• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.2
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• pp.8-17
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• 2000

In this work, two approaches were proposed for the recognition of partial discharge patterns. The first approach was neural network with backpropagation algorithm, and the second approach was angle calculation between t재 operator vectors. PD signals were detected using three electrode systems; IEC(b), needle-plane and CIGRE method II electrode system. Both of neural network and angle comparison method showed good recognition performance for the patterns similar to the trained patterns. And the number of operators to be used had a great influence on the recognition performance to the untrained patterns.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.498-500
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• 1993

This paper presents handover algorithm method using fuzzy rules and neura1 network. In future mobile communication systems, the amount of call requests over a region will increase dramatically. This problem has to be solved by decreasing the cell size. But, this method lets a mobile station switch the a base station at a higher rate. In order to maintain better mobile communication system in a micro or pico cellular system, better handover algorithm must be devoloped. In this paper, we propose a handover algorithm which is based on the fuzzy teory that is applied to make rules with the parameters and neural network that is to learn rules. This new handover algorithm is tested by computer simulation and compared with the conventional algorithms.

• The Journal of the Acoustical Society of Korea
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• v.17 no.2E
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• pp.26-30
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• 1998

In this paper, we compose neural network predictive HMM(NNPHMM) to provide the dynamic feature of the speech pattern for the HMM. The NNPHMM is the hybrid network of neura network and the HMM. The NNPHMM trained to predict the future vector, varies each time. It is used instead of the mean vector in the HMM. In the experiment, we compared the recognition abilities of the one hundred Korean syllables according to the variation of hidden layer, state number and prediction orders of the NNPHMM. The hidden layer of NNPHMM increased from 10 dimensions to 30 dimensions, the state number increased from 4 to 6 and the prediction orders increased from 10 dimensions to 30 dimension, the state number increased from 4 to 6 and the prediction orders increased from the second oder to the fourth order. The NNPHMM in the experiment is composed of multi-layer perceptron with one hidden layer and CMHMM. As a result of the experiment, the case of prediction order is the second, the average recognition rate increased 3.5% when the state number is changed from 4 to 5. The case of prediction order is the third, the recognition rate increased 4.0%, and the case of prediction order is fourth, the recognition rate increased 3.2%. But the recognition rate decreased when the state number is changed from 5 to 6.

• The Journal of the Korea Contents Association
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• v.7 no.9
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• pp.127-138
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• 2007

Due to the development of internet technology and the steady increase of turnover at B2C market many companies put a lot of work into maintaining a good relationship with internet customers. Particularly, analyzing and understanding specific customer groups are essential for effective CRM and marketing strategy Thus, this paper proposes the method to define the customers of online bookstore into several meaningful groups. Five important factors and factor scores for each respondent are obtained by Factor Analysis. Six groups are classified by Cluster Analysis and Analysis of Variance(ANOVA) is used to verify the difference between each group.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.81-89
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• 1996

A design method of fuzzy modeling is presented for the model identification of route choice of traffic problems.The proposed fuzzy modeling implements system structure and parameter identification in the eficient form of""IF..., THEN-.."", using the theories of optimization theory, linguistic fuzzy implication rules. Three kinds ofmethod for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 21,and proposed modified-linear inference (type 3). The fuzzy inference method are utilized to develop the routechoice model in terms of accurate estimation and precise description of human travel behavior. In order to identifypremise structure and parameter of fuzzy implication rules, improved complex method is used and the least squaremethod is utilized for the identification of optimum consequence parameters. Data for route choice of trafficproblems are used to evaluate the performance of the proposed fuzzy modeling. The results show that the proposedmethod can produce the fuzzy model with higher accuracy than previous other studies -BL(binary logic) model,B(production system) model, FL(fuzzy logic) model, NN(neura1 network) model, and FNNs (fuzzy-neuralnetworks) model -.fuzzy-neural networks) model -.