A monitoração de variáveis e o diagnóstico de falhas é um aspecto importante a se considerar seja em plantas nucleares ou indústrias de processos, pois um diagnóstico precoce de falha permite a correção do problema proporcionando a não interrupção da produção e a segurança do operador e, assim, não causando perdas econômicas. O objetivo deste trabalho é, dentro do universo de todas as variáveis monitoradas de um processo, construir um conjunto de variáveis, não necessariamente mínimo, que será a entrada de uma rede neural e, com isso, conseguir monitorar, o maior número possível de variáveis. Esta metodologia foi aplicada ao reator de pesquisas IEA-R1 do IPEN. Para isso, as variáveis Potência do reator, Vazão do primário, Posição de barras de controle/segurança e Diferença de pressão no núcleo do reator D P, foram agrupadas, pois por hipótese quase todas as variáveis monitoradas em um reator nuclear tem relação com alguma dessas ou pode ser resultado da interação de duas ou mais. Por exemplo, a Potência está relacionada ao aumento e diminuição de algumas temperaturas bem como à quantidade de radiação devido à fissão do urânio; as Barras são reguladoras de potência e, por conseqüência podem influenciar na quantidade de radiação e/ou temperaturas; a Vazão do Circuito Primário, responsável pelo transporte de energia e pela conseqüente retirada de calor do núcleo. Assim, tomando o grupo de variáveis mencionadas, calculamos a correlação existente entre este conjunto B e todas as outras variáveis monitoradas (coeficiente de correlação múltipla), isto é, através do cálculo da correlação múltipla, que é uma ferramenta proposta pela teoria das Correlações Canônicas, foi possível calcular o quanto o conjunto B pode predizer cada uma das variáveis monitoradas. Uma vez que não seja possível uma boa qualidade de predição com o conjunto B, é acrescentada uma ou mais variáveis que possuam alta correlação com a variável melhorando a qualidade de predição. Finalmente, uma rede pode ser treinada com o novo conjunto e os resultados quanto a monitoração foram bastante satisfatórios quanto às 64 variáveis monitoradas pelo sistema de aquisição de dados do reator IEA-R1 através de sensores e atuadores , pois com um conjunto de 9 variáveis foi possível monitorar 51 variáveis. / The monitoring of variables and diagnosis of sensor fault in nuclear power plants or processes industries is very important because an early diagnosis allows the correction of the fault and, like this, do not cause the production interruption, improving operators security and its not provoking economics losses. The objective of this work is, in the whole of all variables monitor of a nuclear power plant, to build a set, not necessary minimum, which will be the set of input variables of an artificial neural network and, like way, to monitor the biggest number of variables. This methodology was applied to the IEA-R1 Research Reactor at IPEN. For this, the variables Power, Rate of flow of primary circuit, Rod of control/security and Difference in pressure in the core of the reactor ( D P) was grouped, because, for hypothesis, almost whole of monitoring variables have relation with the variables early described or its effect can be result of the interaction of two or more. The Power is related to the increasing and decreasing of temperatures as well as the amount radiation due fission of the uranium; the Rods are controls of power and influence in the amount of radiation and increasing and decreasing of temperatures and the Rate of flow of primary circuit has function of the transport of energy by removing of heat of the nucleus Like this, labeling B= {Power, Rate of flow of Primary Circuit, Rod of Control/Security and D P} was computed the correlation between B and all another variables monitoring (coefficient of multiple correlation), that is, by the computer of the multiple correlation, that is tool of Theory of Canonical Correlations, was possible to computer how much the set B can predict each variable. Due the impossibility of a satisfactory approximation by B in the prediction of some variables, it was included one or more variables that have high correlation with this variable to improve the quality of prediction. In this work an artificial neural network was trained and the results were satisfactory since the IEA-R1 Data Acquisition System reactor monitors 64 variables and, with a set of 9 input variables resulting from the correlation analysis, it was possible to monitor 51 variables using neural networks.

A monitoração de variáveis e o diagnóstico de falhas é um aspecto importante a se considerar seja em plantas nucleares ou indústrias de processos, pois um diagnóstico precoce de falha permite a correção do problema proporcionando a não interrupção da produção e a segurança do operador e, assim, não causando perdas econômicas. O objetivo deste trabalho é, dentro do universo de todas as variáveis monitoradas de um processo, construir um conjunto de variáveis, não necessariamente mínimo, que será a entrada de uma rede neural e, com isso, conseguir monitorar, o maior número possível de variáveis. Esta metodologia foi aplicada ao reator de pesquisas IEA-R1 do IPEN. Para isso, as variáveis Potência do reator, Vazão do primário, Posição de barras de controle/segurança e Diferença de pressão no núcleo do reator D P, foram agrupadas, pois por hipótese quase todas as variáveis monitoradas em um reator nuclear tem relação com alguma dessas ou pode ser resultado da interação de duas ou mais. Por exemplo, a Potência está relacionada ao aumento e diminuição de algumas temperaturas bem como à quantidade de radiação devido à fissão do urânio; as Barras são reguladoras de potência e, por conseqüência podem influenciar na quantidade de radiação e/ou temperaturas; a Vazão do Circuito Primário, responsável pelo transporte de energia e pela conseqüente retirada de calor do núcleo. Assim, tomando o grupo de variáveis mencionadas, calculamos a correlação existente entre este conjunto B e todas as outras variáveis monitoradas (coeficiente de correlação múltipla), isto é, através do cálculo da correlação múltipla, que é uma ferramenta proposta pela teoria das Correlações Canônicas, foi possível calcular o quanto o conjunto B pode predizer cada uma das variáveis monitoradas. Uma vez que não seja possível uma boa qualidade de predição com o conjunto B, é acrescentada uma ou mais variáveis que possuam alta correlação com a variável melhorando a qualidade de predição. Finalmente, uma rede pode ser treinada com o novo conjunto e os resultados quanto a monitoração foram bastante satisfatórios quanto às 64 variáveis monitoradas pelo sistema de aquisição de dados do reator IEA-R1 através de sensores e atuadores , pois com um conjunto de 9 variáveis foi possível monitorar 51 variáveis. / The monitoring of variables and diagnosis of sensor fault in nuclear power plants or processes industries is very important because an early diagnosis allows the correction of the fault and, like this, do not cause the production interruption, improving operators security and its not provoking economics losses. The objective of this work is, in the whole of all variables monitor of a nuclear power plant, to build a set, not necessary minimum, which will be the set of input variables of an artificial neural network and, like way, to monitor the biggest number of variables. This methodology was applied to the IEA-R1 Research Reactor at IPEN. For this, the variables Power, Rate of flow of primary circuit, Rod of control/security and Difference in pressure in the core of the reactor ( D P) was grouped, because, for hypothesis, almost whole of monitoring variables have relation with the variables early described or its effect can be result of the interaction of two or more. The Power is related to the increasing and decreasing of temperatures as well as the amount radiation due fission of the uranium; the Rods are controls of power and influence in the amount of radiation and increasing and decreasing of temperatures and the Rate of flow of primary circuit has function of the transport of energy by removing of heat of the nucleus Like this, labeling B= {Power, Rate of flow of Primary Circuit, Rod of Control/Security and D P} was computed the correlation between B and all another variables monitoring (coefficient of multiple correlation), that is, by the computer of the multiple correlation, that is tool of Theory of Canonical Correlations, was possible to computer how much the set B can predict each variable. Due the impossibility of a satisfactory approximation by B in the prediction of some variables, it was included one or more variables that have high correlation with this variable to improve the quality of prediction. In this work an artificial neural network was trained and the results were satisfactory since the IEA-R1 Data Acquisition System reactor monitors 64 variables and, with a set of 9 input variables resulting from the correlation analysis, it was possible to monitor 51 variables using neural networks.

by Lo Tai-yan, Milton. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 112-114). / Chapter 1 --- Preliminaries --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.1.1 --- The Noncentral Multivariate F distribution --- p.2 / Chapter 1.1.2 --- The Central Problems and the Approach --- p.4 / Chapter 1.2 --- Concepts and Terminology --- p.7 / Chapter 1.3 --- Choice of Estimates --- p.10 / Chapter 1.4 --- Related Work --- p.11 / Chapter 2 --- Estimation of the noncentrality parameter of a Noncentral Mul- tivariate F distribution --- p.19 / Chapter 2.1 --- Unbiased and Linear Estimators --- p.19 / Chapter 2.1.1 --- The unbiased estimate --- p.20 / Chapter 2.1.2 --- The Class of Linear Estimates --- p.24 / Chapter 2.2 --- Optimal Linear Estimate --- p.32 / Chapter 2.3 --- Nonlinear Estimate --- p.34 / Chapter 2.4 --- Monte Carlo Simulation Study --- p.41 / Chapter 2.5 --- Evaluation and Further Investigation --- p.42 / Chapter 3 --- Estimation of Canonical Correlation Coefficients --- p.73 / Chapter 3.1 --- Preliminary --- p.73 / Chapter 3.2 --- The Estimation Problem --- p.76 / Chapter 3.3 --- Orthogonally Invariant Estimates --- p.77 / Chapter 3.3.1 --- The Unbiased Estimate --- p.78 / Chapter 3.3.2 --- The Class of Linear Estimates --- p.78 / Chapter 3.3.3 --- The Class of Nonlinear Estimates --- p.80 / Chapter 3.4 --- Monte Carlo Simulation Study --- p.87 / Chapter 3.5 --- Evaluation and Further Investigation --- p.89 / Chapter A --- p.104 / Chapter A.1 --- Lemma 3.2 --- p.104 / Chapter A.2 --- Theorem 3.3 Leung(1992) --- p.105 / Chapter A.3 --- The Noncentral F Identity --- p.106 / Chapter B --- Bibliography --- p.111

Machine analysis of human facial and body language is a challenging topic in computer vision, impacting on important applications such as human-computer interaction and visual surveillance. In this thesis, we present research building towards computational frameworks capable of automatically understanding facial expression and behavioural body language. The thesis work commences with a thorough examination in issues surrounding facial representation based on Local Binary Patterns (LBP). Extensive experiments with different machine learning techniques demonstrate that LBP features are efficient and effective for person-independent facial expression recognition, even in low-resolution settings. We then present and evaluate a conditional mutual information based algorithm to efficiently learn the most discriminative LBP features, and show the best recognition performance is obtained by using SVM classifiers with the selected LBP features. However, the recognition is performed on static images without exploiting temporal behaviors of facial expression. Subsequently we present a method to capture and represent temporal dynamics of facial expression by discovering the underlying low-dimensional manifold. Locality Preserving Projections (LPP) is exploited to learn the expression manifold in the LBP based appearance feature space. By deriving a universal discriminant expression subspace using a supervised LPP, we can effectively align manifolds of different subjects on a generalised expression manifold. Different linear subspace methods are comprehensively evaluated in expression subspace learning. We formulate and evaluate a Bayesian framework for dynamic facial expression recognition employing the derived manifold representation. However, the manifold representation only addresses temporal correlations of the whole face image, does not consider spatial-temporal correlations among different facial regions. We then employ Canonical Correlation Analysis (CCA) to capture correlations among face parts. To overcome the inherent limitations of classical CCA for image data, we introduce and formalise a novel Matrix-based CCA (MCCA), which can better measure correlations in 2D image data. We show this technique can provide superior performance in regression and recognition tasks, whilst requiring significantly fewer canonical factors. All the above work focuses on facial expressions. However, the face is usually perceived not as an isolated object but as an integrated part of the whole body, and the visual channel combining facial and bodily expressions is most informative. Finally we investigate two understudied problems in body language analysis, gait-based gender discrimination and affective body gesture recognition. To effectively combine face and body cues, CCA is adopted to establish the relationship between the two modalities, and derive a semantic joint feature space for the feature-level fusion. Experiments on large data sets demonstrate that our multimodal systems achieve the superior performance in gender discrimination and affective state analysis.

This study investigated the significant differences and correlations on college students' quality of life and their occupational selections. A total of 870 college students were stratified randomly selected from 15 Taiwanese colleges. All participants completed "quality of life" and "occupational selections" scales. In addition, 4 target students with either the highest total scores on quality of life or with the highest total scores on occupational selections were recruited for individual follow-up interview. Independent t-test, one-way ANOVA, and canonical correlation assessed the similarities and differences between groups. The initial findings were as follows: 1. College students¡¦ quality of life and occupational selections appear to have upper middle satisfaction and identification. 2. Female college students' mean score on the dimension of "domestic factor' was significantly higher than their male counterparts. 3. The first birth born college students' total mean on the "quality of life' and the dimension of "mental health' were significantly higher than the last birth born college students. 4. College students with high educational expectation had significantly higher mean scores on the "occupational selections", dimensions of "domestic factor' and "occupational / social factor' than these low educational expectation participants. 5. College students from mothers with high educational levels presented significantly higher means on "quality of life ' and the dimension of "occupational / social factor" than their counterparts. 6. College students from mothers with high educational levels presented significantly higher means on "quality of life', "occupational selections' and the dimension of "occupational /social factor' than their counterparts. 7. Students come from private technology colleges presented significantly higher mean scores on "quality of life', "occupational selections', and dimensions of "physical health' ,"individual factor' and "domestic factor' than their counterparts. 8. High academic achievement college students' mean scores on the "quality of life', and the dimensions of "social health' and "individual factor' were significantly higher than these low academic college students. 9. College students from high income families presented significantly higher means on the dimensions of "mental health' and "social health' than these low income families¡¦ college students. 10. There were significant canonical correlations between college students' quality of life and occupational selections. Based on these results, some educational implications and suggestions are discussed.

This study investigated the significant differences and correlations on college students¡¦ reading motivation and their internet literacy. A total of 950 college students were stratified randomly selected from 17 Taiwanese colleges. All participants completed ¡§Reading motivation¡¨ and ¡§Internet literacy¡¨ scales. In addition, 5 target students with highest scores on internet literacy were recruited for standardized test and follow-up interview. Independent t-test, one-way ANOVA, and canonical correlation assessed the similarities and differences between groups. The initial findings were as follows: 1.College students¡¦ reading motivation and internet literacy appear to have moderate performance. 2.Female college students¡¦ mean score on ¡§reading motivation¡¨ was significantly higher than their male counterparts. 3.High academic achievement college students¡¦ mean score on ¡§internet literacy¡¨ was significantly higher than low academic college students. 4.College students who major in engineering, manufacturing domain¡¦s mean score on ¡§internet literacy¡¨, dimensions of ¡§information accessing¡¨, ¡§information sharing¡¨ were significantly higher than social sciences, business management domain¡¦s college students. And college students who major in liberal arts domain¡¦s mean score on dimensions of ¡§information creating¡¨ were significantly higher than agronomy major¡¦s college students. 5.College students with higher reading frequency¡¦s mean score on ¡§reading motivation¡¨ and ¡§internet literacy¡¨ were significantly higher than low reading frequency college students. 6.College students with higher book-borrowing frequency¡¦s mean score on ¡§reading motivation¡¨, dimensions of ¡§information creating¡¨ were significantly higher than low book-borrowing frequency college students. 7.College students consume more time on reading¡¦s mean score on ¡§reading motivation¡¨, dimensions of ¡§information creating¡¨ were significantly higher than these consuming less time on reading¡¦s participants. 8. College students with longer internet seniority¡¦s mean score on ¡§internet literacy¡¨, dimensions of ¡§information accessing¡¨, ¡§information evaluating¡¨ and ¡§information integrating¡¨ were significantly higher than less internet seniority¡¦s college students. 9.College students with higher educational expectation had significantly higher mean scores on ¡§reading motivation¡¨ and ¡§internet literacy¡¨ than these low educational expectation participants. 10.Canonical correlations between college students¡¦ ¡§reading motivation¡¨ and ¡§internet literacy¡¨ were found in this study.

Thesis (Ph.D.)--City University of Hong Kong, 2009. / "Submitted to Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves [52]-58)

Thesis (M.S.)--Mississippi State University. Department of Agricultural and Biological Engineering. / Title from title screen. Includes bibliographical references.

Thesis studied correlations between swallowing accelerometry parameters and anthropometrics in 50 healthy participants. Anthropometrics include: age, gender, weight, height, body fat percent, neck circumference and mandibular length. Dual-axis swallowing signals, from a biaxial accelerometer were obtained for 5-saliva and 10-water (5-wet and 5-wet chin-tuck) swallows per participant. Two patient-independent automatic segmentation algorithms using discrete wavelet transforms of swallowing sequences segmented: 1) saliva/wet swallows and 2) wet chin-tuck swallows. Extraction of swallows hinged on dynamic thresholding based on signal statistics. Canonical correlation analysis was performed on sets of anthropometric and swallowing signal variables including: variance, skewness, kurtosis, autocorrelation decay time, energy, scale and peak-amplitude. For wet swallows, significant linear relationships were found between signal and anthropometric variables. In superior-inferior directions, correlations linked weight, age and gender to skewness and signal-memory. In anterior-posterior directions, age was correlated with kurtosis and signal-memory. No significant relationship was observed for dry and wet chin-tuck swallowing

Thesis studied correlations between swallowing accelerometry parameters and anthropometrics in 50 healthy participants. Anthropometrics include: age, gender, weight, height, body fat percent, neck circumference and mandibular length. Dual-axis swallowing signals, from a biaxial accelerometer were obtained for 5-saliva and 10-water (5-wet and 5-wet chin-tuck) swallows per participant. Two patient-independent automatic segmentation algorithms using discrete wavelet transforms of swallowing sequences segmented: 1) saliva/wet swallows and 2) wet chin-tuck swallows. Extraction of swallows hinged on dynamic thresholding based on signal statistics. Canonical correlation analysis was performed on sets of anthropometric and swallowing signal variables including: variance, skewness, kurtosis, autocorrelation decay time, energy, scale and peak-amplitude. For wet swallows, significant linear relationships were found between signal and anthropometric variables. In superior-inferior directions, correlations linked weight, age and gender to skewness and signal-memory. In anterior-posterior directions, age was correlated with kurtosis and signal-memory. No significant relationship was observed for dry and wet chin-tuck swallowing