• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.126-131
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• 2009

This study was carried out for the reliability of PD(Partial Discharge) pattern recognition. For the pattern recognition, the database for PD was established by use of self-designed insulation defects which occur and were mostly critical in GIS(Gas Insulated Switchgear). The acquired database was analyzed to distinguish patterns by means of PRPD(Phase Resolved Partial Discharge) method and stored to the form with to unite the average amplitude of PD pulse and the number of PD pulse as the input data of neural network. In order to prove the performance of genetic algorithm combined with neural network, the neural networks with trial-and-error method and the neural network with genetic algorithm were trained by same training data and compared to the results of their pattern recognition rate. As a result, the recognition success rate of defects was 93.2% and the neural network train process by use of trial-and-error method was very time consuming. The recognition success rate of defects, on the other hand, was 100% by applying the genetic algorithm at neural network and it took a relatively short time to find the best solution of parameters for optimization. Especially, it could be possible that the scrupulous parameters were obtained by genetic algorithm.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1724-1731
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• 2018

The conventional polynomial neural network (PNN) is a classical flexible neural structure and self-organizing network, however it is not free from the limitation of overfitting problem. In this study, we propose a space search-optimized polynomial neural network (ssPNN) structure to alleviate this problem. Ranking selection is realized by means of ranking selection-based performance index (RS_PI) which is combined with conventional performance index (PI) and coefficients based performance index (CPI) (viz. the sum of squared coefficient). Unlike the conventional PNN, L2-norm regularization method for estimating the polynomial coefficients is also used when designing the ssPNN. Furthermore, space search optimization (SSO) is exploited here to optimize the parameters of ssPNN (viz. the number of input variables, which variables will be selected as input variables, and the type of polynomial). Experimental results show that the proposed ranking selection-based polynomial neural network gives rise to better performance in comparison with the neuron fuzzy models reported in the literatures.

• IE interfaces
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• v.11 no.2
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• pp.39-48
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• 1998

It is strongly believed that Video on Demand(VOD) will become one of the most promising services for Broadband Integrated Services Digital Network(B-ISDN). VOD service can be classified into two types of services: Near VOD(NVOD) and Interactive VOD(IVOD). Although VOD network requires many kinds of resources, we only consider video servers, programs. and storages. In the network. some video servers should be installed at some nodes(especially, at the root node for NVOD service). so that each node with video server stores video programs and distributes stored programs to customers. We take into account three kinds of costs related with the above resources: a program transmission cost, a program storage cost, and a video server installation cost. There exists a trade-off relationship among those three costs according to the locations of video servers and the kinds of programs stored at each video server. Given a multi-level VOD network and the total number of programs being served in the network. the resource. optimization problem for providing both IVOD and NVOD services is to determine where to install video servers, and which and how many programs should be stored at each video server, in order to minimize the total cost. In this paper we develop an efficient dynamic programming algorithm to solve the problem. We also implement the algorithm based on two different service policies assumed in this paper.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.4
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• pp.138-147
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• 1994

Performance management of computer networks is intended to improve a given network performance in order for more efficient information exchange between subsystems of an integrated large-scale system. Improtance of performance management is growing as many function of the large- scale system depend on the quality of communication services provided by the network. The role of performance management is to manipulate the adjustable protocol parameters on line so that the network can adapt itself to a dynamic environment. This can be divided into two subtasks : performance evaluation to find how changes in protocol parameters affect the network performance and decision making to detemine the magnitude and direction of parameter adjustment. This paper is the second part of the two papers focusing on conceptual design, development, and evaluation of performance management for token bus networks. This paper specifically deals with the task of decision making which utilizes the principles of stochastic optimization and learning automata. The developed algorithm can adjuxt four timer settings of a token bus protocol based on the result of performance evaluation. The overall performance management has been evaluated for its efficacy on a network testbed.

• Journal of Korea Water Resources Association
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• v.36 no.2
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• pp.161-172
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• 2003

This study has evaluated the stream gauge network with the main emphasis on if the current stream gauge network can catch the runoff characteristics of the basin. As the evaluation of the stream gauge network in this study does not consider a special purpose of a stream gauge, nor the effect from a hydraulic structure, it becomes an optimization of current stream gauge network under the condition that each stream gauge measures the natural runoff volume. This study has been applied to the Nam-Han River Basin for the optimization of total 31 stream gauge stations using the entropy concept. Summarizing the results are as follows. (1) The unit hydrograph representing the basin response from rainfall can be transferred into a probability density function for the application of the entropy concept to optimize the stream gauge network. (2) Accurate derivation of unit hydrographs representing stream gauge sites was found the most important part for the evaluation of stream gauge network, which was assured in this research by comparing the measured and derived unit hydrographs. (3) The Nam-Han River Basin was found to need at least 28 stream gauge stations, which was derived by considering both the shape of the unit hydrograph and the runoff volume. If considering only the shape of the unit hydrograph, the number of stream gauges required decreases to 23.

• ETRI Journal
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• v.34 no.6
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• pp.892-899
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• 2012

We analyze a wideband spectrum in a cognitive radio (CR) network by employing the optimal adaptive multiband sensing-time joint detection framework. This framework detects a wideband M-ary quadrature amplitude modulation (M-QAM) primary signal over multiple nonoverlapping narrowband Gaussian channels, using the energy detection technique so as to maximize the throughput in CR networks while limiting interference with the primary network. The signal detection problem is formulated as an optimization problem to maximize the aggregate achievable secondary throughput capacity by jointly optimizing the sensing duration and individual detection thresholds under the overall interference imposed on the primary network. It is shown that the detection problems can be solved as convex optimization problems if certain practical constraints are applied. Simulation results show that the framework under consideration achieves much better performance for M-QAM than for binary phase-shift keying or any real modulation scheme.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2226-2229
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• 2003

A new method is presented to construct a classifier. This was accomplished by combining a generalized regression neural network (GRNN) and a genetic algorithm (GA). The classifier constructed in this way is referred to as a GA-GRNN. The GA played a role of controlling training factors simultaneously. In GA optimization, neuron spreads were represented in a chromosome. The proposed optimization method was applied to a data set, consisted of 4 different promoter sequences. The training and test data were composed of 115 and 58 sequence patterns, respectively. The range of neuron spreads was experimentally varied from 0.4 to 1.4 with an increment of 0.1. The GA-GRNN was compared to a conventional GRNN. The classifier performance was investigated in terms of the classification sensitivity and prediction accuracy. The GA-GRNN significantly improved the total classification sensitivity compared to the conventional GRNN. Also, the GA-GRNN demonstrated an improvement of about 10.1% in the total prediction accuracy. As a result, the proposed GA-GRNN illustrated improved classification sensitivity and prediction accuracy over the conventional GRNN.

• Journal of Korea Multimedia Society
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• v.23 no.9
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• pp.1129-1138
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• 2020

The performance of convolutional deep learning networks is generally determined according to parameters of target dataset, structure of network, convolution kernel, activation function, and optimization algorithm. In this paper, a genetic algorithm is used to select the appropriate deep learning model and parameters for Alzheimer's classification and to compare the learning results with preliminary experiment. We compare and analyze the Alzheimer's disease classification performance of VGG-16, GoogLeNet, and ResNet to select an effective network for detecting AD and MCI. The simulation results show that the network structure is ResNet, the activation function is ReLU, the optimization algorithm is Adam, and the convolution kernel has a 3-dilated convolution filter for the accuracy of dementia medical images.

• Journal of KIISE
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• v.41 no.12
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• pp.1145-1151
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• 2014

In this paper, we propose MPLO(Multi-Path routing with Local Optimization) for load balancing in the tactical backbone network. The MPLO manages global metric and local metric separately. The global metric is propagated to other routers via a routing protocol and is used for configuring loop-free multi-path. The local metric reflecting link utilization is used to find an alternate path when congestion occurs. We verified MPLO could effectively distribute user traffic among available routers by simulation.

• Journal of Navigation and Port Research
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• v.37 no.3
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• pp.321-327
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• 2013

As the market of express delivery services expands rapidly, delivery service companies are exposed to severe competition. As a result of the surplus of delivery companies, they are struggling with remaining competitive at a reasonable price with appropriate level of customer satisfaction. To cope with competition pressures, a strategic alliance is suggested as an effective solution to the challenges faced by small and medium enterprises (SMEs) in express delivery services. Therefore, this study suggests a combined optimization and simulation approach to the reconfiguration of an express delivery service network for strategic alliance with respect to strategy partnership of closing/keeping service centers among companies involved and adjustments of their cutoff times. An illustrative numerical example is presented to demonstrate the practicality and efficiency of the approach.