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유영재 대한전기학회 2003 전기학회논문지 D Vol.52 No.2
- This paper describes a new pattern classifier neural network to extract the feature from a letter. The proposed pattern classifier is based on relative distance, which is measure between an input datum and the center of cluster group. So, the proposed classifier neural network is called relative neural network(RNN). According to definitions of the distance and the learning rule, the structure of RNN is designed and the pseudo code of the algorithm is described. In feature extraction of letter, RNN, in spite of deletion of learning rate, resulted in the identical performance with those of winner-take-all(WTA), and self-organizing-map(SOM) neural network. Thus, it is shown that RNN is suitable to extract the feature of a letter.
전위성 3급 부정교합 환자에서의 교합평면의 Decompensation에 대한 임상적 고찰
유영재,이철민,차경석 단국대학교 치의학연구소 1994 논문집 Vol.6 No.1
Transitional Class Ⅲ is mandibular prognathism that functional factor is transitive to skeletal factor. Considerations for the treatment of transitional Class Ⅲ with deep bite are functional factor, LFH & overbite, anterior component of force, inclination of maxillary incisor, occlusal plane angle and residual growth. The purpose of this report is attempt to decompensate transitional class Ⅲ with inadequate compensation of occlusal plane due to functional factor and then compensate residual protrusive causing anatomic factor correctly. Because treatment range that orthodontists can control is limitted to lower facial area, this report deal with occlusal plane angle and functional occlusal plane. In these 3 cases, after assessment of skeletal analysis, adequate compensation pattern of each case was reevaluated and harmonious skeletal pattern was established.