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Neuroscience Bulletin 2024年第7期40卷 905-920页

作者： Xingyu He Vince D.Calhoun Yuhui Du School of Computer and Information Technology Shanxi UniversityTaiyuan 030006China Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State UniversityGeorgia Institute of TechnologyEmory UniversityAtlanta 30303USA

Functional networks(FNs)hold significant promise in understanding brain *** component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determining an optimal model order for ICA remains challenging,leading to criticism about the reliability of FN ***,we propose a SMART(splitting-merging assisted reliable)ICA method that automatically extracts reliable FNs by clustering independent components(ICs)obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model *** extend SMART ICA to multi-subject fMRI analysis,validating its effectiveness using simulated and real fMRI *** on simulated data,the method accurately estimates both group-common and group-unique components and demonstrates robustness to *** two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects,the resulting reliable group-level FNs are greatly similar between the two cohorts,and interestingly the subject-specific FNs show progressive changes while age ***,both small-scale and large-scale brain FN templates are provided as benchmarks for future *** together,SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data,while also providing linkages between different FNs.

关键词： independent component analysis Functional magnetic resonance imaging-Brain functional networks Clustering Multi-model-order

Food safety testing by negentropy-sorted kernel independent component analysis based on infrared spectroscopy

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High-Confidence Computing 2024年第3期4卷 25-31页

作者： Liu Jing Deng Limiao Han Zhongzhi School of Science and Information Science Qingdao Agriculture UniversityQingdao 266109China

In the field of food safety testing,variety,brand,origin,and adulteration are four important *** this study,a novel food safety testing method based on infrared spectroscopy is proposed to investigate these *** transform infrared spectroscopy data are analyzed using negentropy-sorted kernel independent component analysis(NS-kICA)as the feature optimization *** rank the components,negentropy is performed to measure the non-Gaussian independent *** our experiment,the proposed method was run on four datasets to comprehensively investigate the variety,brand,origin,and adulteration of agricultural *** experimental results show that NS-kICA outperforms conventional feature selection *** support vector machine model outperforms the backpropagation artificial neural network and partial least squares *** combination of NS-kICA and support vector machine(SVM)is the best method for achieving high,stable,and efficient recognition *** findings are of great importance for food safety testing.

关键词： Food safety testing Infrared spectroscopy independent component analysis Negentropy

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

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Journal of Central South University 2014年第1期21卷 242-251页

作者： Ajami Ali Daneshvar Mahdi School of Electrical Engineering Azarbaijan Shahid Madani University MAPNA Electrical and Control Engineering and Manufacturing Company (MECO)

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 ***,the aim is to utilize system as a black *** system studied is condenser system of one of MAPNA's power *** 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 ***,the fault sources were diagnosed by ICA *** 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 *** selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.

关键词： condenser fault detection and diagnosis independent component analysis independent component analysis (ICA) principal component analysis (PCA) thermal power plant

An independent component analysis Algorithm through Solving Gradient Equation Combined with Kernel Density Estimation

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Journal of Shanghai Jiaotong university(Science) 2009年第2期14卷 204-209页

作者：薛云峰王宇嘉杨杰 Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Department of Automation Shanghai Jiaotong University

A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient *** iterative method is introduced to solve this equation *** unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density *** simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm.

关键词： independent component analysis blind source separation gradient method kernel density estimation

independent component analysis of streamwise velocity fluctuations in turbulent channel flows

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Theoretical & Applied Mechanics Letters 2022年第4期12卷 233-240页

作者： Ting Wu Guowei He The State Key Laboratory of Nonlinear Mechanics Institute of MechanicsChinese Academy of SciencesBeijing 100190China School of Engineering Sciences University of Chinese Academy of SciencesBeijing 100049China

independent component analysis(ICA)is used to study the multiscale localised modes of streamwise velocity fluctuations in turbulent channel *** aims to decompose signals into independent modes,which may induce spatially localised *** height and size are defined to quantify the spatial position and extension of these ICA modes,*** contrast to spatially extended proper orthogonal decomposition(POD)modes,ICA modes are typically localised in space,and the energy of some modes is distributed across the near-wall *** sizes of ICA modes are multiscale and are approximately proportional to their *** modes can also help to reconstruct the statistics of turbulence,particularly the third-order moment of velocity fluctuations,which is related to the strongest Reynolds shear-stressproducing *** results reported in this paper indicate that the ICA method may connect statistical descriptions and structural descriptions of turbulence.

关键词： independent component analysis Turbulent channel flow Proper orthogonal decomposition Third-order moment Localised modes

independent component analysis with Application to Hot Galvanizing Pickling Waste Liquor Treatment Process

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Independent Component Analysis with Application to Hot Galva...

Source Separation of Diesel Engine Vibration Based on the Empirical Mode Decomposition and independent component analysis

第36届中国控制会议

作者： Song Fan Li Min Zhang Jia Wang College of Electronics and Information Engineering University of Science and Technology Liaoning Lingyuan Steel Co LTD Anshan City Land Resources Survey and Design Institute

ISBN: (纸本)9781538629185

In this paper,the application of independent component analysis（ICA） to statistical process monitoring is *** paper mainly focuses on studying on the fault detection and isolation principle based on the data model of *** of this paper are:（1） for the purpose of fault detection,two monitoring statistics are designated by detailed analysis on the data model of ICA;（2） a semi-supervised Laplacian regularization（SLR） kernel density estimation approach is proposed to determine the normal operation region;（3） Under fault condition,a fault isolation method based on expert knowledge is introduced;（4） Hot Galvanizing Pickling Waste Liquor Treatment Process（HGPWLTP） is taken to evaluate the monitoring performance of the proposed *** experimental results of the proposed process monitoring approaches are achieved.

关键词： Data-driven modeling fault detection and isolation independent component analysis

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Chinese Journal of Mechanical Engineering 2012年第3期25卷 557-563页

作者： DU Xianfeng LI Zhijun BI Fengrong ZHANG Junhong WANG Xia SHAO Kang State Key Laboratory of Engines Tianjin UniversityTianjin 300072China

Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its components in the case of multichannel measurements,such as independent component analysis（ICA）.However,the source separation of vibration signal from single-channel is *** order to study the source separation from single-channel signal for the purpose of source extraction,the combination method of empirical mode decomposition（EMD） and ICA is proposed in diesel engine signal *** performance of the described methods of EMD-wavelet and EMD-ICA in vibration signal application is compared,and the results show that EMD-ICA method outperforms the other,and overcomes the drawback of ICA in the case of single-channel *** independent source signal components can be separated and identified effectively from one-channel measurement by ***,EMD-ICA improves the extraction and identification abilities of source signals from diesel engine vibration measurements.

关键词： empirical mode decomposition independent component analysis source separation single-channel signal

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

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Chinese Physics B 2016年第1期25卷 400-406页

作者：王文波张晓东常毓禅汪祥莉王钊陈希郑雷 College of Science Wuhan University of Science and TechnologyWuhan 430065China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyState Oceanic AdministrationHangzhou 310012China School of Finance Renmin University of ChinaBeijing 100872China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430063China College of Information Science and Engineering Wuhan University of Science and TechnologyWuhan 430081China Wuhan NARI Limited Liability Company of Sate Grid Electric Power Research Institute Wuhan 430074China

In this paper, a new method to reduce noises within chaotic signals based on ICA （independent component analysis） and EMD （empirical mode decomposition） is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the indepen- dent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions （IMFs） of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor.

关键词： independent component analysis empirical mode decomposition chaotic signal denoising

Eliminate indeterminacies of independent component analysis for chemometrics

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Progress in Natural Science:Materials International 2008年第8期18卷 1009-1014页

作者： Zhixiang Yao a,b, Kai Zhang c,*, Huanbin Liu b, Hui Su a a Department of Biological and Chemical Engineering, Guangxi University of Technology, Liuzhou 545006, China b State Key Laboratory of Pulp and Paper-making Engineering, South China University of Technology, Guangzhou 510640, China c State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing 102249, China Department of Biological and Chemical Engineering Guangxi University of Technology Liuzhou 545006 China State Key Laboratory of Pulp and Paper-making Engineering South China University of Technology Guangzhou 510640 China State Key Laboratory of Heavy Oil Processing China University of Petroleum Beijing 102249 China

An improved method has been proposed to eliminate the indeterminacies of independent component analysis (ICA) for chemometrics. Following the arrangement of principal components analysis (PCA), the ICA mixing matrix is selected as signal content indexes, and ICA output are sorted and directed. After many times reputations, independent components (ICs) are paired according to the maximum correlation coefficient, and then the mean values of each IC substitutes the original ICs. This indicates that the ICA indeterminacies are eliminated. A simulation example is tested to validate this improvement. Finally, a set of experimental LC-MS data is processed without any prior knowledge or specific limitation and the results show that the improved ICA can directly separate the mixed signals in chemometrics, and it is simpler and more reasonable than the simple to use interactive self-modeling mixture analysis (SIMPLISMA).

关键词： independent component analysis Indeterminacy in ICA Chemometrics

Study on application of independent component analysis in the CSNS/RCS

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Chinese Physics C 2013年第3期37卷 102-107页

作者：安宇文王生 Institute of High Energy Physics Chinese Academy of Sciences

The China Spallation Neutron Source （CSNS） accelerators consist of a low energy H linac and a high energy proton Rapid Cycling Synchrotron （RCS）. The proton beam is accumulated in the RCS and accelerated from 80MeV to 1.6GeV with a repetition of 25Hz. independent component analysis （ICA） is a robust method for processing the collected data （samples） recorded by the turn-by-turn beam position monitor （BPM）, which was recently applied to the accelerator. The samples are decomposed to source signals, or the so-called independent components, which correspond to the inherent motion of samples, such as betatron motion and synchrotron motion. A study on the application of the ICA method to CSNS/RCS has been made. It shows that the beta function, phase advance, and dispersion can be well reconstructed by using ICA in CSNS/RCS. The effects of BPM errors on the ICA results are also studied. By comparing the different solving methods in ICA, the so-called SOBI has more advantages for isolating the independent components on the application of ICA to CSNS/RCS. Beam emittance dilution in the process of exciting the turn-by-turn samples is considered,and thus an RF kicker is adopted to avoid such emittance growth.

关键词： independent component analysis turn-by-turn BPM RF kicker