• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4144-4165
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• 2018

Software-defined networking (SDN) has emerged as a promising technology for network programmability and experiments. In this work, we focus on virtual network embedding in multiple controllers SDN network. In SDN virtualization environment, virtual SDN networks (vSDNs) operate on the shared substrate network and managed by their each controller, the placement and load of controllers affect vSDN embedding process. We consider controller placement, vSDN embedding, controller adjustment as a joint problem, together considering different quality of service (QoS) requirement for users, formulate the problem into mathematical models to minimize the average time delay of control paths, the load imbalance degree of controllers and embedding cost. We propose a heuristic method which places controllers and partitions control domains according to substrate SDN network, embeds different QoS constraint vSDN requests by corresponding algorithms, and migrates switches between control domains to realize load balance of controllers. The simulation results show that the proposed method can satisfy different QoS requirement of tenants, keep load balance between controllers, and work well in the acceptance ratio and revenue to cost ratio for vSDN embedding.

• 전기학회논문지P
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• 제62권2호
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• pp.65-71
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• 2013

Nonlinear loads with harmonics exist in an actual power system where harmonic currents make voltage distortion. The sum of reactive power measured at individual load is different from the measured reactive power at a bus in a power system with linear and non-linear loads. In this study, ANN(artificial neural network) load modeling technique with consideration of harmonics is introduced for more accurate component load modeling and an impact coefficient is proposed for aggregation of component loads. Results of this research show more accurate load modeling method. Since precise data for power system analysis can be acquired, the proposed method will be used for power system planning and maintenance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1522-1522
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• 1999

This paper proposes the methods of short-term load forecasting using Kohonen neural networks and back-propagation neural networks. Historical load data is divided into 5 patterns for the each seasonal data using Kohonen neural networks and using these results, load forecasting neural network is used for next day hourly load forecasting. Normal days and holidays are forecasted. For load forecasting in summer, max-, and min-temperature data are included in neural networks for a better forecasting accuracy. To show the possibility of the proposed method, it was tested with hourly load data of Korea Electric Power Corporation. (1993-1997)

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2005년도 하계학술대회
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• pp.382-387
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• 2005

There are many studies on arbitrarily divisible load scheduling problem in a distributed computing network consisting of processors interconnected through communication links. It is not efficient to arbitrarily distribute the load that comes into the system. In this paper, how to schedule in case that arbitrarily indivisible load comes into the system is studied. Also, the cases of the divisible load mixed with the indivisible load that come into network were dealt with optimal load distribution in parallel processing system by scheduling applied to linear programming.

• Structural Engineering and Mechanics
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• 제34권2호
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• pp.159-174
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• 2010

Elastic buckling load of perforated steel plates is typically predicted using the finite element or conjugate load/displacement methods. In this paper an artificial neural network (ANN)-based formula is presented for the prediction of the elastic buckling load of rectangular plates having a circular cutout. By using this formula, the elastic buckling load of perforated plates can be calculated easily without setting up an ANN platform. In this study, the center of a circular cutout was chosen at different locations along the longitudinal x-axis of plates subjected to linearly varying loading. The results of the finite element method (FEM) produced by the commercial software package ANSYS are used to train and test the network. The accuracy of the proposed formula based on the trained ANN model is evaluated by comparing with the results of different researchers. The results show that the presented ANN-based formula is practical in predicting the elastic buckling load of perforated plates without the need of an ANN platform.

• 한국융합학회논문지
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• 제9권5호
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• pp.1-6
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• 2018

스마트그리드에서 정확한 단기 부하 예측을 통한 자원의 이용 계획은 에너지 시스템 운영의 불확실성을 줄이고 운영 효율을 높이는데 있어서 매우 중요하다. 단기 부하 예측에 얕은 신경회로망을 포함한 다수의 머신 러닝 기법이 적용되어왔지만 예측 정확도의 개선이 요구되고 있다. 최근에는 컴퓨터 비전이나 음성인식 분야에서 심층 신경회로망의 뛰어난 연구 결과로 인해 심층 신경회로망을 단기 전력수요 예측에 적용해 예측 정확도를 개선하려는 시도가 주목 받고 있다. 본 논문에서는 일별 전력 부하 첨두치를 예측하기 위한 다층신경회로망 구조의 심층 신경회로망 모델을 제안한다. 제안된 심층 신경회로망은 층별 학습이 선행된 후 전체 모델의 학습이 이루어진다. 한국전력거래소에서 얻은 4년 동안의 일별 전력 수요 데이터를 사용, 하루 및 이틀 앞선 전력수요 첨두치를 예측하는 심층 신경회로망 모델을 구축하고 예측 정확도를 비교, 평가한다.

• ETRI Journal
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• 제29권2호
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• pp.255-257
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• 2007

We propose an improved selective randomized load balancing (ISRLB) robust scheme under the hose uncertainty model for a special double-hop routing network architecture. The ISRLB architecture maintains the resilience properties of Valiant's load balancing and reduces the network cost/propagation delay in all other robust routing schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권9호
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• pp.3539-3558
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• 2015

This paper investigates and analyzes the default block placement strategy of HDFS. HDFS is a typical representative distributed file system to stream vast amount of data effectively at high bandwidth to user applications. However, the default HDFS block placement policy assumes that all nodes in the cluster are homogeneous, and places blocks with a simple RoundRobin strategy without considering any nodes' resource characteristics, which decreases self-adaptability of the system. The primary contribution of this paper is the proposition of a network load sensitive block placement strategy. We have implemented our algorithm and justify it through extensive simulations and comparison with similar existing studies. The results indicate that our work not only performs much better in the data distribution but also improves write performance more significantly than the others.

• Journal of Internet Technology
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• 제20권1호
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• pp.293-300
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• 2019

The Internet of Things (IoT) enables embedded devices to connect to the internet either through IP or the web in a physical environment. The increase in performance of wireless access services, adaptive load balancing, and interference routing metric becomes the key challenges in Wireless Mesh Networks (WMN). However, in the case of IoT over WMN, a large number of users generate abundant net flows, which can result in network traffic jam. Therefore, in this paper, we propose a Load Balancing and Interference Delay Aware routing metric algorithm to efficiently address the issues present in the current work. The proposed scheme efficiently utilizes the available mesh station queue information and the number of mesh stations suffering from channel interference in the available path. The simulations results show that the proposed scheme performed superior to the existing routing metrics present in the current literature for similar purposes.

• 대한전기학회논문지:전력기술부문A
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• 제53권10호
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• pp.529-535
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• 2004

Load forecasting is essential in the electricity market for the participants to manage the market efficiently and stably. A wide variety of techniques/algorithms for load forecasting has been reported in many literatures. These techniques are as follows: multiple linear regression, stochastic time series, general exponential smoothing, state space and Kalman filter, knowledge-based expert system approach (fuzzy method and artificial neural network). These techniques have improved the accuracy of the load forecasting. In recent 10 years, many researchers have focused on artificial neural network and fuzzy method for the load forecasting. In this paper, we propose an algorithm of a hybrid load forecasting method using fuzzy linear regression and general exponential smoothing and considering the sensitivities of the temperature. In order to consider the lower load of weekends and Monday than weekdays, fuzzy linear regression method is proposed. The temperature sensitivity is used to improve the accuracy of the load forecasting through the relation of the daily load and temperature. And the normal load of weekdays is easily forecasted by general exponential smoothing method. Test results show that the proposed algorithm improves the accuracy of the load forecasting in 1996.