• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.751-753
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• 2005

지문을 5가지 클래스로 나누는 헨리시스템을 기반으로 신경망이나 SVM(Support Vector Machines) 등과 같은 다양한 패턴분류 기법들이 지문분류에 많이 사용되고 있다. 특히 최근에는 높은 분류 성능을 보이는 SVM 분류기의 결합을 이용한 연구가 활발히 진행되고 있다. 지문은 클래스 구분이 모호한 영상이 많아서 단일결합모델로는 분류에 한계가 있다. 이를 위해 본 논문에서는 새로운 분류기 결합모델인 다중결정템플릿(Multiple Decision Templates, MuDTs)을 제안한다. 이 방법은 하나의 지문클래스로부터 서로 다른 특성을 갖는 클러스터들을 추출하여 각 클러스터에 적합한 결합모델을 생성한다. NIST-database4 데이터로부터 추출한 핑거코드에 대해 실험한 결과. 5클래스와 4클래스 분류문제에 대하여 각각 $90.4\%$와 $94.9\%$의 분류성능(거부율 $1.8\%$)을 획득하였다.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.288-293
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• 2011

To resolve ambiguities in speech act classification, various machine learning models have been proposed over the past 10 years. In this paper, we review these machine learning models and present the results of experimental comparison of three representative models, namely the decision tree, the support vector machine (SVM), and the maximum entropy model (MEM). In experiments with a goal-oriented dialogue corpus in the schedule management domain, we found that the MEM has lighter hardware requirements, whereas the SVM has better performance characteristics.

• Proceedings of the KIPE Conference
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• 2014.11a
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• pp.29-30
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• 2014

전동기의 손실은 크게 동손과 철손으로 구분된다. 하지만 철손은 비선형적인 특성 때문에 제어에서 고려되지 않는 경우가 많았다. 본 논문에서는 동손뿐만 아니라 철손까지 제어에 반영하여 최소 손실로 구동하기 위하여 전동기의 철손을 모델링한다. 이때 구동조건에 따라 최소 손실 점을 실험적으로 측정하여 철손의 계수를 결정하였다. 구성된 손실 모델을 바탕으로 주어진 속도와 토크 조건에서 최적의 전류 지령을 생성하는 최소 손실 벡터제어 알고리즘을 소개하고, 실험을 통해 기존의 제어 방식과의 차이를 검증하여 효용성을 입증한다.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.99-103
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• 2007

Translation equivalence is very important for bilingual lexicography, machine translation system and cross-lingual information retrieval. Extraction of equivalences from bilingual sentence pairs belongs to data mining problem. In this paper, discriminative learning methods are employed to filter translation equivalences. Discriminative features including translation literality, phrase alignment probability, and phrase length ratio are used to evaluate equivalences. 1000 equivalences randomly selected are filtered and then evaluated. Experimental results indicate that its precision is 87.8% and recall is 89.8% for support vector machine.

• Journal of information and communication convergence engineering
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• v.10 no.2
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• pp.168-174
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• 2012

Flash is the most popular 2D animation and game development software, supporting vector and media technology at the core, which enables the development of small and pithy games. It is widely used in Web animation, courseware, TV commercials, game development, and other works of design. In this paper, we propose a control movement function and auxiliary functions for an alpine skiing game based on ActionScript 3.0. The control movement function is designed with moving phases (i.e., free fall, sliding, projectile, and landing). The auxiliary functions include drawing path, start/restart movement, and saving the highest score. In addition, for the visual design of our game, we designed animations in connection with a character and background. In order to facilitate testing the friction, users can input a chosen friction value. Without user input, the friction default is set at 0.97.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.3B no.1
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• pp.23-31
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• 2003

A novel 3D shape optimization algorithm is presented for electromagnetic devices carry-ing eddy current. The algorithm integrates the 3D finite element performance analysis and the steepest descent method with design sensitivity and mesh relocation method. For the design sensitivity formula, the adjoint variable vector is defined in complex form based on the 3D finite element method for eddy current problems. A new 3D mesh relocation method is also proposed using the deformation theory of the elastic body under stress to renew the mesh as the shape changes. The design sensitivity f3r the sur-face nodal points is also systematically converted into that for the design variables for the parameterized optimization application. The proposed algorithm is applied to the optimum design of the tank shield model of the transformer and the effectiveness is proved.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.465-467
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• 1992

This paper shows 3D vision system based on halftone image analysis. Any halftone image has its own surface vector normal to surface patch. To classily the given 3D images, all the patch on 3D object are transformed to black/white halftone. First we extract the general learning patterns which represents required slopes and their attributes. And next we propose 3D segmentation by searching intensity, slope and density. Artificial neural network is found to be very suitable in this approach, because it has powerful learning quality and noise tolerant. In this study, 3D shape reconstruct using pyramidian model. Our results are evaluated to enhance the quality.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.971-972
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• 2008

In this paper, we present a novel method for classifying pornography images using adaptive skin detection. From an input image, we detect initial skin regions and construct an adaptive skin probability density model using color information for the detected skin regions. From the skin probability density model, we extract feature vectors and train the images using Support Vector Machine to classify pornography images.

• The Journal of the Acoustical Society of Korea
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• v.23 no.3E
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• pp.91-97
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• 2004

In this paper, we propose a classification system of TV program scenes based on audio information. The system classifies the video scene into six categories of commercials, basketball games, football games, news reports, weather forecasts and music videos. Two type of audio feature set are extracted from each audio frame-timbral features and coefficient domain features which result in 58-dimensional feature vector. In order to reduce the computational complexity of the system, 58-dimensional feature set is further optimized to yield l0-dimensional features through Sequential Forward Selection (SFS) method. This down-sized feature set is finally used to train and classify the given TV program scenes using κ -NN, Gaussian pattern matching algorithm. The classification result of 91.6% reported here shows the promising performance of the video scene classification based on the audio information. Finally, the system stability problem corresponding to different query length is investigated.

• Annual Conference on Human and Language Technology
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• 1996.10a
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• pp.424-429
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• 1996

본 논문에는 일반문서를 대상으로 하여 그 문사를 하이퍼텍스트(hypertext)로 자동변환하는 기법을 제안하고자 한다. 자동변환의 과정은 대상 문서에서 키워드(keyword)의 인식, 문서를 노드(node) 단위로 분리, 키워드로부터 노드로의 링크(ink) 생성의 3 단계로 이루어 진다. 기존의 연구에서는 문서에서 노드를 분리하는데 구문적 유사도만을 이용하는데, 본 논문에서는 양질의 하이퍼텍스트를 생성하기 위하여 구문적 유사도(syntactic similarity)뿐만 아니라 의미적 유사도(semantic similarity)를 사용한다. 구문적 유사도는 tf*idf와 벡터 곱(vector product)을 이용하고, 의미적 유사도는 시소러스(thesaurus)와 부분부합(partial match)을 이용하여 계산되어 진다. 또 링크 생성시 잘못된 링크의 생성을 막기 위하여 시소러스를 이용하여 시소러스에 존재하는 용어에 한해서 링크를 생성한다.