• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.681-684
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• 2004

본 논문에서는 한국어의 문장패턴을 활용한 형태소 분석기를 설계하였다. 어절기반의 형태소 분석기들이 갖는 형태소 분석 정보는 어절의 품사 등의 기초적 정보만을 포함한다. 본 논문에서 제안하는 문장패턴을 활용한 형태소 분석기는 문장단위의 형태소 분석을 제안하였고 형태소 분석단계에서 구문분석과 문장패턴이 갖는 의미정보를 포함함으로서 분석결과의 활용도를 높이도록 하였다. 제안된 형태소 분석기의 결과를 활용하여 질의 응답시스템, 정보 검색 등의 분야에서 구문분석, 의미분석의 단계를 최소화 하여 결과를 얻을 수 있을 것으로 기대한다.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.88-98
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• 1998

Subspace classifier is a popular pattern recognition method based on Karhunen-Loeve transform. This classifier describes a high dimensional pattern by using a reduced dimensional subspace. Because of the loss of information induced by dimensionality reduction, however, a subspace classifier sometimes shows unsatisfactory recognition performance to the patterns having quite similar principal components each other. In this paper, we propose the use of multiple novelty neural network classifiers constructed on novelty vectors to adopt minor components usually ignored and present a method of improving recognition performance through combining those with the subspace classifier. We develop the proposed classifier on handwritten numeral database and analyze its properties. Our proposed classifier shows better recognition performance compared with other classifiers, though it requires more weight links.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.509-514
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• 2015

In this paper, we introduce a new pattern classifier which is based on the learning algorithm of Extreme Learning Machine the sort of artificial neural networks and fuzzy set theory which is well known as being robust to noise. The learning algorithm used in Extreme Learning Machine is faster than the conventional artificial neural networks. The key advantage of Extreme Learning Machine is the generalization ability for regression problem and classification problem. In order to evaluate the classification ability of the proposed pattern classifier, we make experiments with several machine learning data sets.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4130-4135
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• 2013

This paper is a special-purpose machine studies for processing various patterns on the surface of the stone. We have developed a special-purpose machine that can be applied in various patterns upon the surface treatment of the stone with the water jet. The special-purpose machine is Configured of Transfer mechanism, motion controller, multi-nozzle mechanism, ultra high pressure water control system and S/W. We conducted a performance evaluation experiments of the pattern. We have developed a special-purpose machine with a precision of machining error ${\pm}0.5mm$ and pattern processing of various types.

• Proceedings of the KAIS Fall Conference
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• 2002.05a
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• pp.67-69
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• 2002

주얼리 제품에서 마스터패턴 제작비는 최종제품의 20% 정도로 전체 주얼리제품 시장에서 매우 큰 비중을 차지한다. 기존 주얼리 제품을 제작하는 일반 공정인 ‘디자인 시안→상세도면 제작→1왁스형 제작→석고 플라스크 제작→은 마스터패턴의 제작→왁스패턴의 대량생산→정밀주조→최종제품’의 복잡한 단계를 ‘CAD 디자인 시안→쾌속조형기 듀라폼 음각몰드 제작→저융점 합금으로 마스터패턴 제작→정밀주조→최종제품’의 공정으로 단순화하면서도 대폭시간을 단축할 수 있는 신공정을 제안하였다. 주요공정인 selective laster sintering (SLS)형 쾌속조형기(rapid prototype: RP)를 이용해서 분해온도가 190℃인 듀라폼 분말로 미리 3D CAD로 설계한 직경 20mm의 구, 링 요소를 각각 상하형의 합체형 몰드와 일체형 몰드로 제작하였다. 제작된 몰드에 융점이 70℃인 Pb-Sn-Bi-Cd 저융점 합금을 주입하여 마스터패턴을 제조하였다. 완성된 마스터패턴은 목표형상에 비해 치수 변형율이 2% 이내로 우수하고 주입공정 및 후가공공정이 용이하여 주얼리용 마스터패턴으로 응용이 가능하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.472-479
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• 1999

In this paper, we propose a land cover pattern classifier for remote sensing image by using neuro-fuzzy algorithm. The proposed pattem classifier has a 3-layer feed-forward architecture that is derived from generic fuzzy perceptrons, and the weights are con~posed of h u y sets. We also implement a neuro-fuzzy pattern classification system in the Visual C++ environment. To measure the performance of this, we compare it with the conventional neural networks with back-propagation learning and the Maximum-likelihood algorithms. We classified the remote sensing image into the eight classes covered the majority of land cover feature, selected the same training sites. Experimental results show that the proposed classifier performs well especially in the mixed composition area having many classes rather than the conventional systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1577-1584
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• 2015

In this paper, we propose a method for generating pattern classifier based on LFSR. The proposed pattern classifier bosed on LFSR is easy to see non-reachable state, and we can obtain dependency vector by using the 0-basic path. Also, we propose a method for generating pattern classifiers based on LFSR which correspond to given dependency vector.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1803-1804
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• 2007

개인 사용자의 정보보호를 위한 키보드 타이핑 패턴 인식기를 개발한 바 있었다. 키보드 타이핑 패턴의 스케일링 방식에 따라 신경망 인식기의 성능이 차이가 있을 것이라 기대되어 본 연구에서 이를 수행하였다. 총 3 종류의 방식을 이용하여 스케일링을 하였으며, 그 영향을 인식기의 예측에러, 제 1종과 2정의 인식에러측면에서 분석하고 평가하여 최적의 스케일링 방식을 결정하였다.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.907-916
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• 2004

The present code generation system, developing based on single system, Is not easy for developers or maintenance men to share pattern design information in distribution environment. So in this paper, we design and implement XML as basis of web environment, and JiKU/XML object-oriented code generator using pattern design. We use UML to change pattern design to XML code, and create code, suitable to PIML command, to generate design information designed by UML into XML code. This JiKU/XML Object-oriented Code Generator makes 10-step codes, and can be easily applied to web environment. It complements the disadvantage of present generator, F77/J++, and makes standardization of design because it uses UML and design pattern information. We compare it with present system by implement Eases, and as a result, generator suggested in this study gives more effective function.

• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.946-950
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• 2010

Many sentiment categorization systems based on machine learning methods use morphological analyzers in order to extract linguistic features from sentences. However, the morphological analyzers do not generally perform well in a customer review domain because online customer reviews include many spacing errors and spelling errors. These low performances of the underlying systems lead to performance decreases of the sentiment categorization systems. To resolve this problem, we propose a feature extraction method based on simple longest matching of Eojeol (a Korean spacing unit) and phoneme patterns. The two kinds of patterns are automatically constructed from a large amount of POS (part-of-speech) tagged corpus. Eojeol patterns consist of Eojeols including content words such as nouns and verbs. Phoneme patterns consist of leading consonant and vowel pairs of predicate words such as verbs and adjectives because spelling errors seldom occur in leading consonants and vowels. To evaluate the proposed method, we implemented a sentiment categorization system using a SVM (Support Vector Machine) as a machine learner. In the experiment with Korean customer reviews, the sentiment categorization system using the proposed method outperformed that using a morphological analyzer as a feature extractor.