• 품질경영학회지
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• 제40권3호
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• pp.347-358
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• 2012

Purpose: Given the mixture components - process variables experimental data, we propose the strategy to find the proper combined model. Methods: Process variables are factors in an experiment that are not mixture components but could affect the blending properties of the mixture ingredients. For example, the effectiveness of an etching solution which is measured as an etch rate is not only a function of the proportions of the three acids that are combined to form the mixture, but also depends on the temperature of the solution and the agitation rate. Efficient designs for the mixture components - process variables experiments depend on the mixture components - process variables model which is called a combined model. We often use the product model between the canonical polynomial model for the mixture and process variables model as a combined model. Results: First we choose the reasonable starting models among the class of admissible product models and practical combined models suggested by Lim(2011) based on the model selection criteria and then, search for candidate models which are subset models of the starting model by the sequential variables selection method or all possible regressions procedure. Conclusion: Good candidate models are screened by the evaluation of model selection criteria and checking the residual plots for the validity of the model assumption. The strategy to find the proper combined model is illustrated with examples in this paper.

• 음성과학
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• 제10권4호
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• pp.149-157
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• 2003

In this paper, we proposed the principal component analysis (PCA) fuzzy mixture model for speaker identification. A PCA fuzzy mixture model is derived from the combination of the PCA and the fuzzy version of mixture model with diagonal covariance matrices. In this method, the feature vectors are first transformed by each speaker's PCA transformation matrix to reduce the correlation among the elements. Then, the fuzzy mixture model for speaker is obtained from these transformed feature vectors with reduced dimensions. The orthogonal Gaussian Mixture Model (GMM) can be derived as a special case of PCA fuzzy mixture model. In our experiments, with having the number of mixtures equal, the proposed method requires less training time and less storage as well as shows better speaker identification rate compared to the conventional GMM. Also, the proposed one shows equal or better identification performance than the orthogonal GMM does.

• 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.

• 품질경영학회지
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• 제39권3호
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• pp.400-411
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• 2011

Process variables are factors in an experiment that are not mixture components but could affect the blending properties of the mixture ingredients. For example, the effectiveness of an etching solution which is measured as an etch rate is not only a function of the proportions of the three acids that are combined to form the mixture, but also depends on the temperature of the solution and the agitation rate. Efficient designs for the mixture components-process variables experiments depend on the mixture components-process variables model which is called a combined model. We often use the product model between the canonical polynomial model for the mixture and process variables model as a combined model. In this paper we propose three starting models for the mixture components-process variables experiments. One of the starting model we are considering is the model which includes product terms up to cubic order interactions between mixture effects and the linear & pure quadratic effect of the process variables from the product model. In this paper, we propose a method for finding robust designs and practical designs with respect to D-, G-, and I-optimality for the various starting combined models and then, we find practically efficient and robust designs for estimating the regression coefficients for those models. We find the prediction capability of those recommended designs in the case of three components and three process variables to be good by checking FDS(Fraction of Design Space) plots.

• 설비공학논문집
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• 제5권1호
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• pp.35-43
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• 1993

A numerical model for the solidification of binary mixture is proposed. Numerical model, which employs enthalpy method, is modified from Continuum model, that is, improved relation is proposed for the Enthalpy - Temperature - Concentration - Liquid Mass Fraction. One dimensional example was selected to verify the proposed model. The results show that the new relation can be applied successfully to the solidification or melting of binary mixture.

• 대한음성학회지:말소리
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• 제50호
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• pp.139-150
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• 2004

A Phonetic Tied-Mixture (PTM) model has been proposed as a way of efficient decoding in large vocabulary continuous speech recognition systems (LVCSR). It has been reported that PTM model shows better performance in decoding than triphones by sharing a set of mixture components among states of the same topological location[5]. In this paper we propose a Phonetic Tied-Mixture Syllable (PTMS) model which extends PTM technique up to syllables. The proposed PTMS model shows 13% enhancement in decoding speed than PTM. In spite of difference in context dependent modeling (PTM : cross-word context dependent modeling, PTMS : word-internal left-phone dependent modeling), the proposed model shows just less than 1% degradation in word accuracy than PTM with the same beam width. With a different beam width, it shows better word accuracy than in PTM at the same or higher speed.

• 한국의류학회지
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• 제32권7호
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• pp.1023-1033
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• 2008

The purpose of this study was to segment home-shopper market by using the demographic characteristics. This study enables a better unders landing of home-shoppers and improving the strategy of marketing. The specific objects of this study are as follow: First, it was to exam market segmentations by demographic factors using mixture model. Second, it was to exam shopping orientations of fashion merchandise according to segmentation groups. The data was collected from 637 subjects who had used the home shopping more than one time in a year. The data was analysised through frequencies, factor analysis, ANOVA, Duncan's mutiple range tests with SPSS 12.0 and Mixture model. The results of data are as follows: 1. The result of market segmentation as demographic factor using Mixture model was extracted to 4 market segments called 20's/ unmarried stage, 30's/ children bearing & rearing stage, 40's/ families with children's education stage, 50's/ aging stage. 2. Shopping orientations were extracted to 5 factors called a pleasure oriented, convenience oriented, off-line oriented, human oriented, thrift oriented.

• 한국의류학회지
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• 제26권11호
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• pp.1605-1614
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• 2002

The purpose of this study was to identify the consumers who use the level of price as the indicator of the product quality. In order to implement the purpose of this study, Jeans market had been segmented by the mixture regression model, and price response function was calibrated for each segment. Based on the types of price response function, segments were allocated into one of two groups; the group using the level of price as the quality indicator or the group not using the level of price as that. Then, characteristics of both groups were compared in terms of product attributes and demographic variables. Data were co]looted from the sample of the 23o undergraduate and graduate students in Seoul. For the data analysis, mixture regression model, conjoint analysis, and t-test were used. As a result, jeans market was divided into 5 segments. Segment 1,2,3 were allocated into the group not using the level of price as the quality indicator while segment 4,5 were done into the other group. Significant differences existed between two groups in product attributes, not in demographic variables. Mixture model and conjoint analysis were proved to be an effective set of tools in market segmentation.

• 한국멀티미디어학회논문지
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• 제7권11호
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• pp.1531-1539
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• 2004

본 논문은 입술의 경계선을 효과적으로 추출하는 방법을 제안하였다. 입술 형태는 PDM(Point Distribution Model)과 주성분 분석법을 이용하여 표현하고 입술 경계선은 GLDM(Gray Level Distribution Model)을 기반으로 표현하였다 입술 경계선 추출은 모델에 대한 입력영상의 정확도에 대한 목적함수를 최적화하는 문제로 단순화하였으며, 최적화를 위해 다운힐 심플렉스(Down Hill Simplex) 알고리즘을 이용하였다. 탐색과정에서 지역 최소점으로 수렴하는 문제를 해결하기 위하여 입술 형태 모델의 형태계수를 GMM(Gaussian Mixture Model)을 이용하여 표현하였다. 형태계수에 대한 GMM을 이용하여 입술의 대략적인 형태를 찾고, 이때 사용된 mixture 성분을 이용하여 탐색과정에서 입술의 형태를 조정함으로써 지역 최소점에 수렴하여 입술의 정확한 위치를 찾지 못하는 문제점을 해결하였다. 여러 영상을 대상으로 실험하여 좋은 결과를 얻었다.

• 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.