• Title/Summary/Keyword: 공분산 행렬

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Analysis of Eigenvalues of Covariance Matrices of Speech Signals in Frequency Domain (음성 신호의 주파수 영역에서의 공분산행렬의 고유값 분석)

  • Kim, Seonil
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2015.05a
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    • pp.47-50
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    • 2015
  • Speech Signals consist of signals of consonants and vowels, but the lasting time of vowels is much longer than that of consonants. It can be assumed that the correlations between signal blocks in speech signal is very high. Each speech signal is divided into blocks which have 128 speech data. FFT is applied to each block. Low frequency areas of the results of FFT is taken and Covariance matrix between blocks in a speech signal is extracted and finally eigenvalues of those matrix are obtained. It is studied that what the distribution of eigenvalues of various speech files is. The differences between speech signals and noise signals from cars are also studied.

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Covariance Matrix Estimation with Small STAP Data through Conversion into Spatial Frequency-Doppler Plane (적은 STAP 데이터의 공간주파수-도플러 평면 변환을 이용한 공분산행렬 추정)

  • Hoon-Gee Yang
    • Journal of IKEEE
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    • v.27 no.1
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    • pp.38-44
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    • 2023
  • Performance of a STAP(space-time adaptive processing) algorithm highly depends on how closely the estimated covariance matrix(CM) resembles the actual CM by the interference in CUT(cell under test). A STAP has 2 dimensional data structure determined by the number of array elements and the number of transmitting pulses and both numbers are generally not small. Thus, to meet the degree of freedom(DOF) of the CM, a huge amount of training data is required. This paper presents an algorithm to generate virtual training data from small received data, via converting them into the data in spatial frequency-Doppler plane. We theoretically derive where the clutter exist in the plane and present the procedure to implement the proposed algorithm. Finally, with the simulated scenario of small received data, we show the proposed algorithm can improve STAP performance.

Effect of Bias for Snapshots Using Minimum Variance Processor in MFP (최소분산 프로세서를 사용한 정합장 처리에서 신호단편 수에 따른 바이어스의 영향)

  • 박재은;신기철;김재수
    • The Journal of the Acoustical Society of Korea
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    • v.20 no.7
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    • pp.94-100
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    • 2001
  • When using a sample covariance matrix data in paucity of snapshots, adaptive matched field processing will have problem in inverting covariance matrix due to the rank deficiency. The general solutions are diagonal loading and eigenanalysis methods, but there is a significant bias in the power output. This paper presents a quantitative study of bias of power output and the performance of source localization through the simulation and the measured data analysis in fixed source case using the diagonal loading method for the minimum variance processor. Results show that the bias in power output is reduced and the performance of source localization is improved when the number of snapshots is greater than the number of array sensors.

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Bootstrap inference for covariance matrices of two independent populations (두 독립 모집단의 공분산 행렬에 대한 붓스트랩 추론)

  • 김기영;전명식
    • The Korean Journal of Applied Statistics
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    • v.4 no.1
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    • pp.1-11
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    • 1991
  • It is of great interest to consider the homogeniety of covariance matrices in MANOVA of discriminant analysis. If we lock at the problem of testing hypothesis, H : $\Sigma_1 = \Sigma_2$ from an invariance point of view where $\Sigma_i$ are the covariance matrix of two independent p-variate distribution, the testing problem is invariant under the group of nonsingular transformations and the hypothesis becomes H : $\delta_1 = \delta_2 = \cdots = \delta_p = 1$ where $\delta = (\delta_1, \delta_2, \cdots, \delta_p)$ is a vector of latent roots of $\Sigma$. Bias-corrected estimators of eigenvalues and sampling distribution of the test statistics proposed are obtained. Pooled-bootstrap method also considered for Bartlett's modified likelihood ratio statistics.

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SMOTE by Mahalanobis distance using MCD in imbalanced data (불균형 자료에서 MCD를 활용한 마할라노비스 거리에 의한 SMOTE)

  • Jieun Jung;Yong-Seok Choi
    • The Korean Journal of Applied Statistics
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    • v.37 no.4
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    • pp.455 -465
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    • 2024
  • SMOTE (synthetic minority over-sampling technique) has been used the most as a solution to the problem of imbalanced data. SMOTE selects the nearest neighbor based on Euclidean distance. However, Euclidean distance has the disadvantage of not considering the correlation between variables. In particular, the Mahalanobis distance has the advantage of considering the covariance of variables. But if there are outliers, they usually influence calculating the Mahalanobis distance. To solve this problem, we use the Mahalanobis distance by estimating the covariance matrix using MCD (minimum covariance determinant). Then apply Mahalanobis distance based on MCD to SMOTE to create new data. Therefore, we showed that in most cases this method provided high performance indicators for classifying imbalanced data.

Interblock Information from BIBD Mixed Effects (균형불완비블록설계의 혼합효과에서 블록간 정보)

  • Choi, Jaesung
    • The Korean Journal of Applied Statistics
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    • v.28 no.2
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    • pp.151-158
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    • 2015
  • This paper discusses how to use projections for the analysis of data from balanced incomplete block designs. A model is suggested as a matrix form for the interblock analysis. A second set of treatment effects can be found by projections from the suggested interblock model. The variance and covariance matrix of two estimated vectors of treatment effects is derived. The uncorrelation of two estimated vectors can be verified from their covaraince structure. The fitting constants method is employed for the calculation of block sum of squares adjusted for treatment effects.

공분산 구조를 만족하는 다변량 포아송 확률난수 생성

  • Jeong, Hyeong-Cheol;Kim, Dae-Hak;Jeong, Byeong-Cheol
    • Proceedings of the Korean Statistical Society Conference
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    • 2005.11a
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    • pp.147-152
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    • 2005
  • 본 논문에서는 k개의 포아송 확률변수가 서로 종속 되어 있는 다변량 포아송 분포를 따를 때, 주어진 분산-공분산 행렬 구조를 유지하는 다변량 포아송 확률난수 생성방법에 대해 다루었다. 특히, 확률난수를 생성하기 위해 선형방정식을 푸는 두 가지 수치해석 알고리즘을 제안하였으며, Park 등 (1996)의 다변량 베르누이 확률난수 생성에 활용된 알고리즘과의 연관성을 다루었다.

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Design of Downlink Beamformer for High-quality.High-speed Wireless Multimedia Services (고품질.고속 무선 멀티미디어 서비스를 위한 송신 빔 형성기 설계)

  • 이용주;양승용;김기만
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.5 no.3
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    • pp.459-464
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    • 2001
  • We propose a transmit beamforming algerian for array antenna in FDD (Frequency Division Duplex) environments. The proposed method estimates the directions and spectra of the users, and constructs the spatial covariance matrix of the interferences at the downlink frequency. The weights are computed by that covariance matrix and desired user's direction vector Simulations are performed under Rayleigh fading environments. The proposed method don't need the data feedback, has the enhanced performance in BER (Bit Error Rate).

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A Subspace-based Blind Interference Cancellation for the DS/CDMA System (직접수열 코드분할 다중접속 시스템의 부공간 기반 미상 간섭 제거 기법)

  • 윤연우;김형명
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.26 no.11B
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    • pp.1510-1521
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    • 2001
  • In this paper a subspace-based blind interference cancellation is proposed and its performance is analyzed. Then the blind adaptive implementation is devolped using the improved natural power method which is the signal subspace tracking algorithm. The theoretical analysis shows that when the exact covariance matrix is kown the performance of the proposed detector is the same as that of the decorrelating detector. And when the covariance matrix is estimated the asymptotic results are examined. The results of computer simulation demonstrate that the proposed detector outperforms the previous blind adaptive RLS MOE detector.

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Face Recognition Using PCA and Fuzzy Weighted Average Method (PCA와 퍼지 가중치 평균 기법을 이용한 얼굴 인식)

  • Woo, Young-Woon;Kim, Hyung-Soo;Park, Jae-Min;Cho, Jae-Hyun
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2011.01a
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    • pp.315-316
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    • 2011
  • 일반적으로 영상에서 얼굴 영상을 검출하고 인식하는 알고리즘은 패턴 인식 연구에 있어서 인간과 컴퓨터의 상호작용의 연구라는 면에서 아주 중요한 문제로 연구되어 왔다. 본 논문에서는 고유얼굴을 이용하여 유클리디언 거리법과 퍼지기법의 인식률을 비교해보고자 한다. PCA(Principal Component Analysis) 방식은 우수한 인식 결과를 보장하는 얼굴인식 기법중의 하나이며, 얼굴 영상을 이용하여 공분산 행렬을 계산하고, 공분산 행렬을 통해 생성된 저차원의 벡터, 즉 고유얼굴(Eigenface)을 이용하여 가중치를 계산하고, 이 가중치를 기준으로 인식을 수행하는 기법이다. 이를 기반으로 하여, 본 논문에서는 전처리 과정, 고유얼굴 과정, 유클리디언 거리법 및 퍼지 소속도 함수 설계 과정, 신경망 학습과정, 인식과정으로 구성된 5단계의 얼굴 인식 알고리즘을 제안한다.

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