• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.373-375
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• 2000

By DC load flow approximation, we analyzed marginal cost that is the important factor of price signal for network congestion management and expressed as a function of load. In network congestion, a large scale electric network is partitioned into subnetwork to provide a effetive price signal through zonal pricing. We propose a new network partition technique using marginal cost sensitivity with a variety of load consumption.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.212-218
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• 2021

Low-power and Lossy Networks (LLNs) have become the common network infrastructure for a wide scope of Internet of Things (IoT) applications. For efficient routing in LLNs, IETF provides a standard solution, namely the IPv6 Routing Protocol for LLNs (RPL). It enables effective interconnectivity with IP networks and flexibly can meet the different application requirements of IoT deployments. However, it still suffers from different open issues, particularly in large-scale setups. These include the node unreachability problem which leads to increasing routing losses at RPL sink nodes. It is a result of the event of memory overflow at LLNs devices due to their limited hardware capabilities. Although this can be alleviated by the establishment of multi-sink topologies, RPL still lacks the support for effective load balancing among multiple sinks. In this paper, we address the need for an efficient multi-sink load balancing solution to enhance the performance of PRL in large-scale scenarios and alleviate the node unreachability problem. We propose a new RPL objective function, Multi-Sink Load Balancing Objective Function (MSLBOF), and introduce the Memory Utilization metrics. MSLBOF enables each RPL node to perform optimal sink selection in a way that insure better memory utilization and effective load balancing. Evaluation results demonstrate the efficiency of MSLBOF in decreasing packet loss and enhancing network stability, compared to MRHOF in standard RPL.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2454-2467
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• 2015

Increasing traffic and bandwidth requirements bring challenges to the next generation wireless networks (5G). As one of the main technology in 5G networks, Ultra-Dense Network (UDN) can be used to improve network coverage. In this paper, a radio over fiber based model is proposed to solve the load balancing problem in ultra-dense network. Stochastic differential game is introduced for the load balancing algorithm, and optimal load allocated to each access point (RAP) are formulated as Nash Equilibrium. It is proved that the optimal load can be achieved and the stochastic differential game based scheme is applicable and acceptable. Numerical results are given to prove the effectiveness of the optimal algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.97-106
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• 1999

This paper proposes the structure and the algorithm of the Fuzzy-Neural Controller(FNNC) which is able to adapt itself to unknown plant and the change of circumstances at the Fuzzy Logic Controller(FLC) with the Neural Network. This Learning Fuzzy Logic Controller is made up of Fuzzy Logic controller in charge of a main role and Neural Network of an adaptation in variable circumstances. This construct optimal fuzzy controller applied to the 2-area load frequency control of power system, and then it would examine fitness about parameter variation of plant or variation of circumstances. And it proposes the optimal Scale factor method wsint three preformance functions( E, , U) of system dynamics of load frequency control with error back-propagation learning algorithm. Applying the controller to the model of load frequency control, it is shown that the FNNC method has better rapidity for load disturbance, reduces load frequency maximum deviation and tie line power flow deviation and minimizes reaching and settling time compared to the Optimal Fuzzy Logic Controller(OFLC) and the Optimal Control for optimzation of performance index in past control techniques.

• Journal of Communications and Networks
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• v.17 no.4
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• pp.370-383
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• 2015

Effective link quality estimation is a vital issue for reliable routing in wireless networks. This paper studies the performance of expected transmission count (ETX) under different traffic loads. Although ETX shows good performance under light load, its performance gets significantly worse when the traffic load is high. A broadcast packet storm due to new route discoveries severely affects the link ETX values under high traffic load, which makes it difficult to find a good path. This paper presents the design and implementation of a variation of ETX called high load - ETX (HETX), which reduces the impact of route request broadcast packets to link metric values under high load. We also propose a reliable routing protocol using link quality metrics, which is called link quality distance vector (LQDV). We conducted the evaluation of the performance of three metrics - HETX, ETX and minimum hop-count. The simulation results show that HETX improves the average route throughput by up to 25% over ETX under high traffic load. Minimum hop-count has poor performance compared with both HETX and ETX at all of the different traffic loads. Under light load, HETX and ETX show the same performance.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.4
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• pp.480-487
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• 1999

In this paper we propose a neural network precompensated PID(NNP PID) controller for load frequency control of 2-area power system. While proportional integral derivative(PID) controllers are used in power system they have many problems because of high nonlinearities of the power system So a neural network-based precompensation scheme is adopted into a conventional PID controller to obtain a robust control to the nonlinearities. The applied neural network precompen-sator uses an error back-propagation learning algorithm having error and change of error as inputand considers the changing component of forward term of weighting factor for reducing of learning time. Simulation results show that the proposed control technique is superior to a conventional PID controller and an optimal controller in dynamic responses about load disturbances. The pro-posed technique can be easily implemented by adding a neural network precompensator to an existing PID controller.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.1433-1436
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• 2004

The ad hoc network is a collection of wireless mobile nodes dynamically forming a temporary network without the use of any existing network infrastructure of centralized administration. Load-Balanced Ad hoc Routing(LBAR) protocol is an on-demand routing protocol intended for delay-sensitive applications where users are most concern with packet transmission delay. Although LBAR mechanism is a novel load balancing routing protocol for ad hoc network, it has own limitation in route path maintenance phase. Therefore, in this paper, we propose Advanced Load-Balanced Ad hoc Routing(A-LBAR) that is delay-sensitive and has an efficient path maintenance scheme. The robust path maintenance scheme is maintained by considering about nodal loads all over network and misbehavior of overloaded or selfish nodes. The proposed scheme provides good performance over DSR and AODV in terms of packet delay and packet loss rate when some misbehaving nodes exist in the network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.22-37
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• 2013

Content distribution to a large number of concurrent clients stresses both server and network. While the server limitation can be circumvented by deploying server clusters, the network limitation is far less easy to cope with, due to the difficulty in measuring and balancing network load. In this paper, we use two useful network load metrics, the worst link stress (WLS) and the degree of interference (DOI), and formulate the problem as partitioning the clients into disjoint subsets subject to the server capacity constraint so that the WLS and the DOI are reduced for each session and also well balanced across the sessions. We present a network load-aware partition algorithm, which is practicable and effective in achieving the design goals. Through experiments on PlanetLab, we show that the proposed scheme has the remarkable advantages over existing schemes in reducing and balancing the network load. We expect the algorithm and performance metrics can be easily applied to various Internet applications, such as media streaming, multicast group member selection.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1685-1688
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• 2002

Application Routing Load Balancing (ARLB) is a novel load balancing mode that combines QoS routing and load balancing in per application to support QoS far real-time application based on wired network. Zone Routing Protocol (ZRP) is a recent hybrid proactive/reactive routing approach in an attempt to achieve scalability of ad-hoc network. This routing approach has the potential to be efficient in the generation of control traffic than traditional routing schemes. Up to now, without proper load balancing tools, the ZRP can actually guarantee QoS for delay-sensitive applications when congestion occurred in ad-hoc network. In this paper, we propose the ARLB to improve QoS fur delay-sensitive applications based on ZRP in ad-hoc network when congestion occurred and to be forwarding mechanism fur route coupling to support QoS for real-time applications. The critical point is that the routing metric of ARLB is originally designed for wired network environment. Therefore, we study and present an appropriate metric or cost computation routing of ARLB for recently proposed ZRP over ad-hoc network environment.

• ETRI Journal
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• v.27 no.1
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• pp.110-113
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• 2005

The resilient packet ring (RPR) is a data optimized ring network, where one of the key issues is on load balancing for competing streams of elastic traffic. This paper suggests three efficient traffic loading algorithms on the RPR. For the algorithms, we evaluate their efficiency via analysis or simulation.