• 한국병원경영학회지
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• 제10권4호
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• pp.75-97
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• 2005

The research objective is to empirically investigate the compensation packages maximizing the utilities of internal customers by applying the market segmentation theory. Data was collected from four Korean hospitals in Seoul, Busan and Gyunggi-do. The research is designed to seek the compensation package maximizing the utility of doctors by mixture regression model, which has been applied as latent structure and other type of finite mixture models from various academic fields since early 1980s. The mixture regression model shows the optimal segments number and fuzzy classification for each observation by EM(expectation-maximization algorism). The finite mixture regression model is to unmix the sample, to identify the groups, and to estimate the parameters of the density function underlying the observed data within each group. The doctors were segmented into 5 groups by their preference for the compensation package. The results of this study imply that the utility of doctors increases with differentiated compensation package segmented by their preference.

• 비파괴검사학회지
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• 제27권5호
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• pp.449-458
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• 2007

본 논문에서는 감마선을 이용하여 석유화학 공정설비 내부의 단면영상을 복원하였다. 감마 CT 영상 복원을 위해 $5\;mm{\phi}$ 감마선을 팬텀에 조사하여 NaI(T1) 섬광검출기로 스캔하였으며 반복적인 영상복원 방법인 EM 기법으로 가중치 함수를 비교하였다. 감마 CT 영상이 정확히 복원되었는지 확인하기위하여 3가지 가중치 함수에 대해 히스토그램의 명암값 분포를 비교하였다. 실험 결과를 통해 빔 면적에 의한 가중치 함수로 복원할 경우, 원 영상에 가장 가깝게 복원되는 것을 확인하였다.

• 보건행정학회지
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• 제12권2호
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• pp.1-22
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• 2002

Since the early 1980s scholars have applied latent structure and other type of finite mixture models from various academic fields. Although the merits of finite mixture model are well documented, the attempt to apply the mixture model to medical service has been relatively rare. The researchers aim to try to fill this gap by introducing finite mixture model and segmenting inpatients DB from one general hospital. In section 2 finite mixture models are compared with clustering, chi-square analysis, and discriminant analysis based on Wedel and Kamakura(2000)'s segmentation methodology schemata. The mixture model shows the optimal segments number and fuzzy classification for each observation by EM(expectation-maximization algorism). The finite mixture model is to unfix the sample, to Identify the groups, and to estimate the parameters of the density function underlying the observed data within each group. In section 3 and 4 we illustrate results of segmenting 4510 patients data including menial and ratio scales. And then, we show AHP can be identify the attractiveness of each segment, in which the decision maker can select the best target segment.