• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.1
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• pp.76-84
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• 2002

In this paper, it is studied to remove the baseline wander and to minimize the distortion of ST segment in the noise filtering of ECG. In general, the standard filter and adaptive filter are used to remove the baseline wander of the ECG. But the standard filter is limited because the frequency of the baseline signal is variable and the apative filter is difficult to select the reference signal in case of using the adaptive filter. So we proposed a new method of the structure without reference signal using neural networks. To be convinced of the performance of this method, we used ECG data of MIT-BIHs. and obtained the result of the high performance,(-53.3[dB]) than standard filter(-16.3[dB]) and adaptive filter (-44.9[dB]).

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.4
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• pp.46-53
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• 2008

In this study, we developed RBFN(Radial Basis Function Networks) based load modeling method with harmonic components. The developed method considers harmonic information as well as fundamental frequency and voltage considered as essential factors in conventional method. Thus, the reposed method makes it possible to effectively estimate load characteristics in power lines with harmonics. RBFN has some advantage such as simple structure and rapid computation ability compared with multi-layer perceptorn which is extensively applied for load modeling. To verify the effectiveness, the proposed method has been intensively tested with various dataset acquired under the different frequency and voltage and compared it with conventional methods such as polynomial method, MLPN and RBFN with no harmonic components.

• Korea Journal of Hospital Management
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• v.8 no.1
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• pp.81-94
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• 2003

This paper aims at developing a prediction model and analyzing a sensitivity for the outpatient's overall satisfaction on utilizing hospital services by using data mining techniques within the context of customer satisfaction. From a total of 900 outpatient cases, 80 percent were randomly selected as the training group and the other 20 percent as the validation group. Cases in the training group were used in the development of the CHAID and Neural Networks. The validation group was used to test the performance of these models. The major findings may be summarized as follows: the CHAID provided six useful predictors - satisfaction with treatment level, satisfaction with healthcare facilities and equipments, satisfaction with registration service, awareness of hospital reputation, satisfaction with staffs courtesy and responsiveness, and satisfaction with nurses kindness. The prediction accuracy rates based on MLP (77.90%) is superior to RBF (76.80%).