• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.87-90
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• 1997

Mountain Method의 다른 형태인 Subtractive 클러스터링 알고리듬은 계산이 간단하고 기존의 클러스터링 방법들과는 달리 초기 클러스터 중심의 개수 선정이 필요 없기 때문에 클러스터를 추정하는데 효과적인 알고리듬이다. 또한 클러스터의 간격을 결정하는 파라미터의 값에 따라 클러스터의 개수를 다르게 할 수 있다. 그러나 이 파라미터에 의해 동일한 그룹(Class)내에서 여러 개의 클러스터 중심이 발생될 수도 있다. 본 논문에서는 Subtractive HyperBox 알고리듬을 사용하여 이 파라미터의 영향을 줄이고 발생한 클러스터 중심이 속한 그룹의 경계를 판정함으로서 같은 그룹내에서 하나의 클러스터만 발생하도록 하고, 순차적으로 클러스터링 한 후 결과를 Subtractive 클러스터링 알고리듬과 비교하여 보았다.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.500-506
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• 2012

A method to model left ventricular assist device (LVAD) and detect suction occurrence for safe LVAD operation is presented. An axial flow blood pump as a LVAD has been used to assist patient with heart problems. While an axial flow blood pump, a kind of a non-pulsatile pump, has relative advantages of small size and efficiency compared to pulsatile devices, it has a difficulty in determining a safe pump operating condition. It can show different pump operating statuses such as a normal status and a suction status whether suction occurs in left ventricle or not. A fuzzy subtractive clustering method is used to determine a model of the axial flow blood pump with this pump operating characteristic and the developed pump model can provide blood flow estimates before and after suction occurrence in left ventricle. Also, a fuzzy subtractive clustering method is utilized to develop a suction detection model which can identify whether suction occurs in left ventricle or not.

• The Journal of the Acoustical Society of Korea
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• v.26 no.5
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• pp.199-206
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• 2007

In this paper we propose a new clustering algorithm that performs clustering the feature vectors for the speaker identification. Unlike typical clustering approaches, the proposed method performs the clustering without the initial guesses of locations of the cluster centers and a priori information about the number of clusters. Cluster centers are obtained incrementally by adding one cluster center at a time through the boundary subtractive clustering algorithm. The number of clusters is obtained from investigating the mutual relationship between clusters. The experimental results for artificial datum and TIMIT DB show the effectiveness of the proposed algorithm as compared with the conventional methods.

• Journal of IKEEE
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• v.1 no.1 s.1
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• pp.93-100
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• 1997

In general, fuzzy C-means clustering method was used on the segmentation of true color image. However, this method requires number of clusters as an input. In this study, we suggest new method that uses subtractive and gravity fuzzy C-means clustering. We get number of clusters and initial cluster centers by applying subtractive clustering on color image. After coarse segmentation of the image, we apply gravity fuzzy C-means for optimizing segmentation of the image. We show efficiency of the proposed algorithm by qualitative evaluation.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.239-240
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• 2008

본 논문에서는 Subtractive clustering 알고리즘을 이용한 Fuzzy Radial Basis Function Neural Network (FRBFNN)의 규칙 수를 자동적으로 생성하는 방법을 제시한다. FRBFNN은 멤버쉽 함수로써 기존 RBFNN에서 가우시안이나 타원형 형태의 특정 RBF를 사용하는 구조와 달리 Fuzzy C-Means clustering 알고리즘에서 사용하는 거리에 기한 멤버쉽 함수를 사용하여 전반부의 공간 분할 및 활성화 레벨을 결정하는 구조이다. 본 논문에서는 데이터의 밀집도에 기반을 두어 클러스터링을 하는 Subtractive clustering 알고리즘을 사용하여 퍼지 규칙의 수와 같은 의미를 갖는 분할할 입력공간의 수와 분할된 입력공간의 중심값을 동정하며, Least Square Estimator (LSE) 알고리즘을 사용하여 후반부 다항식의 계수를 추정 한다.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.1-12
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• 2014

This paper implements and improves the performance of high computational subtractive clustering algorithm using a single instruction, multiple data (SIMD) based many-core processor. In addition, this paper implements five different processing element (PE) architectures (PEs=16, 64, 256, 1,024, 4,096) to select an optimal PE architecture for the subtractive clustering algorithm by estimating execution time and energy efficiency. Experimental results using two different medical images and three different resolutions ($128{\times}128$, $256{\times}256$, $512{\times}512$) show that PEs=4,096 achieves the highest performance and energy efficiency for all the cases.

• Journal of Korea Water Resources Association
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• v.37 no.6
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• pp.449-460
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• 2004

The purpose of this study is to test the applicability of neuro-fuzzy system for monthly dam inflow forecasts by using weather forecasting information. The neuro-fuzzy algorithm adopted in this study is the ANFIS(Adaptive neuro-fuzzy Inference System) in which neural network theory is combined with fuzzy theory. The ANFIS model can experience the difficulties in selection of a control rule by a space partition because the number of control value increases rapidly as the number of fuzzy variable increases. In an effort to overcome this drawback, this study used the subtractive clustering which is one of fuzzy clustering methods. Also, this study proposed a method for converting qualitative weather forecasting information to quantitative one. ANFIS for monthly dam inflow forecasts was tested in cases of with or without weather forecasting information. It can be seen that the model performances obtained from the use of past observed data and future weather forecasting information are much better than those from past observed data only.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1280-1284
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• 2004

본 논문에서는 월 댐유입량을 예측하는데 있어서 뉴로-퍼지 시스템의 적용성을 검토하였다. 뉴로-퍼지 알고리즘으로 퍼지이론과 신경망이론의 결합형태인 ANFIS(Adaptive Neuro-Fuzzy Inference System)를 이용하여 모형을 구성하였다. ANFIS의 공간분야에 의한 제어규칙의 선정에 있어 퍼지변수가 증가함에 따라 제어규칙이 기하급수적으로 증가하는 단점을 해결하기 위해 퍼지 클러스터링(Fuzzy flustering)방법 중 하나인 차감 클러스터링(Subtractive Clustering)을 사용하였다. 또한 본 연구에서는 기후인자들을 인력으로 하여 모형을 구성하였으며 각각 학습기간과 검정기간으로 나누어 학습기간에는 모형의 매개변수 최적화를, 검정기간에는 최적화된 모형의 매개변수를 검정하는 순으로 연구를 수행하였다. 예측 길과, ANFIS는 댐유입량 예측시 입력자료의 종류가 많아질수록 예측능력 더욱 정확한 것으로 판단된다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.490-496
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• 2005

In this paper, we propose a self-constructed clustering algorithm based on inference information of the fuzzy model. This method makes it possible to automatically detect and optimize the number of cluster and parameters by using input-output data. The propose method improves the performance of clustering by extended supervised learning technique. This technique uses the output information as well as input characteristics. For effect the similarity measure in clustering, we use the TSK fuzzy model to sent the information of output. In the conceptually, we design a learning method that use to feedback the information of output to the clustering since proposed algorithm perform to separate each classes in input data space. We show effectiveness of proposed method using simulation than previous ones

• 정보저장시스템학회:학술대회논문집
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• 2005.10a
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• pp.83-87
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• 2005

Data storage related with writing and retrieving requires high storage capacity, fast transfer rate and less access time in. Today any data storage system can not satisfy these conditions, but holographic data storage system can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System architecture without mechanical actuating pare is possible, so fast data transfer rate and high storage capacity about 1Tb/cm3 can be realized. In this paper, to correct errors of binary data stored in holographic digital data storage system, find cluster centers using clustering algorithm and reduce intensities of pixels around centers. We archive the procedure by two algorithms of C-mean and subtractive clustering, and compare the results of the two algorithms. By using proper clustering algorithm, the intensity profile of data page will be uniform and the better data storage system can be realized.