• Journal of the Korean Society of Costume
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• v.66 no.3
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• pp.147-161
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• 2016

This study analyzed the effects of job style on job satisfaction and turnover intention of fashion models, and the difference in the job style, job satisfaction and turnover intention by model activities period. Data was collected by surveying fashion models with more than 10 modeling experiences, and 230 responses were used in the data analysis. The results of were as follows: First, the job style of fashion models were classified into professional ability type, social relationship-focused type, future-oriented type and body-boasting type. Job satisfaction was classified into satisfaction with working conditions, satisfaction with model activities, and satisfaction with relationships. Turnover intention was classified into intention to change jobs, and intention to quit modeling. Second, being a professional ability type had a negative effect on satisfaction with working conditions, whereas being a future-oriented type had a positive effect on it. The professional ability type and social relationship-focused type had a positive effect on satisfaction with model activities, and the social relationship-focused type had a positive effect on satisfaction with relationships. Third, the future-oriented type and body-boasting type had a negative effect on the intention to change jobs. The social relationship-focused type, future-oriented type and body-boasting type had a negative effect on the intention to quit modeling. Fourth, there were significant differences in the professional ability type, human relationship-focused type, body-boasting type, intention to change jobs and intention to quit modeling by model activities period. Therefore, it is necessary for domestic fashion models to have the appropriate attitude to develop features and competency required for modeling projects and if improvements are made to enhance job satisfaction of fashion models, the fashion modeling industry is expected to make further developments.

• The KIPS Transactions:PartD
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• v.19D no.1
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• pp.81-94
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• 2012

FORM(Feature-Oriented Reuse Method) is one of representative product line engineering methods. The essence of FORM is the FORM architecture models, which can be reused in the development of multiple products of a software product line. The FORM architecture models, however, have the following problems when applied in practice. First, they are not standardized models like UML(Unified Modeling Language) and therefore they can be constructed only through a specific modeling tool. Second, they do not represent architectural variability explicitly. Instead their variability is only managed through a mapping from a feature model. To address these two problems, we developed at first a method for representing the FORM architecture models using UML, which enables the FORM architecture models to be constructed through various available UML modeling tools. Also, we developed an effective method for representing as well as managing the variability of the FORM architecture models through a mapping from a feature model.

• Journal of Astronomy and Space Sciences
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• v.26 no.3
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• pp.317-328
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• 2009

Relative dynamic models of satellites which describe the relative motion between two satellites is fundamental for research on the formation flying. The accuracy of various linearized or nonlinear models of relative motion is analyzed and compared. A 'Modeling Error Index (MEI)' is defined for evaluating the accuracy of models. The accuracy of the relative dynamic models in various orbit circumstance are obtained by calculating the modeling error with various eccentricities of the chief orbit and distances between the chief and the deputy. It is found that the modeling errors of the relative dynamic models have different values according to the eccentricity, J2 perturbation, and the distance between satellites. Since the evaluated accuracy of various models in this paper means the error of dynamic models of the formation flying, the results of this paper are very useful for choosing the appropriate relative model of the formation flying mission.

• Journal of Environmental Impact Assessment
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• v.19 no.3
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• pp.315-321
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• 2010

ArcView and water network models have been integrated to develop the water network modeling system based upon GIS. To develop this system, pre, main, and post processing systems are required. GIS programming technique was adopted by using the ArcView's script language Avenue. The input data of models have been prepared by using the AutoCAD Map3D through the conversion of modeling input data to GIS data for A city. The modeling has been implemented by using EPANET, WaterCAD, InfoWorks. To develop the post processing system, the modeling results of the water network models have been analyzed by using GIS. During the application process of the developed system to B city with 300,000 population, main problems were found in the constructed GIS DB of that city. Thus, pilot study area of B city has been constructed, and pre-, main, and post-processing techniques were invented based upon GIS. Finally, the problems related to waterworks GIS projects in Korea were discussed and solutions were suggested.

• Structural Engineering and Mechanics
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• v.46 no.2
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• pp.213-229
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• 2013

Although performance based assessment procedures are mainly developed for reinforced concrete and steel buildings, URM (Unreinforced Masonry) buildings occupy significant portion of buildings in earthquake prone areas of the world as well as in IRAN. Variability of material properties, non-engineered nature of the construction and difficulties in structural analysis of masonry walls make analysis of URM buildings challenging. Despite sophisticated finite element models satisfy the modeling requirements, extensive experimental data for definition of material behavior and high computational resources are needed. Recently, nonlinear equivalent frame models which are developed assigning lumped plastic hinges to isotropic and homogenous equivalent frame elements are used for nonlinear modeling of URM buildings. The equivalent frame models are not novel for the analysis of masonry structures, but the actual potentialities have not yet been completely studied, particularly for non-linear applications. In the present paper an effective tool for the non-linear static analysis of 2D masonry walls is presented. The work presented in this study is about performance assessment of unreinforced brick masonry buildings through nonlinear equivalent frame modeling technique. Reliability of the proposed models is tested with a reversed cyclic experiment conducted on a full scale, two-story URM building at the University of Pavia. The pushover curves were found to provide good agreement with the experimental backbone curves. Furthermore, the results of analysis show that EFM (Equivalent Frame Model) with Dolce RO (rigid offset zone) and shell element have good agreement with finite element software and experimental results.

• Journal of KIBIM
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• v.2 no.2
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• pp.37-45
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• 2012

Modular bridge construction enabling better productivity of design and construction by standardized members and robotic construction becomes an important issue in construction industry. Modular structures needs accurate information delivery between design, fabrication and construction processes. BIM (Building Information Modeling) based parametric modeling was proposed for the modular bridge substructure. Considering ranges of parameters of the modular bridge, fixed value, variables and relations were defined and these parametric models were applied to design, analysis and fabrication. Experience from development of new structures can be embedded in the 3D models, and the models provide efficient and precise knowledge delivery.

• Earthquakes and Structures
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• v.18 no.5
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• pp.555-565
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• 2020

The main objective of seismic codes is to prevent structural collapse and ensure life safety. Collapse probability of a structure is usually assessed by making a series of analytical model assumptions. This paper investigates the effect of finite element modeling (FEM) assumptions on the estimated collapse capacity of a reinforced concrete (RC) frame building and points out the modeling limitations. Widely used element formulations and hysteresis models are considered in the analysis. A full-scale, three-story RC frame building was utilized as the experimental model. Alternative finite element models are established by adopting a range of different modeling strategies. Using each model, the collapse capacity of the structure is evaluated via Incremental Dynamic Analysis (IDA). Results indicate that the analytically estimated collapse capacities are significantly sensitive to the utilized modeling approaches. Furthermore, results also show that models that represent stiffness degradation lead to a better correlation between the actual and analytical responses. Results of this study are expected to be useful for in developing proper models for assessing the collapse probability of RC frame structures.

• Computers and Concrete
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• v.16 no.5
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.

• Clinical and Experimental Pediatrics
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• v.65 no.5
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• pp.239-249
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• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• Korean Journal of Radiology
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• v.22 no.10
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• pp.1697-1707
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• 2021

The recent introduction of various high-dimensional modeling methods, such as radiomics and deep learning, has created a much greater diversity in modeling approaches for survival prediction (or, more generally, time-to-event prediction). The newness of the recent modeling approaches and unfamiliarity with the model outputs may confuse some researchers and practitioners about the evaluation of the performance of such models. Methodological literacy to critically appraise the performance evaluation of the models and, ideally, the ability to conduct such an evaluation would be needed for those who want to develop models or apply them in practice. This article intends to provide intuitive, conceptual, and practical explanations of the statistical methods for evaluating the performance of survival prediction models with minimal usage of mathematical descriptions. It covers from conventional to deep learning methods, and emphasis has been placed on recent modeling approaches. This review article includes straightforward explanations of C indices (Harrell's C index, etc.), time-dependent receiver operating characteristic curve analysis, calibration plot, other methods for evaluating the calibration performance, and Brier score.