• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.553-555
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• 2001

기업형 정보 시스템을 개발하는 데 클라이언트 계층, 어플리케이션 서버 계층, 데이터베이스 계층으로 구성된 3계층 아키텍쳐가 널리 사용되고 있다. 따라서 기업형 컴포넌트의 올바른 행위를 시험하기 위해서는 3계층 아키텍처를 고려한 시험 기법이 요구된다. 하지만 기존의 대부분의 컴포넌트 시험 기법들은 클라이언트 계층과 어플리케이션 서버 계층 사이의 관계만을 대상으로 하고 있어서 기업형 컴포넌트 시험에 부족하다. 논문에서는 기업형 컴포넌트의 시험을 위해 클라이언트 계층과 어플리케이션 서버계층 간의 관계만이 아니라 어플리케이션 서버계층과 데이터베이스 서버계층과의 관계를 포함한 시험 기법을 제안한다. 이를 위해 3계층 아키텍쳐를 반영하는 시험모델을 제안했으며 UML/OCL를 컴포넌트의 명세로 사용하여 시험모델을 추출한 뒤 자동으로 시험을 수행하는 시험 환경을 개안했다. 제안된 시험 환경은 일반적인 시험 단계의 뒷부분으로 테스트 케이스를 분석하여 생성하는 것보다는 생성된 시험 사료를 수행시켜 자동으로 시험 과정을 수행하는데 관심을 두고 있다. 제안된 시험환경은 기존의 연구와 달리 3계층 아키텍처를 반영하고 산업체 표준인 UML/OCL을 이용하므로 기업형 응용프로그램의 생산성을 증가시켜 줄 것으로 보인다.

• Annual Conference of KIPS
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• 2010.04a
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• pp.385-388
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• 2010

블로그는 다양한 주제 분야에 대한 내용을 자유롭게 표현할 수 있는 일종의 개인 웹사이트로, 많은 양과 다양성으로 매우 중요한 정보원이 될 수 있다. 블로그는 생산속도가 매우 빠르므로 보다 고품질의 블로그를 선별하는 것이 중요하다. 본 논문에서는 블로그의 본문을 담고 있는 포스트를 대상으로 기계학습 기법을 이용하여 문서의 품질을 자동으로 평가하고자 하였다. 학습을 위한 자질로는 모든 블로그에 공통적으로 적용할 수 있도록 형태소 분석에서 추출한 동사, 부사, 형용사의 내용어만을 선택하였다. 성능 비교를 위해 수작업으로 약 4,600개의 정답 집합을 구축하고, 적합한 기계학습 기법을 찾기 위해 다양한 학습 기법을 사용하여 비교 실험하였다. 실험 결과 Bagging 기법의 성능이 79% F-measure로 가장 좋음을 보여주었다. 한정된 자질을 사용했을 때와 정답 집합의 문서 수 비율이 불균등할 경우 단순함, 유연성, 효율성의 특징을 지닌 Bagging 기법이 적합할 것으로 보인다.

• Annual Conference on Human and Language Technology
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• 2005.10a
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• pp.13-20
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• 2005

이 논문은 공학적인 접근 방법에 기반한 단계적인 전문분야 시소러스 구축 방법을 제안한다. 시소러스 구축 과정은 용어 추출 단계, 용어 분류 단계, 계층 구조 구축 의 3단계로 구성되고, 모든 단계에서 자동 처리와 전문가 검증 작업을 거친다. 추출된 용어를 미리 정해진 분류 체계에 따라 분리한 후 여러 개의 작은 시소러스를 구축하고, 마지막으로 전체 시소러스로 결합한다. 이 방법은 1) 시소러스를 구축하는 복잡도가 줄어들고, 2) 클래스 단위의 작은 시소러스가 다른 전문분야 시소러스에 쉽게 재사용 될 수 있으며, 3) 각 클래스에 포함된 용어들의 분포를 쉽게 판단할 수 있는 장점이 있다. 제안한 방법을 이용하여 한국어 정보기술 분야 시소러스를 구축하였다. 시소러스 구축에 사용된 용어들은 정보기술 분야의 최근의 한국어 신문과 특허 문서에서 추출하였기 때문에 한국에서 만들어진 신조어를 포함한다. 구축된 시소러스는 81 개의 상위 레벨클래스와 1,000개 이상의 용어로 구성된다.

• Journal of Korea Spatial Information System Society
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• v.4 no.2 s.8
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• pp.55-64
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• 2002

VEfficient generation of road data is one of the most important issues in GIS (Geographic Information System). In this paper, we propose a hybrid approach for automatic generation of road data by combining mobile mapping and image processing techniques. Mobile mapping systems have a form of vehicle equipped with CCD camera, GPS, and INS. They can calculate absolute position of objects that appear in acquired image by photogrammetry, but it is labor-intensive and time-consuming. Automatic road detection methods have been studied also by image processing technology. However, the methods are likely to fail because of obstacles and exceptive conditions in the real world. To overcome the problems, we suggest a hybrid method for automatic road generation, by exploiting both GPS/INS data acquired by mobile mapping system and image processing algorithms. We design an estimator to estimate 3-D coordinates of road line and corresponding location in an image. The estimation process reduces complicated image processing operations that find road line. The missing coordinates of road line due to failure of estimation are obtained by cubic spline interpolation. The interpolation is done piecewise, separated by rapid change such as road intersection. We present experimental results of the suggested estimation and interpolation methods with image sequences acquired by mobile mapping system, and show that the methods are effective in generation of road data.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.403-411
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• 2014

In this paper, an algorithm of extracting the features of home appliances and automatically building a database to identify home appliances is designed and presented. For the verification, a software library supporting this algorithm is implemented and added to an power management system server, which was already implemented to support real-time monitoring of home appliances' power consumption status and controlling their power. The implemented system consists of a system server and clients, each of which measures the power consumed by a home appliance plugged in it and transmits the information to the server in real-time over a wireless network. Through experiments, it is verified that it is possible to identify any home appliance connected to a specific client.

• Journal of KIISE:Information Networking
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• v.34 no.4
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• pp.246-255
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• 2007

Due to the bandwidth-consuming characteristics of the heavy-hitter P2P applications, it has become critical to have the capability of pinpointing and mitigating P2P traffic. Traditional port-based classification scheme is no more adequate for this purpose because of newer P2P applications, which incorporating port-hopping techniques or disguising themselves as HTTP-based Internet disk services. Alternatively, packet filtering scheme based on payload signatures suggests more practical and accurate solution for this problem. Moreover, it can be easily deployed on existing IDSes. However, it is significantly difficult to maintain up-to-date signatures of P2P applications. Hence, the automatic signature generation method is essential and will be useful for successful signature-based traffic identification. In this paper, we suggest an automatic signature generation method for Internet disk P2P applications and provide an experimental results on CNU campus network.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.202-210
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• 2002

During the last few years, the extraction and reconstruction of the blood vessels in the medical image has been actively researched and the analysis for the retinal vessel structure has provided important information for diagnosis and remedy of the retinopathy patients. In this research, we propose the algorithm that tracks automatically the entire retinal vessel in retinal image acquired by the ICG(IndoCyanine Green) technology. This algorithm extracts contours and centers by estimating the local maxima and processing directions and detects bifurcations and junctions by comparing direction components of the local maxima from the gradient magnitude profile of each blood vessel. We present experimental results that the entire blood vessel is automatically reconstructed and is excellent in accuracy and connectivity after applying our algorithm to the ICG retinal images of patients.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.141-148
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• 2013

Automated classification systems which utilize machine learning develops classification models through learning process, and then classify unknown data into predefined set of categories according to the model. The performance of machine learning-based classification systems relies greatly upon the quality of features composing classification models. For textual data, we can use their word terms and structure information in order to generate the set of features. Particularly, in order to extract feature from Web documents, we need to analyze tag and hyperlink information. Recent studies on Web document classification focus on feature engineering technology other than machine learning algorithms themselves. Thus this paper proposes a novel method of incorporating feature selection and weighting which can improves classification models effectively. Through extensive experiments using Web-KB document collections, the proposed method outperforms conventional ones.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.29-43
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• 2023

Consumers should look at the ingredients of cosmetics or food for their health and purchase them after checking whether they contain allergy-causing ingredients. Therefore, this paper aimed to develop an artificial intelligence-based smartphone application for automatically recognizing the ingredients displayed on a curved container and delivering it to consumers in an easy-to-understand manner. The app needs to allow consumers to immediately comprehend the restricted ingredients by recognizing the ingredients' words in the cropped image. Two major issues should be solved during the development process: First, although there were flat containers for cosmetics or food, most were curved containers. Thus, it was necessary to recognize the ingredient table displayed on the curved containers. Second, since the ingredients' words were displayed on the curved surface, the transformed or line-changed words also needed to be recognized. The proposed new methods were enough to solve the above two problems. The application developed through various tests verified that there was no problem recognizing the ingredients' words contained in a cylindrical curved container.

• Journal of the Korean Society for information Management
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• v.23 no.1 s.59
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• pp.83-98
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• 2006

This study proposes a template-based method of automatic summarization of multiple news articles using the semantic categories of sentences. First, the semantic categories for core information to be included in a summary are identified from training set of documents and their summaries. Then, cue words for each slot of the template are selected for later classification of news sentences into relevant slots. When a news article is input, its event/accident category is identified, and key sentences are extracted from the news article and filled in the relevant slots. The template filled with simple sentences rather than original long sentences is used to generate a summary for an event/accident. In the user evaluation of the generated summaries, the results showed the 54.l% recall ratio and the 58.l% precision ratio in essential information extraction and 11.6% redundancy ratio.