• Convergence Security Journal
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• v.7 no.2
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• pp.39-47
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• 2007

The scheduling algorithms to provide quality of service (QoS) in broadband convergence network (BcN) are compared and analysed. The main QoS management methods such as traffic classification, traffic processing in the input queue and weighted queueing are first analysed, and then the major scheduling algorithms of round robin, priority and weighted round robin under recently considering for BcN to supply real time multimedia communications are analysed. The simulation results by NS-2 show that the scheduling algorithm with proper weights for each traffic class outperforms the priority algorithm.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.2
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• pp.57-64
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• 2011

The NGN (Next Generation Network) means the kernel infrastructure technology to provide information and communication services which are able to be used at present and future when a ubiquitous computing era has been realized. In other words, NGN can be the frame providing the same information and communication services anytime and anywhere regardless of wire and wireless. The broadband convergence network that has been built in the public institution has established a broadband multimedia communication network supporting voice telephone, task net, internet network, video conference network, voice over IP (VoIP) network and etc. It is possible for a requested bandwidth and services to be served, only if a broadband convergence network provide the interoperability between the various classes which include a transport network layer, network control layer, service control layer and other layers. In this paper, we analyzed the interoperability issues of the present broadband convergence network and propose a guideline for the future one.

• Convergence Security Journal
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• v.20 no.3
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• pp.47-52
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• 2020

The introduction of virtualization technology in the broadcasting field is actively progressing broadcasting service automation and intelligence based on the effective operation of IT resources throughout the broadcasting industry ecosystem. In recent years, there is increasing interest in increasing the flexibility of various broadcasting resources and increasing the efficiency of interworking with other networks through network virtualization of the broadcasting network infrastructure. The fundamental transformation from the broadcasting network to the IP paradigm is facing a situation where it is necessary to solve various problems for the effective interworking of Internet-based service platforms and 5G networks and the development of new convergence services. In other words, for organic and effective interworking with the next-generation broadcasting network represented by ATSC 3.0, a mobile communication network represented by 5G, and the Internet, a number of difficulties must be solved. In this paper, the basic technology and status for the convergence of ATSC 3.0 broadcasting network and mobile communication network represented by 5G was examined, and a plan for the ATSC 3.0 broadcasting network and 5G network to interwork with each other as a network was described.

• Convergence Security Journal
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• v.9 no.2
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• pp.33-39
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• 2009

The problems of current network security system, separated by a single element is checked. To improve this, this thesis is to find vulnerabilities in the network security systems, and network security systems, security equipment, organic to make sure each works is a comprehensive review. Automation also offers a way to check it, it was implemented.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.1
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• pp.1-19
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• 2016

Recently, the emergence of smartphone foster the technological convergence. This convergence no longer takes place within technologies only. Rather, convergence phenomena happen as a form of embodied services. However, previous research on convergence has been subject to the technology-oriented studies, including suggesting patent-based indexes or analyzing technological characteristics. However, investigating technology-service convergence is critical since most of new smart services are technology-based convergence services. Therefore, we analyze the pattern of technology-service convergence which occurs in the smartphone application services. We divided the smatphone application services into four categories, and employ a network analysis to represent the convergence phenomena of each category. Our study is expected to provide meaningful implication in new service development practice.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.2
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• pp.108-114
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• 1996

We present proofs of the stability and convergence of Self-organizing feature map (SOFM) neural network with time-invarient learning rate and binary reinforcement function. One of the major problems in Self-organizing feature map neural network concerns with learning rate-"Kalman Filter" gain in stochsatic control field which is monotone decreasing function and converges to 0 for satisfying minimum variance property. In this paper, we show that the stability and convergence of Self-organizing feature map neural network with time-invariant learning rate. The analysis of the proposed algorithm shows that the stability and convergence is guranteed with exponentially stable and weak convergence properties as well.s as well.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.229-231
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• 2021

고전적인 인공 신경망을 암호 분야에 사용하기 위한 연구들이 이뤄지고 있으며 다양한 암호 관련 분야에서의 사용이 제안되었다. 더 나아가 최근에는 양자 컴퓨터의 연산속도 이점을 활용해서 고전적인 인공 신경망을 학습하기 위한 연구들이 진행되고 있다. 양자컴퓨터의 양자 알고리즘은 기존 컴퓨터에서 보여주지 못한 연산속도를 보여주었으며 앞으로의 잠재력이 기대되고 있다. 본 논문에서는 Quantum Neural Network (QNN)를 활용한 암호 연구 동향에 대해 살펴본다.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.3
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• pp.238-244
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• 2008

Dynamic neural networks have been applied to diverse fields requiring temporal signal processing. This paper presents gamma neural network(GAM) to improve the dynamics of multilayer network. The GAM network uses the gamma memory kernel in the hidden layer of feedforword multilayer network. The GAM network is evaluated in linear and nonlinear system identification, and compared with feedforword(FNN) and recurrent neural networks(RNN) for the relative comparison of its performance. Experimental results show that the GAM network performs better with respect to the convergence and accuracy, indicating that it can be a more effective network than conventional multilayer networks in system identification.

• ETRI Journal
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• v.30 no.2
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• pp.205-215
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• 2008

Many control schemes have been proposed for flow-level traffic control. However, flow-level traffic control is implemented only in limited areas such as traffic monitoring and traffic control at edge nodes. No clear solution for end-to-end architecture has been proposed. Scalability and the lack of a business model are major problems for deploying end-to-end flow-level control architecture. This paper introduces an end-to-end transport architecture and a scalable control mechanism to support the various flow-level QoS requests from applications.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1476-1485
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• 2020

Segmenting OCT retinal images into layers is important to diagnose and understand the progression of retinal diseases or identify potential symptoms. The task of manually identifying these layers is a difficult task that requires a lot of time and effort even for medical professionals, and therefore, various studies are being conducted to automate this using deep learning technologies. In this paper, we use cGAN-based neural network to automatically segmenting OCT retinal images into seven terrain-type regions defined by six layer boundaries. The network is composed of a Segnet-based generator model and a discriminator model. We also proposed a dynamic programming algorithm for refining the outputs of the network. We performed experiments using public OCT image data set and compared its performance with the Segnet-only version of the network. The experimental results show that the cGAN-based network outperforms Segnet-only version.