• ETRI Journal
• /
• v.32 no.3
• /
• pp.351-361
• /
• 2010

Multiple rotation-based transformation (MRBT) was introduced recently for mitigating the apriori-knowledge independent component analysis (AK-ICA) attack on rotation-based transformation (RBT), which is used for privacy-preserving data clustering. MRBT is shown to mitigate the AK-ICA attack but at the expense of data utility by not enabling conventional clustering. In this paper, we extend the MRBT scheme and introduce an augmented rotation-based transformation (ARBT) scheme that utilizes linearity of transformation and that both mitigates the AK-ICA attack and enables conventional clustering on data subsets transformed using the MRBT. In order to demonstrate the computational feasibility aspect of ARBT along with RBT and MRBT, we develop a toolkit and use it to empirically compare the different schemes of privacy-preserving data clustering based on data transformation in terms of their overhead and privacy.

• ETRI Journal
• /
• v.35 no.2
• /
• pp.193-199
• /
• 2013

This paper addresses nonnegative independent component analysis (NICA), with the aim to realize the blind separation of nonnegative well-grounded independent source signals, which arises in many practical applications but is hardly ever explored. Recently, Bertrand and Moonen presented a multiplicative NICA (M-NICA) algorithm using multiplicative update and subspace projection. Based on the principle of the mutual correlation minimization, we propose another novel cost function to evaluate the diagonalization level of the correlation matrix, and apply the multiplicative exponentiated gradient (EG) descent update to it to maintain nonnegativity. An efficient approach referred to as the EG-NICA algorithm is derived and its validity is confirmed by numerous simulations conducted on different types of source signals. Results show that the separation performance of the proposed EG-NICA algorithm is superior to that of the previous M-NICA algorithm, with a better unmixing accuracy. In addition, its convergence speed is adjustable by an appropriate user-defined learning rate.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.1
• /
• pp.1-6
• /
• 2005

In this paper, we propose an emotion recognition method using the facial images and speech signals. Six basic emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. Facia] expression recognition is performed by using the multi-resolution analysis based on the discrete wavelet. Here, we obtain the feature vectors through the ICA(Independent Component Analysis). On the other hand, the emotion recognition from the speech signal method has a structure of performing the recognition algorithm independently for each wavelet subband and the final recognition is obtained from the multi-decision making scheme. After merging the facial and speech emotion recognition results, we obtained better performance than previous ones.

• Journal of Korea Water Resources Association
• /
• v.38 no.12 s.161
• /
• pp.985-994
• /
• 2005

We examined problems of the principal component analysis(PCA), which is able to analyze at the low dimensionality as a methodologv to assess hydrologic time series, and introduced the theory and characteristics of independent component analysis(ICA) that can supplement problems of principal component analysis. We also applied the global sea surface temperature(SST) of the Nino region and assessed the correlation between El $\tilde{n}ino$-Southern Oscillation(ENSO) and SST. The results of examining separation-ability of principal components using mixed signals indicate that the independent component analysis is statistically superior compared to that of the principal component analysis. Finally, we assessed correlation between ENSO and global anomaly SST. The independent component analysis was applied to the $5^{\circ}{\times}5^{\circ}$(latitude and longitude) global anomaly SST in the Nino+3.4 region that is the El $\tilde{n}ino$ observation section. We assessed the correlation with the ENSO years. These results of the analysis show that only one independent component($86\%$) was able to represent the entire behavior and was consistent with the main ENSO years. Finally, we carried out independent component analysis for summer seasonal rainfalls at nine stations and could extract ICs to reflect geographical characteristics. The increasing trend has been shown at IC-1 and IC-2 since 1970s.

• Journal of the Korean Society of Industry Convergence
• /
• v.6 no.1
• /
• pp.23-29
• /
• 2003

This paper proposes an efficient feature extraction of the images by using independent component analysis(ICA) based on neural networks of the hybrid learning algorithm. The proposed learning algorithm is the fixed point(FP) algorithm based on Newton method and moment. The Newton method, which uses to the tangent line for estimating the root of function, is applied for fast updating the inverse mixing matrix. The moment is also applied for getting the better speed-up by restraining an oscillation due to compute the tangent line. The proposed algorithm has been applied to the 10,000 image patches of $12{\times}12$-pixel that are extracted from 13 natural images. The 144 features of $12{\times}12$-pixel and the 160 features of $16{\times}16$-pixel have been extracted from all patches, respectively. The simulation results show that the extracted features have a localized characteristics being included in the images in space, as well as in frequency and orientation. And the proposed algorithm has better performances of the learning speed than those using the conventional FP algorithm based on Newton method.

• Journal of the Ergonomics Society of Korea
• /
• v.27 no.2
• /
• pp.9-14
• /
• 2008

Coherence can be used to evaluate the functional cortical connections between the motor cortex and muscle. This study is to find coherence between EEG (electroencephalogram) and EMG (electromyogram) evoked by movement of a hand. Seven healthy participants were asked to perform thirty repetitive movement of right hand for ten seconds with rest for ten seconds. Specific feature of EEG components has been extracted by ICA (independent component analysis) and coherence between EEG and EMG was analyzed from data measured EEG in five local areas around central part of head and EMG in flexer carpri radialis muscle during grabbing movement. Coherence between EEG and EMG was successfully obtained at 0.025 confidence limit during hand movement and showed significant difference between rest and movement at 13-18Hz.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2002.05a
• /
• pp.73-76
• /
• 2002

간질은 대뇌 신경세포의 순간적인 제어되지 않는 과도한 전기방출로 인하여, 발작적으로 몸의 경련이나, 기타 신경증상 등이 몸으로 표출되는 현상으로 이것이 반복되어 나타나는 현상이다. 간질 진단에 필수적으로 사용되는 뇌파에 혼합된 미지의 성분들로부터 각각의 독립적인 성분으로 분리하는 독립성분분석(ICA)을 적용하여 간질 발작파를 분리하고, 발생위치를 추정하였다. 본 연구에서는 부분발작 환자를 대상으로 간질 발작파가 나타나는 뇌파 신호(18개 채널)에 독립성분분석을 적용하여 18개의 독립성분으로 분리하였다. 또한 발작파(예파(sharp), 극파(spike), 예파와 서파를 동반한 극서파(sharp and slow complexes))가 나오는 간질 발작파의 유형을 분리하였다. 2차원 topological map을 이용하여 발작파의 발생위치를 나타내어 간질 진단에 독립성분분석이 적용될 수 있음을 나타내었다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.189-192
• /
• 2001

뇌 기능의 연구수단으로써 널리 사용되고 있는 뇌파(Electroencephalogram)는 측정시에 노이즈(noise)나 잡파(Artifacts)가 섞여서 측정되기 쉽다. 이러한 노이즈나 잡파들을 제거하기 위하여 미지의 혼합된 신호들을 분리하는데 적용되고 있는 통계적인 처리 방식인 독립성분분석(ICA) 알고리즘을 뇌파에 적용하여 그 결과를 알아보았다. 본 연구에서는 정상인의 안구운동(Eye Movement)상태의 뇌파 신호에 대해서 독립성분분석을 적용하여 안구운동과 관련된 잡파가 포함된 원래의 뇌파신호(Original EEG Signal)와 제거한 다음의 뇌파신호(Corrected EEG Signal)에 대하여 비선형 분석법을 사용하여 두 신호의 유의한 차이점을 밝히고, 분리된 독립 신호들의 해부학적 발생위치 및 분포를 추정하였다.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1998.06e
• /
• pp.327-330
• /
• 1998

독립성분분석(ICA: Independent Component Analysis)이란 특징이 상이한 둘 이상의 신호들이 선형적으로 결합되어 있을 때 이를 효과적으로 분리하는 방법들을 통칭하며 잡음제거, 음질개선 및 신호처리 분야에서 많이 활용되고 있다. 본 논문에서는 전화음성 화자인식 시스템의 성능향상을 위해 독립성분분석을 이용하는 방법을 제안한다. 먼저 화자가 발성한 음성신호의 켑스트럼 계수를 여러 채널 함수들의 선형적인 합으로 가정하고, 독립성분분석을 이용하여 얻은 새로운 켑스트럼 벡터를 학습과 인식에 사용하였다. 실험자료는 잔화음성 화자식별기의 성능평가에 널리 쓰이고 있는 SPIDRE를 사용하였고 regodic 은닉 마코프 모델을 이용하여 문장 독립 화자식별 시스템을 구성하였다. 학습음성의 특징과 실험음성의 특징이 다른 조건에서 기존의 채널 정규화 방법들에 비해 10~15%이상 인식률이 향상되었다.

• Journal of applied mathematics & informatics
• /
• v.26 no.5_6
• /
• pp.1149-1159
• /
• 2008

Blind source extraction (BSE) is a special class of blind source separation (BSS) methods, which only extracts one or a subset of the sources at a time. Based on the time delay of the desired signal, a simple but important extraction algorithm (simplified " BC algorithm")was presented by Barros and Cichocki. However, the performance of this method is not satisfying in some cases for which it only carries out the constrained minimization of the mean squared error. To overcome these drawbacks, ICA with reference (ICA-R) based approach, which considers the higher-order statistics of sources, is added as the second stage for further source extraction. Specifically, BC algorithm is exploited to roughly extract the desired signal. Then the extracted signal in the first stage, as the reference signal of ICA-R method, is further used to extract the desired sources as cleanly as possible. Simulations on synthetic data and real-world data show its validity and usefulness.