• Journal of Korean Society for Quality Management
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• v.41 no.1
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• pp.109-118
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• 2013

Purpose: Given the several proper models for given mixture components-process variables experimental data, we propose a strategy to find the optimal condition in which the performance of the responses is well-behaved under those models. Methods: Given the mixture experimental data with process variables, first we choose the reasonable starting models among the class of admissible product models based on the model selection criteria and then, search for the candidate models that are the subset models of the starting model by the sequential variable selection method or all possible regressions procedure. Good candidate models are screened by the evaluation of model selection criteria and checking the residual plots for the validity of the model assumption. Results: We propose a strategy to find the optimal condition in which the performance of the responses is well-behaved under those good candidate models by adopting the optimization methods developed in multiple responses surface methodology. Conclusion: A strategy is proposed to find the optimal condition in which the performance of the responses is well-behaved under those proper combined models. This strategy to find the optimal condition is illustrated with the example in this paper.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.353-366
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• 2022

Spatial estimation of geoscience data (geo-data) is challenging due to spatial heterogeneity, data scarcity, and high dimensionality. A novel spatial estimation method is needed to consider the characteristics of geo-data. In this study, we proposed the application of Gaussian Mixture Model (GMM) among machine learning algorithms with multivariate data for robust spatial predictions. The performance of the proposed approach was tested through soil chemical concentration data from a former smelting area. The concentrations of As and Pb determined by ex-situ ICP-AES were the primary variables to be interpolated, while the other metal concentrations by ICP-AES and all data determined by in-situ portable X-ray fluorescence (PXRF) were used as auxiliary variables in GMM and ordinary cokriging (OCK). Among the multidimensional auxiliary variables, important variables were selected using a variable selection method based on the random forest. The results of GMM with important multivariate auxiliary data decreased the root mean-squared error (RMSE) down to 0.11 for As and 0.33 for Pb and increased the correlations (r) up to 0.31 for As and 0.46 for Pb compared to those from ordinary kriging and OCK using univariate or bivariate data. The use of GMM improved the performance of spatial interpretation of anthropogenic metals in soil. The multivariate spatial approach can be applied to understand complex and heterogeneous geological and geochemical features.

• Korean Chemical Engineering Research
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• v.49 no.6
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• pp.823-828
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• 2011

Characteristics of water separation in a FWKO(Free Water Knok Out) vessel was investigated to remove water from oil-water mixture. Decane, toluene, and asphalt were used as model oils. Preliminary experiments were carried out for decane in a prototype FWKO vessel. Based on the results of preliminary experiments, the prototype vessel was modified and its performance was evaluated by using toluene. The effects of experimental variables on the separation of oil-water mixture were evaluated in terms of separation efficiency. The experimental variables include water cut(water ratio), number of baffles, residence times, and operation temperatures. The optimum conditions of water separation were found to be 0.8 water cut, 3 baffles, 1,200 sec, and $45^{\circ}C$.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.503-506
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• 2004

Evolutionary operation is useful to improve on-line full-scale manufacturing processes by systematically changing the levels of the process variables without jeopardizing the product. This paper presents a system design for an evolutionary operation software called 'evolutionary optimizer'. The system design is based primarily on data flow diagram. Evolutionary optimizer consists of four modules: factorial design module, many variables module, mixture Production module, and mean/dispersion module.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1249-1254
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• 2008

The crucial part of graphical model is to compute the posterior distribution of parameters plus with the hidden variables given the observed data. In this paper, implementation of variational Bayes method for Gaussian mixture model and derivation of factorial variational approximation have been proposed. This result can be used for data analysis tasks like information retrieval or data visualization.

• Journal of Korean Society for Quality Management
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• v.47 no.2
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• pp.271-281
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• 2019

Purpose: The purpose of this study is to suggest a more efficient distance measure taking into account the relationship between categorical variables for categorical data cluster analysis. Methods: In this study, the association-based dissimilarity was employed to calculate the distance between two categorical data observations and the distance obtained from the association-based dissimilarity was applied to the PAM cluster algorithms to verify its effectiveness. The strength of association between two different categorical variables can be calculated using a mixture of dissimilarities between the conditional probability distributions of other categorical variables, given these two categorical values. In particular, this method is suitable for datasets whose categorical variables are highly correlated. Results: The simulation results using several real life data showed that the proposed distance which considered relationships among the categorical variables generally yielded better clustering performance than the Hamming distance. In addition, as the number of correlated variables was increasing, the difference in the performance of the two clustering methods based on different distance measures became statistically more significant. Conclusion: This study revealed that the adoption of the relationship between categorical variables using our proposed method positively affected the results of cluster analysis.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.31-41
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• 2014

Biplot is a useful graphical method to simultaneously explore the rows and columns of a two-way data matrix. In particular, principal component factor biplot is a graphical method to describe the interrelationship among many variables in terms of a few underlying but unobservable random variables called factors. If we consider the unobservable variables (which are mutually independent and also non-Gaussian), we can apply the independent component analysis decomposing a mixture of non-Gaussian in its independent components. In this case, if we apply the principal component factor analysis, we cannot clearly describe the interrelationship among many variables. Therefore, in this study, we apply the independent component analysis of Jutten and Herault (1991) decomposing a mixture of non-Gaussian in its independent components. We suggest an independent component biplot to interpret the independent component analysis graphically.

• International Journal of Highway Engineering
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• v.11 no.4
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• pp.127-132
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• 2009

This study presents the test results on the compaction characteristics of warm mix asphalt mixtures that include the additive in 3 different mixtures(hot mix asphalt, SBS and SMA). The tests were conducted to find out the compaction characteristics on the compactability with varying compaction time, different amount of the warm mix additive and lowering the compaction temperature. The Superpave gyratory compactor was used to find out the variation of the density when the number of the gyration is varied. A dense mixture and 3 different warm mix additives were employed to find the relationship between compactability and compaction time. The comparison of the compactability with lowering the temperature was conducted using dense mixture, SBS polymer modified mixture and stone matrix asphalt mixture(SMA). The difference of the density of warm mix asphalt mixtures was not found due to the lowering of compaction temperature when it was compared with the standard mixture and the warm mix showed the stable condition in density. In the mean time, depending upon the different warm mix additive and mixture, the difference of density and the variation trend of compaction is found to be existed and shows the relationship between these two variables.

• The Korean Journal of Food And Nutrition
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• v.23 no.1
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• pp.39-44
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• 2010

Retarding retrogradation of Korean rice cakes(Karedduk) with a mixture of trehalose and Sun-Tender added, after 0, 24, and 48 hr of storage at $5^{\circ}C$, was analyzed by Avrami kinetics. A central composite design was used for arrangement of treatment. The two independent variables selected for retarding retrogradation analysis were amounts of trehalose(x) and Sun-Tender(y). Trehalose was added at 0, 3, 6, 9, and 12% levels, and Sun-Tender added at 0, 0.3, 0.6, 0.9, and 1.2% levels, to dry rice flour. The Avrami exponent(n) for the mixtures of 9% trehalose and 0.3% Sun-Tender, and 9% trehalose and 0.9% Sun-Tender were lower than in the control. The time constant(1/k) for the mixture of trehalose and Sun-Tender was higher than in the control. The effect of retarding retrogradation of Korean rice cakes with added mixtures of trehalose and Sun-Tender showed an increasing trend as the amount of trehalose increased. These results suggest that adding a mixture of 9% trehalose and 0.3% Sun-Tender, or 9% trehalose and 0.9% Sun-Tender to Korean rice cakes(Karedduk) is effective for retarding retrogradation.