• Journal of Electrical Engineering and Technology
• /
• v.13 no.2
• /
• pp.608-619
• /
• 2018

As distributed generation (DG) is connected to grid, there is new node-type occurring in distribution network. An efficient algorithm is proposed in this paper to calculate power flow for weakly meshed distribution network with DGs in different load models. The algorithm respectively establishes mathematical models focusing on the wind power, photovoltaic cell, fuel cell, and gas turbine, wherein the different DGs are respectively equivalent to PQ, PI, PQ (V) and PV node-type. When dealing with PV node, the algorithm adopts reactive power compensation device to correct power, and the reactive power allocation principle is proposed to determine reactive power initial value to improve convergence of the algorithm. In addition, when dealing with the weakly meshed network, the proposed algorithm, which builds path matrix based on loop-analysis and establishes incident matrix of node voltage and injection current, possesses good convergence and strong ability to process the loops. The simulation results in IEEE33 and PG&G69 node distribution networks show that with increase of the number of loops, the algorithm's iteration times will decrease, and its convergence performance is stronger. Clearly, it can be effectively used to solve the problem of power flow calculation for weakly meshed distribution network containing different DGs.

• Journal of the Institute of Convergence Signal Processing
• /
• v.7 no.2
• /
• pp.53-59
• /
• 2006

Dynamic neural networks have been applied to diverse fields requiring temporal signal processing such as system identification and signal prediction. This paper proposes the gamma neural network(GAM), which uses gamma memory kernel in the hidden layer of feedforward multilayered network, to improve dynamics of networks and then describes nonlinear adaptive prediction using the proposed network as an adaptive filter. The proposed network is evaluated in nonlinear signal prediction and compared with feedforword(FNN) and recurrent neural networks(RNN) for the relative comparison of prediction performance. Simulation results show that the GAM network performs better with respect to the convergence speed and prediction accuracy, indicating that it can be a more effective prediction model than conventional multilayered networks in nonlinear prediction for nonstationary signals.

• Journal of Internet Computing and Services
• /
• v.22 no.6
• /
• pp.1-8
• /
• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.

• Proceedings of the KIEE Conference
• /
• 2005.05a
• /
• pp.227-229
• /
• 2005

This paper proposes a method of implementation of digital convergence platform(DCP) which enable the service of broadcasting, communication, multimedia and home automation. Also digital convergence platform based on MPEG-21 multimedia framework can be a model to provide a distributed electronic commerce environment of multimedia and to manage of it. Platform hardware is implemented using a general purpose CPU and high performance digital signal processor and has peripheral units for network and multi I/O. It is able to run applications of multimedia which has variable formats on DSP. In addition, a personal transaction of multimedia packaged with MPEG-21 multimedia framework is provided on digital convergence platform. Like this, digital convergence platform bring up a new architecture of multimedia systems using a new generation network.

• 한국IT서비스학회:학술대회논문집
• /
• 2009.05a
• /
• pp.588-592
• /
• 2009

The latest advances in great revolution for digital media and contents industries with Ubiquitous era. Because of spread and supply of various multimedia devices with combination digital with network, we need various type of digital contents. The Digital contents consists of Middleware industries, network business, contents service presenters and users. For supporting digital media. contents and media devices have been converging Digital contents convergence has been proceeding rapidly in media, broadcast and communication field. And also, whole Environment for these industries has been changing, too. Wee need another digital contents convergence for new type of products and services because of convergence of IT and broadcast. In addition, new business trends is very important about these new industrial change and developments with digital contents convergence.

• Convergence Security Journal
• /
• v.4 no.2
• /
• pp.53-60
• /
• 2004

We describe CHAP authentication for roaming service method of visited ISP subscriber on GPRS network. We also illustrate how visited mobile ISP subscriber can access ISP server and authenticate RADIUS in home network via Gateway GPRS Support Node (GGSN) on GPRS/UMTS network for wireless internet service and roaming. For this we propose the modified CHAP message format, PCO Message format at MT, and interworking message and format between GGSN and RADIUS in home ISP network for wireless internet service of mobile ISP subscriber at GPRS network in this paper. We also show authentication results when visited mobile ISP subscriber via CHAP at GPRS network accesses the RADIUS server in home ISP network.

• ETRI Journal
• /
• v.42 no.3
• /
• pp.366-375
• /
• 2020

The network traffic prediction of a smart substation is key in strengthening its system security protection. To improve the performance of its traffic prediction, in this paper, we propose an improved gravitational search algorithm (IGSA), then introduce the IGSA into a wavelet neural network (WNN), iteratively optimize the initial connection weighting, scalability factor, and shift factor, and establish a smart substation network traffic prediction model based on the IGSA-WNN. A comparative analysis of the experimental results shows that the performance of the IGSA-WNN-based prediction model further improves the convergence velocity and prediction accuracy, and that the proposed model solves the deficiency issues of the original WNN, such as slow convergence velocity and ease of falling into a locally optimal solution; thus, it is a better smart substation network traffic prediction model.

• ETRI Journal
• /
• v.26 no.1
• /
• pp.27-37
• /
• 2004

The architecture in a differentiated services (DiffServ) network is based on a simple model that applies a per-class service in the core node of the network. However, because the network behavior is simple, the network structure and provisioning is complicated. If a service provider wants dynamic provisioning or a better bandwidth guarantee, the differentiated services network must use a signaling protocol with QoS parameters or an admission control method. Unfortunately, these methods increase the complexity. To overcome the problems with complexity, we investigated scalable dynamic provisioning for admission control in DiffServ networks. We propose a new scalable $qDPM^2$ mechanism based on a centralized bandwidth broker and distributed measurement-based admission control and movable boundary bandwidth management to support heterogeneous QoS requirements in DiffServ networks.

• Convergence Security Journal
• /
• v.2 no.1
• /
• pp.17-33
• /
• 2002

The major goal of this study is to develop an index that can evaluate the quality of the appropriate network in a series of projections that analyze, design, and then build a network. The existing software engineering and/or the methods of developing a system are limited. The process of defining the requirements in building a network, designing the system, and building the network focuses on arranging the methods of building a network. Based upon this, we tried to develop a necessary index to evaluate the security of a network.

• Journal of Electrical Engineering and information Science
• /
• v.1 no.1
• /
• pp.108-117
• /
• 1996

In this paper, we propose a new type of recurrent neural network architecture in which each output unit is connected with itself and fully-connected with other output units and all hidden units. The proposed recurrent network differs from Jordan's and Elman's recurrent networks in view of functions and architectures because it was originally extended from the multilayer feedforward neural network for improving the discrimination and generalization power. We also prove the convergence property of learning algorithm of the proposed recurrent neural network and analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwritten numeral database of Concordia University of Canada. Experimental results confirmed that the proposed recurrent neural network improves the discrimination and generalization power in recognizing spatial patterns.