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Compressive Sensing Sparse Sampling Method for Composite Material Based on Principal component Analysis

Transactions of Nanjing University of Aeronautics and Astronautics 2018年第2期35卷 282-289页

作者： Sun Yajie Gu Feihong Ji Sai Wang Lihua Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 P. R. China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing 210044 P. R. China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 P. R. China School of Information and Control Nanjing University of Information Science and Technology Nanjing 210014 P. R. China

Signals can be sampled by compressive sensing theory with a much less rate than those by traditional Nyquist sampling theorem,and reconstructed with high probability,only when signals are sparse in the time domain or a transform *** signals are not sparse in real world,but can be expressed in sparse form by some kind of sparse *** used sparse transformations will lose some information,because their transform bases are generally *** this paper,we use principal component analysis for data reduction,and select new variable with low dimension and linearly correlated to the original variable,instead of the original variable with high dimension,thus the useful data of the original signals can be included in the sparse signals after dimensionality reduction with maximize ***,the loss of data can be reduced as much as possible,and the efficiency of signal reconstruction can be ***,the composite material plate is used for the experimental *** experimental result shows that the sparse representation of signals based on principal component analysis can reduce signal distortion and improve signal reconstruction efficiency.

关键词： principal component analysis compressive sensing sparse representation signal reconstruction

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Ageing evaluation of cable insulations subjected to radiation ageing:Application of principal component analyses to Fourier Transform Infra-Red and dielectric spectroscopy

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High Voltage 2022年第4期7卷 652-665页

作者： Simone Vincenzo Suraci Sandrine Amat Lea Hippolyte Astrid Malechaux Davide Fabiani Claire Le Gall Océane Juan Nathalie Dupuy LIMES Department of ElectricalElectronic and Information EngineeringUniversity of BolognaBolognaItaly Aix Marseille University Avignon UniversitéCNRSMarseilleFrance Institut de Radioprotection et de Sûreté Nucléaire MarseilleFrance

The evolution of properties of crosslinked polyethylene(XLPE)-based insulating mate-rials under radio-chemical ageing conditions is presented and *** total,seven different polymer compounds were tested,characterised by an increasing quantity of additives,for example,antioxidants(AOs)and fillers as flame *** were analysed using different testing techniques,that is,Fourier Transform Infra-Red and dielectric *** allowed a broad characterisation of the polymer,bringing to good correlations between the arising of new chemical groups caused by ageing and the modifications on the complex ***-based AOs are found to be the most efficient in terms of protection against *** the contrary,flame retardants are concluded not to have any impact on the material *** data were then treated using the principal component *** data treatment successfully distinguished the material composition(with and without additives)and the ageing *** results confirm the relevance of this analysis in understanding the impact of irradiation on the chemical and functional properties of the polymer materials studied.

关键词： component principal bringing

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Generalized two-dimensional correlation near-infrared spectroscopy and principal component analysis of the structures of methanol and ethanol

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Science China Chemistry 2010年第5期53卷 1154-1159页

作者： Liu Hao Xu JianPing Qu LingBo Xiang BingRen China Pharmaceut Univ Ctr Anal & Measurement Nanjing 210009 Peoples R China Guangdong Food & Drug Vocat Coll Dept Pharm Guangzhou 510520 Guangdong Peoples R China Zhengzhou Univ Dept Chem Zhengzhou 450052 Peoples R China

Liquid state methanol and ethanol under different temperatures have been investigated by FT-NIR(Fourier transform nearinfrared) spectroscopy,generalized two-dimensional(2D) correlation spectroscopy,and PCA(principal component analysis) . First,the FT-NIR spectra were measured over a temperature range of 30-64(or 30-71) °C,and then the 2D correlation spectra were *** near-infrared spectroscopy,generalized 2D correlation spectroscopy,and references,we analyzed the molecular structures(especially the hydrogen bond) of methanol and ethanol,and performed the NIR band assignments. The PCA method was employed to verify the results of the 2D *** study will be helpful to the understanding of these reagents.

关键词： NIR(near-infrared) two-dimensional (2D) correlation spectroscopy principal component analysis (PCA) methanol ethanol

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Identification of the anomaly component using BEMD combined with PCA from element concentrations in the Tengchong tin belt, SW China

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Geoscience Frontiers 2019年第4期10卷 1561-1576页

作者： Yongqing Chen Lina Zhang Binbin Zhao School of the Earth Sciences and Resources China University of GeosciencesBeijing 100083China CNPC Logging Application Research Institute CNPC LoggingXi’an 710000China Sinomine Resource Group Co. Ltd.Beijing 100089China

Concentration of elements or element groups in a geological body is the result of multiple stages of rockforming and ore-forming geological *** ore-forming element group can be identified by PCA(principal component analysis)and be separated into two components using BEMD(bi-dimensional empirical mode decomposition):(1)a high background component which represents the ore-forming background developed in rocks through various geological processes favorable for mineralization(***,sedimentation and/or metamorphism);(2)the anomaly component which reflects the oreforming anomaly that is overprinted on the high background component developed during *** components are used to identify ore-finding targets more effectively than ore-forming element *** steps of data analytical procedures are described in this paper;firstly,the application of PCA to establish the ore-forming element group;secondly,using BEMD on the o re-forming element group to identify the anomaly components created by different types of mineralization processes;and finally,identifying ore-finding targets based on the anomaly *** method is applied to the Tengchong tin-polymetallic belt to delineate ore-finding targets,where four targets for Sn(W)and three targets for Pb-Zn-Ag-Fe polymetallic mineralization are identified and defined as new areas for further *** is shown that BEMD combined with PCA can be applied not only in extracting the anomaly component for delineating the ore-finding target,but also in extracting the residual component for identifying its high background zone favorable for mineralization from its oreforming element group.

关键词： Bi-dimensional empirical mode decomposition(BEMD) Principal component analysis(PCA) Anomaly components Ore-forming element groups Sn(W)and Pb-Zn-Ag-Fe polymetallic deposits Tengchong tin-polymetallic belt

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Multi-component background learning automates signal detection for spectroscopic data

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npj Computational Materials 2019年第1期5卷 484-490页

作者： Sebastian E.Ament Helge S.Stein Dan Guevarra Lan Zhou Joel A.Haber David A.Boyd Mitsutaro Umehara John M.Gregoire Carla P.Gomes Department of Computer Science Cornell UniversityIthacaNY 14850USA Joint Center for Artificial Photosynthesis California Institute of TechnologyPasadenaCA 91125USA Future Mobility Research Department Toyota Research Institute of North AmericaAnn ArborMI 48105USA

Automated experimentation has yielded data acquisition rates that supersede human processing *** Intelligence offers new possibilities for automating data interpretation to generate large,high-quality *** subtraction is a long-standing challenge,particularly in settings where multiple sources of the background signal coexist,and automatic extraction of signals of interest from measured signals accelerates data ***,we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of *** approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background *** the model can incorporate prior knowledge,it does not require knowledge of the signals since the shapes of the background signals,the noise levels,and the signal of interest are simultaneously learned via a probabilistic matrix factorization *** identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets,a transformative capability with many applications in the physical sciences and beyond.

关键词： signal component spectroscopic

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Research on performance of complex networks based on principal component analysis

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The Journal of China Universities of Posts and Telecommunications 2019年第1期26卷 40-48页

作者： Xu Chunxia Qi Xiaogang Liu Lifang School of Mathematics and Statistics Xidian University School of Computer Science and Technology Xidian University

For complex networks, their effectiveness and invulnerability are extremely important. With the development of complex networks, how to evaluate the effectiveness and invulnerability of these networks becomes an important research topic. The relationship among many influencing factors is very complicated, so it is essential to confirm the weighting coefficient of these influencing factors. Principal component analysis(PCA) is proposed to evaluate the performance of complex networks. It can improve one-sidedness of the single evaluation index and select different evaluation models according to different complex networks, which make the evaluation result more accurate. Performance of complex networks can be predicted according to comprehensive evaluation model. To verify the rationality and validity of this method, several small-world networks with different probability values and scale-free network are chosen to evaluate the network performance. Finally, simulation results show that PCA can be applied to performance evaluation of complex networks.

关键词： complex networks network topology principal component analysis performance prediction

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Raman spectrum feature extraction and diagnosis of oil-paper insulation ageing based on kernel principal component analysis

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High Voltage 2021年第1期6卷 51-60页

作者： Dingkun Yang Chen Weigen Shi Haiyang Wan Fu Zhou Yongkuo State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing UniversityShapingba DistrictChongqingChina

Raman spectroscopy, with its specific ability to generate a unique fingerprint‐like spec-trum of certain substances, has attracted much attention in diagnosing the ageing degree of oil-paper insulation. In this study, the feature extraction and ageing diagnosis methods of oil-paper insulation Raman spectroscopy data are further studied. Based on the non‐linear analysis of Raman spectra of different ageing samples, kernel principal component analysis was applied to extract the spectral features, and the back‐propagation neural network was used to build a diagnosis model with high diagnostic accuracy. The results show that Raman spectroscopy combined with kernel principal component analysis and the back‐propagation neural network can diagnose the ageing state of oil-paper insu-lation, with a diagnostic accuracy of 91.43% (64/70). The proposed method provides an effective and feasible method for the ageing assessment of oil‐immersed electrical equipment.

关键词： insulation analysis component

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Genetic inverse algorithm for retrieval of component temperature of mixed pixel by multi-angle thermal infrared remote sensing data

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Science China Earth Sciences 2001年第4期44卷 363-372页

作者：徐希孺陈良富庄家礼 1. LARSIS Institute of Remote Sensing Applications Chinese Academy of Sciences 100101 Beijing China

After carefully studying the results of retrieval of land surface temperature(LST) by multi-channel thermal infrared remote sensing data, the authors of this paper point out that its accuracy and significance for applications are seriously damaged by the high correlation coefficient among multi-channel information and its disablement of direct retrieval of component temperature. Based on the model of directional radiation of non-isothermal mixed pixel, the authors point out that multi-angle thermal infrared remote sensing can offer the possibility to directly retrieve component temperature, but it is also a multi-parameter synchronous inverse problem. The results of digital simulation and field experiments show that the genetic inverse algorithm (GIA) is an effective method to fulfill multi-parameter synchronous retrieval. So it is possible to realize retrieval of component temperature with error less than 1K by multi-angle thermal infrared remote sensing data and GIA.

关键词： multi-angle thermal infrared remote sensing, component temperature of mixed pixel, genetic inverse algorithm.

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Polynomial analysis of canopy spectra and biochemical component content inversion

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Science China Earth Sciences 2005年第10期48卷 1780-1791页

作者： YAN Chunyan1, LIU Qiang1, NIU Zheng1, WANG Jihua2, HUANG Wenjiang2 & LIU Liangyun 2 1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100089, China State Key Laboratory of Remote Sensing Science Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University Beijing China National Engineering Research Center for Information Technology in Agriculture Beijing China

A polynomial expression model was developed in this paper to describe directional canopy spectra, and the decomposition of the polynomial expression was used as a tool for re-trieving biochemical component content from canopy multi-angle spectra. First, the basic formula of the polynomial expression was introduced and the physical meaning of its terms and coeffi-cients was discussed. Based on this analysis, a complete polynomial expression model and its decomposition method were given. By decomposing the canopy spectra simulated with SAILH model, it shows that the polynomial expression can not only fit well the canopy spectra, but also show the contribution of every order scattering to the whole reflectance. Taking the first scattering coefficients a10 and a01 for example, the test results show that the polynomial coefficients reflect very well the hot spot phenomenon and the effects of viewing angles, LAI and leaf inclination angle on canopy spectra. By coupling the polynomial expression with leaf model PROSPECT, a canopy biochemical component content inversion model was given. In the simulated test, the canopy multi-angle spectra were simulated by two different models, SAILH and 4-SCALE re-spectively, then the biochemical component content was retrieved by inverting the coupled polynomial expression + PROSPECT model. Results of the simulated test are promising, and when applying the algorithm to measured corn canopy multi-angle spectra, we also get relatively accurate chlorophyll content. It shows that the polynomial analysis provides a new method to get biochemical component content independent of any specific canopy model.

关键词： polynomial expression, canopy spectra, decomposition, biochemical component content, inversion.

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Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis

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Petroleum Science 2020年第1期17卷 182-195页

作者： Byeongcheol Kang Hyungsik Jung Hoonyoung Jeong Jonggeun Choe Department of Energy Systems Engineering Seoul National UniversitySeoul 08826South Korea

Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir ***,they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir *** stable convergence in ensemble Kalman filter(EnKF),increasing ensemble size can be one of the solutions,but it causes high computational cost in large-scale reservoir *** this paper,we propose a preprocessing of good initial model selection to reduce the ensemble size,and then,EnKF is utilized to predict production performances *** the model selection scheme,representative models are chosen by using principal component analysis(PCA)and clustering *** dimension of initial models is reduced using PCA,and the reduced models are grouped by ***,we choose and simulate representative models from the cluster groups to compare errors of production predictions with historical observation *** representative model with the minimum error is considered as the best model,and we use the ensemble members near the best model in the cluster plane for applying *** demonstrate the proposed scheme for two 3D models that EnKF provides reliable assimilation results with much reduced computation time.

关键词： Channel reservoir characterization Model selection scheme Egg model Principal component analysis(PCA) Ensemble Kalman filter(EnKF) History matching

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