Submitted by Diego Roberto Cintra da Silva null (diegocntr@hotmail.com) on 2017-01-22T14:31:57Z No. of bitstreams: 1 Diego Cintra - Dissertação - Mestrado.pdf: 3332491 bytes, checksum: cb4ba5462d138f7b73df3323acd794c3 (MD5) / Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2017-01-25T16:43:04Z (GMT) No. of bitstreams: 1 silva_drc_me_bauru.pdf: 3332491 bytes, checksum: cb4ba5462d138f7b73df3323acd794c3 (MD5) / Made available in DSpace on 2017-01-25T16:43:04Z (GMT). No. of bitstreams: 1 silva_drc_me_bauru.pdf: 3332491 bytes, checksum: cb4ba5462d138f7b73df3323acd794c3 (MD5) Previous issue date: 2016-12-02 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / As crises que ocorrem no mercado de ações são prejudiciais não só à parte monetária da economia de um país, mas ao desenvolvimento do país como um todo. A crise do subprime em 2008, que se iniciou nos Estados Unidos da América, atingiu o mundo todo, muitos países tiveram quedas significativas do PIB e vários entraram em recessão. Existe, então, o interesse em se compreender a dinâmica das séries temporais de variáveis como retorno e volatilidade das ações negociadas nesse mercado, a fim de compreender as diferenças de seu comportamento em momentos de crise econômica. Com o objetivo de analisar o espectro de correlação da volatilidade de ações no período da crise de 2008 e em suas vizinhanças, foram verificadas 31 ações de empresas pertencentes a diversos setores da economia brasileira, que compuseram entre 2007 e 2011 o Índice Bovespa. Para tal foram utilizados os métodos do Detrended Fluctuation Analisys – DFA e do Detrended Cross-Correlation Analisys – DCCA. Ambos métodos evidenciaram uma significativa mudança na função de probabilidade no período de crise comparativamente aos períodos de sua vizinhança. / Crises occurring in the stock market are harmful not only to the monetary part of a country's economy, but to the development of the country as a whole. The subprime crisis in 2008, which began in the United States of America, hit the whole world, many countries had significant declines in GDP and several went into recession. There is, therefore, an interest in understanding the dynamics of time series of variables such as return and volatility of the shares traded in this market, in order to understand the differences in their behavior in times of economic crisis. With the objective of analyzing the correlation spectrum of stock volatility in the period of the 2008 crisis and its neighborhoods, 31 stocks of companies belonging to various sectors of the Brazilian economy were verified, which made up the Bovespa Index between 2007 and 2011. The methods of Detrended Fluctuation Analyzes - DFA and Detrended Cross-Correlation Analyzes - DCCA were used for this. Both methods evidenced a significant change in the probability function in the period of crisis compared to the periods of its neighborhood.

Big data has received a momentum from both academia and industry. The MapReduce model has emerged into a major computing model in support of big data analytics. Hadoop, which is an open source implementation of the MapReduce model, has been widely taken up by the community. Cloud service providers such as Amazon EC2 cloud have now supported Hadoop user applications. However, a key challenge is that the cloud service providers do not a have resource provisioning mechanism to satisfy user jobs with deadline requirements. Currently, it is solely the user responsibility to estimate the require amount of resources for their job running in a public cloud. This thesis presents a Hadoop performance model that accurately estimates the execution duration of a job and further provisions the required amount of resources for a job to be completed within a deadline. The proposed model employs Locally Weighted Linear Regression (LWLR) model to estimate execution time of a job and Lagrange Multiplier technique for resource provisioning to satisfy user job with a given deadline. The performance of the propose model is extensively evaluated in both in-house Hadoop cluster and Amazon EC2 Cloud. Experimental results show that the proposed model is highly accurate in job execution estimation and jobs are completed within the required deadlines following on the resource provisioning scheme of the proposed model. In addition, the Hadoop framework has over 190 configuration parameters and some of them have significant effects on the performance of a Hadoop job. Manually setting the optimum values for these parameters is a challenging task and also a time consuming process. This thesis presents optimization works that enhances the performance of Hadoop by automatically tuning its parameter values. It employs Gene Expression Programming (GEP) technique to build an objective function that represents the performance of a job and the correlation among the configuration parameters. For the purpose of optimization, Particle Swarm Optimization (PSO) is employed to find automatically an optimal or a near optimal configuration settings. The performance of the proposed work is intensively evaluated on a Hadoop cluster and the experimental results show that the proposed work enhances the performance of Hadoop significantly compared with the default settings.

The stride interval of typical human gait is correlated over thousands of strides. This statistical persistence diminishes with age, disease, and pace-constrained walking. Considering the widespread use of treadmills in rehabilitation and research, it is important to understand the effect of this speed-constrained locomotor modality on stride interval dynamics. To this end, and given that the dynamics of children have been largely unexplored, this study investigated the impact of treadmill walking, both with and without handrail use, on paediatric stride interval dynamics. An initial stationarity analysis of stride interval time series identified both non-stationary and stationary signals during all walking conditions. Subsequent scaling analysis revealed diminished stride interval persistence during unsupported treadmill walking compared to overground walking. Finally, while the correlation between stride interval dynamics and gross energy expenditure was investigated in an effort to elucidate the clinical meaning of persistence, no simple linear correlation was found.

The stride interval of typical human gait is correlated over thousands of strides. This statistical persistence diminishes with age, disease, and pace-constrained walking. Considering the widespread use of treadmills in rehabilitation and research, it is important to understand the effect of this speed-constrained locomotor modality on stride interval dynamics. To this end, and given that the dynamics of children have been largely unexplored, this study investigated the impact of treadmill walking, both with and without handrail use, on paediatric stride interval dynamics. An initial stationarity analysis of stride interval time series identified both non-stationary and stationary signals during all walking conditions. Subsequent scaling analysis revealed diminished stride interval persistence during unsupported treadmill walking compared to overground walking. Finally, while the correlation between stride interval dynamics and gross energy expenditure was investigated in an effort to elucidate the clinical meaning of persistence, no simple linear correlation was found.

Crack initiation and propagation in the bladed disks of aero-engines caused by high-cycle fatigue under cyclic loads could result in the breakdown of the engines if not detected at an early stage. Although a number of fault detection methods have been reported in the literature, it still remains very challenging to develop a reliable online technique to accurately diagnose defects in bladed disks. One of the main challenges is to characterize signals contaminated by noises. These noises caused by very dynamic engine operation environment. This work presents a new technique for engine bladed disk crack detection, which utilizes advanced analysis of clearance and time-of-arrival signals acquired from blade tip sensors. This technique involves two stages of signal processing: 1) signal pre-processing for noise elimination from predetermined causes; and 2) signal post-processing for characterizing crack initiation and location. Experimental results from the spin rig test were used to validate technique predictions.

Submitted by (ana.araujo@ufrpe.br) on 2016-07-06T20:20:42Z No. of bitstreams: 1 Jose Gomes Cabral Neto.pdf: 2927907 bytes, checksum: 48ae7e81516be4443fde4aa11b9564b9 (MD5) / Made available in DSpace on 2016-07-06T20:20:42Z (GMT). No. of bitstreams: 1 Jose Gomes Cabral Neto.pdf: 2927907 bytes, checksum: 48ae7e81516be4443fde4aa11b9564b9 (MD5) Previous issue date: 2013-02-26 / The lack of a better knowledge to further the proper management of water systems and soil of Brazilian semiarid contributes to maintain the social inequalities which are subject to local populations. The adjust of hydrologic data to probability density functions, and the application of Detrended Fluctuation Analysis method to quantify the long-range correlations in non-stationary time series hydrological contribute to a better use of water resources in the environment semiarid and reduction of the risk of economic loss. This way, the information of hydrological variables of blade height and flow of the Stream catchment Jacu in the semiarid and region of Pernambuco were used and it was found that the maximum and minimum blade height and flow of semiarid watershed Jacu best adjusted to Weibull distributions, Gumbel, Log-Normal and Gamma. The Detrended Fluctuation Analysis method showed the existence of persistent long-range correlations, which represents an important property of stochastic processes generating this phenomenon. The series of blade heights showed persistence stronger than the series of flows. In smaller scales fluctuation softer, represented by exponents , and larger scales showed persistent fluctuations, represented by exponents. / A falta do conhecimento científico mais aprofundado para o manejo adequado dos sistemas hídricos e dos solos do semiárido nordestino contribui para manutenção da desigualdade social ao qual estão submetidas às populações locais. O ajuste de dados hidrológicos às distribuições estatísticas e a aplicação do método Detrended Fluctuation Analysis (DFA) para quantificar as correlações de longo alcance nas séries temporais hidrológicas não estacionárias, contribuem para um melhor uso dos recursos hídricos no semiárido e para redução do risco de ocorrência de perdas econômicas. Dessa forma, foram utilizadas informações das variáveis hidrológicas de altura da lâmina de água e vazão da Bacia hidrográfica semiárida do Riacho Jacu de Pernambuco constatando-se que os valores máximos e mínimos de altura da lâmina e vazão da bacia hidrográfica do referido riacho, se ajustaram melhor as distribuições Weibull, Gumbel, Log-Normal e Gama. O método Detrended Fluctuation Analysis indicou a existência de correlações persistentes de longo alcance, que representa uma propriedade importante dos processos estocásticos geradores desse fenômeno. As séries das alturas da lâmina apresentaram persistência mais forte do que as séries das vazões. Nas escalas menores apresentam flutuações mais suaves, representadas pelos expoentes , e para escalas maiores apresentaram flutuações persistentes, representadas pelos expoentes.

Crack initiation and propagation in the bladed disks of aero-engines caused by high-cycle fatigue under cyclic loads could result in the breakdown of the engines if not detected at an early stage. Although a number of fault detection methods have been reported in the literature, it still remains very challenging to develop a reliable online technique to accurately diagnose defects in bladed disks. One of the main challenges is to characterize signals contaminated by noises. These noises caused by very dynamic engine operation environment. This work presents a new technique for engine bladed disk crack detection, which utilizes advanced analysis of clearance and time-of-arrival signals acquired from blade tip sensors. This technique involves two stages of signal processing: 1) signal pre-processing for noise elimination from predetermined causes; and 2) signal post-processing for characterizing crack initiation and location. Experimental results from the spin rig test were used to validate technique predictions.

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