• Proceedings of the KSEEG Conference
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• 2001.04a
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• pp.123-125
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• 2001

• Computational Structural Engineering
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• v.4 no.1
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• pp.95-108
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• 1991

An explicit finite element method can be executed more rapidly and effectively on vector computer than on the scalar computer because it has suitable structures for vector processing. In this paper, an efficient vectorization method of the explicit finite element program on the memory-to-memory type vector computer is proposed. First, the general vectorization method which can be applied regardless of the vector architecture is investigated, then the method which is suitable for the memory-to-memory type vector computer is proposed. To illustrate the usefulness of the proposed vectorization method, DYNA3D, the existing explicit finite element program, is migrated on HDS AS/XL V50 which is the memory-to-memory type vector computer. Performance results on actual test show a vector/scalar speedup is above 2.4.

• The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
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• v.5 no.2
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• pp.20-28
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• 1994

Theoretical radiation patterns for the corrugated horn antenna are analyzed by vector integral method and quasi-optics. The formular of the radiated fields for the corrugated horn antenna can be obtained by the potentials derived the equivalent current sources from hybrid fields at horn aperture and also calculated by the expanded mode set of Gaussian - Laguerre based on the quasi - optics. From comparison of the radiation patterns between two methods for a corrugated horn antenna designed on 85-115 GHz frequency range, the results are coincided well at center frequency but have some errors at each side frequencies 85 and 115 GHz.

• Korean Journal of Remote Sensing
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• v.19 no.4
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• pp.329-339
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• 2003

In this study, an approach of image fusion in decision level has been proposed for unsupervised image classification using the images acquired from multiple sensors with different characteristics. The proposed method applies separately for each sensor the unsupervised image classification scheme based on spatial region growing segmentation, which makes use of hierarchical clustering, and computes iteratively the maximum likelihood estimates of fuzzy class vectors for the segmented regions by EM(expected maximization) algorithm. The fuzzy class vector is considered as an indicator vector whose elements represent the probabilities that the region belongs to the classes existed. Then, it combines the classification results of each sensor using the fuzzy class vectors. This approach does not require such a high precision in spatial coregistration between the images of different sensors as the image fusion scheme of pixel level does. In this study, the proposed method has been applied to multispectral SPOT and AIRSAR data observed over north-eastern area of Jeollabuk-do, and the experimental results show that it provides more correct information for the classification than the scheme using an augmented vector technique, which is the most conventional approach of image fusion in pixel level.

• The Journal of the Korea Contents Association
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• v.5 no.6
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• pp.41-48
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• 2005

This paper presents a face recognition method based on the combination of well-known statistical representations of Principal Component Analysis(PCA), and Linear Discriminant Analysis(LDA) with Radial Basis Function Networks. The original face image is first processed by PCA to reduce the dimension, and thereby avoid the singularity of the within-class scatter matrix in LDA calculation. The result of PCA process is applied to LDA classifier. In the second approach, the LDA process Produce a discriminational features of the face image, which is taken as the input of the Radial Basis Function Network(RBFN). The proposed approaches has been tested on the ORL face database. The experimental results have been demonstrated, and the recognition rate of more than 93.5% has been achieved.

• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.641-644
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• 2008

In order to improve a camera rotation calculation based on the bundle adjustment in Chon's camera motion (Chon and Shankar, 2007, 2008), this paper introduces a method calculating the camera rotation. It estimates a unit vector in the optical axis of a camera through the angles between the optical axis and vectors passing a camera position and ground control points (GCP). The camera position is estimated by using the inner product method proposed by Chon. The horizontal and vertical unit vectors of the camera are determined by using Yakimovsky and Cunningham's camera model (CAHV) (1978).

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.421-434
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• 2005

Machine learning has been extensively studied in recent years as effective tools in pattern classification problem. Although there have been several approaches to machine learning, we focus on the mathematical programming (in particular, multi-objective and goal programming; MOP/GP) approaches in this paper. Among them, Support Vector Machine (SVM) is gaining much popularity recently. In pattern classification problem with two class sets, the idea is to find a maximal margin separating hyperplane which gives the greatest separation between the classes in a high dimensional feature space. However, the idea of maximal margin separation is not quite new: in 1960's the multi-surface method (MSM) was suggested by Mangasarian. In 1980's, linear classifiers using goal programming were developed extensively. This paper proposes a new family of SVM using MOP/GP techniques, and discusses its effectiveness throughout several numerical experiments.

• Journal of Korean Association for Spatial Structures
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• v.8 no.3
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• pp.75-82
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• 2008

In this study, the modified genetic algorithm, D-GA, is proposed. D-GA is a hybrid genetic algorithm combined a simple genetic algorithm and the local search algorithm using direction vectors. Also, two types of direction vectors, learning direction vector and random direction vector, are defined without the sensitivity analysis. The accuracy of D-GA is compared with that of simple genetic algorithm. It is demonstrated that the proposed approach can be an effective optimization technique through a minimum weight structural optimization of ten bar truss.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1991.04a
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• pp.7-9
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• 1991

차량 주행시 내부공간의 소음레벨의 변화를 "Booming"소음이라고 통칭하며, 이 소음의 주 원인은 엔진 회전수의 2차 하모닉(harmonic) 주파수 성분으로 구조적인 경로를 통하여 인체에 전달되게 된다. Booming 소음은 변화가 클 때 탑승자에게 큰 고통을 주며 차량 가치평가에 커다란 마이너스 요인이 된 다. 본 연구에서는 이러한 Booming 현상을 파악하고 대처하는 방법의 하나 로서 벡터해석법을 사용하고자 한다. 사용하고자 한다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1993.04a
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• pp.75-79
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• 1993

본 연구에서는 원통형 셀을 Donnell-Mushitari의 Thin Shell로 모델링하고, 유체의 거동은 Hankel 함수를 배제하고 유한차분법(Finite Difference Method)으로 모델링하여, 상태 벡터(State Vector)해석법, 전달 행렬 및 푸리 에 변환(Fourier Transform)을 사용, 무한 원통형 몰수체의 강제 진동을 해 석하였다.