• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.10a
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• pp.233-236
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• 2002

An efficient method was developed in this study to obtain optimal stacking sequences, thicknesses, and minimum weights of stiffened laminated composite shells under combined loading conditions and stiffener layouts using genetic algorithms (GAs) and finite element analyses. Among many parameters in designing composite laminates determining a optimal stacking sequence that may be formulated as an integer programming problem is a primary concern. Of many optimization algorithms, GAs are powerful methodology for the problem with discrete variables. In this paper the optimal stacking sequence was determined, which gives the maximum critical buckling load factor and the minimum weight as well. To solve this problem, both the finite element analysis by ABAQUS and the GA-based optimization procedure have been implemented together with an interface code. Throughout many parametric studies using this analysis tool, the influences of stiffener sizes and three different types of stiffener layouts on the stacking sequence changes were throughly investigated subjected to various combined loading conditions.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.204-206
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• 2007

In this study, we introduce the optimization method of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation, The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods. Particularly, in parameter identification, we use the UNDX operator which uses multiple parents and generate offsprings around the geographic center off mass of these parents.

• Korean Journal of Computational Design and Engineering
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• v.16 no.6
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• pp.458-466
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• 2011

The morphology of a bone is closely associated with its biomechanical response. Thus, much research has been focused on analyzing the effects of variation of bone morphology with subject-specific models. Subject-specific models, which are generally achieved from 3D imaging devices like CT and MRI, incorporate more of the detailed information that makes a model unique. Hence, it may predict individual responses more accurately. Despite these powerful characteristics, specific models are not easily parameterized to the extent possible with statistical models because of their morphologic complexities. Thus, it is still proven challenging to analyze morphologic variations of subject-specific models across changes due to aging or disease. The aim of this article is to propose a generic and robust parametric morphing method for a subject-specific bone structure. We demonstrate this by using the proposed method on a model of a human proximal femur. Automatic segmentation algorithms are also presented to parameterize the specific model efficiently. A total of 48 femur models were evaluated for defining morphing vector fields. Also, several anatomical and mechanical functions of femur were considered as morphing constraints, and the NURBS interpolating technique was applied in the method to guarantee the generality of our morphed results.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.2 s.344
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• pp.39-43
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• 2006

This paper deals with the AR Identification of unknown system using cumulant, which is the 3rd order statistics of output signal in the presence of the noise signal. The algorithms for identification of unknown system we applies to the AR identification method using the cumulant which is possible to the guarantees of global convergence and the representation of amplitude and phase information of system among with the method of parametric modeling. In the process of identification, we considered unknown system to the one of AR system. After the generation of input signal, it was being passed through the system then We use the its output signal that the noise is added. As a result of identification of AR system by changing the signal to noise ratio, we get the fairly good results compared to original system output values and confirmed that the pole was located in the unit circle of z transform.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.2
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• pp.41-51
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• 2014

This paper describes a method to estimate the noise power using the minimum statistics approach, which was originally proposed for audio processing. The proposed minimum statistics-based method separates a noisy image into multiple frequency bands using the three-level discrete wavelet transform. By assuming that the output of the high-pass filter contains both signal detail and noise, the proposed algorithm extracts the region of pure noise from the high frequency band using an appropriate threshold. The region of pure noise, which is free from the signal detail part and the DC component, is well suited for minimum statistics condition, where the noise power can be extracted easily. The proposed algorithm reduces the computational load significantly through the use of a simple processing architecture without iteration with an estimation accuracy greater than 90% for strong noise at 0 to 40dB SNR of the input image. Furthermore, the well restored image can be obtained using the estimated noise power information in parametric image restoration algorithms, such as the classical parametric Wiener or ForWaRD image restoration filters. The experimental results show that the proposed algorithm can estimate the noise power accurately, and is particularly suitable for fast, low-cost image restoration or enhancement applications.

• Journal of Computational Design and Engineering
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• v.1 no.2
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• pp.116-127
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• 2014

This paper aims to review methods for computing orthogonal projection of points onto curves and surfaces, which are given in implicit or parametric form or as point clouds. Special emphasis is place on orthogonal projection onto conics along with reviews on orthogonal projection of points onto curves and surfaces in implicit and parametric form. Except for conics, computation methods are classified into two groups based on the core approaches: iterative and subdivision based. An extension of orthogonal projection of points to orthogonal projection of curves onto surfaces is briefly explored. Next, the discussion continues toward orthogonal projection of points onto point clouds, which spawns a different branch of algorithms in the context of orthogonal projection. The paper concludes with comments on guidance for an appropriate choice of methods for various applications.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.283-285
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• 2005

To account for the importance of variable in multi-sensor data fusion, random forests are applied to supervised land-cover classification. The random forests approach is a non-parametric ensemble classifier based on CART-like trees. Its distinguished feature is that the importance of variable can be estimated by randomly permuting the variable of interest in all the out-of-bag samples for each classifier. Supervised classification with a multi-sensor remote sensing data set including optical and polarimetric SAR data was carried out to illustrate the applicability of random forests. From the experimental result, the random forests approach could extract important variables or bands for land-cover discrimination and showed good performance, as compared with other non-parametric data fusion algorithms.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.11
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• pp.180-187
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• 2003

A CAD-based seamless design system for MEMS named DS/MEMS was developed which performs coupled-field analysis, optimal and robust design. DS/MEMS has been developed by means of integrating commercial codes and inhouse code-SolidWorks, FEMAP, ANSYS and CA/MEMS. This strategy results in versatility that means to include various analysis model, corresponding analyses and approximated design sensitivity analysis and user friendliness that design variables are taken to be selectable directly from a CAD model, that the problem is formulated under a window environment and that the manual job during optimization process is almost eliminated. DS/MEMS works on a parametric CAD platform, integrating CAD modeling, analysis, and optimization. Nonlinear programming algorithms, the Taguchi method, and response surface method are made available for optimization. One application problem is taken to illustrate the proposed methodology and show the feasibility of DS/MEMS as a practical tool.

• Structural Engineering and Mechanics
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• v.28 no.2
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• pp.239-257
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• 2008

Double-sided punched metal plate timber fasteners present projections on both sides, which offer improved joint fire resistance and better joint aesthetics. In this paper, 3-D nonlinear finite element models were developed to simulate double-sided nail plate fastener timber joints. The models, incorporating orthotropic elasticity, Hill's yield criterion and elasto-plasticity and contact algorithms, are capable of simulating complex contact between the tooth and the timber and between the base plate and the timber in a fastener. Using validated models, parametric studies of the double-sided nail plate joints was undertaken to cover the tooth length and the tooth width. Optimal configuration was assumed to have been attained when increase in nail plate tooth width did not result in a raise in joint capacity, in conjunction with the optimum tooth length. This paper presents the first attempt to model and optimise tooth profile of double-sided nail plate fastener timber joints, which offers rational designs of such fasteners.

• Journal of the Korean Operations Research and Management Science Society
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• v.15 no.1
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• pp.73-82
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• 1990

This paper describes two algorithms to Nonlinear programming problems with equality constraints and with equality and inequality constraints. The first method treats nonlinear programming problems with equality constraints. Utilizing the nonlinear programming problems with equality constraints. Utilizing the nonlinear parametric programming technique, the method solves the problem by imbedding it into a suitable one-parameter family of problems. The second method is to solve a nonlinear programming problem with equality and inequality constraints, by minimizing a square sum of nonlinear functions which is derived from the Kuhn-Tucker condition.