• Proceedings of the Korea Database Society Conference
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• 2000.11a
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• pp.101-108
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• 2000

기업에서 보유한 전문 지식 정보가 급속도로 증가함에 따라 대량의 문서에 저장된 지식 정보를 효과적으로 탐색하여 기업 경영에 활용하기 위한 지식경영시스템 도입이 확산되고 있다. 이러한 지식경영시스템에서 핵심적인 구성 요소는 전문 분야의 지식 정보를 체계적으로 분류하고 효율적으로 검색하기 위한 지식 탐사 기법이다. 본 논문에서는 데이타마이닝 기법을 이용하여 문서를 자동적으로 분류하기 위한 새로운 모델을 제안하였다. 연관 규칙 탐사 알고리즘을 이용하여 학습용 문서 집합으로부터 세부 분야를 대표하는 색인어 집합을 구성하였다. 세부 분야별 색인어 집합에 대하여 전체 문서에 대한 비중에 따라 가중치 배열을 구성하여 문서를 자동으로 분류하기 위한 기준으로 삼았다. 임의의 문서를 자동적으로 분류하는 실험을 통하여 제안된 방법의 효율성을 검정하였다.

• Journal of the Korean Society for information Management
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• v.21 no.1
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• pp.93-117
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• 2004

This study is to develop a hierarchic clustering model fur document classification and browsing in OPAC systems. Two automatic indexing techniques (with and without controlled terms), two term weighting methods (based on term frequency and binary weight), five similarity coefficients (Dice, Jaccard, Pearson, Cosine, and Squared Euclidean). and three hierarchic clustering algorithms (Between Average Linkage, Within Average Linkage, and Complete Linkage method) were tested on the document collection of 175 books and theses on library and information science. The best document clusters resulted from the Between Average Linkage or Complete Linkage method with Jaccard or Dice coefficient on the automatic indexing with controlled terms in binary vector. The clusters from Between Average Linkage with Jaccard has more likely decimal classification structure.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.151-157
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• 2018

Most previous studies of software fault prediction have focused on supervised learning models for binary classification that determines whether an input module has faults or not. However, binary classification model determines only the presence or absence of faults in the module without considering the complex characteristics of the fault, and supervised model has the limitation that it requires a training data set that most development groups do not have. To solve these two problems, this paper proposes severity-based ternary classification model using unsupervised learning algorithms, and experimental results show that the proposed model has comparable performance to the supervised models.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.271-280
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• 2009

In this paper, we propose a smoke detection method using region growing method in outdoor video sequences. Our proposed method is composed of three steps; the initial change area detection step, the boundary finding and expanding step, and the smoke classification step. In the first step, we use a background subtraction to detect changed areas in the current input frame against the background image. In difference images of the background subtraction, we calculate a binary image using a threshold value and apply morphology operations to the binary image to remove noises. In the second step, we find boundaries of the changed areas using labeling algorithm and expand the boundaries to their neighbors using the region growing algorithm. In the final step, ellipses of the boundaries are estimated using moments. We classify whether the boundary is smoke by using the temporal information.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.44-57
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• 1995

In this paper GFI (Generalized Fuzzy Isodata) and FI (Fuzzy Isodata) algorithms are studied and applied to the tire tread pattern classification problem. GFI algorithm which repeatedly grouping the partitioned cluster depending on the fuzzy partition matrix is general form of GI algorithm. In the constructing the binary tree using GFI algorithm cluster validity, namely, whether partitioned cluster is feasible or not is checked and construction of the binary tree is obtained by FDH clustering algorithm. These algorithms show the good performance in selecting the prototypes of each patterns and classifying patterns. Directions of edge in the preprocessed image of tire tread pattern are selected as features of pattern. These features are thought to have useful information which well represents the characteristics of patterns.

• Journal of Information Management
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• v.42 no.2
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• pp.193-210
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• 2011

The study was applied to context window sizes and weighting method to obtain the best performance of word sense disambiguation using support vector machine. The context window sizes were used to a 3-word, sentence, 50-bytes, and document window around the targeted word. The weighting methods were used to Binary, Term Frequency(TF), TF ${\times}$ Inverse Document Frequency(IDF), and Log TF ${\times}$ IDF. As a result, the performance of 50-bytes in the context window size was best. The Binary weighting method showed the best performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.241-247
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• 2007

자궁 경부 세포진 영상의 핵 추출을 위해서는 영상의 배경과 핵 그리고 세포질 영역의 구분이 중요하다. 또한 정상 세포핵과 암종 세포핵의 구분 및 인식을 위해서는 세포핵들의 형태학적 특징을 이용한 분류 기준을 세워야한다. 본 논문에서는 자궁 경부 세포진 영상에서 세포핵의 후보 영역과 핵을 추출하기 위해 현미경 400배율 확대 사진을 획득하는 과정에서 훼손된 컬러 영상을 복원하기 위한 방법으로 Lighting Compensation을 적용하여 영상을 보정한다. 그리고 배경 영역과 세포핵 영역을 구분하기 위해 영상의 R,G,B 영역의 히스토그램의 분포를 이용하여 배경을 제거한다. 배경이 제거된 영상을 그레이 영상으로 변환 한 후, 히스토그램 명암도의 값을 이용하여 세포핵 영역과 세포질을 분류하여 세포핵 영역을 추출한다. 그리고 Kapur 방법을 적용하여 세포핵 영역의 엔트로피 누적확률을 구한 후, 영상을 이진화 한다. Kapur 방법이 적용된 이진화 영상에서 세포핵 영역의 중심과 주위 화소를 비교하는 $3\times3$ 마스크를 적용하여 영상의 미세한 잡음을 제거 한 후, 8방향 윤곽선 추적 알고리즘을 적용하여 최종적으로 세포핵 영역을 추출한다. 추출된 세포핵의 영역을 분류 및 인식하는 과정으로 세포의 외각의 방향성 정보, 핵의 크기, 그리고 면적 비율의 특징을 이용하여 퍼지 소속 함수를 설계한 후, 소속 함수의 소속도를 구하고 퍼지 추론 규칙을 적용하여 자궁 경부 세포진 영상에서 정상 세포핵 및 암종 세포핵을 인식한다.

• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.9-17
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• 2009

License plate recognition is an active research area. but few comparative studies on license plate binarization have been conducted. Many related researchers have experienced similar trial and error for finding an effective binarization method. To reduce this trial and error, this study implemented some binarization methods and quantitatively compared the performance of the methods. The performance evaluation consists of a low level measure and a high level measure, so it can evaluate not only the quality of binarized image itself but also the usefulness of the result. The performance evaluation was separately performed with three groups of images so as to understand the properties of the binarization methods. Experimental results show that the quality of binarization is more dependent on the evenness of illumination than the intensity of illumination. The Otsu's method has acquired the most effective performance in the group of even illumination images and the Niblack's method with parameter correction has shown the best quality in the group of uneven illumination images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4B
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• pp.245-257
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• 2012

As one of the most challenging tasks in designing the Internet routers, packet classification is required to achieve the wire-speed processing for every incoming packet. Packet classification algorithm which applies binary search on trie levels to the area-based quad-trie is an efficient algorithm. However, it has a problem of unnecessary access to a hash table, even when there is no node in the corresponding level of the trie. In order to avoid the unnecessary off-chip memory access, we proposed an algorithm using Bloom filters along with the binary search on levels to multiple disjoint tries. For ACL, FW, IPC sets with about 1000, 5000, and 10000 rules, performance evaluation result shows that the search performance is improved by 21 to 33 percent by adding Bloom filters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2206-2212
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• 2013

We propose a method for the classification of fatty liver by ultrasound imaging using Fuzzy Contrast Enhancement Technique and FCM. ROI images are extracted after removal of information data except ultrasound image of the liver and the kidney then image contrast is improved by Fuzzy Contrast Enhancement Algorithm. The images applied Fuzzy Contrast Enhancement Technique is applied average binarization then ROI images of liver and kidney parenchyma are extracted using Blob algorithm. Representative brightness is extracted in the liver and kidney images using the most frequent brightness level after classification of 10 brightness levels. We applied this method to ultrasound images and a radiologist confirmed the accuracy of diagnosis for fatty liver. This method would be a model for automatic method in the diagnosis of fatty liver.