• Transactions on Electrical and Electronic Materials
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• v.15 no.4
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• pp.175-181
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• 2014

In this paper, the simulation and modeling of a polyethylene/clay nanocomposite were undertaken to predict the nanocomposite's dielectric behavior and to help design a nanocomposite material with optimum electrical properties for electrotechnical or electronic applications. A 3-D simulation model using the finite elements method was employed in order to study the effective permittivity and electric field distribution of two-phase nanocomposite materials for ordered and random distributions of inclusions in a low-loss host matrix such as polyethylene. The influence of the dispersion of reinforcing particles, and of the permittivity and radius of the inclusions, was analysed. The simulation results were compared with alternative, known theoretical solutions obtained from classical models, and were found to be in good agreement. The numerical results also indicate that for fixed volume fractions of nanoparticles the effective permittivity of the mixture, for ordered and random distributions, does not vary with the degree of dispersion. The variation of the effective permittivity with the particle radius is shown, using numerical data, to agree with the analytical modules.

• Steel and Composite Structures
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• v.47 no.1
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• pp.1-17
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• 2023

This paper deals with the free vibration behavior of rotating composite beams reinforced with carbon nanotubes (CNTs) under uniform thermal loads. The temperature-dependent beam material is assumed to be a mixture of single-walled carbon nanotubes (SWCNTs) in an isotropic matrix and five different functionally graded (FG) distributions of CNTs are considered according to the variation along the thickness, namely the UD-uniform, FG-O, FG-V, FG-Λ and FG-X distributions where FG-V and FG-Λ are unsymmetrical patterns. Considering the Timoshenko beam theory (TBT), a new finite element formulation of functionally graded carbon nanotube reinforced composite (FGCNTRC) beam is created for the first time. And the effects of several essential parameters including rotational speed, hub radius, effective material properties, slenderness ratio, boundary conditions, thermal force and moments due to temperature variation are considered in the formulation. By implementing different boundary conditions, some new results of both symmetric and non-symmetrical distribution patterns are presented in tables and figures to be used as benchmark for further validation. In addition, as an alternative advanced composite application for rotating systems exposed to thermal load, the positive effects of CNT addition in improving the dynamic performance of the system have been observed and the results are presented in several tables and figures.

• Journal of the Korea Concrete Institute
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• v.14 no.6
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• pp.813-822
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• 2002

The purpose of the present study is first to refine the mathematical material models for moisture and temperature distributions in early-age concrete and then to incorporate those models into finite element procedure. The three dimensional finite element program developed in the present study can determine the degree of hydration, temperature and moisture distribution in hardening concrete. It is assumed that temperature and humidity fields are fully uncoupled and only the degree of hydration is coupled with two state variables. Mathematical formulation of degree of hydration Is based on the combination of three rate functions of reaction. The effect of moisture condition as well as temperature on the rate of reaction is considered in the degree of hydration model. In moisture transfer, diffusion coefficient is strongly dependent on the moisture content in pore system. Many existing models describe this phenomenon according to the composition of mixture, especially water to cement ratio, but do not consider the age dependency. Microstructure is changing with the hydration and thus transport coefficients at early ages are significantly higher because the pore structure in the cement matrix is more open. The moisture capacity and sink are derived from age-dependent desorption isotherm. Prediction of a moisture sink due to the hydration process, i.e. self-desiccation, is related to autogenous shrinkage, which may cause early-age cracking in high strength and high performance concrete. The realistic models and finite element program developed in this study provide fairly good results on the temperature and moisture distribution for early-age concrete and correlate very well with actual test data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.7
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• pp.883-889
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• 2013

The distribution of a response usually depends on the distribution of a variable. When the distribution of a variable has two different modes, the response also follows a distribution with two different modes. In most reliability analysis methods, the number of modes is irrelevant, but not the type of distribution. However, in actual problems, because information is often provided with two or more modes, it is important to estimate the distributions with two or more modes. Recently, some reliability analysis methods have been suggested for bimodal distributions. In this paper, we review some methods such as the Akaike information criterion (AIC) and maximum entropy principle (MEP) and compare them with the Monte Carlo simulation (MCS) using mathematical examples with two different modes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5095-5111
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• 2016

The Probability Hypothesis Density (PHD) filter is a suboptimal approximation and tractable alternative to the multi-target Bayesian filter based on random finite sets. However, the PHD filter fails to track newborn targets when the target birth intensity is unknown prior to tracking. In this paper, a dual detection-guided newborn target intensity PHD algorithm is developed to solve the problem, where two schemes, namely, a newborn target intensity estimation scheme and improved measurement-driven scheme, are proposed. First, the newborn target intensity estimation scheme, consisting of the Dirichlet distribution with the negative exponent parameter and target velocity feature, is used to recursively estimate the target birth intensity. Then, an improved measurement-driven scheme is introduced to reduce the errors of the estimated number of targets and computational load. Simulation results demonstrate that the proposed algorithm can achieve good performance in terms of target states, target number and computational load when the newborn target intensity is not predefined in multi-target tracking systems.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.173-184
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• 2018

In medical or public health research, it is common to encounter clustered or longitudinal count data that exhibit excess zeros. For example, health care utilization data often have a multi-modal distribution with excess zeroes as well as a multilevel structure where patients are nested within physicians and hospitals. To analyze this type of data, zero-inflated count models with mixed effects have been developed where a count response variable is assumed to be distributed as a mixture of a Poisson or negative binomial and a distribution with a point mass of zeros that include random effects. However, no study has considered a situation where data are also censored due to the finite nature of the observation period or follow-up. In this paper, we present a weighted version of zero-inflated Poisson model with random effects accounting for variable individual follow-up times. We suggested two different types of weight function. The performance of the proposed model is evaluated and compared to a standard zero-inflated mixed model through simulation studies. This approach is then applied to Medicaid data analysis.

• Smart Structures and Systems
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• v.19 no.3
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• pp.309-322
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• 2017

In this paper, the buckling, and free vibration analysis of tapered functionally graded carbon nanotube reinforced composite (FG-CNTRC) micro Reddy beam under longitudinal magnetic field using finite element method (FEM) is investigated. It is noted that the material properties of matrix is considered as Poly methyl methacrylate (PMMA). Using Hamilton's principle, the governing equations of motion are derived by applying a modified strain gradient theory and the rule of mixture approach for micro-composite beam. Micro-composite beam are subjected to longitudinal magnetic field. Then, using the FEM, the critical buckling load, and natural frequency of micro-composite Reddy beam is solved. Also, the influences of various parameters including ${\alpha}$ and ${\beta}$ (the constant coefficients to control the thickness), three material length scale parameters, aspect ratio, different boundary conditions, and various distributions of CNT such as uniform distribution (UD), unsymmetrical functionally graded distribution of CNT (USFG) and symmetrically linear distribution of CNT (SFG) on the critical buckling load and non-dimensional natural frequency are obtained. It can be seen that the non-dimensional natural frequency and critical buckling load decreases with increasing of ${\beta}$ for UD, USFG and SFG micro-composite beam and vice versa for ${\alpha}$. Also, it is shown that at the specified value of ${\alpha}$ and ${\beta}$, the dimensionless natural frequency and critical buckling load for SGT beam is more than for the other state. Moreover, it can be observed from the results that employing magnetic field in longitudinal direction of the micro-composite beam increases the natural frequency and critical buckling load. On the other hands, by increasing the imposed magnetic field significantly increases the stability of the system that can behave as an actuator.

• Proceedings of the Materials Research Society of Korea Conference
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• 2012.05a
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• pp.81.2-81.2
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• 2012

To fabricate a metal mold for injection molding, hot-embossing and imprinting process, mechanical machining, electro discharge machining (EDM), electrochemical machining (ECM), laser process and wet etching ($FeCl_3$ process) have been widely used. However it is hard to get precise structure with these processes. Electrochemical etching has been also employed to fabricate a micro structure in metal mold. A through mask electrochemical micro machining (TMEMM) is one of the electrochemical etching processes which can obtain finely precise structure. In this process, many parameters such as current density, process time, temperature of electrolyte and distance between electrodes should be controlled. Therefore, it is difficult to predict the result because it has low reliability and reproducibility. To improve it, we investigated this process numerically and experimentally. To search the relation between processing parameters and the results, we used finite element simulation and the commercial finite element method (FEM) software ANSYS was used to analyze the electric field. In this study, it was supposed that the anodic dissolution process is predicted depending on the current density which is one of major parameters with finite element method. In experiment, we used stainless steel (SS304) substrate with various sized square and circular array patterns as an anode and copper (Cu) plate as a cathode. A mixture of $H_2SO_4$, $H_3PO_4$ and DIW was used as an electrolyte. After electrochemical etching process, we compared the results of experiment and simulation. As a result, we got the current distribution in the electrolyte and line profile of current density of the patterns from simulation. And etching profile and surface morphologies were characterized by 3D-profiler(${\mu}$-surf, Nanofocus, Germany) and FE-SEM(S-4800, Hitachi, Japan) measurement. From comparison of these data, it was confirmed that current distribution and line profile of the patterns from simulation are similar to surface morphology and etching profile of the sample from the process, respectively. Then we concluded that current density is more concentrated at the edge of pattern and the depth of etched area is proportional to current density.

• Steel and Composite Structures
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• v.35 no.1
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• pp.77-92
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• 2020

The present paper outlined a procedure for geometrically nonlinear dynamic analysis of functionally graded graphene platelets-reinforced (GPLR-FG) nanocomposite cylinder subjected to mechanical shock loading. The governing equation of motion for large deformation problems is derived using meshless local Petrov-Galerkin (MLPG) method based on total lagrangian approach. In the MLPG method, the radial point interpolation technique is employed to construct the shape functions. A micromechanical model based on the Halpin-Tsai model and rule of mixture is used for formulation the nonlinear functionally graded distribution of GPLs in polymer matrix of composites. Energy dissipation in analyses of the structure responding to dynamic loads is considered using the Rayleigh damping. The Newmark-Newton/Raphson method which is an incremental-iterative approach is implemented to solve the nonlinear dynamic equations. The results of the proposed method for homogenous material are compared with the finite element ones. A very good agreement is achieved between the MLPG and FEM with very fine meshing. In addition, the results have demonstrated that the MLPG method is more effective method compared with the FEM for very large deformation problems due to avoiding mesh distortion issues. Finally, the effect of GPLs distribution on strength, stiffness and dynamic characteristics of the cylinder are discussed in details. The obtained results show that the distribution of GPLs changed the mechanical properties, so a classification of different types and volume fraction exponent is established. Indeed by comparing the obtained results, the best compromise of nanocomposite cylinder is determined in terms of mechanical and dynamic properties for different load patterns. All these applications have shown that the present MLPG method is very effective for geometrically nonlinear analyses of GPLR-FG nanocomposite cylinder because of vanishing mesh distortion issue in large deformation problems. In addition, since in proposed method the distributed nodes are used for discretization the problem domain (rather than the meshing), modeling the functionally graded media yields to more accurate results.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1071-1083
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• 2005

By estimating probability distributions of the good solutions in the current population, some researchers try to find the optimal solution more efficiently. Particularly, finite mixtures of distributions have a very useful role in dealing with complex problems. However, it is difficult to choose the number of components in the mixture models and merge superior partial solutions represented by each component. In this paper, we propose a new continuous evolutionary optimization algorithm with distribution estimation by variational Bayesian mixtures of factor analyzers. This technique can estimate the number of mixtures automatically and combine good sub-solutions by sampling new individuals with the latent variables. In a comparison with two probabilistic model-based evolutionary algorithms, the proposed scheme achieves superior performance on the traditional benchmark function optimization. We also successfully estimate the parameters of S-system for the dynamic modeling of biochemical networks.