• Journal of Information Technology Services
• /
• v.2 no.1
• /
• pp.123-133
• /
• 2003

Because of rapid evolution of software technique, numerous software professionals have been concerned with component based development methodologies. However, it is hard to find out a systematic technique for the selection of COTS (Commercial Off The Shelf) component in consumer position. Up to date, the major of component quality evaluation is object-oriented metric based evaluation methodology. But this paper present four step process and evaluation criteria based on MCDM (Multiple Criteria Decision Making) technique for optimal COTS component selection in consumer position. Weconsidered funtionality, efficiency, usability based on ISO/IEC 9126 for quality measurement and executed practical analysis about commercial EJB component in internet. This paper show that the proposed selection technique is applicable to optimal COTS component selection.

• Proceedings of the IEEK Conference
• /
• 2000.07a
• /
• pp.399-402
• /
• 2000

The reduction of chip area and delay is important purpose of Scheduling in High-Level Synthesis. This paper presents a scheduling approach with component selection. After obtaining a initial schedule taking only single-functional u-nits, the component selection of our approach attempts the reduction of chip area and/or delay by the selection more suitable components in a component library using Simulated Annealing.

• Communications for Statistical Applications and Methods
• /
• v.22 no.2
• /
• pp.173-180
• /
• 2015

We propose a selection procedure of principal components in principal component regression. Our method selects principal components using variable selection procedures instead of a small subset of major principal components in principal component regression. Our procedure consists of two steps to improve estimation and prediction. First, we reduce the number of principal components using the conventional principal component regression to yield the set of candidate principal components and then select principal components among the candidate set using sparse regression techniques. The performance of our proposals is demonstrated numerically and compared with the typical dimension reduction approaches (including principal component regression and partial least square regression) using synthetic and real datasets.

• Proceedings of the IEEK Conference
• /
• 2004.08c
• /
• pp.712-716
• /
• 2004

Because of rapid development of software technology, a number of software professionals have been concerned with component-based development methodologies. Up to date, the evaluation of component quality has been focused on object-oriented metric based methodology. But this paper presents the selection process and evaluation criteria based on an MCDM(Multiple Criteria Decision Making) technique for the selection of optimal COTS component from consumers' viewpoints. We considered functionality, efficiency and usability based on ISO/IEC 9126 for quality measurement and conducted practical analysis into commercial EJB component in internet. This paper shows that the proposed selection technique is applicable for the selection of the optimal COTS component.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.23 no.2
• /
• pp.198-209
• /
• 1999

This study reports the selection of dependent variables for momentum equations in general curvilinear coordinates. Catesian, covariant and contravariant velocity components were examined for the dependent variable. The focus of present study is confined to staggered grid system Each dependent variable selected for momentum equations are tested for several flow fields. Results show that the selection of Cartesian and covariant velocity components intrinsically can not satisfy mass conservation of control volume unless additional converting processes ore used. Also, Cartesian component can only be used for the flow field in which main-flow direction does not change significantly. Convergence rate for the selection of covariant velocity component decreases quickly as with the increase of non-orthogonality of grid system. But the selection of contravariant velocity component reduces the total mass residual of discretized equations rapidly to the limit of machine accuracy and the solutions are insensitive to the main-flow direction.

• The KIPS Transactions:PartD
• /
• v.9D no.5
• /
• pp.897-902
• /
• 2002

Because of rapid evolution of software technique, numerous software professionals have been concerned with component based development methodologies. However, it is hard to find out a systematic technique for the selection of COTS (Commercial Off The Shelf) component in consumer position. Up to date, the major of component quality evaluation is object-oriented metric based evaluation methodology. But this paper present four step process and evaluation criteria based on MCDM (Multiple Criteria Decision Making) technique for optimal COTS component selection in consumer position. We considered funtionality, efficiency, usability based on IS0/IEC 9126 for Quality measurement and executed practical analysis about commercial EJB component in internet. This paper show that the proposed selection technique is applicable to optimal COTS component selection.

• The Korean Journal of Applied Statistics
• /
• v.26 no.1
• /
• pp.131-139
• /
• 2013

Support vector machines(SVM) may perform poorly in the presence of noise variables; in addition, it is difficult to identify the importance of each variable in the resulting classifier. A feature selection can improve the interpretability and the accuracy of SVM. Most existing studies concern feature selection in the linear SVM through penalty functions yielding sparse solutions. Note that one usually adopts nonlinear kernels for the accuracy of classification in practice. Hence feature selection is still desirable for nonlinear SVMs. In this paper, we compare the performances of nonlinear feature selection methods such as component selection and smoothing operator(COSSO) and kernel iterative feature extraction(KNIFE) on simulated and real data sets.

• Proceedings of the Korean Statistical Society Conference
• /
• 2003.05a
• /
• pp.151-156
• /
• 2003

Variable selection algorithm for principal component analysis using penalized likelihood method is proposed. We will adopt a probabilistic principal component idea to utilize likelihood function for the problem and use HARD penalty function to force coefficients of any irrelevant variables for each component to zero. Consistency and sparsity of coefficient estimates will be provided with results of small simulated and illustrative real examples.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.12
• /
• pp.2511-2519
• /
• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

• Journal of Korean Institute of Industrial Engineers
• /
• v.40 no.3
• /
• pp.275-282
• /
• 2014

One of the most widely used methods for dimensionality reduction is principal component analysis (PCA). However, the reduced dimensions from PCA do not provide a clear interpretation with respect to the original features because they are linear combinations of a large number of original features. This interpretation problem can be overcome by feature selection approaches that identifying the best subset of given features. In this study, we propose an unsupervised feature selection method based on the geometrical information of PCA loading vectors. Experimental results from a simulation study demonstrated the efficiency and usefulness of the proposed method.