• Economic and Environmental Geology
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• v.28 no.5
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• pp.503-512
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• 1995

Groundwater flow in fracture networks is simulated using a discrete fracture (DF) model which assume that groundwater flows only through the fracture network. This assumption is available if the permeability of rock matrix is very low. It is almost impossible to describe fracture networks perfectly, so a stochastic approach is used. The stochastic approach assumes that the characteristic parameters in fracture network have special distribution patterns. The stochastic model generates fracture networks with some characteristic parameters. The finite element method is used to compute fracture flows. One-dimensional line element is the element type of the finite elements. The simulation results are shown by dominant flow paths in the fracture network. The dominant flow path can be found from the simulated groundwater flow field. The model developed in this study provides the tool to estimate the influences of characteristic parameters on groundwater flow in fracture networks. The influences of some characteristic parameters on the frcture flow are estimated by the Monte Carlo simulation based on 30 realizations.

• Advances in aircraft and spacecraft science
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• v.3 no.4
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• pp.471-502
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• 2016

This paper presents the dynamic instability analysis of un-damped elastically supported piezoelectric functionally graded (FG) beams subjected to in-plane static and dynamic periodic thermomechanical loadings with uncertain system properties. The elastic foundation model is assumed as one parameter Pasternak foundation with Winkler cubic nonlinearity. The piezoelectric FG beam is subjected to non-uniform temperature distribution with temperature dependent material properties. The Young's modulus and Poison's ratio of ceramic, metal and piezoelectric, density of respective ceramic and metal, volume fraction exponent and foundation parameters are taken as uncertain system properties. The basic nonlinear formulation of the beam is based on higher order shear deformation theory (HSDT) with von-Karman strain kinematics. The governing deterministic static and dynamic random instability equation and regions is solved by Bolotin's approach with Newmark's time integration method combined with first order perturbation technique (FOPT). Typical numerical results in terms of the mean and standard deviation of dynamic instability analysis are presented to examine the effect of slenderness ratios, volume fraction exponents, foundation parameters, amplitude ratios, temperature increments and position of piezoelectric layers by changing the random system properties. The correctness of the present stochastic model is examined by comparing the results with direct Monte Caro simulation (MCS).

• Structural Engineering and Mechanics
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• v.68 no.4
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• pp.423-442
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• 2018

In this paper, masonry infilled reinforced concrete (RC) frames are analyzed through a probabilistic approach. A macro-modeling technique, based on an equivalent diagonal pin-jointed strut, has been resorted to for modelling the stiffening contribution of the masonry panels. Since it is quite difficult to decide which mechanical characteristics to assume for the diagonal struts in such simplified model, the strut width is here considered as a random variable, whose stochastic characterization stems from a wide set of empirical expressions proposed in the literature. The stochastic analysis of the masonry infilled RC frame is conducted via the Probabilistic Transformation Method by employing a set of space transformation laws of random vectors to determine the probability density function (PDF) of the system response in a direct manner. The knowledge of the PDF of a set of response indicators, including displacements, bending moments, shear forces, interstory drifts, opens an interesting discussion about the influence of the uncertainty of the masonry infills and the resulting implications in a design process.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.196-197
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• 2006

Preventive maintenance can avail the generation utilities to reduce cost and gain more profit in a competitive supply-side power market. So, it is necessary to perform reliability analysis on the systems in which reliability is essential. In this paper, RCM (Reliability -Centered Maintenance) analytical method is adopted using real historical failure data in Korean power plants. Therefore, the reliability -based Probability model for predicting the failures of components in the power plant is also established, and application to FMECA(Failure Mode Effects and Critical Analysis) consideration of failure probability, Based on the weighting ranking of generating equipments which status to be probability estimation by FMECA. The FMECA is an engineering analysis and a core activity performed by reliability engineers to review the effects of probable failure modes of generating equipments and assemblies of the power system on system performance. The results of this paper show that application of FMECA with stochastic approach to the preventive maintenance can efficiently avail decreasing the cost on maintenance and hence improve the total benefit.

• 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.

• Computers and Concrete
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• v.29 no.1
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• pp.1-14
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• 2022

Several aspects influence corrosive processes in reinforced concrete (RC) structures such as environmental conditions, structural geometry and mechanical properties. Since these aspects present large randomnesses, probabilistic models allow a more accurate description of the corrosive phenomena. Besides, the definition of limit states in the reliability assessment requires a proper mechanical model. In this context, this study proposes a straightforward methodology for the mechanical-probabilistic modelling of RC structures subjected to reinforcements' corrosion. An improved damage approach is proposed to define the limit states for the probabilistic modelling, considering three main degradation phenomena: concrete cracking, rebar yielding and rebar corrosion caused either by chloride or carbonation mechanisms. The stochastic analysis is evaluated by the Monte Carlo simulation method due to the computational efficiency of the Lumped Damage Model for Corrosion (LDMC). The proposed mechanical-probabilistic methodology is implemented in a computational framework and applied to the analysis of a simply supported RC beam and a 2D RC frame. Curves illustrate the probability of failure evolution over a service life of 50 years. Moreover, the proposed model allows drawing the probability of failure map and then identifying the critical failure path for progressive collapse analysis. Collapse path changes caused by the corrosion phenomena are observed.

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.505-514
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• 2020

Since a large amount of training data is typically needed to train Deep Neural Networks (DNNs), a parallel training approach is required to train the DNNs. The Stochastic Gradient Descent (SGD) algorithm is one of the most widely used methods to train the DNNs. However, since the SGD is an inherently sequential process, it requires some sort of approximation schemes to parallelize the SGD algorithm. In this paper, we review various efforts on parallelizing the SGD algorithm, and analyze the computational overhead, communication overhead, and the effects of the approximations.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.602-606
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• 2005

A systematic procedure of probability analysis for general distributions is developed based on the first four moments estimated from polynomial interpolation of the system response function and the Pearson system. The function approximation is based on a specially selected experimental region for accuracy and the number of function evaluations is taken equal to that of the unknown coefficient for efficiency. For this purpose, three error-minimizing conditions are proposed and corresponding canonical experimental regions are formed for popular probability. This approach is applied to study the stochastic properties of the performance functions of a MEMS structure, which has quite large fabrication errors compared to other structures. Especially, the vibratory micro-gyroscope is studied using the statistical moments and probability density function (PDF) of the performance function to be the difference between resonant frequencies corresponding to sensing and driving mode. The results show that it is very sensitive to the fabrication errors and that the types of PDF of each variable also affect the stochastic properties of the performance function although they have same the mean and variance.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.83-92
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• 2011

This paper examines empirically Durham's (2008) asset pricing models to the KOSPI200 index. This model Incorporates the VKOSPI index as a proxy for 1 month integrated volatility. This approach uses option prices to back out implied volatility states with an explicitly speci ed risk-neutral measure and risk premia estimated from the data. The application uses daily observations of the KOSPI200 and VKOSPI indices from January 2, 2003 to September 24, 2010. The empirical results show that non-affine model perform better than affine model.

• Smart Structures and Systems
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• v.11 no.1
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• pp.19-34
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• 2013

The objective of this paper is to develop on-line system parameter estimation and damage detection technique from the response measurements through using the Recursive Covariance-Driven Stochastic Subspace identification (RSSI-COV) approach. To reduce the effect of noise on the results of identification, discussion on the pre-processing of data using recursive singular spectrum analysis (rSSA) is presented to remove the noise contaminant measurements so as to enhance the stability of data analysis. Through the application of rSSA-SSI-COV to the vibration measurement of bridge during scouring experiment, the ability of the proposed algorithm was proved to be robust to the noise perturbations and offers a very good online tracking capability. The accuracy and robustness offered by rSSA-SSI-COV provides a key to obtain the evidence of imminent bridge settlement and a very stable modal frequency tracking which makes it possible for early warning. The peak values of the identified $1^{st}$ mode shape slope ratio has shown to be a good indicator for damage location, meanwhile, the drastic movements of the peak of $2^{nd}$ mode slope ratio could be used as another feature to indicate imminent pier settlement.