• International Journal of Highway Engineering
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• v.15 no.5
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• pp.31-45
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• 2013

PURPOSES : Determination of particle packing model variables that can be used for formulation of new DEM based particle packing model by examining existing particle packing models METHODS : Existing particle packing models are thoroughly examined by analytical reformulation and sensitivity analysis in order to set up DEM based new particle packing model and to determine its variables. All model equations considered in this examination are represented with consistent expressions and are compared to each others to find mathematical and conceptual similarity in expressions. RESULTS : From the examination of existing models, it is observed that the models are very similar in their shapes although the derivation of the models may be different. As well, it is observed that variables used in some existing models are comprehensive enough to estimate particle packing but not applicable to DEM simulation. CONCLUSIONS : A set of variables that can be used in DEM based particle packing model is determined.

• Computers and Concrete
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• v.7 no.4
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• pp.365-386
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• 2010

This paper presents the physical formulation of a 1D material model suitable for seismic applications. It is written within the framework of thermodynamics with internal variables that is, especially, very efficient for the phenomenological representation of material behaviors at macroscale: those of the representative elementary volume. The model can reproduce the main characteristics observed for concrete, that is nonsymetric loading rate-dependent (viscoelasticity) behavior with appearance of permanent deformations and local hysteresis (continuum plasticity), stiffness degradation (continuum damage), cracking due to displacement localization (discrete plasticity or damage). The parameters have a clear physical meaning and can thus be easily identified. Although this point is not detailed in the paper, this material model is developed to be implemented in a finite element computer program. Therefore, for the benefit of the robustness of the numerical implementation, (i) linear state equations (no local iteration required) are defined whenever possible and (ii) the conditions in which the presented model can enter the generalized standard materials class - whose elements benefit from good global and local stability properties - are clearly established. To illustrate the capabilities of this model - among them for Earthquake Engineering applications - results of some numerical applications are presented.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.569-573
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• 2001

An approximate representation of discrete functions {f$_{i,j}\mid$｜i, j=-1, 0, 1, …, N+1}in two variables by a fuzzy system is described. We use the cubic B-splines as fuzzy sets for the input fuzzification and spike functions as the output fuzzy sets. The ordinal number of f$_{i,j}$ in the sorted list is taken to be the out put fuzzy set number in the (i, j) th entry of the fuzzy rule table. We show that the fuzzy system is an exact representation of the cubic spline function s(x, y)=$\sum_{N+1}^{{i,j}=-1}f_{i,j}B_i(x)B_j(y)$ and that the approximation error S(x, y)-f(x, y) is surprisingly O($h^2$) when f(x, y) is three times continuously differentiable. We prove that when f(x, y) is a gray scale image, then the fuzzy system is a smoothed representation of the image and the original image can be recovered exactly from its fuzzy system representation when it is a digitized image.e.

• Structural Engineering and Mechanics
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• v.61 no.3
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• pp.359-370
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• 2017

This study presents a particle swarm optimization algorithm integrated with weighted particle concept and improved fly-back technique. The rationale behind this integration is to utilize the affirmative properties of these new terms to improve the search capability of the standard particle swarm optimizer. Improved fly-back technique introduced in this study can be a proper alternative for widely used penalty functions to handle existing constraints. This technique emphasizes the role of the weighted particle on escaping from trapping into local optimum(s) by utilizing a recursive procedure. On the other hand, it guaranties the feasibility of the final solution by rejecting infeasible solutions throughout the optimization process. Additionally, in contrast with penalty method, the improved fly-back technique does not contain any adjustable terms, thus it does not inflict any extra ad hoc parameters to the main optimizer algorithm. The improved fly-back approach, as independent unit, can easily be integrated with other optimizers to handle the constraints. Consequently, to evaluate the performance of the proposed method on solving the truss weight minimization problems with discrete variables, several benchmark examples taken from the technical literature are examined using the presented method. The results obtained are comparatively reported through proper graphs and tables. Based on the results acquired in this study, it can be stated that the proposed method (integrated particle swarm optimizer, iPSO) is competitive with other metaheuristic algorithms in solving this class of truss optimization problems.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.12
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• pp.3804-3821
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• 1996

The configuration optimization is a structural optimization method which includes the coordinates of a structure as well as the sectional properties in the design variable set. Effective reduction of the weight of discrete structures can be obrained by changing the geometry while satisfying stress, Ei;er bickling, displacement, and frequency constraints, etc. However, the nonlinearity due to the configuration variables may cause the difficulties of the convergence and expensive computational cost. An efficient approximation method for the configuration optimization has been developed to overcome the difficulties. The method approximates the constraint functions based onthe second-order Taylor series expansion with reciprocal design variables. The Hessian matrix is approzimated from the information on previous design points. The developed algotithms are coded and the examples are solved.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.608-613
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• 2017

In this paper, we consider the stability bound for uncertainty of delayed state variables in the linear discrete interval time-varying systems with time-varying delay time. The considered system has an interval time-varying system matrix for non-delayed states and is perturbed by the unstructured time-varying uncertainty in delayed states with time-varying delay time within fixed interval. Compared to the previous results which are derived for time-invariant cases and can not be extended to time-varying cases, the new stability bound in this paper is applicable to time-varying systems in which every factors are considered as time-varying variables. The proposed result has no limitation in applicable systems and is very powerful in the aspects of feasibility compared to the previous. Furthermore. the new bound needs no complex numerical algorithms such as LMI(Linear Matrix Inequality) equation or upper solution bound of Lyapunov equation. By numerical examples, it is shown that the proposed bound is able to include the many existing results in the previous literatures and has better performances in the aspects of expandability and effectiveness.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.309-320
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• 2020

Bayesian networks, also known as directed acyclic graphs (DAG), are used in many areas of medicine, meteorology, and genetics because relationships between variables can be modeled with graphs and probabilities. In particular, Bayesian network classifiers, which are used to predict discrete data, have recently become a new method of data mining. Bayesian networks can be grouped into different models that depend on structured learning methods. In this study, Bayesian network models are learned with various properties of structure learning. The models are compared to the simplest method, the naïve Bayes model. Classification results are compared by applying learned models to various real data. This study also compares the relationships between variables in the data through graphs that appear in each model.

• Journal of Energy Engineering
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• v.16 no.1 s.49
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• pp.46-52
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• 2007

The piecewise constant angular approximation is developed to replace the conventional angular quadrature sets in the solution of the second-order, multi-dimensional $S_{N}$ neutron transport equations. The newly generated quadrature sets by this method substantially mitigate ray effects and can be used in the same manner as the conventional quadrature sets are used. The discrete-ordinates and the piecewise-constant approximations are applied to both the first-order Boltzmann and the second-order form of neutron transport equations in treating angular variables. The result is that the mitigation of ray effects is only achieved by the piecewise-constant method, in which new angular quadratures are generated by integrating angle variables over the specified region. In other sense, the newly generated angular quadratures turn out to decrease the contribution of mixed-derivative terms in the even-parity equation that is one of the second-order neutron transport equation. This result can be interpreted as the entire elimination or substantial mitigation of ray effect are possible in the simplified even-parity equation which has no mixed-derivative terms.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.6
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• pp.20-26
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• 2002

This paper deals with the guaranteed cost control problems for a class of discrete-time linear uncertain systems with time-varying delay. The uncertain systems under consideration depend on time-varying norm-bounded parameter uncertainties. We address the existence condition and the design method of the memoryless state feedback control law such that the closed loop system not only is quadratically stable but also guarantees an adequate level of performance for all admissible uncertainties. Through some changes of variables and Schur complement, It is shown that the sufficient condition can be rewritten as an LMI(linear matrix inequality) form in terms of all variables.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.792-797
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• 2011

This paper discusses the application of a hybrid discretiziation method for the discretization procedure that needs to be included in discrete particle swarm optimization (DPSO) for the problem of allocating PV (photovoltaic) systems onto distribution power systems. For this purpose, this paper proposes a rule-based expert system considering the objective function value and its optimizing speed as the input parameters and applied it to the PV allocation problem including discrete decision variables. For multi-level discretization, this paper adopts a hybrid method combined with a simple rounding and sigmoid funtion based 3-step and 5-step quantization methods, and the application of the rule based expert system proposing the adequate discretization method at each PSO iteration so that the DPSO with the hybrid discretization can provide better performance than the previous DPSO.