp.1242-1245
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• 2018

In this paper, we have implemented a network that overcomes the over-blurring phenomenon that occurs when removing Gaussian noise. In the conventional filtering method, blurring of the original image is performed to remove noise, thereby eliminating high frequency components such as edges and corners. We propose a network that reducing over blurring while maintaining denoising performance by adding denoised high frequency components to denoisers based on CNN.

• Journal of Communications and Networks
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• v.9 no.4
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• pp.499-510
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• 2007

Shortest path tree(SPT) construction is essential in high performance routing in an interior network using link state protocols. When some links have new state values, SPTs may be rebuilt, but the total rebuilding of the SPT in a static way for a large computer network is not only computationally expensive, unnecessary modifications can cause routing table instability. This paper presents a new update algorithm, dynamic shortest path tree(DSPT) that is computationally economical and that maintains the unmodified nodes mostly from an old SPT to a new SPT. The proposed algorithm reduces redundancy using a dynamic update approach where an edge becomes the significant edge when it is extracted from a built edge list Q. The average number of significant edges are identified through probability analysis based on an arbitrary tree structure. An update derived from significant edges is more efficient because the DSPT algorithm neglect most other redundant edges that do not participate in the construction of a new SPT. Our complexity analysis and experimental results show that DSPT is faster than other known methods. It can also be extended to solve the SPT updating problem in a graph with negative weight edges.