• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.581-587
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• 2003

독립 요소 분석 (Independent Component Analysis: ICA)는 텍스처를 의미 있는 특징으로 변환하는 강인한 영상 필터를 생성하기 위한 확률적 방법이다. ICA는 고차통계적 특성을 사용하여 ICA 필터와 독립 요소를 동시에 학습한다. 제안한 분류 방법은 fast ICA 알고리즘을 사용하여 KOMPSAT 영상으로부터 ICA 필터를 생성한 다음, 필터에 의해 투영된 텍스처들의 특징들을 독립 평면상에서 무감독 방법으로 분류한다. KOMPSAT 영상은 텍스처 성분이 뚜렷하지 않는 영역이 존재하기 때문에 본 논문에서는 투영된 특징 값들과 윈도우 내의 정규화된 평균 화소값으로 특징 벡터를 재구성하였다. 분류 방법으로는 K-means 클러스터링을 적용하였다. 6.6m 해상도를 가진 KOMPSAT 흑백 영상에 대해 제안한 방법은 우수한 분류 성능을 보인다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.2 s.314
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• pp.10-17
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• 2007

The echo signal on ultrasonic transducer is a mixed signal from tissues, blood vessel walls, blood cells and noise. In this mixed-signal, the signal reflected from tissues and blood vessel walls is called clutter. It is necessary to extract pure blood signal from this mixed-signal, when measuring blood flow velocity with medical ultrasonic system The quality of measured blood flow velocity is highly dependent on sufficient attenuation of the clutter signals. In this paper, we suggest a clutter rejection method using ICA For simulation, the echo signals are generated by Field n ultrasonic simulation program In this echo signals, independent signals are separated by using ICA Then the blood signal is obtained from the separated signals. Blood flow velocity is measured by 2D autocorrelation method. We compare ICA clutter rejection method with PCA-based eigen filter method using both measured blood flow velocity profiles by 2D autocorrelation. In simulation results, ICA clutter rejection method can be better applied measuring blood flow velocity in noisy echo signals.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.159-166
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• 2009

In this paper, we analyse the noise robustness of MLPs(Multilayer perceptrons) through deriving the probability density function(p.d.f.) of output nodes with additive input noises and the misclassification ratio with the integral form of the p.d.f. functions. Also, we propose linear preprocessing methods to improve the noise robustness. As a preprocessing stage of MLPs, we consider ICA(independent component analysis) and PCA(principle component analysis). After analyzing the noise reduction effect using PCA or ICA in the viewpoints of SNR(Singal-to-Noise Ratio), we verify the preprocessing effects through the simulations of handwritten-digit recognition problems.

• ETRI Journal
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• v.30 no.6
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• pp.868-870
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• 2008

The rotation-based transformation (RBT) for privacy preserving data mining is vulnerable to the independent component analysis (ICA) attack. This paper introduces a modified multiple-rotation-based transformation technique for special mining applications, mitigating the ICA attack while maintaining the advantages of the RBT.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1568-1572
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• 2004

This paper proposes a new online calibration algorithm for the array antenna system. As you know, the several previous calibration methods for the mutual coupling did not estimate but measure mutual coupling effect at the real or test-bed system directly. Therefore we suggest some idea to compensate the calibration errors due to mutual coupling effect and mismatch in cables and electronic modules without the off-line calibration. In this work, we can calibrate the array antenna system under the operation of the system using Independent Component Analysis(ICA). This is what is called an online calibration. As you know, the ICA method has permutation and scaling problems. However, we solve problems of the ICA method and apply it to the calibration of an array antenna. The method simultaneously estimates the DOA(Direction of Arrival) of the signals, and calibrates the array for that specific angle. The proposed algorithm is evaluated by computer simulation and its behavior is illustrated by a numerical example.

• Journal of Biomedical Engineering Research
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• v.29 no.5
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• pp.355-363
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• 2008

In order to extract only the alpha activity related signals from EEG recordings, we have applied Constrained Independent Component Analysis (cICA), a new extension of ICA in which some a priori knowledge of the alpha activity is utilized to extract only desired components. Its extraction (or filtering) performance has been compared to that of the conventional band-pass filtering via the scalp alpha power maps and cortical source maps of the alpha activity. Our results demonstrate that the alpha power maps and cortical source maps from the cICA-extracted alpha signals reveal more focalized alpha generating regions of the brain than those from the band-pass filtered alpha EEG signals. Furthermore they match more closely the activated regions of the brain mapped using fMRI, validating our results. We believe that the cICA-based filtering approach of EEG signals is a more effective means of extracting a specific brain activity reflected in EEG signals that will result in more accurate source localization or imaging maps.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.135-148
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• 2006

Independent component analysis (ICA) is a statistical method for transforming an observed high-dimensional multivariate data into statistically independent components. ICA has been applied increasingly in wide fields of spectrum application since ICA is able to extract unknown components of a mixture from spectra. We focus on application of ICA for separating independent sources and predicting each composition using extracted components. The theory of ICA is introduced and an application to a metal surface spectra data will be described, where subsequent analysis using non-negative least square method is performed to predict composition ratio of each sample. Furthermore, some simulation experiments are performed to demonstrate the performance of the proposed approach.

• The Journal of the Acoustical Society of Korea
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• v.22 no.6
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• pp.452-456
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• 2003

We discuss how to reduce the number of inverse matrix and its dimensions requested in MLLR framework for speaker adaptation. To find a smaller set of variables with less redundancy, we adapt PCA (principal component analysis) and ICA (independent component analysis) that would give as good a representation as possible. The amount of additional computation when PCA or ICA is applied is as small as it can be disregarded. 10 components for ICA and 12 components for PCA represent similar performance with 36 components for ordinary MLLR framework. If dimension of SI model parameter is n, the amount of computation of inverse matrix in MLLR is proportioned to O(n⁴). So, compared with ordinary MLLR, the amount of total computation requested in speaker adaptation is reduced by about 1/81 in MLLR with PCA and 1/167 in MLLR with ICA.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.121-130
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• 2009

In this paper, feature extraction methods, which is one field of reducing dimensions of high-dimensional data, are empirically investigated. We selected the traditional PCA(Principal Component Analysis), ICA(Independent Component Analysis), NMF(Non-negative Matrix Factorization), and sNMF(Sparse NMF) for comparisons. ICA has a similar feature with the simple cell of V1. NMF implemented a "parts-based representation in the brain" and sNMF is a improved version of NMF. In order to visually investigate the extracted features, handwritten digits are handled. Also, the extracted features are used to train multi-layer perceptrons for recognition test. The characteristic of each feature extraction method will be useful when applying feature extraction methods to many real-world problems.

• Journal of Biomedical Engineering Research
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• v.29 no.4
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• pp.329-336
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• 2008

Recently, causality analysis of source time series extracted from EEG or MEG signals is becoming of great importance in human brain mapping studies and noninvasive diagnosis of various brain diseases. Two approaches have been widely used for the analyses: one is independent component analysis (ICA), and the other is multiple signal classification (MUSIC). To the best of our knowledge, however, any comparison studies to reveal the difference of the two approaches have not been reported. In the present study, we compared the performance of the two different techniques, ICA and MUSIC, especially focusing on how accurately they can estimate and separate various brain electrical signals such as linear, nonlinear, and chaotic signals without a priori knowledge. Results of the realistic simulation studies, adopting directed transfer function (DTF) and Granger causality (GC) as measures of the accurate extraction of source time series, demonstrated that the MUSIC-based approach is more reliable than the ICA-based approach.