• Title/Summary/Keyword: Independent component analysis(ICA)

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Estimation of Pure Component Fractions in a Mixture Using Independent Component Analysis (독립성분분석을 이용한 혼합물내의 순수물질 구성비 추정)

  • Jeon Chi-Hyeok;Lee Hye-Seon;Park Hae-Sang;Hong Jae-Hwa
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2006.05a
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    • pp.1066-1070
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    • 2006
  • Independent component analysis (ICA) is a statistical method for linearly transforming observed high-dimensional multivariate data into several statistically independent components. ICA has gained wide-spread attention in a variety of fields including spectrum application. We focus on the application of ICA for separating independent sources from a set of mixtures and estimating their fractions in a mixture. The proposed method of estimating fractions is based on the regression model subject to the non-negativity constraint on coefficients. Simulation experiments are performed to demonstrate the performance of the proposed approach.

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The Motion Artifact Reduction in Photoplethysmography Using Independent Component Analysis (독립 요소 분석을 통한 Photoplethysmography에서의 동잡음 제거)

  • 김경하;유선국;김병수;김남현
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.52 no.10
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    • pp.598-605
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    • 2003
  • In this paper, we propose the method that separates PPG signal and motion artifact signal from two input signals using new independent component analysis algorithm in time domain. In order to eliminate the large level artifact efficiently, block interleaving. lowpass time filtering and innovation processing technique were applied in ICA preprocessing, and FastICA algorithm were applicable. Experiments are made with the numerical simulation and the real PPG signal including four kinds of motion artifact pattern. Our results show that ICA can effectively detect, separate and remove motion artifact in input signals. Then from the separated signals we restore the original PPG signal and propose a new method which computes SpO$_2$ using ICA mixing matrix.

The Use of Local Outlier Factor(LOF) for Improving Performance of Independent Component Analysis(ICA) based Statistical Process Control(SPC) (LOF를 이용한 ICA 기반 통계적 공정관리의 성능 개선 방법론)

  • Lee, Jae-Shin;Kang, Bok-Young;Kang, Suk-Ho
    • Journal of the Korean Operations Research and Management Science Society
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    • v.36 no.1
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    • pp.39-55
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    • 2011
  • Process monitoring has been emphasized for the monitoring of complex system such as chemical processing industries to achieve the efficiency enhancement, quality management, safety improvement. Recently, ICA (Independent Component Analysis) based MSPC (Multivariate Statistical Process Control) was widely used in process monitoring approaches. Moreover, DICA (Dynamic ICA) has been introduced to consider the system dynamics. However, the existing approaches show the limitation that their performances are strongly dependent on the statistical distributions of control variables. To improve the limitation, we propose a novel approach for process monitoring by integrating DICA and LOF (Local Outlier Factor). In this paper, we aim to improve the fault detection rate with the proposed method. LOF detects local outliers by using density of surrounding space so that its performance is regardless of data distribution. Therefore, the proposed method not only can consider the system dynamics but can also assure robust performance regardless of the statistical distributions of control variables. Comparison experiments were conducted on the widely used benchmark dataset, Tennessee Eastman process (TE process), and showed the improved performance than existing approaches.

Performance of music section detection in broadcast drama contents using independent component analysis and deep neural networks (ICA와 DNN을 이용한 방송 드라마 콘텐츠에서 음악구간 검출 성능)

  • Heo, Woon-Haeng;Jang, Byeong-Yong;Jo, Hyeon-Ho;Kim, Jung-Hyun;Kwon, Oh-Wook
    • Phonetics and Speech Sciences
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    • v.10 no.3
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    • pp.19-29
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    • 2018
  • We propose to use independent component analysis (ICA) and deep neural network (DNN) to detect music sections in broadcast drama contents. Drama contents mainly comprise silence, noise, speech, music, and mixed (speech+music) sections. The silence section is detected by signal activity detection. To detect the music section, we train noise, speech, music, and mixed models with DNN. In computer experiments, we used the MUSAN corpus for training the acoustic model, and conducted an experiment using 3 hours' worth of Korean drama contents. As the mixed section includes music signals, it was regarded as a music section. The segmentation error rate (SER) of music section detection was observed to be 19.0%. In addition, when stereo mixed signals were separated into music signals using ICA, the SER was reduced to 11.8%.

Analyzing Exon Structure with PCA and ICA of Short-Time Fourier Transform

  • Hwang Changha;Sohn Insuk
    • Proceedings of the Korean Statistical Society Conference
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    • 2004.11a
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    • pp.79-84
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    • 2004
  • We use principal component analysis (PCA) to identify exons of a gene and further analyze their internal structures. The PCA is conducted on the short-time Fourier transform (STFT) based on the 64 codon sequences and the 4 nucleotide sequences. By comparing to independent component analysis (ICA), we can differentiate between the exon and intron regions, and how they are correlated in terms of the square magnitudes of STFTs. The experiment is done on the gene F56F11.4 in the chromosome III of C. elegans. For this data, the nucleotide based PCA identifies the exon and intron regions clearly. The codon based PCA reveals a weak internal structure in some exon regions, but not the others. The result of ICA shows that the nucleotides thymine (T) and guanine (G) have almost all the information of the exon and intron regions for this data. We hypothesize the existence of complex exon structures that deserve more detailed analysis.

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Features for Figure Speech Recognition in Noise Environment (잡음환경에서의 숫자음 인식을 위한 특징파라메타)

  • Lee, Jae-Ki;Koh, Si-Young;Lee, Kwang-Suk;Hur, Kang-In
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • v.9 no.2
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    • pp.473-476
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    • 2005
  • This paper is proposed a robust various feature parameters in noise. Feature parameter MFCC(Mel Frequency Cepstral Coefficient) used in conventional speech recognition shows good performance. But, parameter transformed feature space that uses PCA(Principal Component Analysis)and ICA(Independent Component Analysis) that is algorithm transformed parameter MFCC's feature space that use in old for more robust performance in noise is compared with the conventional parameter MFCC's performance. The result shows more superior performance than parameter and MFCC that feature parameter transformed by the result ICA is transformed by PCA.

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Performance Improvement of Speech Recognition Based on Independent Component Analysis (독립성분분석법을 이용한 음성인식기의 성능향상)

  • 김창근;한학용;허강인
    • Proceedings of the Korea Institute of Convergence Signal Processing
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    • 2001.06a
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    • pp.285-288
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    • 2001
  • In this paper, we proposed new method of speech feature extraction using ICA(Independent Component Analysis) which minimized the dependency and correlation among speech signals on purpose to separate each component in the speech signal. ICA removes the repeating of data after finding the axis direction which has the greatest variance in input dimension. We verified improvement of speech recognition ability with training and recognition experiments when ICA compared with conventional mel-cepstrum features using HMM. Also, we can see that ICA dealt with the situation of recognition ability decline that is caused by environmental noise.

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A noise tolerance of Independent Component analysis in image classification in comparision with Principal Component Analysis (독립성분해석을 이용한 영상분리에 있어서의 잡음 허용에 관한 주성분해석과의 비교)

  • Hong, Jun-Sik;Ryu, Jeong-Woong
    • Proceedings of the KIEE Conference
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    • 2001.07d
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    • pp.2810-2812
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    • 2001
  • 본 논문에서는 독립성분해석을 이용한 영상분리에 있어서의 잡음에 대한 강인성에 대한 주성분해석과 비교 연구를 함으로써, 독립성분해석(Independent Component Analysis, ICA)기법의 효율성을 고찰하고 분석하고자 한다. 원래의 인식 시스템 모델에 잡음을 주었을 때, ICA를 이용한 영상 분리의 잡음에 대한 강인성은 주성분 해석(Principal Component Analysis, PCA)기법에서 보다 더 잡음에 강인한 성질을 내포하고 있는데, 이는 PCA 보다 ICA가 분리하려는 영상정보의 상호관계를 더 약화시키는 작용을 하기 때문이다. 이러한 특성은 모의실험을 통해 확인되었다.

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Effect of Sparse Decomposition on Various ICA Algorithms With Application to Image Data

  • Khan, Asif;Kim, In-Taek
    • Proceedings of the IEEK Conference
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    • 2008.06a
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    • pp.967-968
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    • 2008
  • In this paper we demonstrate the effect of sparse decomposition on various Independent Component Analysis (ICA) algorithms for separating simultaneous linear mixture of independent 2-D signals (images). We will show using simulated results that sparse decomposition before Kernel ICA (Sparse Kernel ICA) algorithm produces the best results as compared to other ICA algorithms.

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Comparison of Analysis Performance of Additive Noise Signals by Independent Component Analysis (독립성분분석법에 의한 잡음첨가신호의 분석성능비교)

  • Cho Yong-Hyun;Park Yong-Soo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.15 no.3
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    • pp.294-299
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    • 2005
  • This paper presents the separation performance of the linearly mixed image signals with additive noises by using an independent component analyses(ICAs) of the fixed-point(FP) algorithm based on Newton and secant method, respectively. The Newton's FP-ICA uses the slope of objective function, and the secant's FP-ICA also uses the tangent line of objective function. The 2 kinds of ICA have been applied to the 2 dimensional 2-image with $512\times512$ pixels. Then Gaussian noise and Laplacian noise are added to the mixed images, respectively. The experimental results show that the Newton's FP-ICA has better the separation speed than secant FP-ICA and the secant's FP-ICA has also the better separation rate than Newton's FP-ICA. Especially, the Newton and secant method gives relatively larger improvement degree in separation speed and rate as the noise increases.