• Title/Summary/Keyword: 부공간 분해

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A Big Data Analysis by Between-Cluster Information using k-Modes Clustering Algorithm (k-Modes 분할 알고리즘에 의한 군집의 상관정보 기반 빅데이터 분석)

  • Park, In-Kyoo
    • Journal of Digital Convergence
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    • v.13 no.11
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    • pp.157-164
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    • 2015
  • This paper describes subspace clustering of categorical data for convergence and integration. Because categorical data are not designed for dealing only with numerical data, The conventional evaluation measures are more likely to have the limitations due to the absence of ordering and high dimensional data and scarcity of frequency. Hence, conditional entropy measure is proposed to evaluate close approximation of cohesion among attributes within each cluster. We propose a new objective function that is used to reflect the optimistic clustering so that the within-cluster dispersion is minimized and the between-cluster separation is enhanced. We performed experiments on five real-world datasets, comparing the performance of our algorithms with four algorithms, using three evaluation metrics: accuracy, f-measure and adjusted Rand index. According to the experiments, the proposed algorithm outperforms the algorithms that were considered int the evaluation, regarding the considered metrics.

Robust Multi-channel Wiener Filter for Suppressing Noise in Microphone Array Signal (마이크로폰 어레이 신호의 잡음 제거를 위한 강인한 다채널 위너 필터)

  • Jung, Junyoung;Kim, Gibak
    • Journal of Broadcast Engineering
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    • v.23 no.4
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    • pp.519-525
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    • 2018
  • This paper deals with noise suppression of multi-channel data captured by microphone array using multi-channel Wiener filter. Multi-channel Wiener filter does not rely on information about the direction of the target speech and can be partitioned into an MVDR (Minimum Variance Distortionless Response) spatial filter and a single channel spectral filter. The acoustic transfer function between the single speech source and microphones can be estimated by subspace decomposition of multi-channel Wiener filter. The errors are incurred in the estimation of the acoustic transfer function due to the errors in the estimation of correlation matrices, which in turn results in speech distortion in the MVDR filter. To alleviate the speech distortion in the MVDR filter, diagonal loading is applied. In the experiments, database with seven microphones was used and MFCC distance was measured to demonstrate the effectiveness of the diagonal loading.

Improved speech enhancement of multi-channel Wiener filter using adjustment of principal subspace vector (다채널 위너 필터의 주성분 부공간 벡터 보정을 통한 잡음 제거 성능 개선)

  • Kim, Gibak
    • The Journal of the Acoustical Society of Korea
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    • v.39 no.5
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    • pp.490-496
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    • 2020
  • We present a method to improve the performance of the multi-channel Wiener filter in noisy environment. To build subspace-based multi-channel Wiener filter, in the case of single target source, the target speech component can be effectively estimated in the principal subspace of speech correlation matrix. The speech correlation matrix can be estimated by subtracting noise correlation matrix from signal correlation matrix based on the assumption that the cross-correlation between speech and interfering noise is negligible compared with speech correlation. However, this assumption is not valid in the presence of strong interfering noise and significant error can be induced in the principal subspace accordingly. In this paper, we propose to adjust the principal subspace vector using speech presence probability and the steering vector for the desired speech source. The multi-channel speech presence probability is derived in the principal subspace and applied to adjust the principal subspace vector. Simulation results show that the proposed method improves the performance of multi-channel Wiener filter in noisy environment.

Genetically Optimization of Fuzzy C-Means Clustering based Fuzzy Neural Networks (Subtractive Clustering 알고리즘을 이용한 퍼지 RBF 뉴럴네트워크의 동정)

  • Choi, Jeoung-Nae;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Proceedings of the KIEE Conference
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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.

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Identification of Fuzzy-Radial Basis Function Neural Network Based on Mountain Clustering (Mountain Clustering 기반 퍼지 RBF 뉴럴네트워크의 동정)

  • Choi, Jeoung-Nae;Oh, Sung-Kwun;Kim, Hyun-Ki
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.1 no.3
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    • pp.69-76
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    • 2008
  • This paper concerns Fuzzy Radial Basis Function Neural Network (FRBFNN) and automatic rule generation of extraction of the FRBFNN by means of mountain clustering. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values (degree of membership) directly rely on the computation of the relevant distance between data points. Also, we consider high-order polynomial as the consequent part of fuzzy rules which represent input-output characteristic of sup-space. The number of clusters and the centers of clusters are automatically generated by using mountain clustering method based on the density of data. The centers of cluster which are obtained by using mountain clustering are used to determine a degree of membership and weighted least square estimator (WLSE) is adopted to estimate the coefficients of the consequent polynomial of fuzzy rules. The effectiveness of the proposed model have been investigated and analyzed in detail for the representative nonlinear function.

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Gauss-Newton Based Estimation for Moving Emitter Location Using TDOA/FDOA Measurements and Its Analysis (TDOA/FDOA 정보를 이용한 Gauss-Newton 기법 기반의 이동 신호원 위치 및 속도 추정 방법과 성능 분석)

  • Kim, Yong-Hee;Kim, Dong-Gyu;Han, Jin-Woo;Song, Kyu-Ha;Kim, Hyoung-Nam
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.6
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    • pp.62-71
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    • 2013
  • The passive emitter location method using TDOA and FDOA measurements has higher accuracy comparing to the single TDOA or FDOA based method. Moreover, it is able to estimate the velocity vector of a moving platform. Recently, several non-iterative methods were suggested using the nuisance parameter but the common reference sensor is needed for each pair of sensors. They show also relatively low performance in the case of a long range between the sensor groups and the emitter. To solve this, we derive the estimation method of the position and velocity of a moving platform based on the Gauss-Newton method. In addition, to analyze the estimation performance of the position and velocity, respectively, we decompose the CRLB matrix into each subspace. Simulation results show the estimation performance of the derived method and the CEP planes according to the given geometry of the sensors.