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.

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.

No description available.

Cognitive processes heavily rely on a dedicated spatio-temporal architecture of the underlying neural system - the brain. The spatial aspect is substantiated by the modularization as it has been brought to light in much detail by recent sophisticated neural imaging investigations. The time aspect is less well investigated although the role of time is prominent in several approaches to understanding the organization of the information processing in the brain. By way of example we mention (i) the synchronization hypothesis for the resolution of the binding problem, cf. [5] [4], [3] and the efforts to relate the information contained in observed spike rates back to the neuronal mechanisms underlying the cognitive event. In particular, in Refs. [1], [2] Amit et. al. tried to bridge the gap between the Miyashita data [10] and the hypothesis that associative memory is realized by the (strange) attractor states of dynamical systems.

Research in damage detection and structural health monitoring in engineering systems during their service life has received increasing attention because of its importance and benefits in maintenance and rehabilitation of structure. Though the concept of vibration-based damage detection has been in existence for decades, and several procedures have been proposed to date, its practical applications remain limited, considering the increased utilization of sensors to measure structural response at multiple points. In this thesis, use of acceleration response of the structure as a method of global damage detection is explored using instantaneous frequency and stiffness degradation methods. Instantaneous frequency was estimated using continuous wavelet transform of measured acceleration response of the structure subjected to ground motion. Complex Morlet Wavelet was used in the time-frequency analysis due to its ability to provide sufficient resolution in both time and frequency domains. This ability is important in analyzing nonstationary signals like earthquake response of structure containing sharp changes in the signal. The second method, called the stiffness degradation analysis, is based on estimating the time-varying stiffness. This estimation is done by fitting a moving least-square line to the force-displacement loop for the duration of the ground motion.A four-story shear building is used as the model structure for numerical analysis. Two damage scenarios are considered: single damage instant and multiple damage instants. Both scenarios assume that the damage occurs at a single location. In the numerical simulations, damage was modeled as a reduction in the stiffness of the first floor, and accelerations were computed at floor levels using state-space model. The two methods were compared in terms of their damage detection ability and it was shown that both methods can be used in detecting damage and the time at which the damage occurs. These methods can later be extended by simultaneously considering the correlations of responses at all floor levels. This extension may enable locating the damage and quantifying the severity of the damage.

Vector born disease account for about one third of all cases of emerging diseases. Culex sp., particularly, is one of the most important mosquito vectors transmitting important diseases such as the West Nile virus, filariasis and related encephalitis. Because there are no vaccines available the most effectual means to prevent infections from the above diseases, is to target mosquitos to prevent bites and disease transmission. However, to be effective such a strategy, it is important to predict the temporal change in mosquito abundance as well as to study how it is affected by weather conditions. This dissertation is devoted on the development of new methods to predict arthropod vector dynamics and with emphasis on the development of stochastic models and computational methods for predicting Culex sp. abundance in Northern Greece. The current dissertation is divided in three parts. The first part explores the non-trivial associations between Culex sp. mosquito abundance and weather variables using traditional and straightforward novel techniques. The information from the first part was a prerequisite for developing a series of stochastic prediction models based on the most detrimental factors affecting mosquito abundance. In the second part, a series of conventional and conditional stochastic Markov chain models are applied for the first time to predict the non-linear dynamics of Culex sp. adult abundance. In the third part of the dissertation a soft computing approach is introduced to model the population dynamics of Culex sp. and a series of autoregressive artificial neural networks are implied. Finally, the information of the models is extrapolated and a machine learning algorithm is proposed to be used for predicting arthropod vector dynamics having practical implications for public health decision making. Based on the current results there was a high and positive correlation between temperature and mosquito abundance during both observation years (r = 0.6). However, a very poor correlation was observed between rain and weekly mosquito abundances (r = 0.29), as well as between wind speed (r = 0.29), respectively. Additionally, according to the multiple linear regression model the effect of temperature, was significant. The continuous power spectrum of the mosquito abundance counts and mean temperatures depict in most cases similar power for periods which are close to 1 week, indicating the point of the lowest variance of the time series, although appearing on slightly different moments of time. The cross wavelet coherent analysis showed that inter weekly cycles with a period between 2 and 3 weeks between mosquito abundance and temperature were coherent mostly during the first and the last weeks of the season. Hence, the wavelet analysis shows a progressive oscillation in mosquito occurrences with time, which is higher at the start and the end of the season. Moreover, in contrast with standard methods of analysis, wavelets can provide useful insights into the time-resolved oscillation structure of mosquito data and accompanying revealing a non-stationary association with temperature. According to the correlation results a climate-conditioned Markov Chain (CMC) model was developed and applied for the first time to predict the dynamics of vectors of important medical diseases. Temporal changes in mosquito population profiles were generated to simulate the probabilities of a high population impact. The probabilities achieved from the trained model are very near to the observed data and the CMC model satisfactorily describes the temporal evolution of the mosquito population process. In general, our numerical results indicate that it is more likely for the population system to move into a state of high population level, when the former is a state of a low population level than the opponent. Field data on frequencies of successive mosquito population levels, which were not used for the data inferred MC modeling, were assembled to obtain an empirical intensity transition matrix and the observed frequencies. The findings match to a certain degree the empirical results in which the probabilities follow analogous patterns while no significant differences were observed between the transition matrices of the CMC model and the validation data (ChiSq=14.58013, df=24, p=0.9324451). Furter, a soft system computing modeling approach was followed to simulate and predict Culex sp. abundances. Three dynamic artificial neural network (ANNs) models were developed and applied to describe and predict the non-linear incidence and time evolution of a medical important mosquito species Culex sp. in Northern Greece. The first is a simple nonlinear autoregressive ANN model that used lagged population values as inputs, the second is an exogenous non-linear autoregressive recurrent neural network (NARX), which is designed to take as inputs the temperature as exogenous variable and mosquito abundance as endogenous. Finally, the third model is a focused time-delay neural network (FTD), which takes in to account only the temperature variable as input to provide forecasts of the mosquito abundance as target variable. All three models behaved well considering the non-linear nature of the adult mosquito abundance data. However, the NARX model, which takes in to account temperature, showed the best overall modelling performances. Nevertheless, although, the NARX model predicted slight better (R=0.623) compared to the FTD model (R=0.534), the advantage of the FTD over the NARX neural network model is that it can be applied in the case where past values of the population system, here mosquito abundance, are not available for their forecasting. This is very important considering that arthropod vector data are not always available as climatic data. Concluding, the proposed methods for simulating and predicting mosquito dynamics are recommended as viable for modeling vector disease population dynamics in order to make real-time recommendations utile for dynamic health policies decision making. The proposed stochastic models, as well as the current computational and machine learning techniques, of this work provide an accurate abstraction of the arthropod vector population progress observed within the dataset used for their generation. Nevertheless, the current study may consider also as a new entry point into the extensive literature of ecological modelling, medical entomology, as well as in simulating arthropod vector diseases epidemics. From a public health standpoint, the current models have the potential to be integrated into a decision support system allowing health policy makers in their planning to initiate specific management actions against the period of high activity of mosquito adults.

Managing traffic during oversaturated conditions is a current challenge for practitioners due to the lack of adequate tools that can handle such situations. Unlike under-saturated conditions, operation of traffic signal systems during congestion requires careful consideration and analysis of the underlying causes of the congestion before developing mitigation strategies. The objectives of this research are to provide a practical guidance for practitioners to identify oversaturated scenarios and to develop a multi-objective methodology for selecting and evaluating mitigation strategy/ or combinations of strategies based on a guiding principles. The research focused on traffic control strategies that can be implemented by traffic signal systems. The research did not considered strategies that deals with demand reduction or seek to influence departure time choice, or route choice. The proposed timing methodology starts by detecting network's critical routes as a necessary step to identify the traffic patterns and potential problematic scenarios. A wide array of control strategies are defined and categorized to address oversaturation problematic scenarios. A timing procedure was then developed using the principles of oversaturation timing in cycle selection, split allocation, offset design, demand overflow, and queue allocation in non-critical links. Three regimes of operation were defined and considered in oversaturation timing: (1) loading, (2) processing, and (3) recovery. The research also provides a closed-form formula for switching control plans during the oversaturation regimes. The selection of optimal control plan is formulated as linear integer programming problem. Microscopic simulation results of two arterial test cases revealed that traffic control strategies developed using the proposed framework led to tangible performance improvements when compared to signal control strategies designed for operations in under-saturated conditions. The generated control plans successfully manage to allocate queues in network links. / Ph. D.

A utilização de modelos de teletrafego que contemplem caractersticas tais como autossimilaridade e dependencia de longa duraçao tem se mostrado cada vez mais como sendo ponto-chave na correta caracterizaçao do teletrafego Local Area Network (LAN) e Wide Area Network (WAN) [1, 2]. Tal caracterizaçao e necessaria para o monitoramento e controle de teletrafego em redes convergentes [3]. Nesse contexto, a questão da estimaçao precisa do parâmetro de autossimilaridade, denominado de parâmetro de Hurst, torna-se essencial. Entretanto, estudos comprovam que, alem da dependência de longa duraçao, redes WAN podem, não raramente, apresentar caractersticas mistas de dependência de longa e de curta duraçao [4, 5]. Enquanto vasta literatura cientca, tanto teorica como pratica, tem abordado com anco a questão da acuracia de diversos estimadores para o parâmetro de Hurst [6, 7, 8, 9], pouca atenção tem sido dada a questão da estimação deste parâmetro na presenca de dependência de curta duração. O presente trabalho de pesquisa concentrou-se no estudo dos metodos de estimaçao do parametro de Hurst baseados no espectro wavelet, em particular atraves do metodo de Abry-Veitch [10] { baseado na transformada Discrete Wavelet Transform (DWT) { e atraves do espectro obtido atraves da transformada Discrete Wavelet Packet Transform (DWPT). Os resultados baseados no metodo de Abry-Veitch demonstram que, atraves de um ajuste apropriado dos par^ametros de estimaçao, tal metodo permite uma estimaçao robusta na presenca de componentes com dependencia de curta duraçao, mesmo em situaçoes de mudanca de regime de tal componente, caracterstica desejavel para a estimaçao em tempo real do parametro de Hurst. Entretanto, a dispersao consideravel apresentada, em alguns casos, pelas estimativas do metodo de Abry-Veitch, motivou o estudo da utilizaçao do espectro wavelet obtido via transformada DWPT para realizaçao da estimaçao do parametro de Hurst. Os resultados indicam que a utilizaçao de tal transformada gera um espectro wavelet tal que e possvel detectar a presenca ou não de componentes com dependencia de curta duraçao. Ao final, os resultados da pesquisa realizada são sumarizados e utilizados em uma proposta de mecanismo de estimaçao do parametro de Hurst em tempo real, na presenca simultanea de componentes de dependencia de longa e curta duracão. / The use of network trac models that hold self-similar and long-range dependence characteristics have shown to be a key element on the correct characterization of Local Area Network (LAN) and Wide Area Network (WAN) network trac [1, 2]. Such characterization is necessary to monitor and control the network trac in converged networks [3]. In this context, the accurate estimation of the selfsimilarity parameter, named Hurst parameter, is a major issue. However, studies show that, besides the long-range dependence, WAN network trac may, not uncommonly, present mixed long and short-range dependence characteristics [4, 5]. While great part of either theoretical or practical scientic literature has been focused on the issue of Hurst parameter estimator accuracy [6, 7, 8, 9], little attention has been given to the estimation of such parameter in the presence of short-range dependence. This research work has focused on the study of the Hurst parameter estimation methods based on the wavelet spectrum, specially through the Abry-Veitch method [10] { which is based on the Discrete Wavelet Transform (DWT) transform { and through the wavelet spectrum based on the Discrete Wavelet Packet Transform (DWPT) transform. The results based on the Abry-Veitch method show that, through a suitable adjustment of the estimation parameters, such method yields a robust estimation in the presence of short-range dependence components, even in changing conditions of such component, a desirable characteristic for the real-time estimation of the Hurst parameter. However, the signi cant dispersion presented, occasionally, by the Abry-Veitch method estimates motivated the research of the usage of the wavelet spectrum obtained via DWPT transform to estimate the Hurst parameter. The results show that the usage of such transform generates such a wavelet spectrum that it is possible to detect whether short-range dependence components are present, or not, in the analyzed series. At the end, the research results are summarized and used to propose a realtime Hurst parameter estimation mechanism, in the presence of simultaneous long- and short-range dependence components.

A utilização de modelos de teletrafego que contemplem caractersticas tais como autossimilaridade e dependencia de longa duraçao tem se mostrado cada vez mais como sendo ponto-chave na correta caracterizaçao do teletrafego Local Area Network (LAN) e Wide Area Network (WAN) [1, 2]. Tal caracterizaçao e necessaria para o monitoramento e controle de teletrafego em redes convergentes [3]. Nesse contexto, a questão da estimaçao precisa do parâmetro de autossimilaridade, denominado de parâmetro de Hurst, torna-se essencial. Entretanto, estudos comprovam que, alem da dependência de longa duraçao, redes WAN podem, não raramente, apresentar caractersticas mistas de dependência de longa e de curta duraçao [4, 5]. Enquanto vasta literatura cientca, tanto teorica como pratica, tem abordado com anco a questão da acuracia de diversos estimadores para o parâmetro de Hurst [6, 7, 8, 9], pouca atenção tem sido dada a questão da estimação deste parâmetro na presenca de dependência de curta duração. O presente trabalho de pesquisa concentrou-se no estudo dos metodos de estimaçao do parametro de Hurst baseados no espectro wavelet, em particular atraves do metodo de Abry-Veitch [10] { baseado na transformada Discrete Wavelet Transform (DWT) { e atraves do espectro obtido atraves da transformada Discrete Wavelet Packet Transform (DWPT). Os resultados baseados no metodo de Abry-Veitch demonstram que, atraves de um ajuste apropriado dos par^ametros de estimaçao, tal metodo permite uma estimaçao robusta na presenca de componentes com dependencia de curta duraçao, mesmo em situaçoes de mudanca de regime de tal componente, caracterstica desejavel para a estimaçao em tempo real do parametro de Hurst. Entretanto, a dispersao consideravel apresentada, em alguns casos, pelas estimativas do metodo de Abry-Veitch, motivou o estudo da utilizaçao do espectro wavelet obtido via transformada DWPT para realizaçao da estimaçao do parametro de Hurst. Os resultados indicam que a utilizaçao de tal transformada gera um espectro wavelet tal que e possvel detectar a presenca ou não de componentes com dependencia de curta duraçao. Ao final, os resultados da pesquisa realizada são sumarizados e utilizados em uma proposta de mecanismo de estimaçao do parametro de Hurst em tempo real, na presenca simultanea de componentes de dependencia de longa e curta duracão. / The use of network trac models that hold self-similar and long-range dependence characteristics have shown to be a key element on the correct characterization of Local Area Network (LAN) and Wide Area Network (WAN) network trac [1, 2]. Such characterization is necessary to monitor and control the network trac in converged networks [3]. In this context, the accurate estimation of the selfsimilarity parameter, named Hurst parameter, is a major issue. However, studies show that, besides the long-range dependence, WAN network trac may, not uncommonly, present mixed long and short-range dependence characteristics [4, 5]. While great part of either theoretical or practical scientic literature has been focused on the issue of Hurst parameter estimator accuracy [6, 7, 8, 9], little attention has been given to the estimation of such parameter in the presence of short-range dependence. This research work has focused on the study of the Hurst parameter estimation methods based on the wavelet spectrum, specially through the Abry-Veitch method [10] { which is based on the Discrete Wavelet Transform (DWT) transform { and through the wavelet spectrum based on the Discrete Wavelet Packet Transform (DWPT) transform. The results based on the Abry-Veitch method show that, through a suitable adjustment of the estimation parameters, such method yields a robust estimation in the presence of short-range dependence components, even in changing conditions of such component, a desirable characteristic for the real-time estimation of the Hurst parameter. However, the signi cant dispersion presented, occasionally, by the Abry-Veitch method estimates motivated the research of the usage of the wavelet spectrum obtained via DWPT transform to estimate the Hurst parameter. The results show that the usage of such transform generates such a wavelet spectrum that it is possible to detect whether short-range dependence components are present, or not, in the analyzed series. At the end, the research results are summarized and used to propose a realtime Hurst parameter estimation mechanism, in the presence of simultaneous long- and short-range dependence components.

Os modelos ARFIMA de memória longa mostraram-se nesse trabalho mais versáteis à análise da persistência em séries temporais em comparação aos modelos ARIMA. As funções impulso-resposta dos modelos de integração fracionária indicam que essa classe de modelos capta mais adequadamente as informações contidas nas baixas freqüências das séries e, portanto, estes modelos são mais capacitados para avaliar como os choques econômicos são acomodados no médio e longo prazo. Os estudos simulatórios mostraram que os testes de raiz unitária aplicados a processos com memória longa possuem baixo poder, e que os estimadores por máxima verossimilhança e os baseados no espectro de ondaletas são eficientes para estimar o parâmetro de integração fracionária. Os estudos empíricos encontraram componentes altamente persistentes nas séries brasileiras do produto, desemprego e consumo. A análise de co-integração fracionária refutou os resultados do arcabouço I(1)-I(0) que sugerem a não co-integração entre as séries consumo das famílias e renda disponível. A variabilidade relativa dessas séries foi analisada por meio da análise em multiresolução de ondaletas. Concluiu-se que, nas baixas escalas, a variabilidade entre as séries varia em função da escala temporal envolvida. A doutrina da paridade do poder de compra com dados brasileiros foi revisitada por meio da análise de co-integração fracionária. / The long-memory ARFIMA models proved to be more versatile in this study to the analysis of endurance in time series compare to the ARIMA models. The impulse-response functions of the fractionally integrated models indicate that this class of models more adequately gathers the data enclosed in the low frequencies of the series and thus these models are more befitted to evaluate how economic shocks are settled in the medium and long terms. Simulation studies unveiled that the unit root tests applied to long-memory processes have low power, and that the maximum likelihood estimators as well as those based on wavelet spectrum are efficient in estimating the fractional difference parameter. Empirical studies have found highly persistent components in the Brazilian series of the product, unemployment and consumption. The fractional co-integration analysis rebutted the results of the I(1)-I(0) framework, which suggest the non co-integration between the series of families\' consumption and the disposable income. The relative variability of these series was investigated through a wavelet multiresolution analysis. It was concluded that, in small scales, the variability between the series changes according to the time scale involved. The Purchasing Power Parity doctrine with Brazilian data has been revisited through the fractional co-integration analysis.