• Land and Housing Review
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• v.11 no.2
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• pp.95-104
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• 2020

This study aims to explore a purely mechanistic pavement analysis approach where viscoelasticity and fracture of asphalt mixtures are considered to accurately predict deformation and damage behavior of flexible pavements. To do so, the viscoelastic and fracture properties of designated pavement materials are obtained through experiments and a fully mechanistic damage analysis is carried out using a finite element method (FEM). While modeling crack development can be done in various ways, this study uses the cohesive zone approach, which is a well-known fracture mechanics approach to efficiently model crack initiation and propagation. Different pavement configurations and traffic loads are considered based on three main functional classes of roads suggested by FHWA i.e., arterial, collector and local. For each road type, three different material combinations for asphalt concrete (AC) and base layers are considered to study damage behavior of pavement. A concept of the approach is presented and a case study where three different material combinations for AC and base layers are considered is exemplified to investigate progressive damage behavior of pavements when mixture properties and layer configurations were altered. Overall, it can be concluded that mechanistic pavement modeling attempted in this study could differentiate the performance of pavement sections due to varying design inputs. The promising results, although limited yet to be considered a fully practical method, infer that a few mixture tests can be integrated with the finite element modeling of the mixture tests and subsequent structural modeling of pavements to better design mixtures and pavements in a purely mechanistic manner.

• Smart Media Journal
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• v.4 no.3
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• pp.50-55
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• 2015

In this paper, we present background modeling method based on Gaussian mixture model to subtract background for night-time video surveillance. In night-time video, it is hard work to distinguish the object from the background because a background pixel is similar to a object pixel. To solve this problem, we change the pixel of input frame to more advantageous value to make the Gaussian mixture model using scaled histogram stretching in preprocessing step. Using scaled pixel value of input frame, we then exploit GMM to find the ideal background pixelwisely. In case that the pixel of next frame is not included in any Gaussian, the matching test in old GMM method ignores the information of stored background by eliminating the Gaussian distribution with low weight. Therefore we consider the stacked data by applying the difference between the old mean and new pixel intensity to new mean instead of removing the Gaussian with low weight. Some experiments demonstrate that the proposed background modeling method shows the superiority of our algorithm effectively.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3390-3397
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• 2022

Among the different techniques available, nuclear methods, including gamma-gamma logging tools, are of special importance. Though the real environment which surrounds the drilled borehole is a complex fractured medium which the fluid can flow through the porosities, simulation studies generally use the traditional model of a homogeneous mixture of formation and the liquid. Considering a previously published study, which shows that modeling of fluid flow in fractured reservoirs and simulating the formation as an inhomogeneous fractured medium leads to different results compared with those of homogeneous mixture, here we study the effect of the presence of drilling fluid (mudcake) on the response of the detectors in both the models. To study this effect, a typical gamma-gamma logging tool was modeled by using the MCNPX Monte Carlo code. The results show that the responses of the detectors in the mixture model in the presence of various thicknesses of mudcake are sensitive to the density of the formation material. However, this effect is not notable in the inhomogeneous fractured medium. These results emphasize the importance of the model employed for simulation of the medium in gamma-gamma well-logging.

• Korean Journal of Childcare and Education
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• v.16 no.1
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• pp.115-143
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• 2020

Objective: The purpose of this study was to examine underlying types of developmental trajectories of adaptation to child care among infants and toddlers. This study also aimed to identify latent classes in their child care adaptation types in order to find predictors that account for individual differences. Methods: Participants were 420 mothers of infants and toddlers and 123 teachers. The levels of child care adaptation of participating infants and toddlers were rated monthly from early April to June, 2019. The collected data were analyzed using growth mixture modeling, latent class analysis and multinominal logistic analysis. Results: The results of growth trajectories of child care adaptation showed there were two to four latent groups by dimension of child care adaptation. Also, the groups of individual dimensions of child care adaptation were classified into three latent classes, which were 'complying and positive group', 'negative group', and 'individualized group. Multinominal logistic analysis revealed that children's age, gender, and temperament differentiated the three latent classes of adaptation to child care. Conclusion/Implications: The results show individual characteristics that infants and toddlers possess should be prudently considered in order for successful adaptation to child care.

• Journal of Aerospace System Engineering
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• v.1 no.2
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• pp.37-42
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• 2007

This paper proposes a numerical modeling approach to simulate the hybrid combustion phenomena. From the physical understandings of hybrid combustion, the computational domain was separated into three regions: the solid fuel, gas phase reactive flow, and the interface between solid and fluid. Moreover, for the accurate calculation, computational grids for these regions was generated at every time step considering the instantaneous moving interface which are governed by the balance equations using thermal pyrolysis. In the domain of reactive flow, by virtue of diffusion flame structure, turbulent combustion modeling was introduced using either mixture fraction approach or mean reaction rate approach.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.110.1-110
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• 2002

We propose a novel neuro-fuzzy system based on an efficient clustering method. It is a very useful method that improves the performance of a fuzzy model with small number of fuzzy rules. The fuzzy clustering methods are studied in the wide range of fuzzy modeling. One of them, the grid partition method has problem of exponentially increasing number of rules when the dimension of input or number of membership function is linearly increased. On the other hand, the Expectation Maximization algorithm is an efficient estimation for unknown parameters of the Gaussian mixture model. Here it is noted that the parameters can be used for fuzzy clustering method. In a fuzzy modeling, it is desired that...

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.863-871
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• 2010

Until now, many studies related circular data are carried out, but the focuses are mainly on mildly peaked symmetric or asymmetric cases. In this paper we studied a modeling process for sharply peaked asymmetric circular data. By using wrapped Laplace, which was firstly introduced by Jammalamadaka and Kozbowski (2003), and its mixture distributions, we considered the model fitting problem of multi-modal circular data as well as unimodal one. In particular we suggested EM algorithm to find ML estimates of the mixture of wrapped Laplace distributions. Simulation results showed that the suggested EM algorithm is very accurate and useful.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8C
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• pp.788-794
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• 2003

It is very important to construct statistical model in order to exactly estimate the signal variance from the noisy image. By using estimated variance of original image, in general, Wiener filter is constructed, and it is applied to the noisy image. In this paper, we propose a new statistical mixture modeling of wavelet coefficients for image denoising. Firstly, a simple classification method is used to construct a significance map that captures significant property of wavelet coefficients. Based upon the significance map, the state probabilities of mixture model is computed, and signal variance is estimated by using them. Experimental results show that the proposed method yields 0.1-0.2㏈ higher PSNR than conventional methods for image denoising.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.241-250
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• 2010

Over the past few decades, several studies have been made on the modeling of circular data. But these studies focused mainly on the symmetrical cases including von Mises distribution. Recently, many studies with skew-normal distribution have been conducted in the linear case. In this paper, we dealt the problem of fitting of non-symmetrical circular data with wrapped skew-normal distribution which can be derived by using the principle of wrapping. Wrapped skew-normal distribution is very flexible to asymmetical data as well as to symmetrical data. Multi-modal data are also fitted by using the mixture of wrapped skew-normal distributions. To estimate the parameters of mixture, we suggested the EM algorithm. Finally we verified the accuracy of the suggested algorithm through simulation studies. Application with real data is also considered.

• Journal of information and communication convergence engineering
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• v.11 no.2
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• pp.95-102
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• 2013

This paper presents a stochastic driver behavior modeling framework which takes into account both individual and general driving characteristics as one aggregate model. Patterns of individual driving styles are modeled using a Dirichlet process mixture model, as a non-parametric Bayesian approach which automatically selects the optimal number of model components to fit sparse observations of each particular driver's behavior. In addition, general or background driving patterns are also captured with a Gaussian mixture model using a reasonably large amount of development data from several drivers. By combining both probability distributions, the aggregate driver-dependent model can better emphasize driving characteristics of each particular driver, while also backing off to exploit general driving behavior in cases of unseen/unmatched parameter spaces from individual training observations. The proposed driver behavior model was employed to anticipate pedal operation behavior during car-following maneuvers involving several drivers on the road. The experimental results showed advantages of the combined model over the model adaptation approach.