• Journal of Electrical Engineering and Technology
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• 제11권2호
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• pp.309-319
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• 2016

This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

• 정보처리학회논문지D
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• 제9D권5호
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• pp.939-944
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• 2002

In this paper, we propose and apply new classification method to the remotely sensed image acquired from airborne multi-spectral scanner. This is a neuro-fuzzy image classifier derived from the generic model of a 3-layer fuzzy perceptron. We implement a classification software system with the proposed method for land cover image classification. Comparisons with the proposed and maximum-likelihood classifiers are also presented. The results show that the neuro-fuzzy classification method classifies more accurately than the maximum likelihood method. In comparing the maximum-likelihood classification map with the neuro-fuzzy classification map, it is apparent that there is more different as amount as 7.96% in the overall accuracy. Most of the differences are in the "Building" and "Pine tree", for which the neuro-fuzzy classifier was considerably more accurate. However, the "Bare soil" is classified more correctly with the maximum-likelihood classifier rather than the neuro-fuzzy classifier.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.48-53
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• 1999

The paper presents an automated method for generating fuzzy rules and fuzzy membership functions for pattern classification from training sets of examples and an application to the land-cover classification. Initially, fuzzy subspaces are created from the partitions formed by the minimum and maximum of individual feature values of each class. The initial membership functions are determined according to the generated fuzzy partitions. The fuzzy subspaces are further iteratively partitioned if the user-specified classification performance has not been archived on the training set. Our classifier was trained and tested on patterns consisting of the DN of each band, (XS1, XS2, XS3), extracted from KITSAT-3 multispectral scene. The result represents that our classification method has higher generalization power.

• 대한전자공학회논문지TE
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• 제37권3호
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• pp.114-122
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• 2000

본 논문에서는 기존의 Fuzzy C-Means, Nearest Neighborring Classification, FMMCNN, Fuzzy -ART등에서 사용하였던 정형에 근거한 분류에서 유기될 수 있던 판단 오류를 최소화하기 위해 단 한가지의 형태적 특징을 갖고 있는 정형에 의존하지 않고 분류를 수행하는 방법을 제안하고i파 한다. 이를 위해 본 논문에서는 주어진 학습 데이터로 학습하는 과정에서 볼록 다면체를 적응적으로 생성하고 다면체의 구조를 수정하는 퍼지 신경회로망을 설계하였다. 따라서, 본 방법은 순차적으로 입력되는 데이터를 분류하여 패턴 유형들을 생성하는 기능을 갖게된다. 본 방법의 유용성을 증명하기 위해, Hyperbox를 정형으로 하는 FMMCNN과의 다양한 시뮬레이션 비교를 수행하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권1호
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.67-72
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• 2013

Cloud detection and analysis from satellite images has been a topic of research in many atmospheric and environmental studies; however, it still is a challenging task for many reasons. In this paper, we propose a new method for cloud-type classification using fuzzy logic. Knowing that visible-light images of clouds contain thickness related information, while infrared images haves height-related information, we propose a two-layered fuzzy logic based on the input source to provide us with a relatively clear-cut threshold in classification. Traditional noise-removal methods that use reflection/release characteristics of infrared images often produce false positive cloud areas, such as fog thereby it negatively affecting the classification accuracy. In this study, we used the color information from source images to extract the region of interest while avoiding false positives. The structure of fuzzy inference was also changed, because we utilized three types of source images: visible-light, infrared, and near-infrared images. When a cloud appears in both the visible-light image and the infrared image, the fuzzy membership function has a different form. Therefore we designed two sets of fuzzy inference rules and related classification rules. In our experiment, the proposed method was verified to be efficient and more accurate than the previous fuzzy logic attempt that used infrared image features.

• 대한전기학회논문지
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• 제43권7호
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• pp.1193-1205
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• 1994

This paper presents a fuzzy system that adaptively classifies subimages to four classes according to image activity distribution. In adaptive transform image coding, subimage classification improves the compression performance by assigning different bit maps to different classes. A conventional classification method sorts subimages by their AC energy and divides them to classes with equal number of subimages. The fuzzy system provides more flexible classification to natural images with various distribution of image details than does the conventional method. Clustering of training data in the input-output product space generated the fuzzy rules for subimage classification. The fuzzy system of small number of fuzzy rules successfully classified subimages to improve the compression performance of the transform image coding without sorting of AC energies.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.447-450
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• 2004

This paper proposes remotely sensed image classification method by fuzzy c-means clustering algorithm using average intra-cluster distance. The average intra-cluster distance acquires an average of the vector set belong to each cluster and proportionates to its size and density. We perform classification according to pixel's membership grade by cluster center of fuzzy c-means clustering using the mean-values of training data about each class. Fuzzy c-means algorithm considered membership degree for inter-cluster of each class. And then, we validate degree of overlap between clusters. A pixel which has a high degree of overlap applies to the maximum likelihood classification method. Finally, we decide category by comparing with fuzzy membership degree and likelihood rate. The proposed method is applied to IKONOS remote sensing satellite image for the verifying test.

• 한국지능시스템학회논문지
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• 제18권3호
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• pp.360-366
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• 2008

퍼지 규칙기반 분류 시스템에서 초기의 퍼지 분할은 주어진 데이터가 가진 속성들의 도메인을 고려함으로서 결정되어지고, 최적의 분류 경계면은 초기에 정의된 퍼지 분할의 파라미터들을 조정함으로서 찾을 수 있다. 본 논문에서는 학습과정들을 사용하지 않고 패턴분류의 성능을 최대화하기 위해 통계적 정보에 기반을 둔 퍼지 분할의 선택방법을 제안한다. 제안된 방법에서 통계적 정보는 주어진 수치적인 데이터로부터 각 입력 속성의 '불확실성 영역', 즉 패턴분류문제에서 분류 경계면이 결정되는 영역을 추출하기 위해 사용되었다. 또한 통계적인 정보에 의해서 생성된 퍼지 분할구간에 대응하는 후보 규칙들을 추출하기 위한 방법과 그 후보 규칙들 간의 커플링 문제를 최소화하기 위한 방법도 추가적으로 논의하였다. 실험에서는 제안된 방법의 효용성을 보이기 위해 IRIS와 New Thyroid Cancer 데이터를 사용한 기존 패턴분류 방법들과의 분류 정확성을 비교하였고, 그 결과들로부터 제안된 방법이 기존의 방법들보다 더 좋은 분류 정확성을 제공함을 확인할 수 있었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(5)
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• pp.273-276
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• 2002

This paper has design of ECG pattern classification system using decision of fuzzy IF-THEN rules and neural network. each fuzzy IF-THEN rule in our classification system has antecedent lingustic values and a single consequent class. we use a fuzzy reasoning method based on a single winner rule in the classification phase. this paper in, the MIT/BIH arrhythmia database for the source of input signal is used in order to evaluate the performance of the proposed system. From the simulation results, we can effectively pattern classification by application of learned from neural networks.