• Journal of the Korean Society of Safety
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• v.27 no.2
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• pp.105-110
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• 2012

This study deals with the accident models of rotary. The objectives is to develop the models by day and nighttime. In pursuing the above, this study gives particular attentions to collecting the data of geometric structure and accidents of 20 rotaries and developing the Poisson and negative binomial regression models using NLOGIT 4.0. The main results are as follows. First, the numbers of accident of nighttime (1.03 per 1,000 entering vehicles) were analyzed to be very higher than those of day (0.47 per 1,000 entering vehicles). Second, 4 Poisson models which were all statistically significant were developed, in which the dependent variable were both the number of accident and EPDO (equivalent property damage only). Finally, the number of entry/exit ($X_1$) and the number of entering lane ($X_5$) in the models of the number of accident, and $X_1$ in the EPDO models were adopted as the common variables. The variables were analyzed to be all positive to the dependent variables.

• Korean Journal of Computational Design and Engineering
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• v.9 no.2
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• pp.122-132
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• 2004

Progressive mesh representation and generation have become one of the most important issues in network-based computer graphics. However, current researches are mostly focused on triangular mesh models. On the other hand, solid models are widely used in industry and are applied to advanced applications such as product design and virtual assembly. Moreover, as the demand to share and transmit these solid models over the network is emerging, the generation and the transmission of progressive solid models depending on specific engineering needs and purpose are essential. In this paper, we present a Cellular Topology-based approach to generating and transmitting progressive solid models from a feature-based solid model for internet-based design and collaboration. The proposed approach introduces a new scheme for storing and transmitting solid models over the network. The Cellular Topology (CT) approach makes it possible to effectively generate progressive solid models and to efficiently transmit the models over the network with compact model size. Thus, an arbitrary solid model SM designed by a set of design features is stored as a much coarser solid model SM/sup 0/ together with a sequence of n detail records that indicate how to incrementally refine SM/sup 0/ exactly back into the original solid model SM = SM/sup 0/.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1278-1281
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• 2004

Several workers reported the relationship between osteoporosis and age-related reductions in the BV/TV (bone volume fraction) of vertebral trabecular bones. However, there were few micro finite element (micro-FE) models to account for the treatments of the osteoporotic trabecular bone. In the present study, micro-FE models of osteoporotic and hormone-treated bone models were constructed to analyze the effect of specimen location and boundary condition on mechanical characteristics of hormone treatment model for osteoporotic trabecular bone. Top and bottom sections of specimens were also investigated individually to study the effect of specimen location. Hormone-treated models were allowed to have the same relative BV/TV (13.4％) as that used in models of previous researchers. The present study reported the elastic and plastic characteristics of the osteoporosis and hormone-treated bone models. In the present study, in-situ boundary condition was applied to the simulated compression tests for in-vivo condition of vertebral trabecular bone. The present study indicated that the hormone therapy was likely to improve the mechanical characteristics of osteoporotic bones and the mechanical characteristics of vertebral trabecular bone specimen were dependent on the captured location and boundary condition.

• Journal of Educational Research in Mathematics
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• v.25 no.3
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• pp.407-429
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• 2015

This study aims to find the elementary mathematics teachers' satisfaction, availability, and needs, based on the mathematics teaching models in current mathematics curriculum. The satisfaction on current mathematics teaching models is about 80%, but the frequency of usage of the models is a bit low because the models are used once a unit or a semester. Among other subjects, the teachers prefer the teaching models of social studies or science, because the models are convenient in applying models to their teaching. We proposed a few ideas to enhance the availability of mathematics teaching models including the consideration on a variety of content areas of mathematics, students' differences of their mathematics levels, and the teaching and learning methods in mathematics curriculum.

• Journal of the Earthquake Engineering Society of Korea
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• v.22 no.7
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• pp.401-409
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• 2018

Performance-based seismic evaluation is usually done by considering simplified models for the liquid storage tanks therefore, it is important to validate those simplified models before conducting such evaluation. The purpose of this study is to compare the seismic response results of the FSI (fluid-structure interaction) model and the simplified models for the cylindrical liquid storage tanks and to verify the applicability of the simplified models for estimating failure probability. Seismic analyses were carried out for two types of storage tanks with different aspect ratios (H/D) of 0.45 and 0.86. FSI model represents detailed 3D fluid-structure interaction model and simplified models are modeled as cantilever mass-spring model, frame type mass-spring model and shell type mass-spring model, considering impulsive and convective components. Seismic analyses were performed with modal analysis followed by time history analysis. Analysis results from all the models were verified by comparing with the results calculated by the code and literature. The results from simplified models show good agreement with the ones from detailed FSI model and calculated results from code and literature, confirming that all three types of simplified models are very valid for conducting failure probability analysis of the cylindrical liquid storage tanks.

• International Journal of Highway Engineering
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• v.14 no.5
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• pp.133-143
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• 2012

PURPOSES: Using the collected data for crash, traffic volume, and design elements on ramps between 2007 and 2009, this research effort was initiated to develop traffic crash prediction models for expressway ramps. METHODS: Three negative binomial regression models and three zero-inflated negative binomial regression models were developed for individual ramp types, including direct, semi-direct and loop, respectively. For validating the developed models, authors compared the estimated crash frequencies with actual crash frequencies of twelve randomly selected interchanges, the ramps of which have not been used for model developing. RESULTS: The results show that the negative binomial regression models for direct, semi-direct and loop ramps showed 60.3%, 63.8% and 48.7% error rates on average whereas the zero-inflated negative binomial regression models showed 82.1%, 120.4% and 57.3%, respectively. CONCLUSIONS: Conclusively, the negative binomial regression models worked better in traffic crash prediction than the zero-inflated negative binomial regression models for estimating the frequency of traffic accidents on expressway ramps.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.123-136
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• 2015

Science has developed with great achievements after Galileo's discovery of the law depicting a relationship between observable variables. However, many natural phenomena have been better explained by models including unobservable random effects. A mixed effect model was the first statistical model that included unobservable random effects. The importance of the mixed effect models is growing along with the advancement of computational technologies to infer complicated phenomena; subsequently mixed effect models have extended to various statistical models such as hierarchical generalized linear models. Hierarchical likelihood has been suggested to estimate unobservable random effects. Our special issue about mixed effect models shows how they can be used in statistical problems as well as discusses important needs for future developments. Frequentist and Bayesian approaches are also investigated.

• Journal of KIBIM
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• v.9 no.4
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• pp.41-50
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• 2019

Digital models for a cable-stayed bridge are defined considering data-driven engineering from design to construction. Algorithms for digital object generation of each component of the cable-stayed bridge were developed. Using these algorithms, Level-3 BIM practices can be realized from design stages. Based on previous practices, digital object library can be accumulated. Basic digital models are modified according to given design conditions by a designer. Once design models are planned, various applications using the models are linked the models such as estimation, drawings and mechanical properties. Federated bridge models are delivered to construction stages. In construction stage, the models can be efficiently revised according to the changed situations during construction phases. In this paper, measured coordinates are imported to the model generation algorithms and revised models are obtained. Augmented reality devices and their applications are proposed. AR simulations in construction site and in office condition are tested. From this pilot test of digital models, it can be said that Level-3 BIM practices can be realized by using in-house modeling algorithms according to different purposes.

• Computers and Concrete
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• v.26 no.6
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• pp.533-546
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• 2020

The prediction of spall response of reinforced concrete members like columns and slabs have been attempted by earlier researchers with analytical solutions, as well as with empirical models developed from data generated from physical or numerical experiments, with different degrees of success. In this article, compared to the empirical models, more versatile and accurate models are developed based on model-free approach of artificial neural network (ANN). Synthetic data extracted from the results of numerical experiments from literature have been utilized for the purpose of training and testing of the ANN models. For two concrete members, namely, slabs and columns, different sets of ANN models were developed, each of which proved to have definite advantages over the corresponding empirical model reported in literature. In case of slabs, for all three categories of spall, the ANN model results were superior to the empirical models as evaluated by the various performance metrics, such as correlation, root mean square error, mean absolute error, maximum overestimation and maximum underestimation. The ANN models for each category of column spall could handle three variables together: namely, depth, spacing of longitudinal and transverse reinforcement, as contrasted to the empirical models that handled one variable at a time, and at the same time yielded comparable performance. The application of the ANN models for spall prediction of concrete slabs and columns developed in this study has been discussed along with their limitations.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.