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새로운 시신경 테 너비의 규칙을 이용한 정상안과 녹내장안의 구별
이명원,신종훈,기창원,Myung Won Lee,Jong Hoon Shin,Chang Won Kee 대한안과학회 2014 대한안과학회지 Vol.55 No.1
Purpose: To evaluate the diagnostic ability of the modified ISNT rule (disc rim thickness of the smaller of inferior and superior > the larger of nasal and temporal) for normal and glaucomatous eyes compared to the classic ISNT rule (disc rim thickness of inferior > superior > nasal > temporal). Methods: Color stereo optic disc photographs of 113 normal subjects and 108 open angle glaucoma patients with early and moderate stage were morphometrically evaluated. The classic ISNT rule and the modified ISNT rule were assessed by masked evaluation of disc photographs at the 3, 6, 9 and 12 o’clock positions. Results: Among normal subjects, 58 of 113 eyes (51.3%) were normal and in open angle glaucoma patients, 104 of 108 eyes (96.3%) were abnormal with the classic ISNT rule. Among normal subjects, 98 of 113 eyes (94.2%) were normal and in open angle glaucoma patients, 102 of 108 eyes (94.4%) were abnormal with the modified ISNT rule. The modified ISNT rule was more accurate than the classic ISNT rule in terms of Cohen’s Kappa analysis used for discriminating between normal and glaucomatous eyes. Conclusions: The modified ISNT rule is useful for differentiating between normal and glaucomatous optic nerves and easily applied in clinical practice. J Korean Ophthalmol Soc 2014;55(1):93-101
A Study on New Performance Index of Granular Models
이명원,곽근창 한국정보기술학회 2016 한국정보기술학회논문지 Vol.14 No.11
In this paper, we propose the performance evaluation of granular model instead of the conventional performance index, which is a root mean square error. In contrast to most fuzzy models developed in the previous researches, the outputs obtained by granular models have the form of information granules rather than plain numeric entities. Thus, we use a concept of information granularity as a network of information granules with contexts produced in the output space and a set of information granules formed in the input space. Granular model is constructed by information granules that are extracted by specialized context-based fuzzy clustering. Furthermore, this model is designed by the rules connecting the association of information granules obtained in input-output space. The performance evaluation method of granular model needs new criterion, because the output of granular model has triangular fuzzy number. The performance index is expressed by the product between fuzzy coverage of predicted output and specificity of information granules. Thus, we can quantify the model output representing information granules. Granular models offer a new performance evaluation method of system modeling by designing models at the level of information granules. For this, we deal with a synthesis function approximation and nonlinear regression and demonstrate the effectiveness of the proposed performance evaluation in the design of granular model.