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

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

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

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.87-90
• /
• 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 IKEEE
• /
• v.1 no.1 s.1
• /
• pp.93-100
• /
• 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.

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

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.2
• /
• pp.123-129
• /
• 2017

This paper proposes a new nonnegative matrix factorization (NMF) based direction-of-arrival (DOA) estimation method for multiple sound sources using a dual microphone array. First of all, sound signals coming from the dual microphone array are segmented into consecutive analysis frames, and a steered-response power phase transform (SRP-PHAT) beamformer is applied to each frame so that stereo signals of each frame are represented in a time-direction domain. The time-direction outputs of SRP-PHAT are stored for a pre-defined number of frames, which is referred to as a time-direction block. Next, In order to estimate DOAs robust to noise, each time-direction block is normalized along the time by using a block subtraction technique. After that, an unsupervised NMF method is applied to the normalized time-direction block in order to cluster the directions of each sound source in a multiple sound source environments. In particular, the activation and basis matrices are used to estimate the number of sound sources and their DOAs, respectively. The DOA estimation performance of the proposed method is evaluated by measuring a mean absolute error (MAE) and the standard deviation of errors between the oracle and estimated DOAs under a three source condition, where the sources are located in [$-35{\circ}$, 5m], [$12{\circ}$, 4m], and [$38{\circ}$, 4.m] from the dual microphone array. It is shown from the experiment that the proposed method could relatively reduce MAE by 56.83%, compared to a conventional SRP-PHAT based DOA estimation method.