• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.41-48
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• 2011

In feature selection, the selective evaluation scheme for Ant Colony Optimization(ACO) has recently been proposed, which reduces computational load by excluding unnecessary or less promising candidate solutions from the actual evaluation. Its superiority was supported by experimental results. However the experiment seems to be not statistically sufficient since it used only one dataset. The aim of this paper is to analyze convergence characteristics of the selective evaluation scheme and to make the conclusion more convincing. We chose three datasets related to handwriting, medical, and speech domains from UCI repository whose feature set size ranges from 256 to 617. For each of them, we executed 12 independent runs in order to obtain statistically stable data. Each run was given 72 hours to observe the long-time convergence. Based on analysis of experimental data, we describe a reason for the superiority and where the scheme can be applied.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.159-164
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• 2005

In a computing environment connected pan-based computers and a server by Internet, This paper describes a language learning system evaluating the quality of a handwriting string. For the purpose of the system, this paper explains how to retrieve reference data from a database, how to evaluate the quality of a handwriting string using global and local features. The Proposed system can evaluate the qualify of a handwriting string as well as a handwriting character. The qualify can be computed in the case of different language between reference and input. Therefore, we expect that the system is very useful not only for training on handwriting but also learning a language.

• Journal of the Korean Society of Radiology
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• v.6 no.4
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• pp.305-311
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• 2012

This study compares the TE and UTE is to evaluate. We was programming by DWT of Matlab Tool-box for evaluation. M-program used feature value extract between TE Images and UTE Images. Two images using the extracted feature values were compared. Comparison of similar features two images phase was found to have value.

• The KIPS Transactions:PartB
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• v.11B no.5
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• pp.581-586
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• 2004

In this paper, we proposed a content-based image retrieval using a color feature for central region and its optimized comparing bin method. Human's visual characteristic is influenced by existent of central object. So we supposed that object is centrally located in image and then we extract color feature at central region. When the background of image is simple, the retrieval result can be bad affected by major color of background. Our method overcome this drawback as a result of the human visual characteristic. After we transform Image into HSV color space, we extract color feature from the quantized image with 16 level. The experimental results showed that the method using the eight high rank bin is better than using the 16 bin The case which extracts the feature with image's central region was superior compare with the case which extracts the feature with the whole image about 5%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.342-345
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• 2014

Network intrusion detection system based on machine learning methods is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features from generally used features to detect network intrusion requires extensive computing resources. For instance, the number of possible feature combinations from given n features is $2^n-1$. In this paper, to tackle this problem we propose a optimal feature selection algorithm. Proposed algorithm is based on the local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In addition, the accuracy of clusters which obtained using selected feature components and k-means clustering algorithm is adopted to evaluate a feature assembly. In order to estimate the performance of our proposed algorithm, comparing with a method where all features are used on NSL-KDD data set and multi-layer perceptron.

• Science of Emotion and Sensibility
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• v.18 no.3
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• pp.71-80
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• 2015

Minimalism means abstinence characterized by simplicity, clarity, repetition, and exclusion while skeuomorphism means visual metaphor design characterized by retro, function, and emotion. Mobile platform interface has been developed based on those two aesthetic characteristics and user experience is evaluated using apple iOS6 and iOS7 representing skeuomorphism and minimalism respectively in this paper. Those two aesthetic designs on typography, color, and icon were tested with a sample of 35 undergraduate participants in the repeated measures design and the results showed that participants distinguished two types of aesthetic designs, and evaluated that iOS7 is superior to iOS6 on typography and identity. Comparing the levels of the three variables, aesthetic of typography, and accuracy, aesthetic and consistency of icon design were significantly differentiated and this means that although accuracy of icon design of iOS6 is superior to iOS7, iOS7 is superior in the rest of it. Overall, the participants had a positive evaluation toward iOS7.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.853-867
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• 2005

A number of content-based image search methods have been proposed to this point. Each of these systems uses different image data and generates different data depending on the extraction method of different characteristics that the search capabilities of each system cannot be compared and assessed. In particular, there is a problem of applying the identical image data onto the contents based image search system on the web that cannot be compared and assessed. To resolve such a problem, the XML-based FEMAL is hereby presented for extracting data of characteristics generated from specific search system in a way that can be recognized from other starch system. In the experiment using FEMAL, the extract data for characteristics is mutually communicated and integrated and the comparison assessment of search capability is seemed to be available.

• Journal of the Korean Society for Nondestructive Testing
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• v.14 no.3
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• pp.194-205
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• 1994

이종재료 접합재의 연구개발(R&D)이나 품질보증(QA)분야에서 최적 접합조건의 신속한 결정과 사용중 접합재의 접합강도에 결정적으로 영향을 미치는 접합계면의 박리 미접합부등의 비파괴진단 평가기법에 적극 활용되고 있는 초음파비파괴평가(UNDE)기법의 주된 특징과 적용한계, 향후 연구되어야할 과제등을 최근의 특징적인 연구경향과 적용예를 중심으로 소개하고자 한다. 본고(本稿)에서 기술하는 초음파비파괴평가기법은 부분적으로는 기술의 안정화단계에 까지 상당히 접근하여, 현재 국내 반도체산업의 품질보증분야에서는 접합재료 접합계면의 비파괴적해석에 그 유용성이 어느정도 확인되고 있으나 정량적비파괴평가(QNDE)와 검사시스템의 꽃이라 불리는 전문가시스템화에의 접근에는 아직 해결되어야할 문제가 많다. 따라서, 저자들은 앞으로 접합재료 접합계면의 비파괴적해석에 관련한 연구사례와 결과를 정량적비파괴평가의 중요성과 방향성에 초점을 두고 본학회지를 통하여 계속 연재할 계획이다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.706-708
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• 2005

본 연구에서는 다단계 필터와 복합형 신경망을 사용하는 얼굴 검출 시스템에서 FMM 모델을 이용한 특징선정 기법을 소개한다. 색상, 모션 및 명암을 이용한 다단계 필터는 검출 대상 영역의 개수를 줄임으로써 시스템의 실시간 검출기능을 가능하게 한다. 신경망을 이용한 특징추출 단계에서는 대상영역의 기본 특징으로부터 일련의 특징지도를 생성하게 된다. 이 과정에서 패턴 분류 신경망의 입력으로 사용되는 특징집합이 지나치게 커짐으로써 신경망의 규모와 계산량이 방대해지는 단정을 갖는다. 이에 본 논문에서는 FMM 모델의 수정된 특성으로부터 특징과 각 클래스에 대한 상호 연관도 요소를 정의하고, 이로부터 특징의 상대적 중요도를 평가함으로써 성능의 저하 없이 최적의 특징집합을 선정하는 방법론을 소개한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.257-260
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• 2011

본 논문에서는 수화인식을 위한 신경망에서 특징추출과 분류단계의 방법론과, 특징 선별 기법을 통하여 분류기의 규모를 최적화 하는 방법을 고찰한다. 색상 및 움직임정보로부터 특징영역의 시간에 따른 변화를 3 차원 볼륨형태의 데이터로 표현하며, 이로부터 특징지도를 생성하는 과정에서 특징영역의 위치에 대한 변이를 보완하는 방법을 고려한다. 특징추출과정과 패턴 분류과정에서 점진적 학습이 가능한 모델과 특징 수를 효과적으로 줄일 수 있는 방법론을 제시하였으며, 학습된 신경망으로부터 특징과 패턴 클래스간의 상대적 연관성 척도를 정의하여 특징을 선별하도록 하였다. 제안된 내용에 대하여 여섯 가지 수화패턴에 대상으로 한 실험을 통하여 그 유용성을 평가하였다.