Orientador: Sérgio Tonini Button / Dissertação (mestrado profissional) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-18T21:08:57Z (GMT). No. of bitstreams: 1 Possamai_Lisiane_M.pdf: 4008102 bytes, checksum: b6e9b8d71cd5acc093a6d8f5074751db (MD5) Previous issue date: 2011 / Resumo: Este trabalho tem como objetivo principal avaliar se a determinação da concentração de metais em óleo lubrificante por ICP-OES (Espectrometria de Emissão Ótica com Plasma Indutivamente Acoplado, do inglês Inductively Coupled Plasma Optical Emission Spectroscopy), é capaz de auxiliar no diagnóstico de eventuais falhas por desgaste em motores Diesel não detectadas no ensaio funcional de produção (amaciamento). O tratamento dos dados foi realizado empregando-se métodos estatísticos. Parâmetros como carga de trabalho, rotação, pressões, temperaturas, assim como a severidade do desgaste, podem não ser suficientes para manifestar a falha durante o tempo de exposição do motor ao ensaio, postergando a sua ocorrência para a planta do cliente (montadora) ou para o campo. O conhecimento da concentração dos elementos metálicos em óleo lubrificante é útil para a avaliação de desgastes de componentes específicos de motores permitindo uma intervenção preventiva a fim de evitar falhas catastróficas. A partir da análise dos dados históricos disponíveis na empresa definiu-se o conceito de assinatura de desgaste e buscou-se conhecer o comportamento dos resultados das análises de óleo, quando se constatou variabilidade significativa dos valores que pode ser explicada por erros sistêmicos e aleatórios. A validação da técnica por ICP-OES para detecção de desgaste prematuro em motores foi realizada a partir da reprodução do modo de falha mais comumente encontrado em motores de combustão interna, que é o engripamento de biela. Simulou-se a contaminação do sistema tribológico casquilho, moente e óleo, considerando-se o agente de contaminação externa proveniente da região de fechamento da capa da biela. Conforme esperado, os motores foram aprovados dentro dos parâmetros de controle existentes. Após a desmontagem dos motores evidenciouse que o sistema tribológico fora seriamente comprometido por desgaste do casquilho (bronzina), que apresenta um revestimento superficial de alumínio e estanho. Na análise univariada de metais dissolvidos no óleo lubrificante identificou-se a presença de estanho, o que não é esperado encontrar neste modelo de motor. No tratamento multivariado foi possível analisar a correlação dos metais dissolvidos no óleo cuja análise estatística possibilitou distinguir motores conformes de não conformes de forma quantitativa e objetiva. Desta forma, conclui-se que o método de análise de metais dissolvidos em óleo lubrificante por ICP-OES é eficaz, se mostrando sensível na detecção de desgaste do sistema casquilho-moente / Abstract: The main goal of this study is to evaluate if the determination of metal concentration in lubricant oil by ICP-OES (Inductively Coupled Plasma Optical Emission Spectroscopy) is capable to help on eventual wear failures diagnosis on Diesel engines that are not detected in the production test cycle (running in). The data processing was done using statistical methods. Parameters like load, engine speed, pressures, temperatures, as well wear severity, cannot be enough to show the failure during the engine test time postponing the failure occurrence to OEM or field. The metallic elements concentration knowledge in lubricant oil is useful to evaluate specific engines parts wear allowing a preventive action in order to avoid catastrophic failures. Starting from the historic data analysis available in the company it was defined the concept of wear signature looking for oil analysis results behavior understanding, where it was noticed significant values variability due to random and systemic errors. The ICP-OES validation for premature wearing detection on engines was done starting from most usual failure mode found on internal combustion engines that is connecting rod scuffing. The tribological system bearing, crankshaft pin and oil was set to simulate a failure by an external contamination coming from the connecting rod cracked assembly area. As expected, the engines were approved according to current controls. After engine disassembly it was noticed that the tribological system was heavy damaged due to bearing wear whose surface layer composition is made of aluminum and tin. In the univariate oil analysis of metals dissolved it was detected tin which is not expected to be finding in this engine model. In the multivariate data processing it was possible to analyze the dissolved metals correlation whose statistical analysis make possible to distinguish conforming engines from non-conforming engines in a quantitative and objective way. So it is possible to conclude that the ICP-OES method to analyze dissolved metal in oil is effective showing sensibility to detect wear between crankshaft pin and connecting rod bearing system / Mestrado / Projetos / Mestre em Engenharia Automobilistica

With the evolution in the game industry and other virtual environments, demands on what comes with an application is higher than ever before. This leads to many companies trying to to procedurally generate content in order to save up on storage space and get a wider variety of content. It has become essential to infuse immersion in such application and some companies has even gone as far as to let the player recreate him- or herself to be the hero or heroine of the game. Even so, many AAA companies refrain from using face segmentation software as it gives the power of adding game content by the end users, and that may lead to an increased risk of offensive content, that goes against company standards and policy, to enter their application. By taking the concept of procedural generation and applying this together with face segmentation, placing a Principal Component Analysis (PCA) based texturization model, we allow for a controlled yet functioning face texturization in a run-time virtual environment. In this project we use MatLab to create a controlled Eigen space, infuses this into an application built in Unity 3D using UMA, and lets smaller recreation vectors, that spans a few kilobytes as most, to create textures in run-time. In doing so, we can project faces onto the Eigen space and get fully functioning and texturized characters, able to use ready animations and controllers of the developer’s choice. These Eigen spaces may cost more storage space and loading times up to a limit, but can in turn generate a seemingly endless variation of textural content dynamically. In order to see what potential users prioritize when it comes to applications like these, we conducted a survey where the responders saw variations of this technique and were able to express their view on attributes expected from a “good” (from their point of view) application. In the end we have a UMA ready set of scripts, and a one-time use system to create Eigen spaces for the applications to use it. We worked in close relation with Högström’s Selfie to Avatar face segmentation software and proved the concept in Unity 3D applications.

Arbetet som presenteras i den här rapporten handlar om ansiktsigenkänning från webbkameror med hjälp av principal component analysis samt artificiella neurala nätverk av typen feedforward. Arbetet förbättrar tekniken med hjälp av filterbaserade metoder som bland annat används inom ansiktsdetektering. Dessa filter bygger på att skicka med redundant data av delregioner av ansiktet.

Behavioural syndromes, defined as suites of correlated behaviours across different contexts, are used to characterize individual variability in behaviours. Males of the weakly electric fish species, Apteronotus leptorhynchus, produce electro-communication signals called chirps. Chirps are thought to be involved in agonistic signalling, as their relative incidence increases during agonistic conspecific interactions. However, high levels of individual variability in aggression obscure the role of chirps in mediating aggression. Here, I tested the presence of an aggression-boldness behavioural syndrome, and then considered the implications such a syndrome would have on chirping behaviours. Behavioural tests in anti-predation, object novelty, feeding, conspecific intrusion and novel environment exploration contexts revealed a syndrome involving only object novelty and feeding. We found no correlation between chirping behaviour and the assessed behaviours. Our results demonstrate that chirps represent a more complex communication system than previously suggested.

Recently, automatic demographic classification has found its way into embedded applications such as targeted advertising in mobile devices, and in-car warning systems for elderly drivers. In this thesis, we present a complete framework for video-based gender classification and age estimation which can perform accurately on embedded systems in real-time and under unconstrained conditions. We propose a segmental dimensionality reduction technique utilizing Enhanced Discriminant Analysis (EDA) to minimize the memory and computational requirements, and enable the implementation of these classifiers for resource-limited embedded systems which otherwise is not achievable using existing resource-intensive approaches. On a multi-resolution feature vector we have achieved up to 99.5% compression ratio for training data storage, and a maximum performance of 20 frames per second on an embedded Android platform. Also, we introduce several novel improvements such as face alignment using the nose, and an illumination normalization method for unconstrained environments using bilateral filtering. These improvements could help to suppress the textural noise, normalize the skin color, and rectify the face localization errors. A non-linear Support Vector Machine (SVM) classifier along with a discriminative demography-based classification strategy is exploited to improve both accuracy and performance of classification. We have performed several cross-database evaluations on different controlled and uncontrolled databases to assess the generalization capability of the classifiers. Our experiments demonstrated competitive accuracies compared to the resource-demanding state-of-the-art approaches.

The objective of this study is to investigate automatic recognition of speech-evoked auditory brainstem responses (speech-evoked ABR) to the five English vowels (/a/, /ae/, /ao (ɔ)/, /i/ and /u/). We used different automatic speech recognition methods to discriminate between the responses to the vowels. The best recognition result was obtained by applying principal component analysis (PCA) on the amplitudes of the first ten harmonic components of the envelope following response (based on spectral components at fundamental frequency and its harmonics) and of the frequency following response (based on spectral components in first formant region) and combining these two feature sets. With this combined feature set used as input to an artificial neural network, a recognition accuracy of 83.8% was achieved. This study could be extended to more complex stimuli to improve assessment of the auditory system for speech communication in hearing impaired individuals, and potentially help in the objective fitting of hearing aids.

Research into traumatic brain injury (TBI) mechanisms is essential for the development of methods to prevent its occurrence. One of the most common ways to incur a TBI is from falls, especially for the young and very old. The purpose of this thesis was to investigate how the acceleration loading curves influenced the occurrence of different types of TBI, namely: epidural hematoma, subdural hematoma, subarachnoid hemorrhage, and contusion. This investigation was conducted in three parts. The first study conducted reconstructions of 20 TBI cases with varying outcomes using MADYMO, Hybrid III, and finite element methodologies. This study provided a dataset of threshold values for each of the TBI injuries measured in parameters of strain and stress. The results of this study indicated that using a combined reconstructive approach produces results which are in keeping with the literature for TBI. The second study examined how the characteristics of the loading curves which were produced from each reconstruction influenced the outcome using a principal components analysis. It was found that the duration of the event accounted for much of the variance in the results, followed with the acceleration components. Different curve characteristics also accounted for differing amounts of variance in each of the lesion types. Study 3 examined how the dynamic response of the impact influenced where in the brain a subdural hematoma (SDH) could occur. It was found that the largest magnitudes of acceleration produced SDH in the parietal lobe, and the lowest in the occipital lobe. Overall this thesis examined the mechanism of injury for TBI using a large dataset with methodologies which complement each other’s limitations. As a result in depth information of the nature of TBI was attained and information provided which may be used to improve future protection and standard development.

[pt] Identificar um indivíduo a partir de uma imagem de face é uma tarefa simples para seres humanos e extremamente difícil para a Visão Computacional. Esta questão tem motivado diversos grupos de pesquisa em todo o mundo, especialmente a partir de 1993. Inúmeros trabalhos realizados até o momento encaram uma imagem digital de n pixels como um vetor num espaço n-dimensional, onde n é em geral muito grande. Imagens de rostos humanos possuem, contudo, grande redundância: todas contém dois olhos, um nariz, uma boca, e etc. É possível, portanto, trabalhar em uma base deste espaço em que faces possam ser adequadamente caracterizadas a partir de um conjunto de p componentes, onde p é muito menor quen. É com este enfoque que o presente trabalho estuda sistemas de reconhecimento de faces que consistem de um estágio de redução de dimensionalidade, realizado pela técnica de Análise de Componentes Principais (PCA), seguido de um modelo classificador. No estágio da PCA, as imagens de n pixels são transformadas em vetores de p características a partir de um conjunto de treinamento. Três classificadores conhecidos na literatura são estudados: os classificadores de distância (EUclideana e de Mahalanobis), a rede neural de Funções Base Radiais (RBF), e o classificador de Fisher. Este trabalho propõe, ainda, um novo classificador que introduz o conceito de Matrizes de Covariança Misturadas (MPM) no classificador gaussiano de Máxima Probabilidade. Os quatros classificadores são avaliados através da variação de seus respectivos parâmetros e utilizam como imagens o banco de faces da Olivetti. Nos experimentos realizados para comparar tais abordagens, o novo classificador proposto atingiu as maiores taxas de reconhecimento e apresentou menorsensibilidade à escolha do conjunto de faces de treinamento. / [en] Identifying an individual based on a face image is a simple task for humans to perform and a very difficult one for Vision Computing. Since 1993, several research groups in all over the world have been studied this problem. Most of the methods proposed for recognizing the identity of an individual represent a n intensity pixel image as a n- dimensional vector, when, in general, n is a very large number value. Face images are highly redundant, since every individual has two eyes, one nose, one mouth and so on. Then, instead of using n intensity values, it is generally possible to characterize an image instance by a set of p features, for p < < n. This work studies face recognition systems consisting of a PCA stage for dimensionality reduction followed by a classifier. The PCA stage takes the n-pixels face images and produces the corresponding p most expensive features, based on the whole available training set. Three classifiers proposed in the literature are studied: the Euclidean and Mahalanobis distances, the RBF neural network, and the Fisher classifier. This work also proposes a new classifier, which introduces the concept of Mixture Covariance Matrices (MPM) in the Minimum Total Probality of Misclassification rule for normal populations. The four classifiers are evaluated using the Olivetti Face Database varying their parameters in a wide range. In the experiments carried out to compare those approaches the new proposed classifier reached the best recognition rates and showed to be less sensitive to the choice of the training set.

abstract: Functional regression models are widely considered in practice. To precisely understand an underlying functional mechanism, a good sampling schedule for collecting informative functional data is necessary, especially when data collection is limited. However, scarce research has been conducted on the optimal sampling schedule design for the functional regression model so far. To address this design issue, efficient approaches are proposed for generating the best sampling plan in the functional regression setting. First, three optimal experimental designs are considered under a function-on-function linear model: the schedule that maximizes the relative efficiency for recovering the predictor function, the schedule that maximizes the relative efficiency for predicting the response function, and the schedule that maximizes the mixture of the relative efficiencies of both the predictor and response functions. The obtained sampling plan allows a precise recovery of the predictor function and a precise prediction of the response function. The proposed approach can also be reduced to identify the optimal sampling plan for the problem with a scalar-on-function linear regression model. In addition, the optimality criterion on predicting a scalar response using a functional predictor is derived when the quadratic relationship between these two variables is present, and proofs of important properties of the derived optimality criterion are also provided. To find such designs, an algorithm that is comparably fast, and can generate nearly optimal designs is proposed. As the optimality criterion includes quantities that must be estimated from prior knowledge (e.g., a pilot study), the effectiveness of the suggested optimal design highly depends on the quality of the estimates. However, in many situations, the estimates are unreliable; thus, a bootstrap aggregating (bagging) approach is employed for enhancing the quality of estimates and for finding sampling schedules stable to the misspecification of estimates. Through case studies, it is demonstrated that the proposed designs outperform other designs in terms of accurately predicting the response and recovering the predictor. It is also proposed that bagging-enhanced design generates a more robust sampling design under the misspecification of estimated quantities. / Dissertation/Thesis / Doctoral Dissertation Statistics 2020

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