• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.6
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• pp.284-289
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• 2002

The transmission networks do not consist of perfect conductors and a percentage of the power generated is therefore lost before it reaches the loads. Since this network loss contributes to the cost of suppling power to consumers, it must be considered that the most efficient dispatch and location of generators and loads are to be achieved. In this paper, marginal loss factors are calculated for 12 load levels that represent the impact of marginal network losses on nodal prices at the transmission network connection points at which generators are located. Based on comparison analysis of marginal loss factors on 12 load levels, we found the MLF characteristics in KOREA.

• Journal of Industrial Technology
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• v.27 no.B
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• pp.185-192
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• 2007

Transcoding video proxy is necessary to support various bandwidth requirements for mobile multimedia and to provide adapting video streams to mobile clients. Caching algorithms for proxy are to reduce the network traffic between the content servers and the proxy. This paper proposes a Meta-tailed caching for transcoding proxy that is efficient to lower network load and CPU load. Caching of two different data types - transcoded video, and metadata - provides a foundation to achieve superior balance between network resource and computation resource at transcoding proxies. Experimental results show that the Meta-tailed caching lowers at least 10% of CPU-load and at least 9% of network-load at a transcoding proxy.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.9
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• pp.567-569
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• 2005

NARX(Nonlinear AutoRegressive with eXogenous input) neural network was used for prediction of nuclear reactor behavior which was influenced by control rods in short-term period and also by the concentration of xenon and boron in long-term period in load following operations. The developed model was designed to predict reactor power, xenon worth and axial offset with different burnup states when control rods and boron were adjusted in load following operations. Data of the Korea Next Generation PWR were collected by ONED94 code. The test results presented exhibit the capability of the NARX neural network model to capture the long term and short term dynamics of the reactor core and the developed model seems to be utilized as a handy tool for the use of a plant simulation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1001-1006
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• 2017

In this study the NARX was proposed as a novel approach to forecast electric load more accurately. The NARX model is a recurrent dynamic network. ISO-NewEngland dataset was employed to evaluate and validate the proposed approach. Obtained results were compared with NAR network and some other popular statistical methods. This study showed that the proposed approach can be applied to forecast electric load and NARX has high potential to be utilized in modeling dynamic systems effectively.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.447-449
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• 1996

This paper describes an algorithm for short term load forecasting using the Kohonen neural network. Single layer Kohonen neural network presents a lot of advantageous features for practical application. It takes less training time compared to other networks such as BP network, and moreover, its self organized feature can amend the distorted data. The originality of proposed approach is to use a Kohonen map toclassify data representing load patterns and to use directly the information stored in the weight vectors of the Kohonen map to pridict the load. Proposed method was tested with KEPCO hourly record(1993-1995) show better forecasting results compared with conventional exponential smoothing method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.7A
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• pp.757-767
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• 2008

Start Ongoing next generation networks are expected to be deployed over current existing networks, in the form of overlayed heterogeneous networks, in particular, in hot spot areas. Therefore, it will be necessary to develop an interworking technique such as load balancing, to achieve increased overall resource utilization in the various heterogeneous networks. In this paper, we present a new load balancing mechanism termed 'soft' load balancing where the IP(Internet Protocol) traffic of a user is divided into sub-traffic, each of which flows into a different access network. The terminology of soft load balancing involves the use of both load sharing and handover techniques. Through a numerical analysis, we obtain an optimal LBR (Load Balancing Ratio) for determining the volume of traffic delivered to each network over an overlayed multi-cell environment. Using the optimal LBR, a more reliable channel transmission can be achieved by reducing the outage probability efficiently for a given user traffic.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.314-317
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• 2000

Cluster system provides attractive scalability in terms of computation power and memory size. With the advances in high speed computer network technology, cluster systems are becoming increasingly competitive compared to expensive parallel machines. In parallel processing program, each task load is difficult to predict before running the program and each task is interdependent each other in many ways. Load imbalancing induces an obstacle to system performance. Most of researches in load balancing were concerned with distributed system but researches in cluster system are few. In cluster system, the dynamic load balancing algorithm which evaluates each processor's load in runtime is purpose that the load of each node are evenly distributed. But, if communication cost or node complexity becomes high, it is not effective method for all nodes to attend load balancing process. In that circumstances, it is good to reduce the number of node which attend to load balancing process. We have modeled cluster systems and proposed marginal dynamic load balancing algorithms suitable for that circumstances.

• Journal of Digital Contents Society
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• v.18 no.5
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• pp.983-987
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• 2017

Multi-homing mobile router separates network for user connection and network for internet access using various interfaces for internet access. This paper proposes a load balancing metric in order that multi-network mobile router distributes its traffic to one of several heterogeneous network interfaces. To evaluate the performance of the load balancing metric, experiments on traffic balancing is performed on real commercial networks were used in Korea and Hong Kong.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.2982-3003
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• 2014

With the explosive deployment of the wireless communications technology, the increased QoS requirement has sparked keen interest in network planning and optimization. As the major players in wireless network optimization, the BS's resource utilization and mobile user's QoS can be improved a lot by the load-balancing technology. In this paper, we propose a load-balancing strategy that uses Coordinated Multiple Points (CoMP) technology among the Base Stations (BS) to effectively extend network coverage and increase edge users signal quality. To use universally, different patterns of load-balancing based on CoMP are modeled and discussed. We define two QoS metrics to be guaranteed during CoMP load balancing: call blocking rate and efficient throughput. The closed-form expressions for these two QoS metrics are derived. The load-balancing capacity and QoS performances with different CoMP patterns are evaluated and analyzed in low-dense and high-dense traffic system. The numerical results present the reasonable CoMP load balancing pattern choice with guaranteed QoS in each system.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.9
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• pp.417-424
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• 2002

This paper presents an industrial peak load management system for the peak demand control. Kohonen neural network and wavelet transform based techniques are adopted for industrial peak load forecasting that will be used as input data of the peak demand control. Firstly, one year of historical load data of a steel company were sorted and clustered into several groups using Kohonen neural network and then wavelet transforms are applied with Biorthogonal 1.3 mother wavelet in order to forecast the peak load of one minute ahead. In addition, for the peak demand control, composite fuzzy model is proposed and implemented in this work. The results are compared with those of conventional model, fuzzy model and composite model, respectively. The outcome of the study clearly indicates that the composite fuzzy model approach can be used as an attractive and effective means of the peak demand control.