• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2164-2167
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• 2003

In this paper we propose effective speech recognizer through recognition experiments for three feature parameters(PCA, ICA and MFCC) using SVM(Support Vector Machine) classifier In general, SVM is classification method which classify two class set by finding voluntary nonlinear boundary in vector space and possesses high classification performance under few training data number. In this paper we compare recognition result for each feature parameter and propose ICA feature as the most effective parameter

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.461-466
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• 2005

The purpose of this study is to classify rural villages through the amenity investigation by a village unit. PCA(Principal component analysis) is used for the classification of rural villages. The principal components of rural villages are deduced scale, population, infrastructure, traffic, education welfare and sightseeing by PCA.

• Proceedings of the IEEK Conference
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• 2004.06a
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• pp.171-174
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• 2004

Packet classification is an essential architectural component in implementing the quality-of-service (QoS) in today's Internet which provides a best-effort service to ail of its applications. Multiple header fields of incoming packets are compared against a set of rules in packet classification, the highest priority rule among matched rules is selected, and the packet is treated according to the action of the rule. In this Paper, we proposed a new packet classification scheme based on parallel multiple hashing on tuple spaces. Simulation results using real classifiers show that the proposed scheme provides very good performance on the required number of memory accesses and the memory size compared with previous works.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.241-247
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• 2006

We propose a character recognition system to extract the component reference names from printed circuit boards (PCBs) automatically. The names are written in horizontal, vertical, reverse-horizontal and reverse-vertical directions. Also various symbols and figures are included in PCBs. To recognize the character and orientation effectively, we divide the recognizer into two stages: character classification stage and orientation classification stage. The character classification stage consists of two sub-recognizers and a verifier. The rotaion-invarint features of input pattern are then used to identify the character independent of orientation. Each recognizer is implemented as a neural network, and the weight values of verifier are obtained by genetic algorithm. In the orientation classification stage, the input pattern is compared with reference patterns to identify the orientation. Experimental results are presented to verify the usefulness of the proposed system.

• KSTLE International Journal
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• v.1 no.1
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• pp.34-42
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• 2000

A method and results of classification of four different metallic wear debris were presented by using their color features. The color image of wear debris was used far the initial data, and the color properties of the debris were specified by HSI color model. Particles were characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used fer the definition of a classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.59-62
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• 2000

A software application builder has designed and partially implemented a E-Business software system using several reusable in-house software components. The builder finds an externally available third-party software components that satisfies solve desired functionality or behavior. We need systematic classification of the component from the domain. We propose a reference architecture of E-Business domain. It is used to search and reuse requiring components.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.292-294
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• 2003

본 논문에서는 독립성분분석(Independent Component Analysis； 이하 ICA)에서의 제안된 그레이 블록 거리 알고리즘(Grey Block Algorithm, 이하 GBD)을 이용한 영상 분류 방법을 제안한다. 이 제안된 방법은 기존의 GBD 알고리듬을 이용한 경우보다 k가 감소할 때 그 편차는 적어 좋은 영상 분류 특징을 보임을 모의 실험을 통하여 확인할 수 있었다.

• Journal of Information Technology Services
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• v.5 no.2
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• pp.137-153
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• 2006

SOA(Service Oriented Architecture) can be a technique to make compassable software from mapping business service to software component. To support effective SOA, it is important that services have to be defined or classified more independently for dynamic and reusable composition. Several methods have been issued but no ways to defined service granularities, service type or service unit. In this paper we introduce 3 level views, service level, service granularity to reuse effectively. And, we suggest service definition guidelines using them.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.543-553
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• 2002

Principal component analysis is applied to reduce p-dimensions into q-dimensions ( $q {\leq} p$). Any partition of a collection of data points with p and q variables generated by the application of six hierarchical clustering methods is re-classified by discriminant analysis. From the application of discriminant analysis through each hierarchical clustering method, correct classification ratios are obtained. The results illustrate which method is more reasonable in exploratory data analysis.

• Communications for Statistical Applications and Methods
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• v.20 no.6
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• pp.491-498
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• 2013

We consider support vector machines(SVM) to predict Y with p numerical variables $X_1$, ${\ldots}$, $X_p$. This paper aims to build a biplot of p explanatory variables, in which the first dimension indicates the direction of SVM classification and/or regression fits. We use the geometric scheme of kernel principal component analysis adapted to map n observations on the two-dimensional projection plane of which one axis is determined by a SVM model a priori.