• Food Science and Industry
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• v.52 no.1
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• pp.20-31
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• 2019

Sensory perception of food/beverage products is one of the most important quality factors to determine consumer acceptability and thus sensory discrimination methodology has been a vital tool for quality management. Signal detection theory(SDT) and Thurstonian modeling provide the most advanced psychometric approach to modeling various discrimination methods. In these theories, perceptual and cognitive decisional factors are considered so that, a fundamental measure of sensory difference (d') can be computed, independent of test methods used. In this paper, sensory discrimination analysis based on SDT and Thurstonian modeling is introduced for more accurate and systematic applications of sensory and hedonic quality management in industry. Ways to realize the statistical power and relative sensitivity of sensory discrimination methods theorized in SDT and Thurstonian modeling in practice, are also discussed by using a case study of the Nongshim quality management program for drinking water in which SDT A-Not A test methodology was further optimized.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.20 no.4
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• pp.429-453
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• 2022

Numerical modeling and scenario composition are needed to characterize the geological environment of the disposal site and analyze the long-term evolution of natural barriers. In this study, processes and features of the hydro-mechanical behavior of natural barriers were categorized and represented using the interrelation matrix proposed by SKB and Posiva. A hydro-mechanical coupled model was evaluated for analyzing stress field changes and fracture zone re-activation. The processes corresponding to long-term evolution and the hydro-mechanical mechanisms that may accompany critical processes were identified. Consequently, practical numerical methods could be considered for these geological engineering issues. A case study using a numerical method for the stability analysis of an underground disposal system was performed. Critical stress distribution regime problems were analyzed numerically by considering the strata's movement. Another case focused on the equivalent continuum domain composition under the upscaling process in fractured rocks. Numerical methods and case studies were reviewed, confirming that an appropriate and optimized modeling technique is essential for studying the stress state and geological history of the Korean Peninsula. Considering the environments of potential disposal sites in Korea, selecting the optimal application method that effectively simulates fractured rocks should be prioritized.

• Cartoon and Animation Studies
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• s.37
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• pp.143-158
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• 2014

In the modeling processes of 3D computer animation, methods to build optimal work conditions to realize real forms for more efficient works have been advanced. Digital sculpting software, published in 1999, ZBrush has been positioned as an essential factor in character model work requiring of realistic descriptions through different manufacturing methods from previous modeling work processes and easy shape realization. Their functional areas are expanding. So, in this production case paper, as a method to product more optimized animation character models, the efficiency of production method linking digital sculpting software (Z-Brush) and animation production software (Maya) was deliberated and its consequences and implications are suggested. To this end, first the technical features of polygon modeling and Retopology were reviewed. Second, based on it, the efficiency of animation character modeling work processes through step linking ZBrush and Maya suggested in this paper was analyzed. Third, based on the features drawn before, in order to prove the hypothesis on modeling optimization method suggested in this paper, the production process of character Dumvee from a short animation film, 'Cula & Mina' was analyzed as an example. Through this study, it was found that technical approach easiness and high level of completion could be realized through two software linked work processes. This study is considered to be a reference for optimizing production process of related industries or modeling-related classes by deliberating different modeling process linked systems.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.543-559
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• 2017

Tree-based regression and classification ensembles form a standard part of the data-science toolkit. Many commonly used methods take an algorithmic view, proposing greedy methods for constructing decision trees; examples include the classification and regression trees algorithm, boosted decision trees, and random forests. Recent history has seen a surge of interest in Bayesian techniques for constructing decision tree ensembles, with these methods frequently outperforming their algorithmic counterparts. The goal of this article is to survey the landscape surrounding Bayesian decision tree methods, and to discuss recent modeling and computational developments. We provide connections between Bayesian tree-based methods and existing machine learning techniques, and outline several recent theoretical developments establishing frequentist consistency and rates of convergence for the posterior distribution. The methodology we present is applicable for a wide variety of statistical tasks including regression, classification, modeling of count data, and many others. We illustrate the methodology on both simulated and real datasets.

• Journal of Korea Multimedia Society
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• v.19 no.11
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• pp.1900-1908
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• 2016

Recent times have seen the introduction of modeling technologies using implicit surface and marching cubes algorithm in the field of computer graphics. Implicit surface modeling is used to express characters or fluid. This study presents a calculation method for the density of curve skeletal primitives using parametric curve and implements an implicit surface modeling tool by utilizing Maya API. Skeletal primitives can be assembled and utilized in character modeling using the implemented modeling tool. Results could be obtained more effectively compared to existing particle-based methods.

• Korean Journal of Computational Design and Engineering
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• v.5 no.4
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• pp.293-300
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• 2000

The non-manifold geometric modeling technique is to improve design process and to Integrate design, analysis, and manufacturing by handling mixture of wireframe model, surface model, and solid model in a single data structure. For the non-manifold geometric modeling, Euler operators and other high level modeling methods are necessary. Boolean operation is one of the representative modeling method for the non-manifold geometric modeling. This thesis studies Boolean operations of non-manifold model with the data structure of selective storage. The data structure of selective storage is improved non-manifold data structure in that existing non-manifold data structures using ordered topological representation method always store non-manifold information even if edges and vortices are in the manifold situation. To implement Boolean operations for non-manifold model, intersection algorithm for topological cells of three different dimensions, merging and selection algorithm for three dimensional model, and Open Inventor(tm), a 3D toolkit from SGI, are used.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.534-538
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• 2008

Maya currently is used form various area in 3D graphics. Maya provide that modeling methods are NURBs, Polygon, and Subdivision. There are special feature their modeling method. So we need to modeling convert. After modeling convert, there is no UV node. In this paper, we study creating techniques UV node which NURBs modeling convert Subdivsion modeling. Moreover, we present prototype implementation.

• The Journal of Society for e-Business Studies
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• v.15 no.2
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• pp.191-203
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• 2010

Recently applications of RFID technologies in production and logistics systems are expanding. In this research, we deal with workflow modeling methods for handling RFID-tagged parts. We verified that the material flow processes in a RFID system can be designed and assessed using workflow modeling notations, and suggested available process patterns using BPMN. In addition, we proposed an algorithm to monitor the exact status of flows and determine whether some of the events are ghost reads or not by referring predefined workflow definitions. The major contribution of this research is that it has demonstrated how well-established workflow modeling methods can be applied to RFID-based systems.

• International Journal of Highway Engineering
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• v.19 no.4
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• pp.1-7
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• 2017

PURPOSES : This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors. METHODS: Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling. RESULTS : The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables. CONCLUSIONS : Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.

• Women's Health Nursing
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• v.29 no.2
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• pp.128-136
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• 2023

Purpose: Topic modeling is a text mining technique that extracts concepts from textual data and uncovers semantic structures and potential knowledge frameworks within context. This study aimed to identify major keywords and network structures for each major topic to discern research trends in women's health nursing published in the Korean Journal of Women Health Nursing (KJWHN) using text network analysis and topic modeling. Methods: The study targeted papers with English abstracts among 373 articles published in KJWHN from January 2011 to December 2021. Text network analysis and topic modeling were employed, and the analysis consisted of five steps: (1) data collection, (2) word extraction and refinement, (3) extraction of keywords and creation of networks, (4) network centrality analysis and key topic selection, and (5) topic modeling. Results: Six major keywords, each corresponding to a topic, were extracted through topic modeling analysis: "gynecologic neoplasms," "menopausal health," "health behavior," "infertility," "women's health in transition," and "nursing education for women." Conclusion: The latent topics from the target studies primarily focused on the health of women across all age groups. Research related to women's health is evolving with changing times and warrants further progress in the future. Future research on women's health nursing should explore various topics that reflect changes in social trends, and research methods should be diversified accordingly.