• Culinary science and hospitality research
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• v.14 no.2
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• pp.205-214
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• 2008

The purpose of this study was to measure the moderating effect of belief homogeneity on the relation-ships among attitudinal ambivalence, behavior intention and consumption behavior. The questionnaire, which consisted of items to measure the constructs of belief homogeneity, attitudinal ambivalence, behavior intention and consumption behavior, were completed by 338 subjects in Jeonnam area. Moderated regression analysis was used to measure the moderating effect of belief homogeneity. To test validity and reliability of constructs, factor analysis and Cronbach's $\alpha$ were used in this study. Results of the study demonstrated that the moderated regression analysis result for the data also indicated a better model fit in Model 2 than Model 1. In the Model 1, the main effects of behavior intention and attitudinal ambivalence on consumption behavior were statistically significant. In the Model 2, the main effects of behavior intention, belief homogeneity and attitudinal ambivalence on consumption behavior were statistically significant. The interactional effects of belief homogeneity$\times$attitudinal ambivalence on consumption behavior were statistically significant. Moreover, the effects of attitudinal ambivalence on consumption behavior were statistically significant at all levels of belief homogeneity, except for when homogeneity was high.

• Journal of the Korean Statistical Society
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• v.34 no.3
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• pp.185-195
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• 2005

This article is concerned with the intraclass correlation in survey sampling. From a design-based viewpoint the intraclass correlation is generalized to a finite population with unequal sized clusters. Under simple random cluster sampling the intraclass correlation is given in an explicit form, which is a generalization of the usual one. The range of it is found and the design effect is expressed by means of it. An example is given to compare the intraclass correlation with the homogeneity measure numerically, which shows that two measures are not the same except some limited cases.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1386-1394
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• 2016

Pyramid fusion usually adjusts the Laplacian weights of pixels of the input images by evaluating predefined criteria. This has advantages that it can selectively express intense color and enhance the contrast when applied to HDR exposure fusion. But it may cause noise because the weights are determined by pixel importance without considering the interdependent pixel relationship that constitutes a scene. This paper proposes a fusion method using simple weight criteria generated from the gray pixel values, which is expected to preserve the interdependent relationship and improve execution speed. In order to evaluate the performance of the proposed method we examine a homogeneity measure, H and compare the execution time for both methods. The proposed method is found to be more advantageous with respect to homogeneity and execution speed.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.148-150
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• 2006

In this paper, we propose a new method for detection of mass-type breast cancer in dense mammogram. As the proposed method analyzes texture of the breast tissue using method by fusing Homogeneity and Ranklets, improve problem of traditional method. Homogeneity gives the measure of uniform density, and Ranklets determine orientation selective property at vertical, horizontal and diagonal in mass region. The proposed method is suitable to dense mammogram with tangled normal tissue and cancer tissue. SVM(Support Vector Machine) classifier is used for effective detection of mass-type breast cancer in dense mammogram.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.15-23
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• 2021

Most of the open-source decision tree algorithms are based on three splitting criteria (Entropy, Gini Index, and Gain Ratio). Therefore, the advantages and disadvantages of these three popular algorithms need to be studied more thoroughly. Comparisons of the three algorithms were mainly performed with respect to the predictive performance. In this work, we conducted a comparative experiment on the splitting criteria of three decision trees, focusing on their interpretability. Depth, homogeneity, coverage, lift, and stability were used as indicators for measuring interpretability. To measure the stability of decision trees, we present a measure of the stability of the root node and the stability of the dominating rules based on a measure of the similarity of trees. Based on 10 data collected from UCI and Kaggle, we compare the interpretability of DT (Decision Tree) algorithms based on three splitting criteria. The results show that the GR (Gain Ratio) branch-based DT algorithm performs well in terms of lift and homogeneity, while the GINI (Gini Index) and ENT (Entropy) branch-based DT algorithms performs well in terms of coverage. With respect to stability, considering both the similarity of the dominating rule or the similarity of the root node, the DT algorithm according to the ENT splitting criterion shows the best results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.244-248
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• 2012

F-Measure is one of the external measures for evaluating the validity of clustering results. Though it has clear advantages over other widely used external measures such as Purity and Entropy, F-Measure has inherently been less sensitive than other validity measures. This insensitivity owes to the definition of F-Measure that counts only most influential portions. In this research, we present $F_n$-Measure, an external cluster evaluation measure based on F-Measure. $F_n$-Measure is so sensitive that it can detect their difference in the cases that F-Measure cannot detect the difference in clustering results. We compare $F_n$-Measure to F-Measure for a few clustering results and show which measure draws better result based upon homogeneity and completeness.

• Journal of the Korean Magnetics Society
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• v.8 no.2
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• pp.86-92
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• 1998

Homegeneous feedstock is necessary to make plastic magents with uniform magnetic properties, therefore the optrimized mixing route and the homogeneity evaluation method are demanded. In this paper, method of homogeneity evaluation and effect of homogeneity on the magnetic prperites were investigated using Sr-ferrite /EVA plastic magnets. The feedstocks with different homogeneity were prepared using batch mixer and single screw extruder. The homogeneities of feedstocks were tested by torgue sensor, capilary rheometer, and measurement of magnetic properties. Mixing torque measurement using torque sensor was an effective method to determine the critical powder loading, but it was nor suitable to suitable to determine the feedstock mixing quality. Particle alignment measurement of a plastic magent was very accurate to evaluate the homogeneity, but expensive equipments were required to make and measure the samples. Pressure measurement using capillary rheometer was a very effective and easy method with high accuracy. Homogeneous feedstock increased the particle alignment of plastic magnet. Remanet flux density and maximum energy product increased linearly and quadratically with increasing particle alignment, respectively.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1642-1645
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• 2002

In this paper, I propose a measure of the status of a rule base that can be used to predict the degree of difficulty in the maintenace of a rule base.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.3
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• pp.213-219
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• 2000

Combination of AHP priorities is essential in combining opinions of multiple experts. There are two ways to get combined priorities: one is to combine the pairwise matrices and obtain the priority from it and another is to combine the individual priorities. In this paper, we use a Dirichlet probability model to combine the priorities from multiple experts. The resulting combined priority is an expected value of the model, which is a function of some measure of the homogeneity and credibility of the group of experts. We give some interpretations of this measure and illustrate them by numerical example.

• Journal of Korean Society for Quality Management
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• v.28 no.1
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• pp.119-131
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• 2000

This paper suggests a new diagnostic measure for detecting influential observations in linear discriminant analysis (LDA). It is developed from a Bayesian point of view using a default Bayes factor obtained from the imaginary training sample methodology. The Bayes factor is taken as a criterion for testing homogeneity of covariance matrices in LDA model. It is noted that the effect of an observation over the criterion is fully explained by the diagnostic measure. We suggest a graphical method that can be taken as a tool for interpreting the diagnostic measure and detecting influential observations. Performance of the measure is examined through an illustrative example.