• Journal of Institute of Control, Robotics and Systems
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• 제13권6호
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• pp.513-518
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• 2007

In this paper, we propose a fault diagnosis scheme tor induction motor by adopting a hierarchical classifier consisting of k-Nearest Neighbors(k-NN) and Support Vector Machine(SVM). First, some motor conditions are classified by a simple k-NN classifier in advance. And then, more complicated classes are distinguished by SVM. To obtain the normal and fault data, we established an experimental unit with induction motor system and data acquisition module. Feature extraction is performed by Principal Component Analysis(PCA). To show its effectiveness, the proposed fault diagnostic system has been intensively tested with various data acquired under the different electrical and mechanical faults with varying load.

• Proceedings of the Korean Information Science Society Conference
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• 한국정보과학회 2000년도 봄 학술발표논문집 Vol.27 No.1 (B)
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• pp.262-264
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• 2000

텍스트 문서 분류는 텍스트 형태로 주어진 문서를 종류별로 구분하는 작업으로 웹페이지 검색, 뉴스 그룹 검색, 메일 필터링 등이 분야에 응용될 수 있는 기반 작업이다. 지금까지 문서를 분류하는데는 k-NN, 신경망 등 여러 가지 기계학습 기법이 이용되어 왔다. 이 논문에서는 베이지안망을 이용해서 텍스트 문서 분류를 행한다. 베이지안망은 다수의 변수들간의 확률적 관계를 표현하는 그래프 모델로 DAG 형태인 망 구조와 각 노드에 연관된 지역확률분포로 구성된다. 그래프 모델을 사용할 경우 학습에 이용되는 각 속성들간의 관계를 사람이 알아보기 쉬운 형태로 학습할 수 있다는 장점이 있다. 실험 데이터로는 Reuters-21578 문서분류데이터를 이용했으며 베이안망의 성능은 나이브 베이즈 분류기와 비슷했다.

• Journal of Korea Multimedia Society
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• 제9권3호
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• pp.279-286
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• 2006

Classification method used in this paper is applied for the first time to Korean traditional music. Among the frequency distribution vectors, average peak value is suggested and proved effective comparing to previous classification success rate. Mean, variance, spectral centroid, average peak value and ZCR are used to classify Korean traditional musical instruments. To achieve Korean traditional instruments automatic classification, Spectral analysis is used. For the spectral domain, Various functions are introduced to extract features from the data files. k-NN classification algorithm is applied to experiments. Taegum, gayagum and violin are classified in accuracy of 94.44% which is higher than previous success rate 87%.

• Journal of Intelligence and Information Systems
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• 제15권2호
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• pp.1-13
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• 2009

Scene classification and concept-based procedures have been the great interest for image categorization applications for large database. Knowing the category to which scene belongs, we can filter out uninterested images when we try to search a specific scene category such as beach, mountain, forest and field from database. In this paper, we propose an adaptive segmentation method for real-world natural scene classification based on a semantic modeling. Semantic modeling stands for the classification of sub-regions into semantic concepts such as grass, water and sky. Our adaptive segmentation method utilizes the edge detection to split an image into sub-regions. Frequency of occurrences of these semantic concepts represents the information of the image and classifies it to the scene categories. K-Nearest Neighbor (k-NN) algorithm is also applied as a classifier. The empirical results demonstrate that the proposed adaptive segmentation method outperforms the Vogel and Schiele's method in terms of accuracy.

• Proceedings of the Korean Society for Information Management Conference
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• 한국정보관리학회 2014년도 제21회 학술대회 논문집
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• pp.127-130
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• 2014

이 연구에서는 도서관의 주요 장서에 해당하는 단행본 도서에 대한 자동 분류를 적용가능한지 알아보고자 하였다. 분류자질로 메타데이터인 서명, 목차, 책 소개를 사용하였으며, 다양한 자질 가중치를 적용하여 581건의 단행본 도서를 통해 kNN 분류기의 분류성능을 파악하였다. 실험 결과 이들 메타데이터를 모두 사용하였을 때 가장 좋은 분류성능을 가져왔으며, 실험문헌집단의 규모가 작은 한계가 있지만 로그 TF를 취한 가중치 방법이 좋은 성능을 가져왔다.

• Journal of the Korean Society for information Management
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• 제36권4호
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• pp.83-105
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• 2019

This study aims to suggest an effective method for the automatic classification of keywords with similar patterns by calculating pattern similarity of temporal data. For this, large scale news on the Web were collected and time series data composed of 120 time segments were built. To make training data set for the performance test of the proposed model, 440 representative keywords were manually classified according to 8 types of trend. This study introduces a Dynamic Time Warping(DTW) method which have been commonly used in the field of time series analytics, and proposes an application model, MA-DTW based on a Moving Average(MA) method which gives a good explanation on a tendency of trend curve. As a result of the automatic classification by a k-Nearest Neighbor(kNN) algorithm, Euclidean Distance(ED) and DTW showed 48.2% and 66.6% of maximum micro-averaged F1 score respectively, whereas the proposed model represented 74.3% of the best micro-averaged F1 score. In all respect of the comprehensive experiments, the suggested model outperformed the methods of ED and DTW.

• Journal of KIISE:Software and Applications
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• 제35권9호
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• pp.562-571
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• 2008

For the purpose of building domain ontology, this paper proposes a methodology for building core ontology first, and then enriching the core ontology with the concepts and relations in the domain thesaurus. First, the top-level concept taxonomy of the core ontology is built using domain dictionary and general domain thesaurus. Then, the concepts of the domain thesaurus are classified into top-level concepts in the core ontology, and relations between broader terms (BT) - narrower terms (NT) and related terms (RT) are classified into semantic relations defined for the core ontology. To classify concepts, a two-step approach is adopted, in which a frequency-based approach is complemented with a similarity-based approach. To classify relations, two techniques are applied: (i) for the case of insufficient training data, a rule-based module is for identifying isa relation out of non-isa ones; a pattern-based approach is for classifying non-taxonomic semantic relations from non-isa. (ii) For the case of sufficient training data, a maximum-entropy model is adopted in the feature-based classification, where k-NN approach is for noisy filtering of training data. A series of experiments show that performances of the proposed systems are quite promising and comparable to judgments by human experts.

• Annual Conference on Human and Language Technology
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• 한국정보과학회언어공학연구회 2007년도 제19회 한글 및 한국어 정보처리 학술대회
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• pp.31-37
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• 2007

본 논문에서는 분야 시소러스의 개념과 관계를 이용하여 코아 온톨로지를 확장하는 방법을 제안한다. 분야 시소러스의 개념을 코아 온톨로지의 상위 개념으로 분류하고, 시소러스에서의 광의어(Broader Term: BT)-협의어(Narrower Term: NT) 및 광의어-관련어(Related Term: RT)들 사이의 관계는 코아 온톨로지에서 정의한 의미관계로 분류한다. 유사도와 빈도수 기반의 방법으로 개념 분류를 수행하였고, 관계 분류에서는 두 가지 방법을 적용하였는데, (i) 훈련데이터가 부족한 경우를 위하여 규칙기반 방법으로 BT-NT/RT관계를 isa와 기타 관계(non-isa관계)로 분류하고, 패턴기반 방법으로 non-isa관계를 온톨로지를 위한 의미관계로 분류한다. (ii) 훈련데이터를 충분히 가지고 있을 경우, 최대 엔트로피 모델(MEM)을 적용한 분류 방법을 사용하되, kNN방법으로 훈련데이터를 정제하였다. 본 논문에서 제안한 방법으로 시스템을 구축하였고, 실험 결과, 시스템 성능이 사람에 의한 판단 결과와 비교 가능한 수준이었다.

• Journal of the Korean Society for information Management
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• 제23권2호
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• pp.265-285
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• 2006

In text categorization a certain level of correctness of labels assigned to training documents is assumed without solid knowledge on that of real-world collections. Our research attempts to explore the quality of pre-assigned subject categories in a real-world collection, and to identify the relationship between the quality of category assignment in training set and text categorization performance. Particularly, we are interested in to what extent the performance can be improved by enhancing the quality (i.e., correctness) of category assignment in training documents. A collection of 1,150 abstracts in computer science is re-classified by an expert group, and divided into 907 training documents and 227 test documents (15 duplicates are removed). The performances of before and after re-classification groups, called Initial set and Recat-1/Recat-2 sets respectively, are compared using a kNN classifier. The average correctness of subject categories in the Initial set is 16%, and the categorization performance with the Initial set shows 17% in $F_1$ value. On the other hand, the Recat-1 set scores $F_1$ value of 61%, which is 3.6 times higher than that of the Initial set.

• Journal of the Institute of Electronics Engineers of Korea CI
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• 제43권6호
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• pp.26-33
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• 2006

Film restoration is to detect the location and extent of defected regions from a given movie film, and if present, to reconstruct the lost information of each region. It has gained increasing attention by many researchers, to support multimedia service of high quality. In general, an old film is degraded by dust, scratch, flick, and so on. Among these, the most frequent degradation is the scratch. So far techniques for the scratch restoration have been developed, but they have limited applicability when dealing with all kinds of scratches. To fully support the automatic scratch restoration, the system should be developed that can detect all kinds of scratches from a given frame of old films. This paper presents a neurual network (NN)-based texture classifier that automatically detect all kinds of scratches from frames in old films. To facilitate the detection of various scratch sizes, we use a pyramid of images generated from original frames by having the resolution at three levels. The image at each level is scanned by the NN-based classifier, which divides the input image into scratch regions and non-scratch regions. Then, to reduce the computational cost, the NN-based classifier is only applied to the edge pixels. To assess the validity of the proposed method, the experiments have been performed on old films and animations with all kinds of scratches, then the results show the effectiveness of the proposed method.