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Littérature scientifique sur le sujet « Linear principal componetns analysis »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 10 mars 2023

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Articles de revues sur le sujet "Linear principal componetns analysis"

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Török, Evelin, István Komlósi, Béla Béri, Imre Füller, Barnabás Vágó, and János Posta. "Principal component analysis of conformation traits in Hungarian Simmental cows." Czech Journal of Animal Science 66, No. 2 (2021): 39–45. http://dx.doi.org/10.17221/155/2020-cjas.

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The aim of the current research was to analyze the linear type traits of Hungarian Simmental dual-purpose cows scored in the first lactation using principal component analysis and cluster analysis. Data collected by the Association of Hungarian Simmental Breeders were studied during the work. The filtered database contained the results of 8 868 cows, born after 1997. From the evaluation of main conformation traits, the highest correlations (r = 0.35, P &lt; 0.05) were found between mammary system and feet and legs traits. Within linear type traits, the highest correlation was observed between rump length and rump width (r = 0.81, P &lt; 0.05). Using the principal component analysis, main conformation traits were combined into groups. There were three factors having 84.5 as total variance ratio after varimax rotation. Cluster analysis verified the results of the principal component analysis as most of the trait groups were similar. The strongest relationship was observed between feet and legs and mammary system (main conformation traits) and between rump length and rump width (linear type traits).

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Hiden, H. G., M. J. Willis, M. T. Tham, and G. A. Montague. "Non-linear principal components analysis using genetic programming." Computers & Chemical Engineering 23, no. 3 (1999): 413–25. http://dx.doi.org/10.1016/s0098-1354(98)00284-1.

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Zhang, J., A. J. Morris, and E. B. Martin. "Process Monitoring Using Non-Linear Principal Component Analysis." IFAC Proceedings Volumes 29, no. 1 (1996): 6584–89. http://dx.doi.org/10.1016/s1474-6670(17)58739-x.

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Ruessink, B. G., I. M. J. van Enckevort, and Y. Kuriyama. "Non-linear principal component analysis of nearshore bathymetry." Marine Geology 203, no. 1-2 (2004): 185–97. http://dx.doi.org/10.1016/s0025-3227(03)00334-7.

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Jia, F., E. B. Martin, and A. J. Morris. "Non-linear principal components analysis for process fault detection." Computers & Chemical Engineering 22 (March 1998): S851—S854. http://dx.doi.org/10.1016/s0098-1354(98)00164-1.

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Rattan, S. S. P., B. G. Ruessink, and W. W. Hsieh. "Non-linear complex principal component analysis of nearshore bathymetry." Nonlinear Processes in Geophysics 12, no. 5 (2005): 661–70. http://dx.doi.org/10.5194/npg-12-661-2005.

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Abstract. Complex principal component analysis (CPCA) is a useful linear method for dimensionality reduction of data sets characterized by propagating patterns, where the CPCA modes are linear functions of the complex principal component (CPC), consisting of an amplitude and a phase. The use of non-linear methods, such as the neural-network based circular non-linear principal component analysis (NLPCA.cir) and the recently developed non-linear complex principal component analysis (NLCPCA), may provide a more accurate description of data in case the lower-dimensional structure is non-linear. NLPCA.cir extracts non-linear phase information without amplitude variability, while NLCPCA is capable of extracting both. NLCPCA can thus be viewed as a non-linear generalization of CPCA. In this article, NLCPCA is applied to bathymetry data from the sandy barred beaches at Egmond aan Zee (Netherlands), the Hasaki coast (Japan) and Duck (North Carolina, USA) to examine how effective this new method is in comparison to CPCA and NLPCA.cir in representing propagating phenomena. At Duck, the underlying low-dimensional data structure is found to have linear phase and amplitude variability only and, accordingly, CPCA performs as well as NLCPCA. At Egmond, the reduced data structure contains non-linear spatial patterns (asymmetric bar/trough shapes) without much temporal amplitude variability and, consequently, is about equally well modelled by NLCPCA and NLPCA.cir. Finally, at Hasaki, the data structure displays not only non-linear spatial variability but also considerably temporal amplitude variability, and NLCPCA outperforms both CPCA and NLPCA.cir. Because it is difficult to know the structure of data in advance as to which one of the three models should be used, the generalized NLCPCA model can be used in each situation.

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Kambhatla, Nandakishore, and Todd K. Leen. "Dimension Reduction by Local Principal Component Analysis." Neural Computation 9, no. 7 (1997): 1493–516. http://dx.doi.org/10.1162/neco.1997.9.7.1493.

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Reducing or eliminating statistical redundancy between the components of high-dimensional vector data enables a lower-dimensional representation without significant loss of information. Recognizing the limitations of principal component analysis (PCA), researchers in the statistics and neural network communities have developed nonlinear extensions of PCA. This article develops a local linear approach to dimension reduction that provides accurate representations and is fast to compute. We exercise the algorithms on speech and image data, and compare performance with PCA and with neural network implementations of nonlinear PCA. We find that both nonlinear techniques can provide more accurate representations than PCA and show that the local linear techniques outperform neural network implementations.

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Purviance, J. E., M. C. Petzold, and C. Potratz. "A linear statistical FET model using principal component analysis." IEEE Transactions on Microwave Theory and Techniques 37, no. 9 (1989): 1389–94. http://dx.doi.org/10.1109/22.32222.

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Jiang, Jian-Hui, Ji-Hong Wang, Xia Chu, and Ru-Qin Yu. "Neural network learning to non-linear principal component analysis." Analytica Chimica Acta 336, no. 1-3 (1996): 209–22. http://dx.doi.org/10.1016/s0003-2670(96)00359-5.

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Chan. "Face Biometrics Based on Principal Component Analysis and Linear Discriminant Analysis." Journal of Computer Science 6, no. 7 (2010): 693–99. http://dx.doi.org/10.3844/jcssp.2010.693.699.

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Thèses sur le sujet "Linear principal componetns analysis"

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Shannak, Kamal Majed. "On Non-Linear Principal Component Analysis for Process Monitoring." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/ShannakKM2004.pdf.

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Jia, Feng. "Application of linear and non-linear principal component analysis in multivariate statistical process control." Thesis, University of Newcastle Upon Tyne, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311152.

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MENNI, CRISTINA. "Population stratification in genome-wide association studies: a comparison among different multivariate analysis methods for dimensionality reduction." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19317.

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INTRODUCTION: Genome-wide association studies (GWAS) are large-scale association mapping using SNPs, making no assumptions on the genomic location of the causal variant. They hold substantial promise for unraveling the genetic basis of common human diseases. A well known problem with such studies is population stratification (PS), a form of confounding which arises when there are two or more strata in the study population, and both the risk of disease and the frequency of marker alleles differ between strata. It therefore may appear that the risk of disease is related to the marker alleles when in fact it is not. Many statistical methods were developed to account for PS so that association studies could proceed even in the presence of structure and for GWAS, linear principal components analysis (PCA) represents a sort of gold-standard. PCA uses genotype data to extract continuous (principal) axis of variation, which can be used to adjust for association attributable to ancestry along each axis. The assumption underlying PCA, however, is that the variable under studies are continuous and so SNPs are quantified by fixing for each marker a reference and a variant allele and by counting the number of mutations. This implies that the distance between homozygous wild type and heterozygous is the same as the distance between heterozygous and homozygous mutant and it thus implies an additive model of inheritance. This model is very conservative, is very static and most importantly it is not necessarily the correct one. AIM: The aim of this thesis is to treat SNPs as ordinal qualitative variables. This means that there is a distance between homozygous wild type, heterozygous and homozygous mutant, but that the distance between each pair is not necessarily the same. So, we no longer assume any model of inheritance and can potentially better capture some information that linear PCA misses out. METHODS: We apply a multivariate technique to reduce dimensionality in the presence of non-metric data known as non linear principal components analysis (NLPCA, also known as PRINCALS: Principal components analysis by means of alternating least squares). PRINCALS belongs to “Gifi’ s system”, a unified theoretical framework under which many well known descriptive multivariate techniques are organised. We apply both PCA and PRINCALS to a sample dataset of 90 individuals belonging to three very distinct subpopulations and 1,000 randomly chosen uncorrelated SNPs and compare the results graphically, using Procrustean superimposition approach and the test Protest and finally with a scenarios analysis. RESULTS: When we compare the performances of PCA and PRINCALS, we find that the two methods yield similar scores for markers with a low/null genotypic variability across the study sample, while scores differ as the level of genotypic variability increases. This suggests that the two methods capture intra-subject variability differently. Procrustes analysis and scenarios analysis confirm this. Indeed, the matrix of principal components obtained with PCA and the matrix of dimensions obtained with PRINCALS are shown to be statistically different by the test PROTEST and, in the scenarios analysis, we find that, as the level of PS increases, PRINCALS appears to outperform PCA. CONCLUSION: PCA and PRINCALS behave differently. Validation analyses are needed to confirm these results.

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Archer, Cynthia. "A framework for representing non-stationary data with mixtures of linear models /." Full text open access at:, 2002. http://content.ohsu.edu/u?/etd,585.

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Savery, James Roy. "A modular non-linear approach to empirical principal component analysis based process modelling." Thesis, University College London (University of London), 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417693.

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Pascoto, Tamara Vieira. "Análises fatorial e de componentes principais aplicadas ao estudo dos fatores influenciadores de processos erosivos /." Bauru, 2020. http://hdl.handle.net/11449/192266.

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Orientador: Anna Silvia Palcheco Peixoto<br>Resumo: A erosão é um problema ambiental em que a perda de solo pode acarretar problemas econômicos e, quando próximos a urbanizações, problemas sociais. A cidade de São Manuel está situada no interior de São Paulo e apresenta, tanto solos argilosos com baixa suscetibilidade a erosão, como solos arenosos com alta suscetibilidade a erosão. Uma vez que existem áreas suscetíveis à erosão próximas à área urbana capazes de colocar a população em risco, surgiu a necessidade de analisá-las a fim de auxiliar políticas públicas para minimizar suas consequências. Com isso, a presente pesquisa propôs o desenvolvimento de uma metodologia para gerar índices de erosão, por Análise de Componente Principal (PCA) e por Análise Fatorial, fundamentado em alguns dos principais fatores influenciadores nos processos erosivos que ocorrem na área urbana do município. Nessa etapa foram considerados: textura do solo; declividade; permeabilidade; uso e ocupação; pluviosidade; e erodibilidade dos solos. Inicialmente, foram levantadas as feições erosivas existentes na área urbana, espacializadas e classificadas. Entre as 9 feições espacializadas, duas eram provenientes de processos fluviais, duas estavam recuperadas, restando cinco feições erosivas lineares para serem estudadas. Uma das cinco, apesar de estar estabilizada, apresentou um avanço significativo em um dos braços. Das feições estudadas, apenas uma foi classificada como ravina, sendo as demais classificadas como voçorocas. Após levantados os fatores in... (Resumo completo, clicar acesso eletrônico abaixo)<br>Abstract: Erosion is an environmental problem where soil loss can lead either to economic problems or, whether close to urbanization, social problems. São Manuel town is located in the interior of São Paulo and has both clayey soils with low susceptibility to erosion and sandy soils with high susceptibility to erosion. Since there are areas susceptible to erosion close to the urban area capable of putting the population at risk, the need arose to analyze them in order to assist public policies to minimize their consequences. Therefore, this research proposed the development of a methodology to generate erosion indexes, by Principal Component Analysis (PCA) and Factorial Analysis, based on some of the main factors influencing erosion processes that occur in the urban area of the municipality. At this stage the following factors were considered: soil texture; slope; permeability; use and occupation; rainfall; and soil erodibility. Initially, the erosive features existing in the urban area were surveyed, spatialized and classified. Among the 9 spatialized features, two were from fluvial processes, two were recovered, leaving five linear erosive features to be studied. One of the five, despite being stabilized, presented a significant advance in one of the arms. Of the studied features, only one was classified as ravine, the others being classified as gullies. After surveyed the influencing factors, they were evaluated according to two methodologies: Method A - it was based on the analysis... (Complete abstract click electronic access below)<br>Mestre

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Marbach, Matthew James. "Use of principal component analysis with linear predictive features in developing a blind SNR estimation system /." Full text available online, 2006. http://www.lib.rowan.edu/find/theses.

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Varanis, Marcus Vinicius Monteiro 1979. "Detecção de falhas em motores elétricos através da transformada wavelet packet e métodos de redução de dimensionalidade." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/265889.

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Orientador: Robson Pederiva<br>Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica<br>Made available in DSpace on 2018-08-26T01:39:31Z (GMT). No. of bitstreams: 1 Varanis_MarcusViniciusMonteiro_D.pdf: 5116959 bytes, checksum: b16ac36565b93c6bf49eb1863f7e9823 (MD5) Previous issue date: 2014<br>Resumo: Motores elétricos são componentes de grande importância na maioria dos equipamentos de plantas industriais. As diversas falhas que ocorrem nas máquinas de indução podem gerar consequências severas no processo industrial. Os principais problemas estão relacionados à elevação dos custos de produção, piora nas condições do processo e de segurança e, sobretudo piora na qualidade do produto final. Muitas destas falhas mostram-se progressivas. Neste trabalho, apresenta-se uma contribuição ao estudo de Técnicas de Processamento de Sinais Baseadas na Transformada Wavelet para extração de parâmetros de Energia e Entropia a partir de sinais de vibração para detecção de falhas no regime não-estacionário (parada e partida do motor). Em conjunto com a transformada Wavelet utilizam-se métodos de redução de dimensionalidade como, a análise em componentes principais (PCA e a análise Linear Discriminante (LDA). O uso de uma bancada experimental mostra que os resultados da classificação têm alta precisão<br>Abstract: Electric motors are very important components in most industrial plants equipment. The several faults occurring in induction machines can generate severe consequences in the industrial process. The main problems are related to high production costs, worsening the conditions of process and security, and especially poor quality of the final product. Many of these failures are shown progressive. This work presents a contribution to the study of Signal Processing Techniques Based on Wavelet Packet Transform for extracting parameters of Energy and Entropy, together makes the use of dimensionality reduction methods like the Principal components Analysis (PCA) and Linear Dscriminant Analysis (LDA). This analysis is done from the acquisition of vibration signals in Non-Stationary state (stop and start the engine). The results show that the performance of classification has high accuracy based on experimental work<br>Doutorado<br>Mecanica dos Sólidos e Projeto Mecanico<br>Doutor em Engenharia Mecânica

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Khosla, Nitin, and n/a. "Dimensionality Reduction Using Factor Analysis." Griffith University. School of Engineering, 2006. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20061010.151217.

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In many pattern recognition applications, a large number of features are extracted in order to ensure an accurate classification of unknown classes. One way to solve the problems of high dimensions is to first reduce the dimensionality of the data to a manageable size, keeping as much of the original information as possible and then feed the reduced-dimensional data into a pattern recognition system. In this situation, dimensionality reduction process becomes the pre-processing stage of the pattern recognition system. In addition to this, probablility density estimation, with fewer variables is a simpler approach for dimensionality reduction. Dimensionality reduction is useful in speech recognition, data compression, visualization and exploratory data analysis. Some of the techniques which can be used for dimensionality reduction are; Factor Analysis (FA), Principal Component Analysis(PCA), and Linear Discriminant Analysis(LDA). Factor Analysis can be considered as an extension of Principal Component Analysis. The EM (expectation maximization) algorithm is ideally suited to problems of this sort, in that it produces maximum-likelihood (ML) estimates of parameters when there is a many-to-one mapping from an underlying distribution to the distribution governing the observation, conditioned upon the obervations. The maximization step then provides a new estimate of the parameters. This research work compares the techniques; Factor Analysis (Expectation-Maximization algorithm based), Principal Component Analysis and Linear Discriminant Analysis for dimensionality reduction and investigates Local Factor Analysis (EM algorithm based) and Local Principal Component Analysis using Vector Quantization.

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Khosla, Nitin. "Dimensionality Reduction Using Factor Analysis." Thesis, Griffith University, 2006. http://hdl.handle.net/10072/366058.

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In many pattern recognition applications, a large number of features are extracted in order to ensure an accurate classification of unknown classes. One way to solve the problems of high dimensions is to first reduce the dimensionality of the data to a manageable size, keeping as much of the original information as possible and then feed the reduced-dimensional data into a pattern recognition system. In this situation, dimensionality reduction process becomes the pre-processing stage of the pattern recognition system. In addition to this, probablility density estimation, with fewer variables is a simpler approach for dimensionality reduction. Dimensionality reduction is useful in speech recognition, data compression, visualization and exploratory data analysis. Some of the techniques which can be used for dimensionality reduction are; Factor Analysis (FA), Principal Component Analysis(PCA), and Linear Discriminant Analysis(LDA). Factor Analysis can be considered as an extension of Principal Component Analysis. The EM (expectation maximization) algorithm is ideally suited to problems of this sort, in that it produces maximum-likelihood (ML) estimates of parameters when there is a many-to-one mapping from an underlying distribution to the distribution governing the observation, conditioned upon the obervations. The maximization step then provides a new estimate of the parameters. This research work compares the techniques; Factor Analysis (Expectation-Maximization algorithm based), Principal Component Analysis and Linear Discriminant Analysis for dimensionality reduction and investigates Local Factor Analysis (EM algorithm based) and Local Principal Component Analysis using Vector Quantization.<br>Thesis (Masters)<br>Master of Philosophy (MPhil)<br>School of Engineering<br>Full Text

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Livres sur le sujet "Linear principal componetns analysis"

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Ahmed, S. E. (Syed Ejaz), 1957- editor of compilation, ed. Perspectives on big data analysis: Methodologies and applications : International Workshop on Perspectives on High-Dimensional Data Anlaysis II, May 30-June 1, 2012, Centre de Recherches Mathématiques, University de Montréal, Montréal, Québec, Canada. American Mathematical Society, 2014.

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Hall, Peter. Principal component analysis for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.8.

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This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how principal component functions are linked to the amount of probability mass contained in a small ball around a given, fixed function, and how this property can be used to define a simple, easily estimable density surrogate. The article concludes by explaining the use of PCA for estimating log-density.

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Soilihi, Nizar. Data Analysis: New on Functional Principal Component Analysis and on Functional Linear Regression Modeling. Independently Published, 2020.

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James, Gareth. Sparseness and functional data analysis. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.11.

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This article considers two functional data analysis settings where sparsity becomes important: the first involves only measurements at a relatively sparse set of points and the second relates to variable selection in a functional case. It begins with a discussion of two data sets that fall into the ‘sparsely observed’ category, the ‘growth’ data and the ‘nephropathy’ data, both of which are used to illustrate alternative approaches for analysing sparse functional data. It then examines different classes of methods that can be applied to functional data, such as basis functions, mixed-effects models and local smoothing techniques, as well as specific methodologies for dealing with sparse functional data in the principal components, clustering, classification, and regression settings. Finally, it describes two approaches for performing regressions involving a functional predictor and a scalar response: SASDA (sequential algorithm for selecting design automatically) and FLiRTI (Functional Linear Regression That’s Interpretable).

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Ferraty, Frédéric, and Yves Romain, eds. The Oxford Handbook of Functional Data Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.001.0001.

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This handbook presents the state-of-the-art of the statistics dealing with functional data analysis. With contributions from international experts in the field, it discusses a wide range of the most important statistical topics (classification, inference, factor-based analysis, regression modeling, resampling methods, time series, random processes) while also taking into account practical, methodological, and theoretical aspects of the problems. The book is organised into three sections. Part I deals with regression modeling and covers various statistical methods for functional data such as linear/nonparametric functional regression, varying coefficient models, and linear/nonparametric functional processes (i.e. functional time series). Part II considers related benchmark methods/tools for functional data analysis, including curve registration methods for preprocessing functional data, functional principal component analysis, and resampling/bootstrap methods. Finally, Part III examines some of the fundamental mathematical aspects of the infinite-dimensional setting, with a focus on the stochastic background and operatorial statistics: vector-valued function integration, spectral and random measures linked to stationary processes, operator geometry, vector integration and stochastic integration in Banach spaces, and operatorial statistics linked to quantum statistics.

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Veech, Joseph A. Habitat Ecology and Analysis. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198829287.001.0001.

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Habitat is crucial to the survival and reproduction of individual organisms as well as persistence of populations. As such, species-habitat relationships have long been studied, particularly in the field of wildlife ecology and to a lesser extent in the more encompassing discipline of ecology. The habitat requirements of a species largely determine its spatial distribution and abundance in nature. One way to recognize and appreciate the over-riding importance of habitat is to consider that a young organism must find and settle into the appropriate type of habitat as one of the first challenges of life. This process can be cast in a probabilistic framework and used to better understand the mechanisms behind habitat preferences and selection. There are at least six distinctly different statistical approaches to conducting a habitat analysis – that is, identifying and quantifying the environmental variables that a species most strongly associates with. These are (1) comparison among group means (e.g., ANOVA), (2) multiple linear regression, (3) multiple logistic regression, (4) classification and regression trees, (5) multivariate techniques (Principal Components Analysis and Discriminant Function Analysis), and (6) occupancy modelling. Each of these is lucidly explained and demonstrated by application to a hypothetical dataset. The strengths and weaknesses of each method are discussed. Given the ongoing biodiversity crisis largely caused by habitat destruction, there is a crucial and general need to better characterize and understand the habitat requirements of many different species, particularly those that are threatened and endangered.

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Makatjane, Katleho, and Roscoe van Wyk. Identifying structural changes in the exchange rates of South Africa as a regime-switching process. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/919-8.

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Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The former approach is used to reduce the dimensionality of the data using an orthogonal linear transformation by preserving the statistical variance of the data, with the proviso that a new trait is non-linearly independent, and it identifies the number of regime switches that are to be used in the Markov-switching model. The latter is used to partition the variance in each regime by allowing an estimation of multiple break transitions. The transition breakpoints estimates derived from this machine learning approach produce results that are comparable to other methods on similar system sizes. Application of these methods shows that the machine learning approach can also be employed to identify structural changes as a regime-switching process. During times of financial crisis, the growing concern over exchange rate volatility, including its adverse effects on employment and growth, broadens the debates on exchange rate policies. Our results should help the South African monetary policy committee to anticipate when exchange rates will pick up and be prepared for the effects of periods of high exchange rates.

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Boothroyd, Andrew T. Principles of Neutron Scattering from Condensed Matter. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198862314.001.0001.

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The book contains a comprehensive account of the theory and application of neutron scattering for the study of the structure and dynamics of condensed matter. All the principal experimental techniques available at national and international neutron scattering facilities are covered. The formal theory is presented, and used to show how neutron scattering measurements give direct access to a variety of correlation and response functions which characterize the equilibrium properties of bulk matter. The determination of atomic arrangements and magnetic structures by neutron diffraction and neutron optical methods is described, including single-crystal and powder diffraction, diffuse scattering from disordered structures, total scattering, small-angle scattering, reflectometry, and imaging. The principles behind the main neutron spectroscopic techniques are explained, including continuous and time-of-flight inelastic scattering, quasielastic scattering, spin-echo spectroscopy, and Compton scattering. The scattering cross-sections for atomic vibrations in solids, diffusive motion in atomic and molecular fluids, and single-atom and cooperative magnetic excitations are calculated. A detailed account of neutron polarization analysis is given, together with examples of how polarized neutrons can be exploited to obtain information about structural and magnetic correlations which cannot be obtained by other methods. Alongside the theoretical aspects, the book also describes the essential practical information needed to perform experiments and to analyse and interpret the data. Exercises are included at the end of each chapter to consolidate and enhance understanding of the material, and a summary of relevant results from mathematics, quantum mechanics, and linear response theory, is given in the appendices.

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Frew, Anthony. Air pollution. Edited by Patrick Davey and David Sprigings. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199568741.003.0341.

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Any public debate about air pollution starts with the premise that air pollution cannot be good for you, so we should have less of it. However, it is much more difficult to determine how much is dangerous, and even more difficult to decide how much we are willing to pay for improvements in measured air pollution. Recent UK estimates suggest that fine particulate pollution causes about 6500 deaths per year, although it is not clear how many years of life are lost as a result. Some deaths may just be brought forward by a few days or weeks, while others may be truly premature. Globally, household pollution from cooking fuels may cause up to two million premature deaths per year in the developing world. The hazards of black smoke air pollution have been known since antiquity. The first descriptions of deaths caused by air pollution are those recorded after the eruption of Vesuvius in ad 79. In modern times, the infamous smogs of the early twentieth century in Belgium and London were clearly shown to trigger deaths in people with chronic bronchitis and heart disease. In mechanistic terms, black smoke and sulphur dioxide generated from industrial processes and domestic coal burning cause airway inflammation, exacerbation of chronic bronchitis, and consequent heart failure. Epidemiological analysis has confirmed that the deaths included both those who were likely to have died soon anyway and those who might well have survived for months or years if the pollution event had not occurred. Clean air legislation has dramatically reduced the levels of these traditional pollutants in the West, although these pollutants are still important in China, and smoke from solid cooking fuel continues to take a heavy toll amongst women in less developed parts of the world. New forms of air pollution have emerged, principally due to the increase in motor vehicle traffic since the 1950s. The combination of fine particulates and ground-level ozone causes ‘summer smogs’ which intensify over cities during summer periods of high barometric pressure. In Los Angeles and Mexico City, ozone concentrations commonly reach levels which are associated with adverse respiratory effects in normal and asthmatic subjects. Ozone directly affects the airways, causing reduced inspiratory capacity. This effect is more marked in patients with asthma and is clinically important, since epidemiological studies have found linear associations between ozone concentrations and admission rates for asthma and related respiratory diseases. Ozone induces an acute neutrophilic inflammatory response in both human and animal airways, together with release of chemokines (e.g. interleukin 8 and growth-related oncogene-alpha). Nitrogen oxides have less direct effect on human airways, but they increase the response to allergen challenge in patients with atopic asthma. Nitrogen oxide exposure also increases the risk of becoming ill after exposure to influenza. Alveolar macrophages are less able to inactivate influenza viruses and this leads to an increased probability of infection after experimental exposure to influenza. In the last two decades, major concerns have been raised about the effects of fine particulates. An association between fine particulate levels and cardiovascular and respiratory mortality and morbidity was first reported in 1993 and has since been confirmed in several other countries. Globally, about 90% of airborne particles are formed naturally, from sea spray, dust storms, volcanoes, and burning grass and forests. Human activity accounts for about 10% of aerosols (in terms of mass). This comes from transport, power stations, and various industrial processes. Diesel exhaust is the principal source of fine particulate pollution in Europe, while sea spray is the principal source in California, and agricultural activity is a major contributor in inland areas of the US. Dust storms are important sources in the Sahara, the Middle East, and parts of China. The mechanism of adverse health effects remains unclear but, unlike the case for ozone and nitrogen oxides, there is no safe threshold for the health effects of particulates. Since the 1990s, tax measures aimed at reducing greenhouse gas emissions have led to a rapid rise in the proportion of new cars with diesel engines. In the UK, this rose from 4% in 1990 to one-third of new cars in 2004 while, in France, over half of new vehicles have diesel engines. Diesel exhaust particles may increase the risk of sensitization to airborne allergens and cause airways inflammation both in vitro and in vivo. Extensive epidemiological work has confirmed that there is an association between increased exposure to environmental fine particulates and death from cardiovascular causes. Various mechanisms have been proposed: cardiac rhythm disturbance seems the most likely at present. It has also been proposed that high numbers of ultrafine particles may cause alveolar inflammation which then exacerbates preexisting cardiac and pulmonary disease. In support of this hypothesis, the metal content of ultrafine particles induces oxidative stress when alveolar macrophages are exposed to particles in vitro. While this is a plausible mechanism, in epidemiological studies it is difficult to separate the effects of ultrafine particles from those of other traffic-related pollutants.

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Chapitres de livres sur le sujet "Linear principal componetns analysis"

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Balzano, Simona, Maja Bozic, Laura Marcis, and Renato Salvatore. "On Predicting Principal Components Through Linear Mixed Models." In Statistical Learning and Modeling in Data Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69944-4_3.

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Visentin, Andrea, Steven Prestwich, and S. Armagan Tarim. "Robust Principal Component Analysis by Reverse Iterative Linear Programming." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46227-1_37.

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Deshmukh, Rushali A., Prachi Jadhav, Sakshi Shelar, Ujwal Nikam, Dhanshri Patil, and Rohan Jawale. "Stock Price Prediction Using Principal Component Analysis and Linear Regression." In Emerging Technologies in Data Mining and Information Security. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4052-1_28.

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Abusham, Eimad Eldin, David Ngo, and Andrew Teoh. "Fusion of Locally Linear Embedding and Principal Component Analysis for Face Recognition (FLLEPCA)." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552499_37.

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Wang, Mei. "An Improved Image Segmentation Algorithm Based on Principal Component Analysis." In Proceedings of the 9th International Symposium on Linear Drives for Industry Applications, Volume 4. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40640-9_101.

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Wang, Sai, and Huiwen Deng. "Face Recognition Using Principal Component Analysis-Fuzzy Linear Discriminant Analysis and Dynamic Fuzzy Neural Network." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27323-0_73.

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Tessitore, Gerarda, and Simona Balbi. "An Algorithm for Detecting the Number of Knots in Non Linear Principal Component Analysis." In COMPSTAT. Physica-Verlag HD, 1996. http://dx.doi.org/10.1007/978-3-642-46992-3_64.

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Dhankhar, Amita, and Kamna Solanki. "Predicting Student’s Performance Using Linear Kernel Principal Component Analysis and Recurrent Neural Network (LKPCA-RNN) Model." In Proceedings of Data Analytics and Management. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6285-0_51.

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Lohmann, G., D. Y. von Cramon, and A. C. F. Colchester. "A Construction of an Averaged Representation of Human Cortical Gyri Using Non-linear Principal Component Analysis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11566489_92.

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Fan, Kai, Conall O’Sullivan, Anthony Brabazon, and Michael O’Neill. "Non-linear Principal Component Analysis of the Implied Volatility Smile using a Quantum-inspired Evolutionary Algorithm." In Natural Computing in Computational Finance. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77477-8_6.

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Actes de conférences sur le sujet "Linear principal componetns analysis"

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Pei, Yan. "Linear Principal Component Discriminant Analysis." In 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2015. http://dx.doi.org/10.1109/smc.2015.368.

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Hiden, H. G. "Non-linear principal components analysis using genetic programming." In Second International Conference on Genetic Algorithms in Engineering Systems. IEE, 1997. http://dx.doi.org/10.1049/cp:19971197.

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Ramrath, L., M. Miinchhof, and R. Isermann. "Local linear neural networks based on principal component analysis." In 2006 American Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/acc.2006.1657185.

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Su, Hang, and Xuansheng Wang. "Principal Component Analysis in Linear Discriminant Analysis Space for Face Recognition." In 2014 5th International Conference on Digital Home (ICDH). IEEE, 2014. http://dx.doi.org/10.1109/icdh.2014.13.

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Horsthemke, William H., and Daniela S. Raicu. "Organ analysis and classification using principal component and linear discriminant analysis." In Medical Imaging, edited by Josien P. W. Pluim and Joseph M. Reinhardt. SPIE, 2007. http://dx.doi.org/10.1117/12.708032.

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Wang, Huiyuan, Zengfeng Wang, Yan Leng, and Xiaojuan Wu. "Face Recognition Combing Principal Component Analysis and Fractional-step Linear Discriminant analysis." In 2006 8th international Conference on Signal Processing. IEEE, 2006. http://dx.doi.org/10.1109/icosp.2006.345816.

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Vigon, L. "Adaptive non-linear principal component analysis of a saccade related EEG component." In DERA/IEE Workshop Intelligent Sensor Processing. IEE, 2001. http://dx.doi.org/10.1049/ic:20010114.

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Santos, A. D. F., M. F. M. Silva, C. S. Sales, J. C. W. A. Costa, and E. Figueiredo. "Applicability of linear and nonlinear principal component analysis for damage detection." In 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2015. http://dx.doi.org/10.1109/i2mtc.2015.7151383.

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Gencturk, B., V. V. Nabiyev, and A. Ustubioglu. "ACromegaly Pre-Diagnosis Based On Principal Component And Linear Discriminant Analysis." In 2013 21st Signal Processing and Communications Applications Conference (SIU). IEEE, 2013. http://dx.doi.org/10.1109/siu.2013.6531306.

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Su, Yuanhang, Yuzhong Huang, and C. C. Jay Kuo. "Efficient Text Classification Using Tree-structured Multi-linear Principal Component Analysis." In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8545832.

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Rapports d'organisations sur le sujet "Linear principal componetns analysis"

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Warrick, Arthur W., Gideon Oron, Mary M. Poulton, Rony Wallach, and Alex Furman. Multi-Dimensional Infiltration and Distribution of Water of Different Qualities and Solutes Related Through Artificial Neural Networks. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7695865.bard.

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The project exploits the use of Artificial Neural Networks (ANN) to describe infiltration, water, and solute distribution in the soil during irrigation. It provides a method of simulating water and solute movement in the subsurface which, in principle, is different and has some advantages over the more common approach of numerical modeling of flow and transport equations. The five objectives were (i) Numerically develop a database for the prediction of water and solute distribution for irrigation; (ii) Develop predictive models using ANN; (iii) Develop an experimental (laboratory) database of water distribution with time; within a transparent flow cell by high resolution CCD video camera; (iv) Conduct field studies to provide basic data for developing and testing the ANN; and (v) Investigate the inclusion of water quality [salinity and organic matter (OM)] in an ANN model used for predicting infiltration and subsurface water distribution. A major accomplishment was the successful use of Moment Analysis (MA) to characterize “plumes of water” applied by various types of irrigation (including drip and gravity sources). The general idea is to describe the subsurface water patterns statistically in terms of only a few (often 3) parameters which can then be predicted by the ANN. It was shown that ellipses (in two dimensions) or ellipsoids (in three dimensions) can be depicted about the center of the plume. Any fraction of water added can be related to a ‘‘probability’’ curve relating the size of the ellipse (or ellipsoid) that contains that amount of water. The initial test of an ANN to predict the moments (and hence the water plume) was with numerically generated data for infiltration from surface and subsurface drip line and point sources in three contrasting soils. The underlying dataset consisted of 1,684,500 vectors (5 soils×5 discharge rates×3 initial conditions×1,123 nodes×20 print times) where each vector had eleven elements consisting of initial water content, hydraulic properties of the soil, flow rate, time and space coordinates. The output is an estimate of subsurface water distribution for essentially any soil property, initial condition or flow rate from a drip source. Following the formal development of the ANN, we have prepared a “user-friendly” version in a spreadsheet environment (in “Excel”). The input data are selected from appropriate values and the output is instantaneous resulting in a picture of the resulting water plume. The MA has also proven valuable, on its own merit, in the description of the flow in soil under laboratory conditions for both wettable and repellant soils. This includes non-Darcian flow examples and redistribution and well as infiltration. Field experiments were conducted in different agricultural fields and various water qualities in Israel. The obtained results will be the basis for the further ANN models development. Regions of high repellence were identified primarily under the canopy of various orchard crops, including citrus and persimmons. Also, increasing OM in the applied water lead to greater repellency. Major scientific implications are that the ANN offers an alternative to conventional flow and transport modeling and that MA is a powerful technique for describing the subsurface water distributions for normal (wettable) and repellant soil. Implications of the field measurements point to the special role of OM in affecting wettability, both from the irrigation water and from soil accumulation below canopies. Implications for agriculture are that a modified approach for drip system design should be adopted for open area crops and orchards, and taking into account the OM components both in the soil and in the applied waters.

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