Abundance quantification by independent component analysis of hyperspectral imagery for oil spill coverage calculation

The estimation of oil spill coverage is an important part of monitoring of oil spills at sea.The spatial resolution of images collected by airborne hyper-spectral remote sensing limits both the detection of oil spills and the accuracy of estimates of their size.We consider at-sea oil spills with zonal distribution in this paper and improve the traditional independent component analysis algorithm.For each independent component we added two constraint conditions:non-negativity and constant sum.We use priority weighting by higher-order statistics,and then the spectral angle match method to overcome the order nondeterminacy.By these steps,endmembers can be extracted and abundance quantified simultaneously.To examine the coverage of a real oil spill and correct our estimate,a simulation experiment and a real experiment were designed using the algorithm described above.The result indicated that,for the simulation data,the abundance estimation error is 2.52% and minimum root mean square error of the reconstructed image is 0.030 6.We estimated the oil spill rate and area based on eight hyper-spectral remote sensing images collected by an airborne survey of Shandong Changdao in 2011.The total oil spill area was 0.224 km^2,and the oil spill rate was 22.89%.The method we demonstrate in this paper can be used for the automatic monitoring of oil spill coverage rates.It also allows the accurate estimation of the oil spill area.

Fault diagnosis method for an Aeroengine Based on Independent Component Analysis and the Discrete Hidden Markov Model

The vibration signals of an aeroengine are a very important information source for fault diagnosis and condition monitoring. Considering the nonstationarity and low repeatability of the vibration signals, it is necessary to find a corresponding method for feature extraction and fault recognition. In this paper, based on Independent Component Analysis （ICA） and the Discrete Hidden Markov Model （DHMM）, a new fault diagnosis approach named ICA-DHMM is proposed. In this method, ICA separates the source signals from the mixed vibration signals and then extracts features from them, DHMM works as a classifier to recognize the conditions of the aeroengine. Compared with the DHMM, which use the amplitude spectrum of mixed signals as feature parameters, experimental results show this method has higher diagnosis accuracy.

Two Dimensional Spatial Independent Component Analysis and Its Application in fMRI Data Process

One important application of independent component analysis （ICA） is in image processing. A two dimensional （2-D） composite ICA algorithm framework for 2-D image independent component analysis （2-D ICA） is proposed. The 2-D nature of the algorithm provides it an advantage of circumventing the roundabout transforming procedures between two dimensional （2-D） image deta and one-dimensional （l-D） signal. Moreover the combination of the Newton （fixed-point algorithm） and natural gradient algorithms in this composite algorithm increases its efficiency and robustness. The convincing results of a successful example in functional magnetic resonance imaging （fMRI） show the potential application of composite 2-D ICA in the brain activity detection.

SIGNAL FEATURE EXTRACTION BASED UPON INDEPENDENT COMPONENT ANALYSIS AND WAVELET TRANSFORM

It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent component analysis (ICA) method is combined withwavelet to de-noise. Firstly, The sampled signal can be separated with ICA, then the function offrequency band chosen with multi-resolution wavelet transform can be used to judge whether thestochastic disturbance singular signal is interfused. By these ways, the vibration signals can beextracted effectively, which provides favorable condition for subsequent feature detection ofvibration signal and fault diagnosis.

Independent component analysis approach for fault diagnosis of condenser system in thermal power plant

A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.

Study of engine noise based on independent component analysis

Independent component analysis was applied to analyze the acoustic signals from diesel engine. First the basic prin-ciple of independent component analysis (ICA) was reviewed. Diesel engine acoustic signal was decomposed into several inde-pendent components (ICs); Fourier transform and continuous wavelet transform (CWT) were applied to analyze the independent components. Different noise sources of the diesel engine were separated, based on the characteristics of different component in time-frequency domain.

Independent Component Analysis Based Blind Adaptive Interference Reduction and Symbol Recovery for OFDM Systems

To overcome the inter-carrier interference （ICI） of orthogonal frequency division multiplexing （OFDM） systems subject to unknown carrier frequency offset （CFO） and multipath, this paper develops a blind adaptive interference suppression scheme based on independent component analysis （ICA）. Taking into account statistical independence of subcarriers＇ signals of OFDM, the signal recovery mechanism is investigated to achieve the goal of blind equalization. The received OFDM signals can be considered as the mixed observation signals. The effect of CFO and multipath corresponds to the mixing matrix in the problem of blind source separation （BSS） framework. In this paper, the ICA- based OFDM system model is built, and the proposed ICA-based detector is exploited to extract source signals from the observation of a received mixture based on the assumption of statistical independence between the sources. The blind separation technique can increase spectral efficiency and provide robustness performance against erroneous parameter estimation problem. Theoretical analysis and simulation results show that compared with the conventional pilot-based scheme, the improved performance of OFDM systems is obtained by the proposed ICA-based detection technique.

Online Batch Process Monitoring Based on Just-in-Time Learning and Independent Component Analysis

A new method was developed for batch process monitoring in this paper. In the devdopad method, just-in-time learning （ JITL ） and independent component analysis （ICA） were integrated to build JITL-ICA monitoring scheme. JITL was employed to tackle with the characteristics of batch process such as inherent time- varying dynamics, multiple operating phases, and especially the case of uneven length stage. According to new coming test data, the most correlated segmentation was obtained from batch-wise unfolded training data by JITL. Then, ICA served as the principal components extraction approach. Therefore, the non.Gaussian distributed data can also be addressed under this modeling framework. The effectiveness and superiority of JITL-ICA based monitoring method was demonstrated by fed-batch penicillin fermentation.

Foreground Detection Based on Nonlinear Independent Component Analysis

Motionless foreground objects are key targets in applications of home care monitoring and abandoned object detection, and pose a great challenge to foreground detection. Most algorithms incorporate the motionless foreground objects into their background models because they have to adapt to environmental changes. To overcome this challenge, a foreground detection method based on nonlinear independent component analysis （ICA） was proposed. Considering that each video frame was actually a nonlinear mixture of the background image and the foreground image, the nonlinear ICA was employed to accurately separate the independent components from each frame. Then, the entropy of grayscale image was calculated to classify which resulting independent component was the foreground image. The proposed nonlinear ICA model was trained offiine and this model was not updated online, so the method can cope with the motionless foreground objects. Experimental results demonstrate that, the method achieves remarkable results and outperforms several advanced methods in dealing with the motionless foreground objects.

Detection and Separation of Event-related Potentials from Multi-Artifacts Contaminated EEG by Means of Independent Component Analysis

Event-related potentials (ERP) is an important type of brain dynamics in human cognition research. However, ERP is often submerged by the spontaneous brain activity EEG, for its relatively tiny scale. Further more, the brain activities collected from scalp electrodes are often inevitably contaminated by several kinds of artifacts, such as blinks, eye movements, muscle noise and power line interference. A new approach to correct these disturbances is presented using independent component analysis (ICA). This technique can effectively detect and extract ERP components from the measured electrodes recordings even if they are heavily contaminated. The results compare favorably to those obtained by parametric modeling. Besides, auto-adaptive projection of decomposed results to ERP components was also given. Through experiments, ICA proves to be highly capable of ERP extraction and S/N ratio improving.

An Improved Fixed-point Algorithm for Independent Component Analysis of Functional MRI Data

The fixed-point algorithm and infomax algorithm are two of the most popular algorithms in independent component analysis(ICA).However,it is hard to take both stability and speed into consideration in processing functional magnetic resonance imaging(fMRI)data.In this paper,an optimization model for ICA is presented and an improved fixed-point algorithm based on the model is proposed.In the new algorithms a small step size is added to increase the stability.In order to accelerate the convergence,an improvement on Newton method is made,which makes cubic convergence for the new algorithm.Applying the algorithm and two other algorithms to invivo fMRI data,the results show that the new algorithm separates independent components stably,which has faster convergence speed and less computation than the other two algorithms.The algorithm has obvious advantage in processing fMRI signal with huge data.

A Simple and Accurate ICA Algorithm for Separating Mixtures of Up to Four Independent Components

这份报纸用二的明确的关上的形式为独立部件分析(集成通信适配器) 介绍一个算法 -- ，三 -- 并且没有任何近似，四维的反对称的矩阵 exponentials，搜索方向和矩阵 exponentials 能基于是直接在每次重复计算了。另外，二个错误为在另外的工作被建立的四维的反对称的矩阵 exponentials 的表示被改正了。模拟证明算法快收敛并且能为多达四个独立部件的混合物比著名扩大 InfoMax 和 FastICA 算法完成更好的性能。

Process Monitoring Based on Independent Component Contribution

Independent component analysis( ICA) has been widely applied to the monitoring of non-Gaussian processes. Despite lots of applications,there is no universally accepted criterion to select the dominant independent components( ICs). Moreover, how to determine the number of dominant ICs is still an open question. To further address this issue,a novel process monitoring based on IC contribution( ICC) is proposed from the perspective of information storage. Based on the ICC with each variable,the dominant ICs can be obtained and the number of dominant ICs is determined objectively. To further preserve the process information, the remaining ICs are not useless. As a result,all the ICs are regarded to be divided into dominant and residual subspaces. The monitoring models are established respectively in each subspace, and then Bayesian inference is applied to integrating monitoring results of the two subspaces. Finally, the feasibility and effectiveness of the proposed method are illustrated through a numerical example and the Tennessee Eastman process.

基于SOA-VMD-ICA的海水泵激励源特征提取方法

针对海水泵复杂多源激励特征提取问题,提出了一种海鸥优化算法(SOA)、变分模态分解(VMD)和独立分量分析(ICA)相结合的海水泵激励源特征提取方法。基于单通道测量信号,采用VMD算法与SOA算法选取信号平方包络谱峭度统计量作为适应度函数,寻优获取模态分解数量K、惩罚系数α及特征模态函数(IMF)分量。采用信号排列熵作为噪声检验函数,合理选取排列熵阈值,对IMF分量进行噪声筛选,获取非噪声IMF分量信号。将非噪声IMF分量与原输入信号组合,采用快速独立成分分析(Fast-ICA)算法计算得到激励源信号向量,从而实现激励源特征信号的提取。通过实船海水泵激励源特征提取试验及对比分析,验证了所提方法的有效性。研究结果表明,所提的SOA-VMD-ICA方法能满足单通道测量条件海水泵激励源特征提取准确性要求。

A post-modification approach to independent compo-nent analysis for resolution of overlapping GC/MS signals: from independent components to chemical components

Independent component analysis (ICA) has demonstrated its power to extract mass spectra from over-lapping GC/MS signal. However, there is still a problem that mass spectra with negative peaks at some m/z will be obtained in the resolved results when there are overlapping peaks in the mass spectra of a mixture. Based on a detail theoretical analysis of the preconditions for ICA and the non-negative property of GC/MS signals, a post-modification based on chemical knowledge (PMBK) strategy is pro-posed to solve this problem. By both simulated and experimental GC/MS signals, it was proved that the PMBK strategy can improve the resolution effectively.

Novel infrared and visible image fusion method based on independent component analysis

The goal of infrared （IR） and visible image fu- sion is for the fused image to contain IR object features from the IR image and retain the visual details provided by the visible image. The disadvantage of traditional fusion method based on independent component analysis （ICA） is that the primary feature information that describes the IR objects and the secondary feature information in the IR image are fused into the fused image. Secondary feature information can de- press the visual effect of the fused image. A novel ICA-based IR and visible image fusion scheme is proposed in this paper. ICA is employed to extract features from the infrared image, and then the primary and secondary features are distinguished by the kurtosis information of the ICA base coefficients. The secondary features of the IR image are discarded during fu- sion. The fused image is obtained by fusing primary features into the visible image. Experimental results show that the pro- posed method can provide better perception effect.

基于CEEMDAN和ICA的油气勘探大地电磁噪声消除方法

大地电磁(MT)信号中的噪声会严重影响大地电磁勘探的观测数据,导致后续反演、解释工作出现严重偏差,影响油气勘探效果。为此,提出了一种基于带自适应噪声的完全集合经验模态分解(CEEMDAN)和独立分量分析(ICA)的噪声消除方法。该方法将经验模态分解(EMD)中的CEEMDAN方法与盲源分离(BSS)中的ICA方法进行有效结合。首先,利用改进的端点检测技术识别电磁信号中的有噪声信号分段;其次,利用CEEMDAN方法对其进行分解,提取具有代表性的固有模态分量(IMF)分量并进行ICA处理,达到消除噪声的目的;然后,利用获得的独立分量对有用MT信号进行逆向重构;最后,将未受噪声污染的MT信号与去噪后的有用MT信号进行拼接,获得最终的消噪后的完整MT信号。对合成信号和实测MT数据的实验结果表明,该方法能有效消除MT信号中的噪声。

ICA Based Identification of Time-Varying Linear Causal Model

Recently, several approaches have been proposed to discover the causality of the time-independent or fixed causal model. However, in many realistic applications, especially in economics and neuroscience, causality among variables might be time-varying. A time-varying linear causal model with non-Gaussian noise is considered and the estimation of the causal model from observational data is focused. Firstly, an independent component analysis(ICA) based two stage method is proposed to estimate the time-varying causal coefficients. It shows that, under appropriate assumptions, the time varying coefficients in the proposed model can be estimated by the proposed approach, and results of experiment on artificial data show the effectiveness of the proposed approach. And then, the granger causality test is used to ascertain the causal direction among the variables. Finally, the new approach is applied to the real stock data to identify the causality among three stock indices and the result is consistent with common sense.

State Inspection for Transmission Lines Based on Independent Component Analysis

Monitoring transmission towers is of great importance to prevent severe thefts on them and ensure the reliability and safety of the power grid operation.Independent component analysis(ICA) is a method for finding underlying factors or components from multivariate statistical data based on dimension reduction methods,and it is applicable to extract the non-stationary signals.FastICA based on negentropy is presented to effectively extract and separate the vibration signals caused by human activity in this paper.A new method combined empirical mode decomposition(EMD) technique with the adaptive threshold method is applied to extract the vibration pulses,and suppress the interference signals.The practical tests demonstrate that the method proposed in the paper is effective in separating and extracting the vibration signals.

Unsupervised linear spectral mixture analysis with AVIRIS data

A new algorithm for unsupervised hyperspectral data unmixing is investigated, which includes a modified minimum noise fraction （MNF） transformation and independent component analysis （ICA）. The modified MNF transformation is used to reduce noise and remove correlation between neighboring bands. Then the ICA is applied to unmix hyperspectral images, and independent endmembers are obtained from unmixed images by using post-processing which includes image segmentation based on statistical histograms and morphological operations. The experimental results demonstrate that this algorithm can identify endmembers resident in mixed pixels. Meanwhile, the results show the high computational efficiency of the modified MNF transformation. The time consumed by the modified method is almost one fifth of the traditional MNF transformation.