• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.19-26
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• 2012

The rapid increase of information imposes new demands of content management. The purpose of automatic audio segmentation and classification is to meet the rising need for efficient content management. With this reason, this paper proposes a high-accuracy algorithm that segments audio signals and classifies them into different classes such as speech, music, silence, and environment sounds. The proposed algorithm utilizes support vector machine (SVM) to detect audio-cuts, which are boundaries between different kinds of sounds using the parameter sequence. We then extract feature vectors that are composed of statistical data and they are used as an input of fuzzy c-means (FCM) classifier to partition audio-segments into different classes. To evaluate segmentation and classification performance of the proposed SVM-FCM based algorithm, we consider precision and recall rates for segmentation and classification accuracy for classification. Furthermore, we compare the proposed algorithm with other methods including binary and FCM classifiers in terms of segmentation performance. Experimental results show that the proposed algorithm outperforms other methods in both precision and recall rates.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.185-191
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• 2007

Remote sensing images are multispectral image data collected from several band divided by wavelength ranges. The classification of remote sensing images is the method of classifying what has similar spectral characteristics together among each pixel composing an image as the important algorithm in this field. This paper presents a pattern classification method of remote sensing images by applying a possibilistic fuzzy c-means (PFCM) algorithm. The PFCM algorithm is a hybridization of a FCM algorithm, which adopts membership degree depending on the distance between data and the center of a certain cluster, combined with a PCM algorithm, which considers class typicality of the pattern sets. In this proposed method, we select the training data for each class and perform supervised classification using the PFCM algorithm with spectral signatures of the training data. The application of the PFCM algorithm is tested and verified by using Landsat TM and IKONOS remote sensing satellite images. As a result, the overall accuracy showed a better results than the FCM, PCM algorithm or conventional maximum likelihood classification(MLC) algorithm.

• Journal of Korean Society of Transportation
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• v.21 no.5
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• pp.7-18
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• 2003

Due to the changes of shipping and logistic environment, Asian ports today face severe competition. To be a mega-hub port, Asian ports have achieved a big scale development. For these reasons, it has been widely recognized as an important study to analyze and evaluate characteristics of Asian ports, from the standpoint of Korea where Busan Port is located. Although some previous studies have been reported, most of them have been beyond the scope of Asian ports and analyzed the world's major ports; moreover, the studied ports have been about the ports which are well known from the previous research and reports. So, most studies is unlikely to be used as substantial indicators from the perspective of Busan Port. In addition. most of the existing studies have used hierarchical evaluation algorithm for port ranking, such as AHP (analytical hierarchy process) and clustering analysis. However, these two methods have fundamental weaknesses from the algorithm perspective. The aim of this study is to classify major Asian ports based on competitiveness level. Especially. in order to overcome serious problem of the existing studies, major Asian ports were analyzed by using objective indicators. and Fuzzy C-Means algorithm, which alleviates the weakness of the clustering method. It was found that 10 ports of 16 major Asian ports have their own phases and were classified into 4 port groups. This result implies that some ports have higher potential as ports to lead some zones in Asia. Based on those results. present status and future direction of Busan port were discussed as well.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1905_1906
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• 2009

본 논문은 데이터의 군집화를 효율적으로 수행하기 위하여 새로운 군집화 알고리즘을 제안한다. 제안되는 군집화 알고리즘은 Fuzzy C-Means (FCM)에 기반을 두는데, FCM 알고리즘은 모든 데이터에 대한 거리에 기본을 둔 멤버쉽을 기초로 하기 때문에 잡음에 약한 제약을 지니고 있었다. 이를 개선하기 위하여, 제안되었던 PCM(Probabilistic C-Means), FPCM(Fuzzy PCM), PFCM(Probabilistic FCM) 등 여러가지 알고리즘이 제안 되었다. 그러나 이들 알고리즘들은 초기 파라미터값 설정과 과다한 계산양에 따른 문제가 증가하였으며, 또한 잡음에 어느 정도 민감한 문제점을 지니고 있었다. 이 논문에서는 잡음에 대해 효과적으로 대응할 수 있는 새로운 군집화 알고리즘을 제안하고, 전통적인 군집화를 위한 Iris 데이터에 대한 실험을 통하여 효용성을 확인하였다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1536-1544
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• 1995

We propose a design scheme of a binary decision tree and apply it to the tire tread pattern recognition problem. In this scheme, a binary decision tree is constructed by using fuzzy C-means( FCM ) algorithm. All the available features are used while clustering. At each node, the best feature or feature subset among these available features is selected based on proposed similarity measure. The decision tree can be used for the classification of unknown patterns. The proposed design scheme is applied to the tire tread pattern recognition problem. The design procedure including feature extraction is described. Experimental results are given to show the usefulness of this scheme.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.1527-1530
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• 2009

유비쿼터스 환경의 확산에 따른 다양한 보안문제의 발생은 센서의 정보를 이용한 상황인식 보안 서비스의 필요성을 증대시키고 있다. 본 논문에서는 FCM (Fuzzy C-Means) 클러스터링과 다변량 퍼지 결정트리 (Multivariate Fuzzy Decision Tree)를 이용하여 센서의 정보를 분류함으로써 사용자의 상황을 인식하고, 사용자가 처한 상황에 따라 다양한 수준의 보안기술을 유연하게 적용할 수 있는 상황인식 보안 서비스를 제안한다. 제안 모델은 기존에 많이 연구되어 오던 고정된 규칙을 기반으로 하는 RBAC(Role-Based Access Control)계열의 모델보다 더욱 유연하고 적합한 결과를 보여주고 있다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.267-271
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• 2007

본 논문은 원본이미지와 은닉이미지의 좋은 압축률과 만족할만한 이미지의 질, 그리고 외부공격에 강인한 이미지은닉의 한 방법으로 특이치 분해와 퍼지 군집화를 이용한 벡터양자화를 이용한 워터마킹 방법을 소개하였다. 실험에서는 은닉된 이미지의 비가시성과 외부공격에 대한 강인성을 증명하였다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2102-2107
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• 2008

This paper demonstrates a enhanced FCM to identify the causes of ground faults in power distribution systems. The discrimination scheme which can automatically recognize the fault causes is proposed using Fuzzy RBF networks. By using the actual fault data, it is shown that the proposed method provides satisfactory results for identifying the fault causes.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.12
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• pp.102-107
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• 2009

As technology gets developed, medical equipments are also modernized and leading-edge systems, such as PACS become popular. Many scientists noticed importance of medical image processing technology. Technique of region segmentation is the first step of digital medical image processing. Segmentation technique helps doctors to find out abnormal symptoms early, such as tumors, edema, and necrotic tissue, and helps to diagnoses correctly. Segmentation of white matter, gray matter and CSF of a brain image is very crucial part. However, the segmentation is not easy due to ambiguous boundaries and inhomogeneous physical characteristics. The rate of incorrect segmentation is high because of these difficulties. Fuzzy-based segmentation algorithms are robust to even ambiguous boundaries. In this paper a modified Fuzzy-based segmentation algorithm is proposed to handle the noise of MR scanners. A proposed algorithm requires minimal computations of mean and variance of neighbor pixels to adjust a new neighbor list. With the addition of minimal compuation, the modified FCM(mFCM) lowers the rate of incorrect clustering below 30% approximately compared the traditional FCM.

• Journal of Intelligence and Information Systems
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• v.13 no.1
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• pp.81-90
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• 2007

This paper shows an intelligent disease diagnosis system for public. Our system deals with 30 diseases and their typical symptoms selected based on the report from Ministry of Health and Welfare, Korea. Technically, the system uses a modified FCM algorithm for clustering diseases and the input vector consists of the result of user-selected questionnaires. The modified FCM algorithm improves the quality of clusters by applying symmetrically measure based on the fuzzy theory so that the clusters are relatively sensitive to the shape of the pattern distribution. Furthermore, we extract the highest 5 diseases only related to the user-selected questionnaires based on the fuzzy membership function between questionnaires and diseases in order to avoid diagnosing unrelated disease.