• Smart Structures and Systems
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• 제9권6호
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• pp.505-534
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• 2012

The present work deals with second order statistics of post buckling response of piezoelectric laminated composite cylindrical shell panel subjected to hygro-thermo-electro-mechanical loading with random system properties. System parameters such as the material properties, thermal expansion coefficients and lamina plate thickness are assumed to be independent of the temperature and electric field and modeled as random variables. The piezoelectric material is used in the forms of layers surface bonded on the layers of laminated composite shell panel. The mathematical formulation is based on higher order shear deformation shell theory (HSDT) with von-Karman nonlinear kinematics. A efficient $C^0$ nonlinear finite element method based on direct iterative procedure in conjunction with a first order perturbation approach (FOPT) is developed for the implementation of the proposed problems in random environment and is employed to evaluate the second order statistics (mean and variance) of the post buckling load of piezoelectric laminated cylindrical shell panel. Typical numerical results are presented to examine the effect of various environmental conditions, amplitude ratios, electrical voltages, panel side to thickness ratios, aspect ratios, boundary conditions, curvature to side ratios, lamination schemes and types of loadings with random system properties. It is observed that the piezoelectric effect has a significant influence on the stochastic post buckling response of composite shell panel under various loading conditions and some new results are presented to demonstrate the applications of present work. The results obtained using the present solution approach is validated with those results available in the literature and also with independent Monte Carlo Simulation (MCS).

• Nuclear Engineering and Technology
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• 제51권3호
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• Earthquakes and Structures
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• 제20권1호
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• pp.71-86
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• 2021

This paper focuses on the development and assessment of the expected damage for the rocking response of rigid anchored blocks, with irregular geometry and non-uniform mass distribution, considering the site conditions and the seismicity of Mexico City. The non-linear behavior of the restrainers is incorporated to evaluate the pure tension and tension-shear failure mechanisms. A probabilistic framework is performed covering a wide range of block sizes, slenderness ratios and eccentricities using physics-based ground motion simulation. In order to incorporate the uncertainties related to the propagation of far-field earthquakes with a significant contribution to the seismic hazard at study sites, it was simulated a set of scenarios using a stochastic summation methods of small-earthquakes records, considered as Empirical Green's Function (EGFs). As Engineering Demand Parameter (EDP), the absolute value of the maximum block rotation normalized by the body slenderness, as a function of the peak ground acceleration (PGA) is adopted. The results show that anchorages are more efficient for blocks with slenderness ratio between two and three, while slenderness above four provide a better stability when they are not restrained. Besides, there is a range of peak intensities where anchored blocks located in soft soils are less vulnerable with respect to those located in firm soils. The procedure used in here allows to take decisions about risk, reliability and resilience assessment of different types of contents, and it is easily adaptable to other seismic environments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권2호
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• pp.73-83
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• 2014

Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.

• 대한기계학회논문집B
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• 제21권3호
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• pp.350-357
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• 1997

The transient incompressible flow behind the axisymmetric bluff body is numerically simulated using the random vortex method(RVM). Based on the vorticity formulation of the unsteady Navier-Stokes equations, the Lagrangian approach with a stochastic simulation of diffusion using random walk technique is employed to account for the transport processes of the vortex elements. The numerical solutions for 2-dimensional recirculating flow behind a backward-facing step in the laminar range of Reynolds number are compared with experimental data. The present simulation focuses on the transitional flow regime where the recirculation zone behind the bluff body becomes highly unsteady and large-scale vortex eddies are shed from the bluff body wake due to intrinsic shear layer instabilities. The unsteady vertical flow structures and the mixing characteristics behind the bluff body are discussed in detail.

• 한국환경과학회지
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• 제22권4호
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• pp.415-424
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• 2013

In this paper, time series of soil moisture were measured for a steep forest hillslope to model and understand distinct hydrological behaviours along two different transects. The transfer function analysis was presented to characterize temporal response patterns of soil moisture for rainfall events. The rainfall is a main driver of soil moisture variation, and its stochastic characteristic was properly treated prior to the transfer function delineation between rainfall and soil moisture measurements. Using field measurements for two transects during the rainy season in 2007 obtained from the Bumrunsa hillslope located in the Sulmachun watershed, a systematic transfer functional modeling was performed to configure the relationships between rainfall and soil moisture responses. The analysis indicated the spatial variation pattern of hillslope hydrological processes, which can be explained by the relative contribution of vertical, lateral and return flows and the impact of transect topography.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.540-544
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• 2009

Hydrologic pattern under climate change has been paid attention to as one of the most important issues in hydrologic science group. Rainfall and runoff is a key element in the Earth's hydrological cycle, and associated with many different aspects such as water supply, flood prevention and river restoration. In this regard, a main objective of this study is to evaluate design flood using simulation techniques which can consider a full spectrum of uncertainty. Here we utilize a weather state based stochastic multivariate model as conditional probability model for simulating the rainfall field. A major premise of this study is that large scale climatic patterns are a major driver of such persistent year to year changes in rainfall probabilities. Uncertainty analysis in estimating design flood is inevitably needed to examine reliability for the estimated results. With regard to this point, this study applies a Bayesian Markov Chain Monte Carlo scheme to the NWS-PC rainfall-runoff model that has been widely used, and a case study is performed in Soyang Dam watershed in Korea. A comprehensive discussion on design flood under climate change is provided.

• 한국연소학회지
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• 제12권1호
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• pp.1-10
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• 2007

Analytical investigation of acoustic wave scattering from turbulent premixed flames was conducted to evaluate the acoustic energy amplification/damping. Such acoustic energy change is attributed to the acoustic velocity jump due to flame's heat release. Small perturbation method up to second order and stochastic analysis were utilized to formulate net acoustic energy and the energy transfer from coherent to incoherent energy. Randomly wrinkled flame surface is responsible for the energy transfer from coherent to incoherent field. Nondimensional parameters that govern net acoustic energy were determined: rms height and correlation length of flame front, incident wave frequency, incidence angle, and temperature ratio. The dependence of net acoustic energy upon these parameters is illustrated by numerical simulations in case of Gaussian statistics of flame front. Total net energy was amplified and the major factors that affect such energy amplification are incidence angle and temperature ratio. Coherent (incoherent) energy is damped (amplified) with rms height and correlation length of flame front.

• Wind and Structures
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• 제15권3호
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• pp.223-245
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• 2012

Multivariate simulation is necessary for cases where non-Gaussian processes at spatially distributed locations are desired. A simulation algorithm to generate non-Gaussian wind pressure fields is proposed. Gaussian sample fields are generated based on the spectral representation method using wavelet transforms method and then mapped into non-Gaussian sample fields with the aid of a CDF mapping transformation technique. To illustrate the procedure, this approach is applied to experimental results obtained from wind tunnel tests on the domes. A multivariate Gaussian simulation technique is developed and then extended to multivariate non-Gaussian simulation using the CDF mapping technique. It is proposed to develop a new wavelet-based CDF mapping technique for simulation of multivariate non-Gaussian wind pressure process. The efficiency of the proposed methodology for the non-Gaussian nature of pressure fluctuations on separated flow regions of different rise-span ratios of domes is also discussed.

• Communications for Statistical Applications and Methods
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• 제8권3호
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• pp.625-632
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• 2001

Principal component analysis(PCA) is a multivariate technique falling under the general title of factor analysis. The purpose of PCA is to Identify the dependence structure behind a multivariate stochastic observation In order to obtain a compact description of it. In engineering field PCA is utilized mainly (or data compression and restoration. In this paper we propose a new robust Hebbian algorithm for robust PCA. This algorithm is based on a hyperbolic tangent function due to Hampel ef al.(1989) which is known to be robust in Statistics. We do two experiments to investigate the performance of the new robust Hebbian learning algorithm for robust PCA.