• Journal of Communications and Networks
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• v.13 no.6
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• pp.633-638
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• 2011

In this paper, we consider resource allocation with proportional fairness in the downlink orthogonal frequency division multiple access relay networks, in which relay nodes operate in decode-and-forward mode. A joint optimization problem is formulated for relay selection, subcarrier assignment and power allocation. Since the formulated primal problem is nondeterministic polynomial time-complete, we make continuous relaxation and solve the dual problem by Lagrangian dual decomposition method. A near-optimal solution is obtained using Karush-Kuhn-Tucker conditions. Simulation results show that the proposed algorithm provides superior system throughput and much better fairness among users comparing with a heuristic algorithm.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.461-464
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• 1998

Lateral Information Propagation Neural Networks (LIPN) is proposed for on-line interpolation. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D LIPN hardware has been implemented with general purpose analog ICs to test the interpolation capability of the proposed neural networks. Experiments with static and dynamic signals have been done upon the LIPN hardware.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.331-333
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• 1994

A learning method for induction motor trajectory using neuro-fuzzy networks (NFN) based on fusion of fuzzy logic theory and neural networks is proposed. The premise and consequent parameters of the NFN affecting the controllers performances are modified during the learning stages by the proposed learning method to implement an optimal controller only with pre-determined target trajectory and the least amount of knowledge about an induction motor. The induction motor position control system is simulated to verify the effectiveness of the learned NF controller(NFC). The simulation results shows that the proposed learning method has good dynamic performance and small steady state error.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.234-238
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• 1996

In this paper, Plasma-Enhanced Chemical Vapor Deposition (PECVD) modeling using Polynomial Neural Networks (PNN) has been introduced. The deposition of SiO2 was characterized via a 25-1 fractional factorial experiment, was used to train PNNs using predicted squared error (PSE). The optimal neural network structure and learning parameters were determined by means of a second fractional factorial experiment. The optimized networks minimized both learning and prediction error. From these PNN process models, the effect of deposition conditions on film properties has been studied. The deposition experiments were carried out in a Plasma Therm 700 series PECVD system. The models obtained will ultimately be used for several other manufacturing applications, including recipe synthesis and process control.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.606-609
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• 1995

This paper presents discriminate between magnetizing inrush and internal faults of power transformer by artificial neural networks trained with preprocessing of fault discriminant. The proposed neural networks contain multi-layer perceptron using back-propagation learning algorithm with logistic sigmoid activation function. For this training and test, we used the relaying signals obtained from the EMTP simulation of model power system. It is shown that the proposed transformer protection system by neural networks never misoperated.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.7
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• pp.355-362
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• 2003

The fault analysis problem of a distribution network has many difficulties comes from the unbalance of loads or networks and the lacks of load information. The unbalance of loads or networks make the fault location difficult when it use the classical sequence transformation. Moreover the amount of load in the distribution networks fluctuates with time. This paper introduces a recent fault location algorithm using iterative method which handle the unbalance of the problem. But, the fault location errors comes from the load fluctuations still left. For the real application of the new fault location algorithm in distribution networks, this paper studied the effect of the load fluctuations in the algorithm.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2447-2455
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• 2018

Security is more important in many sensor applications. The node replication attack is a major issue on sensor networks. The replicated node can capture all node details. Node Replication attacks use its secret cryptographic key to successfully produce the networks with clone nodes and also it creates duplicate nodes to build up various attacks. The replication attacks will affect in routing, more energy consumption, packet loss, misbehavior detection, etc. In this paper, a Secure-Efficient Centralized approach is proposed for detecting a Node Replication Attacks in Wireless Sensor Networks for Static Networks. The proposed system easily detects the replication attacks in an effective manner. In this approach Secure Cluster Election is used to prevent from node replication attack and Secure Efficient Centralized Approach is used to detect if any replicated node present in the network. When comparing with the existing approach the detection ratio, energy consumption performs better.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.8
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• pp.1615-1623
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• 2009

In order to develop reliable on-site partial discharge (PD) pattern recognition algorithm, we introduce Type-2 Fuzzy Neural Networks (T2FNNs) optimized by means of Particle Swarm Optimization(PSO). T2FNNs exploit Type-2 fuzzy sets which have a characteristic of robustness in the diverse area of intelligence systems. Considering the on-site situation where it is not easy to obtain voltage phases to be used for PRPDA (Phase Resolved Partial Discharge Analysis), the PD data sets measured in the laboratory were artificially changed into data sets with shifted voltage phases and added noise in order to test the proposed algorithm. Also, the results obtained by the proposed algorithm were compared with that of conventional Neural Networks(NNs) as well as the existing Radial Basis Function Neural Networks (RBFNNs). The T2FNNs proposed in this study were appeared to have better performance when compared to conventional NNs and RBFNNs.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.17 no.8
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• pp.898-903
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• 2004

In this paper, neural networks is studied to apply as a PD source classification in XLPE power cable specimen. For treeing discharge sources in the specimen, three defected models are made. And these data making use of a computer-aided discharge analyser, statistical and other discharge parameters is calculated to discrimination between different models of discharge sources. And also these parameter is applied to classify PD sources by neural networks. Neural Networks has good recognition rate for three PD sources.

• Journal of Communications and Networks
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• v.16 no.5
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• pp.559-567
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• 2014

In this paper, a framework for power allocation of downlink transmissions in orthogonal frequency division multiple access-based decode-and-forward cellular relay networks is investigated. We consider a system with a single base station communicating with multiple users assisted by multiple relays. The relays have limited power which must be divided among the users they support in order to maximize the data rate of the whole network. Advanced power allocation schemes are crucial for such networks. The optimal relay power allocation which maximizes the data rate is proposed as an upper bound, by finding the optimal power requirement for each user based on knapsack problem formulation. Then by considering the fairness, a new relay power allocation scheme, called weighted-based scheme, is proposed. Finally, an efficient power reallocation scheme is proposed to efficiently utilize the power and improve the data rate of the network. Simulation results demonstrate that the proposed power allocation schemes can significantly improve the data rate of the network compared to the traditional scheme.