• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.41 no.4
• /
• pp.314-318
• /
• 2013

Tests and evaluations following the U.S. MIL-HDBK/STANDARD were successfully conducted to assure the performance of the air-to-surface guidance kit which was developed first in Korea. Various ground tests confirmed the operation capability and reliability of the guidance kit, and flight tests proved very good mid-range gliding performance and accuracy of the gliding bomb which was a general purpose bomb with the guidance kit.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.21 no.3
• /
• pp.295-304
• /
• 2008

This paper comprises two specific objectives. The first is to examine the applicability of Ordinary Kriging interpolation(OK) to finite element method that is based on variogram modeling in conjunction with different allowable limits of separation distance. The second is to investigate the accuracy according to theoretical variograms such as polynomial, Gauss, and spherical models. For this purpose, the weighted least square method is applied to obtain the estimated new stress field from the stress data at the Gauss points. The weight factor is determined by experimental and theoretical variograms for interpolation of stress data apart from the conventional interpolation methods that use an equal weight factor. The validity of the proposed approach has been tested by analyzing two numerical examples. It is noted that the numerical results by Gauss model using 25% allowable limit of separation distance show an excellent agreement with theoretical solutions in literature.

• The Korean Journal of Applied Statistics
• /
• v.16 no.2
• /
• pp.261-271
• /
• 2003

Support Vector Machines (SVMs) provide a powerful performance of the learning system. Generally, SVMs tend to make overfitting. For the purpose of overcoming this difficulty, the definition of soft margin has been introduced. In this case, it causes another difficulty to decide the weight for slack variables reflecting soft margin classifiers. Especially, the error of soft margin algorithm can be bounded by a target margin and some norms of the slack vector. In this paper, we formulate a new soft margin algorithm considering the bound of corruption by noise in data directly. Additionally, through a numerical example, we compare the proposed method with a conventional soft margin algorithm.