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Relevant principal component analysis applied to the characterisation of Portuguese heather honey

Martins, Rui C.; Lopes, Victor V.; Valentão, Patrícia; Carvalho, João C. M. F.; Isabel, Paulo; Amaral, Maria T.; Batista, Maria T.; Andrade, Paula B.; Silva, Branca M.
Fonte: Taylor & Francis Publicador: Taylor & Francis
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
95.8%
The main purpose of this study was the characterisation of ‘Serra da Lousã’ heather honey by using novel statistical methodology, relevant principal component analysis, in order to assess the correlations between production year, locality and composition. Herein, we also report its chemical composition in terms of sugars, glycerol and ethanol, and physicochemical parameters. Sugars profiles from ‘Serra da Lousã’ heather and ‘Terra Quente de Tra´ s-os-Montes’ lavender honeys were compared and allowed the discrimination: ‘Serra da Lousã’ honeys do not contain sucrose, generally exhibit lower contents of turanose, trehalose and maltose and higher contents of fructose and glucose. Different localities from ‘Serra da Lousã ’ provided groups of samples with high and low glycerol contents. Glycerol and ethanol contents were revealed to be independent of the sugars profiles. These data and statistical models can be very useful in the comparison and detection of adulterations during the quality control analysis of ‘Serra da Lousã’ honey.; http://dx.doi.org/10.1080/14786410701825004

Utilização de análise de componentes principais em séries temporais; Use of principal component analysis in time series

Teixeira, Sérgio Coichev
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 12/04/2013 PT
Relevância na Pesquisa
95.97%
Um dos principais objetivos da análise de componentes principais consiste em reduzir o número de variáveis observadas em um conjunto de variáveis não correlacionadas, fornecendo ao pesquisador subsídios para entender a variabilidade e a estrutura de correlação dos dados observados com uma menor quantidade de variáveis não correlacionadas chamadas de componentes principais. A técnica é muito simples e amplamente utilizada em diversos estudos de diferentes áreas. Para construção, medimos a relação linear entre as variáveis observadas pela matriz de covariância ou pela matriz de correlação. Entretanto, as matrizes de covariância e de correlação podem deixar de capturar importante informações para dados correlacionados sequencialmente no tempo, autocorrelacionados, desperdiçando parte importante dos dados para interpretação das componentes. Neste trabalho, estudamos a técnica de análise de componentes principais que torna possível a interpretação ou análise da estrutura de autocorrelação dos dados observados. Para isso, exploramos a técnica de análise de componentes principais para o domínio da frequência que fornece para dados autocorrelacionados um resultado mais específico e detalhado do que a técnica de componentes principais clássica. Pelos métodos SSA (Singular Spectrum Analysis) e MSSA (Multichannel Singular Spectrum Analysis)...

Focused Principal Component Analysis : a graphical method for exploring dietary patterns; Análise de Componente Principal Focada : um método gráfico para explorar padrões alimentares

Canuto, Raquel; Camey, Suzi Alves; Gigante, Denise Petrucci; Menezes, Ana Maria Baptista; Olinto, Maria Teresa Anselmo
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Artigo de Revista Científica Formato: application/pdf
ENG
Relevância na Pesquisa
95.82%
O presente estudo teve objetivo de apresentar a Análise de Componentes Principais Focada (ACPF) como um método exploratório para investigar padrões alimentares a partir de características da amostra. Para exemplificar utilizou-se as variáveis idade, renda e escolaridade de um banco de dados de 1.968 adultos. O consumo alimentar foi obtido através questionário de frequência alimentar (QFA) com 26 itens alimentares. As análises foram realizadas no programa R. Os gráficos gerados evidenciaram iniquidades socioeconômicas na conformação dos padrões alimentares. Alimentos integrais, frutas e verduras foram diretamente correlacionados com renda e escolaridade, e cereais refinados, gordura animal e pão branco tiveram associação inversa. A idade mostrou-se como associada inversamente a alimentos fast-food e industrializados e, diretamente, a um padrão “saudável” que inclui frutas, salada verde e outros vegetais. De maneira fácil e direta, a ACPF permitiu a visualização de correlações entre alimentos a partir de variáveis escolhidas como foco.; The aim of the present study was to introduce Focused Principal Component Analysis (FPCA) as a novel exploratory method for providing insight into dietary patterns that emerge based on a given characteristic of the sample. To demonstrate the use of FPCA...

Genetic parameter estimates and principal component analysis of breeding values of reproduction and growth traits in female Canchim cattle

Buzanskas, M. E.; Savegnago, R. P.; Grossi, D. A.; Venturini, G. C.; Queiroz, S. A.; Silva, L. O C; Júnior, R. A. A. Torres; Munari, D. P.; Alencar, M. M.
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Artigo de Revista Científica Formato: 775-781
ENG
Relevância na Pesquisa
95.88%
Phenotypic data from female Canchim beef cattle were used to obtain estimates of genetic parameters for reproduction and growth traits using a linear animal mixed model. In addition, relationships among animal estimated breeding values (EBVs) for these traits were explored using principal component analysis. The traits studied in female Canchim cattle were age at first calving (AFC), age at second calving (ASC), calving interval (CI), and bodyweight at 420 days of age (BW420). The heritability estimates for AFC, ASC, CI and BW420 were 0.03±0.01, 0.07±0.01, 0.06±0.02, and 0.24±0.02, respectively. The genetic correlations for AFC with ASC, AFC with CI, AFC with BW420, ASC with CI, ASC with BW420, and CI with BW420 were 0.87±0.07, 0.23±0.02, -0.15±0.01, 0.67±0.13, -0.07±0.13, and 0.02±0.14, respectively. Standardised EBVs for AFC, ASC and CI exhibited a high association with the first principal component, whereas the standardised EBV for BW420 was closely associated with the second principal component. The heritability estimates for AFC, ASC and CI suggest that these traits would respond slowly to selection. However, selection response could be enhanced by constructing selection indices based on the principal components. © CSIRO 2013.

Predicting effects of toxic events to anaerobic granular sludge with quantitative image analysis and principal component analysis

Costa, J. C.; Alves, M. M.; Ferreira, E. C.
Fonte: Universidade do Minho Publicador: Universidade do Minho
Tipo: Conferência ou Objeto de Conferência
Publicado em 24/06/2008 ENG
Relevância na Pesquisa
95.83%
Detergents and solvents are included in the list of compounds that can be inhibitory or toxic to anaerobic digestion processes. Industrial cleaning stages/processes produce vast amounts of contaminated wastewater. In order to optimize the control of these wastewaters it is important to know and predict the effects on the activity and physical properties of anaerobic aggregates in an early stage. Datasets gathering morphological, physiological and reactor performance information were created from three toxic shock loads (SL1 – 1.6 mgdetergent/L; SL2 – 3.1 mgdetergent/L; SL3 – 40 mgsolvent/L). The use of Principal Component Analysis (PCA) allowed the visualization of the main effects caused by the toxics, by clustering the samples according to its operational phase, exposure or recovery. The morphological parameters showed to be sensitive enough to detect the operational problems even before the COD removal efficiency decreased. Its high loadings in the plane defined by the first and second principal components, which gathers the higher variability in datasets, express the usefulness of monitor the biomass morphology in order to achieve a suitable control of the process. PCA defined a new latent variable t[1], gathering the most relevant variability in dataset...

Principal component analysis and quantitative image analysis to predict effects of toxics in anaerobic granular sludge

Costa, J. C.; Alves, M. M.; Ferreira, E. C.
Fonte: Elsevier Ltd. Publicador: Elsevier Ltd.
Tipo: Artigo de Revista Científica
Publicado em /02/2009 ENG
Relevância na Pesquisa
95.83%
Principal component analysis (PCA) was applied to datasets gathering morphological, physiological and reactor performance information, from three toxic shock loads (SL1 – 1.6 mgdetergent/L; SL2 – 3.1 mgdetergent/L; SL3 – 40 mgsolvent/L) applied in an expanded granular sludge bed (EGSB) reactor. The PCA allowed the visualization of the main effects caused by the toxics, by clustering the samples according to its operational phase, exposure or recovery. The aim was to investigate the variables or group of variables that mostly contribute for the early detection of operational problems. The morphological parameters showed to be sensitive enough to detect the operational problems even before the COD removal efficiency decreased. As observed by the high loadings in the plane defined by the first and second principal components. PCA defined a new latent variable t[1], gathering the most relevant variability in dataset, that showed an immediate variation after the toxics were fed to the reactors. t[1] varied 262%, 254% and 80%, respectively, in SL1, SL2 and SL3. The high loadings/weights of the morphological parameters associated with this new variable express its influence in shock load monitoring and control, and consequently in operational problems recognition.; Fundação para a Ciência e a Tecnologia (FCT) -Bolsa SFRH/BD/13317/2003...

Assessment of walker-assisted gait based on Principal Component Analysis and wireless inertial sensors

Martins,Maria; Elias,Arlindo; Cifuentes,Carlos; Alfonso,Manuel; Frizera,Anselmo; Santos,Cristina; Ceres,Ramón
Fonte: SBEB - Sociedade Brasileira de Engenharia Biomédica Publicador: SBEB - Sociedade Brasileira de Engenharia Biomédica
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/09/2014 EN
Relevância na Pesquisa
95.85%
INTRODUCTION:This study investigates a gait research protocol to assess the impact of a walker model with forearm supports on the kinematic parameters of the lower limb during locomotion. METHODS: Thirteen healthy participants without any history of gait dysfunction were enrolled in the experimental procedure. Spatiotemporal and kinematic gait parameters were calculated by using wireless inertial sensors and analyzed with Principal Component Analysis (PCA). The PCA method was selected to achieve dimension reduction and evaluate the main effects in gait performance during walker-assisted gait. Additionally, the interaction among the variables included in each Principal Component (PCs) derived from PCA is exposed to expand the understanding of the main differences between walker-assisted and unassisted gait conditions. RESULTS:The results of the statistical analysis identified four PCs that retained 65% of the data variability. These components were associated with spatiotemporal information, knee joint, hip joint and ankle joint motion, respectively. CONCLUSION: Assisted gait by a walker model with forearm supports was characterized by slower gait, shorter steps, larger double support phase and lower body vertical acceleration when compared with normal...

Optimization of end milling parameters under minimum quantity lubrication using principal component analysis and grey relational analysis

Murthy,K. Sundara; Rajendran,I.
Fonte: Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM Publicador: Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/09/2012 EN
Relevância na Pesquisa
95.82%
Machining is the major reliable practice in accomplishment of metal cutting industries. The accelerated growing competition demands top superior and large quantity with low cost products. Metal working fluids have significant fragment of manufacturing cost and causes ecological impacts and health problems. This work attempts to advance a competent machining alignment with no ecological impacts. The prediction of quality characteristics and enhancement of machining field are consistently accepting great interest in machining sectors to compress the accomplishment costs. In this paper, GA based ANN prediction model proposes to envisage the quality characteristics of surface roughness and tool wear. The comparison of predicted and experimental values acknowledges the precision of the model. The end milling experiments are conducted beneath minimum quantity lubrication. This paper as well deals with the multiple objective optimization with principal component analysis, grey relational analysis and Taguchi method. ANOVA was carried out to determine each parameter contribution percentage on quality characteristics. The results show that cutting speed is the most influencing parameter followed by feed velocity, lubricant flow rate and depth of cut. The confirmation tests acknowledge that the proposed multiple-objective methodology is able in determining optimum machining parameters for minimum surface roughness and tool wear.

Principal Component Analysis applied to digital image compression

Santo,Rafael do Espírito
Fonte: Instituto Israelita de Ensino e Pesquisa Albert Einstein Publicador: Instituto Israelita de Ensino e Pesquisa Albert Einstein
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/06/2012 EN
Relevância na Pesquisa
95.92%
OBJECTIVE: To describe the use of a statistical tool (Principal Component Analysis – PCA) for the recognition of patterns and compression, applying these concepts to digital images used in Medicine. METHODS: The description of Principal Component Analysis is made by means of the explanation of eigenvalues and eigenvectors of a matrix. This concept is presented on a digital image collected in the clinical routine of a hospital, based on the functional aspects of a matrix. The analysis of potential for recovery of the original image was made in terms of the rate of compression obtained. RESULTS: The compressed medical images maintain the principal characteristics until approximately one-fourth of their original size, highlighting the use of Principal Component Analysis as a tool for image compression. Secondarily, the parameter obtained may reflect the complexity and potentially, the texture of the original image. CONCLUSION: The quantity of principal components used in the compression influences the recovery of the original image from the final (compacted) image.

Interpreting variability in global SST data using independent component analysis and principal component analysis

Westra, S.; Brown, C.; Lall, U.; Koch, I.; Sharma, A.
Fonte: John Wiley & Sons Ltd Publicador: John Wiley & Sons Ltd
Tipo: Artigo de Revista Científica
Publicado em //2010 EN
Relevância na Pesquisa
95.89%
Component extraction techniques are used widely in the analysis and interpretation of high-dimensional climate datasets such as global sea surface temperatures (SSTs). Principal component analysis (PCA), a frequently used component extraction technique, provides an orthogonal representation of the multivariate dataset and maximizes the variance explained by successive components. A disadvantage of PCA, however, is that the interpretability of the second and higher components may be limited. For this reason, a Varimax rotation is often applied to the PCA solution to enhance the interpretability of the components by maximizing a simple structure. An alternative rotational approach is known as independent component analysis (ICA), which finds a set of underlying ‘source signals’ which drive the multivariate ‘mixed’ dataset. Here we compare the capacity of PCA, the Varimax rotation and ICA in explaining climate variability present in globally distributed SST anomaly (SSTA) data. We find that phenomena which are global in extent, such as the global warming trend and the El Niño-Southern Oscillation (ENSO), are well represented using PCA. In contrast, the Varimax rotation provides distinct advantages in interpreting more localized phenomena such as variability in the tropical Atlantic. Finally...

Modeling multivariable hydrological series: principal component analysis or independent component analysis?

Westra, S.; Brown, C.; Lall, U.; Sharma, A.
Fonte: Amer Geophysical Union Publicador: Amer Geophysical Union
Tipo: Artigo de Revista Científica
Publicado em //2007 EN
Relevância na Pesquisa
95.87%
The generation of synthetic multivariate rainfall and/or streamflow time series that accurately simulate both the spatial and temporal dependence of the original multivariate series remains a challenging problem in hydrology and frequently requires either the estimation of a large number of model parameters or significant simplifying assumptions on the model structure. As an alternative, we propose a relatively parsimonious two-step approach to generating synthetic multivariate time series at monthly or longer timescales, by first transforming the data to a set of statistically independent univariate time series and then applying a univariate time series model to the transformed data. The transformation is achieved through a technique known as independent component analysis (ICA), which uses an approximation of mutual information to maximize the independence between the transformed series. We compare this with principal component analysis (PCA), which merely removes the covariance (or spatial correlation) of the multivariate time series, without necessarily ensuring complete independence. Both methods are tested using a monthly multivariate data set of reservoir inflows from Colombia. We show that the discrepancy between the synthetically generated data and the original data...

Snapshots of complexity: using motion capture and principal component analysis to reconceptualize dance

Vincs, K.; Barbour, K.
Fonte: Taylor & Francis (Routledge) Publicador: Taylor & Francis (Routledge)
Tipo: Artigo de Revista Científica
Publicado em //2014 EN
Relevância na Pesquisa
95.82%
This article brings together the disparate worlds of dance practice, motion capture and statistical analysis. Digital technologies such as motion capture offer dance artists new processes for recording and studying dance movement. Statistical analysis of these data can reveal hidden patterns in movement in ways that are semantically ‘blind’, and are hence able to challenge accepted culturo-physical ‘grammars’ of dance creation. The potential benefit to dance artists is to open up new ways of understanding choreographic movement. However, quantitative analysis does not allow for the uncertainty inherent in emergent, artistic practices such as dance. This article uses motion capture and principal component analysis (PCA), a common statistical technique in human movement recognition studies, to examine contemporary dance movement, and explores how this analysis might be interpreted in an artistic context to generate a new way of looking at the nature and role of movement patterning in dance creation.; Kim Vincs and Kim Barbour

Utilização da análise dos componentes principais na caracterização dos sedimentos de superfície de fundo da Baía da Ilha Grande (RJ); Use of the principal component analysis in the study of the superficial sediments of Ilha Grande Bay (Rio de Janeiro State)

Mahiques, Michel Michaelovitch de; Furtado, Valdenir Veronese
Fonte: Universidade de São Paulo. Instituto Oceanográfico Publicador: Universidade de São Paulo. Instituto Oceanográfico
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; ; Formato: application/pdf
Publicado em 01/01/1989 POR
Relevância na Pesquisa
95.82%
A Análise dos Componentes Principais foi aplicada sobre três conjuntos de dados, gerados a partir dos resultados de análise granulométrica de 153 amostras de sedimentos superficiais da Baía da Ilha Grande, Estado do Rio de Janeiro. Os resultados obtidos demonstram as vantagens da utilização do método na interpretação dos diferentes fácies texturais dos sedimentos da área, em comparação com os métodos de classificação de Shepard (1954) e Folk & Ward (1957). Os resultados demonstram também ser o conjunto de dados de freqüências de classes granulométricas o melhor parâmetro a ser utilizado na análise.; The Principal Component Analysis was applied to three sets of data, generated from the results of grain size analysis of 153 surface sediment samples of Ilha Grande Bay, Rio de Janeiro. State. The results show the advantages of the method in the interpretation of the different textural facies of the sediments of the area, in comparison to the classification methods of Shepard (1954) and Folk & Ward (1957). The results show also that the grain size frequencies are the best set of data for the application of the analysis.

Perceptual audio classification using principal component analysis

Burka, Zak
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
Relevância na Pesquisa
95.82%
The development of robust algorithms for the recognition and classification of sensory data is one of the central topics in the area of intelligent systems and computational vision research. In order to build better intelligent systems capable of processing environmental data accurately, current research is focusing on algorithms which try to model the types of processing that occur naturally in the human brain. In the domain of computer vision, these approaches to classification are being applied to areas such as facial recognition, object detection, motion tracking, and others. This project investigates the extension of these types of perceptual classification techniques to the realm of acoustic data. As part of this effort, an algorithm for audio fingerprinting using principal component analysis for feature extraction and classification was developed and tested. The results of these experiments demonstrate the feasibility of such a system, and suggestions for future implementation enhancements are examined and proposed.

Use of principal component analysis and the $GE$-biplot for the graphical exploration of gene expression data.

Pittelkow, Yvonne; Wilson, Susan
Fonte: International Biometrics Society Publicador: International Biometrics Society
Tipo: Artigo de Revista Científica
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95.9%
This note is in response to Wouters at al. (2003. Biometrics 59, 1131-1130) who compared three methods for exploring gene expression data. Contrary to their summary that principal component analysis is not very informative, we show that it is possible to

Uso de Imagens Digitais e Análise de Componentes Principais na Identificação dos Níveis de Cr (VI) em Amostras de Solos; Use of Digital Images and Principal Component Analysis for the Identification of Cr (VI) Levels in Soil Samples

Luciana F. Oliveira; Natália T. Canevari; Amanda Jesus; Edenir R. Pereira Filho; UFSCar
Fonte: Revista Virtual de Química Publicador: Revista Virtual de Química
Tipo: ; Formato: binary/octet-stream
Publicado em 06/05/2013 PT
Relevância na Pesquisa
95.86%
A proposição de métodos simples e rápidos para a identificação dos níveis de Cr (VI) em amostras de solos é desejável para nortear estratégias de remediação. O presente trabalho teve como objetivo desenvolver um procedimento para a identificação de amostras de solos com concentrações de Cr (VI) superiores aos valores estabelecidos pelas legislações internacionais. Uma amostra de solo foi fortificada com concentrações de Cr (VI) que variaram de 0 a 20 mg kg-1 (total de 61 fortificações) e posteriormente submetidas a extração alcalina. Os extratos foram colocados em placas de Petri, aos quais se adicionou difenilcarbazida 0,2 % (m v-1) como reagente colorimétrico e H2SO4 (5 mol L-1) para o ajuste do pH. Após o desenvolvimento da coloração, as placas foram posicionadas em um scanner comercial e obtidas imagens da parte inferior. As imagens foram tratadas com programas computacionais para cálculo dos seguintes descritores de cores (R, G, B, H, S, V, r, g, b e L) e, efetuou-se uma análise por ACP (Análise de componentes principais - Principal Component Analysis). Houve uma boa separação entre os valores acima e abaixo da legislação italiana, a qual define um valor máximo de 2,0 mg kg-1 para Cr (VI). Também foram utilizados os valores de Cr (VI) das legislações do Canadá e da Suécia e...

Estimation of the underlying structure of systematic risk with the use of principal component analysis and factor analysis

Ladrón de Guevara Cortés,Rogelio; Torra Porras,Salvador
Fonte: Facultad de Contaduría y Administración, UNAM Publicador: Facultad de Contaduría y Administración, UNAM
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/09/2014 EN
Relevância na Pesquisa
95.87%
We present an improved methodology to estimate the underlying structure of systematic risk in the Mexican Stock Exchange with the use of Principal Component Analysis and Factor Analysis. We consider the estimation of risk factors in an Arbitrage Pricing Theory (APT) framework under a statistical approach, where the systematic risk factors are extracted directly from the observed returns on equities, and there are two differentiated stages, namely, the risk extraction and the risk attribution processes. Our empirical study focuses only on the former; it includes the testing of our models in two versions: returns and returns in excess of the riskless interest rate for weekly and daily databases, and a two-stage methodology for the econometric contrast. First, we extract the underlying systematic risk factors by way of both, the standard linear version of the Principal Component Analysis and the Maximum Likelihood Factor Analysis estimation. Then, we estimate simultaneously, for all the system of equations, the sensitivities to the systematic risk factors (betas) by weighted least squares. Finally, we test the pricing model with the use of an average cross-section methodology via ordinary least squares, corrected by heteroskedasticity and autocorrelation consistent covariances estimation. Our results show that although APT is very sensitive to the extraction technique utilized and to the number of components or factors retained...

Principal Component Analysis to study spatial variability of errors in the INSAT derived quantitative precipitation estimates over Indian monsoon region

ROY BHOWMIK,S. K.; SEN ROY,S.
Fonte: Centro de Ciencias de la Atmósfera, UNAM Publicador: Centro de Ciencias de la Atmósfera, UNAM
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/10/2006 EN
Relevância na Pesquisa
95.92%
In this paper, Principal Component Analysis has been applied to investigate the spatial variability of errors in the INSAT derived quantitative precipitation estimates (QPE) over the Indian monsoon region, using daily rainfall analysis (at the same resolution) for the period from 1 June to 30 August of summer monsoon 2001. The study shows that the QPE errors have certain spatial variability. The orographic rainfall is significantly underestimated along the Western Ghats and along the foothills of the Himalayas, where the root mean square errors are also very large. Otherwise, the performance of the QPE is reasonably good over the rest of the region. The first principal component, which explains about 5.1% of the variance, corresponds to the onset phase of the monsoon during June, when strong positive loadings dominate over the southern parts of the country. The second principal component explaining about 4.2% of the variance, has strong positive loading in the intermittent presence of the monsoon low pressure system over the east central parts of the country. The third principal component which explains 3.3% of the variance is associated with the monsoon trough at the normal position, and the fourth principal component which explains 3.1% of the variance is associated with the monsoon trough at the southern position.

Fault Detection in a Heat Exchanger, Comparative Analysis between Dynamic Principal Component Analysis and Diagnostic Observers

Tudón Martínez,Juan C.; Morales Menéndez,Rubén; Ramírez Mendoza,Ricardo A.; Garza Castañón,Luis E.; Vargas Martínez,Adriana
Fonte: Centro de Investigación en computación, IPN Publicador: Centro de Investigación en computación, IPN
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/03/2011 EN
Relevância na Pesquisa
95.83%
A comparison between the Dynamic Principal Component Analysis (DPCA) method and a set of Diagnostic Observers (DO) under the same experimental data from a shell and tube industrial heat exchanger is presented. The comparative analysis shows the detection properties of both methods when sensors and/or actuators fail online, including scenarios with multiple faults. Similar metrics are defined for both methods: robustness, quick detection, isolability capacity, explanation facility, false alarm rates and multiple faults identifiability. Experimental results show the principal advantages and disadvantages of both methods. DO showed quicker detection for sensor and actuator faults with lower false alarm rate. Also, DO can isolate multiple faults. DPCA required a minor training effort; however, it can not identify two or more sequential faults.

Quantification of not-dipolar components of atrial depolarization by principal component analysis of the P-wave

Censi,Federica; Calcagnini,Giovanni; Bartolini,Pietro; Ricci,Renato Pietro; Santini,Massimo
Fonte: Istituto Superiore di Sanità Publicador: Istituto Superiore di Sanità
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/06/2012 EN
Relevância na Pesquisa
95.84%
BACKGROUND: Principal component analysis (PCA) of the T-wave has been demonstrated to quantify the dipolar and not-dipolar components of the ventricular activation, the latter reflecting repolarization heterogeneity. Accordingly, the PCA of the P-wave could help in analyzing the heterogeneous propagation of sinus impulses in the atria, which seems to predispose to fibrillation. AIM: The aim of this study is to perform the PCA of the P-wave in patients prone to atrial fibrillation (AF). METHODS: PCA is performed on P-waves extracted by averaging technique from ECG recordings acquired using a 32-lead mapping system (2048 Hz, 24 bit, 0-400 Hz bandwidth). We extracted PCA parameters related to the dipolar and not dipolar components of the P-wave using the first 3 eigenvalues and the cumulative percent of variance explained by the first 3 PCs (explained variance EV). RESULTS AND CONCLUSIONS: We found that the EV associated to the low risk patients is higher than that associated to the high risk patients, and that, correspondingly, the first eigenvalue is significantly lower while the second one is significantly higher in the high risk patients respect to the low risk group. Factor loadings showed that on average all leads contribute to the first principal component.