O grande aumento da capacidade de integração de transistores em um único circuito integrado tem exigido grande e constante evolução na metodologia de projeto e práticas de implementação de sistemas eletrônicos embarcados. Tal capacidade de integração resultou no surgimento de sistemas sobre silício (SoCs). O projeto de tais sistemas, mais complexos que seus predecessores, alteram significativamente os fluxos tradicionais de concepção de sistemas, fazendo surgir estratégias tais quais reuso, projetos orientados a plataformas, assim como modelagens e simulações em diferentes níveis de abstração. Um dos diferentes níveis de abstração estudados é o analítico, onde os sistemas são modelados através de representações abstratas. A adoção de modelos analíticos apresenta vantagens, como alta velocidade de execução (permitindo um grande número de análises de modelos diferentes) e facilidade de alteração. No entanto, por se tratarem de modelagens distantes, em termos de abstração, de implementações reais, podem oferecer prognósticos não exatos. Faz-se então necessária a investigação de metodologias que tenham como propósito o aperfeiçoamento de tais modelos em termos de acurácia e fidelidade. O presente trabalho apresenta uma metodologia de modelagem analítica para avaliação de desempenho de sistemas-sobre-silício orientada a aplicação de processadores de redes de pacotes. A metodologia de Network Calculus, a ser implementada nos estágios iniciais de projeto de sistemas-sobre-silício baseados em plataforma, contribui para reduzir o espaço de avaliação de projeto. Trata do equacionamento analítico de representações abstratas das cargas de entrada e também da capacidade de processamento de recursos, visando obter prognósticos mais pessimistas e mais otimistas de parâmetros como latência, requisição de buffer e utilização do sistema, descrito de modo abstrato através de grafos. / The great increase in terms of integration capacity of transistors on integrated circuits has demanded great and constant evolution in the design methodology and practical implementation of embedded electronic systems. Such capacity of integration resulted in the sprouting of systems-on-chips (SoCs). The design of such systems, more complex than their predecessors, significantly changes the traditional flow in the conception of systems, bringing up strategies such like reuse, platform based design, as well as modeling and simulation in different abstraction levels. One of the different abstraction levels under study is the analytical one, where the systems are shaped through abstract representations. The adoption of analytical models presents advantages, such as high speed of execution (allowing a great number of analyses of different models) and easiness for alteration. However, due to their distant representation models, in terms of abstraction, from real implementations, they cannot offer accurate prognostics on several design metrics. Therefore, it is necessary the investigation on methodologies aiming to the enhancement of such models in terms of accuracy and fidelity. The present work shows a methodology of analytical modeling for evaluation of system-on-chip performance guided to the application of network processors of packages. The methodology of Network Calculus, to be implemented in the initial steps of of system-on-chip´s design cycle, contributes to reduce the design space exploration. It deals with the building of analytical equations for abstract representations of workloads and also the processing capacity of resources, aiming at to get most pessimistic and most optimistic prognostics of parameters such like latency, buffer requirements and the system utilization, described in abstract way through graphs.

Submitted by Haroudo Xavier Filho (haroudo.xavierfo@ufpe.br) on 2016-03-11T13:40:44Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao - Eliomar Gomes Campos - Mestrado Ciência da computação.pdf: 5177532 bytes, checksum: 4fc152f297f19c035a0affba1640dcc3 (MD5) / Made available in DSpace on 2016-03-11T13:40:44Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao - Eliomar Gomes Campos - Mestrado Ciência da computação.pdf: 5177532 bytes, checksum: 4fc152f297f19c035a0affba1640dcc3 (MD5) Previous issue date: 2015-08-03 / FACEPE / Composite web services, also known as mashups, are useful to build added-value products in the web. Cloud computing environments have been widely used for hosting web services due to the possibility of increasing or decreasing available resources through automatic mechanisms (i.e.: auto scaling). Such elastic behavior ease the task of reaching satisfactory performance on peaks of demand without wasting resources. It is hard to determine the right components to tune such systems performance when eventually needed. This study evaluates the performance of auto scaling mechanisms for private clouds hosting an event recommendation web service. A hierarchical modeling approach is used to cope with the complexity of such a system, and represent specific details of these mechanisms. Our study applies parametric sensitivity analysis from several performance metrics of the models, such as mean execution time of the auto scaling monitoring, mean time of VMs instantiation, and the mean response time perceived by the web service user. We also have carried a General Full Factorial Experiment, in order to calculate the relevance and effects of each factor involved in the processes of auto scaling and virtual machines (VMs) instantiation. For the auto scaling monitoring, we analyze the factors: collection period of a metric, number of monitored virtual machines, and the time of monitoring of a metric. Regarding the instantiation process, the following factors have been chosen: VM type, VM image size, and VM caching. This analysis allows checking the impact of parameters on the system response time and pointing out effective ways for improvement of performance. / Serviços web compostos, também conhecidos como mashups, são úteis para construir produtos de valor agregado na web. Ambientes de computação em nuvem têm sido amplamente utilizados para hospedar serviços web, devido à possibilidade de aumentar ou diminuir os recursos disponíveis através de mecanismos automáticos (i.e.: escala automática). Tal comportamento elástico facilita a tarefa de alcançar um desempenho satisfatório nos picos de demanda sem desperdiçar recursos. É difícil determinar os componentes certos para ajustar o desempenho desses sistemas eventualmente, quando necessário. Este estudo avalia o desempenho dos mecanismos de escala automática e elasticidade para nuvens privadas hospedando um serviço web de recomendação de eventos. Uma abordagem de modelagem hierárquica é utilizada afim de lidar com a complexidade de tal sistema, e representar detalhes específicos desses mecanismos. Nosso estudo aplicou análise de sensibilidade paramétrica a partir de várias métricas de desempenho dos modelos, tais como o tempo médio de execução do monitoramento de escala automática, tempo médio da instanciação de VMs e o tempo médio da resposta percebida pelo usuário do serviço web. Realizamos também um Experimento Geral Fatorial Completo, com o objetivo de calcular os efeitos e relevâncias de cada fator envolvido nos processos escala automática e instanciação de máquinas virtuais (virtual machines - VMs). Para o monitoramento de escala automática, analisamos os fatores: período de coleta de uma métrica, número de máquinas virtuais monitoradas, e o tempo de monitoração de uma métrica. Quanto ao processo de instanciação, os seguintes fatores foram escolhidos: tipo de VM, tamanho da imagem da VM, e cache da VM. Estas análises permitem verificar o impacto dos parâmetros sobre o tempo de resposta do sistema e apontar formas eficazes de melhoria do desempenho.

Submitted by Rafael Santana (rafael.silvasantana@ufpe.br) on 2017-05-04T17:58:30Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tese_rubens_digital_biblioteca_08092016.pdf: 4506490 bytes, checksum: 251226257a6b659a6ae047e659147a8a (MD5) / Made available in DSpace on 2017-05-04T17:58:30Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tese_rubens_digital_biblioteca_08092016.pdf: 4506490 bytes, checksum: 251226257a6b659a6ae047e659147a8a (MD5) Previous issue date: 2016-03-01 / CAPES / Cloud computing paradigm is able to reduce costs of acquisition and maintenance of computer systems, and enables the balanced management of resources according to the demand. Hierarchical and composite analytical models are suitable for describing performance and dependability of cloud computing systems in a concise manner, dealing with the huge number of components which constitute such kind of system. That approach uses distinct sub-models for each system level and the measures obtained in each sub-model are integrated to compute the measures for the whole system. Identification of bottlenecks in hierarchical models might be difficult yet, due to the large number of parameters and their distribution among distinct modeling levels and formalisms. This thesis proposes methods for evaluation and detection of bottlenecks of cloud computing systems. The methodology is based on hierarchical modeling and parametric sensitivity analysis techniques tailored for such a scenario. This research introduces methods to build unified sensitivity rankings when distinct modeling formalisms are combined. These methods are embedded in the Mercury software tool, providing an automated sensitivity analysis framework for supporting the process. Distinct case studies helped in testing the methodology, encompassing hardware and software aspects of cloud systems, from basic infrastructure level to applications that are hosted in private clouds. The case studies showed that the proposed approach is helpful for guiding cloud systems designers and administrators in the decision-making process, especially for tune-up and architectural improvements. It is possible to employ the methodology through an optimization algorithm proposed here, called Sensitive GRASP. This algorithm aims at optimizing performance and dependability of computing systems that cannot stand the exploration of all architectural and configuration possibilities to find the best quality of service. This is especially useful for cloud-hosted services and their complex underlying infrastructures. / O paradigma de computação em nuvem é capaz de reduzir os custos de aquisição e manutenção de sistemas computacionais e permitir uma gestão equilibrada dos recursos de acordo com a demanda. Modelos analíticos hierárquicos e compostos são adequados para descrever de forma concisa o desempenho e a confiabilidade de sistemas de computação em nuvem, lidando com o grande número de componentes que constituem esse tipo de sistema. Esta abordagem usa sub-modelos distintos para cada nível do sistema e as medidas obtidas em cada sub-modelo são usadas para calcular as métricas desejadas para o sistema como um todo. A identificação de gargalos em modelos hierárquicos pode ser difícil, no entanto, devido ao grande número de parâmetros e sua distribuição entre os distintos formalismos e níveis de modelagem. Esta tese propõe métodos para a avaliação e detecção de gargalos de sistemas de computação em nuvem. A abordagem baseia-se na modelagem hierárquica e técnicas de análise de sensibilidade paramétrica adaptadas para tal cenário. Esta pesquisa apresenta métodos para construir rankings unificados de sensibilidade quando formalismos de modelagem distintos são combinados. Estes métodos são incorporados no software Mercury, fornecendo uma estrutura automatizada de apoio ao processo. Uma metodologia de suporte a essa abordagem foi proposta e testada ao longo de estudos de casos distintos, abrangendo aspectos de hardware e software de sistemas IaaS (Infraestrutura como um serviço), desde o nível de infraestrutura básica até os aplicativos hospedados em nuvens privadas. Os estudos de caso mostraram que a abordagem proposta é útil para orientar os projetistas e administradores de infraestruturas de nuvem no processo de tomada de decisões, especialmente para ajustes eventuais e melhorias arquiteturais. A metodologia também pode ser aplicada por meio de um algoritmo de otimização proposto aqui, chamado Sensitive GRASP. Este algoritmo tem o objetivo de otimizar o desempenho e a confiabilidade de sistemas em cenários onde não é possível explorar todas as possibilidades arquiteturais e de configuração para encontrar a melhor qualidade de serviço. Isto é especialmente útil para os serviços hospedados na nuvem e suas complexas

Les travaux de cette thèse portent sur la méthodologie de conception des Machines Synchrones à Aimants Permanents (MSAP) avec pour application le véhicule électrique. Dans une première partie, nous avons abordé du contexte de l'étude et de la problématique du dimensionnement. A l'occasion, nous avons montré qu'il est nécessaire d'adopter une méthodologie de design appropriée en fonction de l'avancement de chaque projet dans le cadre du processus de conception. Nous avons alors proposé différents niveaux de modélisation afin de repérer la machine optimale face au cahier des charges pour ensuite la caractériser plus finement et l'optimiser. La seconde partie du rapport traite donc de la modélisation et de l'optimisation de la MSAP. En premier lieu, un modèle analytique permet d'évaluer les performances globales des centaines de machines très rapidement. Ce premier calcul permet de sélectionner les meilleures machines à l'application pour ensuite les optimiser. Puis, le second niveau de modélisation se base sur les réseaux de réluctances. Ce niveau plus fin permet d'une part de retrouver les performances affinées des MSAP et également de procéder à l'optimisation. Enfin, la dernière partie du rapport est consacrée à l'optimisation de la MSAP en vue d'améliorer grandement les performances et de satisfaire au mieux au cahier des charges. / The works of this thesis concern the design methodology of Permanent Magnet Synchronous Machine (PMSM) for an electric vehicle application. The first part of this report introduces the context of the study and the problem of design. Then, we have shown that it is necessary to adopt an appropriate design methodology based on the progress of each project in the design process. Thus, we proposed different levels of modeling to identify the optimal machine against the specifications, and then characterize it more finely and optimize. The second part of the report deals with the modeling and optimization of the PMSM. First, an analytical model is employed to evaluate the overall performance of hundreds of machines very quickly. The first calculation is used to select the best machine for the application and then optimize them. Then, the second level of modeling is based on reluctance networks. This model allows finer resolution of the PMSM and also permits to perform optimization. The last part of the report is devoted to the optimization of the PMSM in order to greatly improve performances and meet the specifications.

<p>We are in the midst of a technological revolution (popularly known as Industrie 4.0 or 4th Industrial Revolution) where our cars are being equipped with hundreds of sensors that make them safer, homes are becoming smarter, industry yields are at an all-time high, and internet-of-things is a reality. This was largely possible due to the developments in communication, electronics, motor controls, robotics, cyber security, software, efficient power distribution, etc. One of the major propellants of the 4th Industrial revolution is the ever-expanding applications of power electronics devices. All electrical energy will be provided, handled, and consumed through power electronics devices in the near future. Therefore, the reliability of power electronics devices will be instrumental in driving future technological advances. </p> <p> </p> <p><br></p><p>A myriad of devices is categorized as power electronics devices, and in the heart of those devices are the transistors. Although Silicon-based transistors still dominate the power electronics market, a paradigm shift towards wide bandgap semiconductors, such as silicon carbide (SiC), gallium nitride (GaN), beta-gallium oxide etc., is underway. However, realizing the full potential of these devices demands unconventional design, layout, and reliability. </p> <p> </p> <p>In this thesis, we try to establish a generalized model of reliability for power and logic transistors. We start by defining a comprehensive, substrate-, self-heating-, and reliability-aware safe operating area (SOA) that analytically establishes the optimum and self-consistent trade-off among breakdown voltage, power consumption, operating frequency, heat dissipation, and reliability before actual device fabrication. Then we take a deeper look into the reliability of individual transistors (a beta-gallium oxide transistor and a Silicon-based LDMOS), to test the predictions by the safe operating area, using both experiments and simulations. In the beta-gallium oxide transistor, we studied its implementation in a DC-DC voltage converter and concluded that the self-heating is a performance bottleneck and suggested approaches to alleviate it. For the LDMOS transistor, we investigated the hot carrier degradation (HCD) using experiments and simulations. We established that the HCD degradation kinetics is universal, and physics is the same as a classical transistor, despite a complicated geometry. Finally, we studied the correlation between HCD and radiation in LDMOS used in space shuttles, airplanes, etc., to determine its lifetime. </p><p><br></p> <p> </p> <p>We have holistically analyzed the reliability of power transistors by extending the theories of logic transistors in this thesis. Therefore, this thesis takes us a step closer to a generalized reliability model for power transistors by developing a comprehensive and predictive model for the safe operating area, encompassing all sources of stresses (e.g., electrical, thermal, and radiation) it experiences during operation.</p>

In this work, we focus on enhancing the efficacy of predictive modeling in operational management decision making in two different settings: Essay 1 focuses on demand forecasting for the companies and the second study utilizes longitudinal data to analyze the illicit drug seizure and overdose deaths in the United States. In Essay 1, we utilize an operational system (newsvendor model) to evaluate the forecast method outcome and provide guidelines for forecast method (the exponential smoothing model) performance assessment and judgmental adjustments. To assess the forecast outcome, we consider not only the common forecast error minimization approach but also the profit maximization at the end of the forecast horizon. Including profit in our assessment enables us to determine if error minimization always results in maximum profit. We also look at the different levels of profit margin to analyze their impact on the forecasting method performance. Our study also investigates how different demand patterns influence maximizing the forecasting method performance. Our study shows that the exponential smoothing model family has a better performance in high-profit products, and the rate of decrease in performance versus demand uncertainty is higher in a stationary demand environment.In the second essay, we focus on illicit drug overdose death rate. Illicit drug overdose deaths are the leading cause of injury death in the United States. In 2017, overdose death reached the highest ever recorded level (70,237), and statistics show that it is a growing problem. The age adjusted rate of drug overdose deaths in 2017 (21.7 per 100,000) is 9.6% higher than the rate in 2016 (19.8 per 100,000) (U. S. Drug Enforcement Administration, 2018, p. V). Also, Marijuana consumption among youth has increased since 2009. The magnitude of the illegal drug trade and its resulting problems have led the government to produce large and comprehensive datasets on a variety of phenomena relating to illicit drugs. In this study, we utilize these datasets to examine how marijuana usage among youth influence excessive drug usage. We measure excessive drug usage in terms of drug overdose death rate per state. Our study shows that illegal marijuana consumption increases excessive drug use. Also, we analyze the pattern of most frequently seized illicit drugs and compare it with drugs that are most frequently involved in a drug overdose death. We further our analysis to study seizure patterns across layers of heroin and cocaine supply chain across states. This analysis reveals that most active layers of the heroin supply chain in the American market are retailers and wholesalers, while multi-kilo traffickers are the most active players in the cocaine supply chain. In summary, the studies in this dissertation explore the use of analytical, descriptive, and predictive models to detect patterns to improve efficacy and initiate better operational management decision making.

In this paper, we look at the shear-out failure of carbon fiber reinforced plastics connections in the automotive industry. Contrary to the aircraft industry, the boundary conditions of automotive applications favor this failure mode strongly. Moreover, the use of other joining technologies than that used in the aircraft industry, such as joining by forming, leads to new challenges. The different influences, typical for joining by forming, on ultimate shear-out strength were first investigated separately and then transferred and validated on connections related to praxis by an analytical model. Special attention was given to effects that resulted from oversized pre-holes, acting clamping forces, and the reduced quality of the laminates in the immediate vicinity of the joint due to the joining process.

Les travaux présentés dans cette thèse portent sur la modélisation et l’optimisation de machines électriques pour des applications à entraînement direct. Ils s’inscrivent dans un contexte de réduction de l’utilisation des aimants en terres rares et d’amélioration du rendement énergétique. Un état de l’art des machines électriques est réalisé et l’accent est mis sur les machines à flux radial et axial pour les applications à fort couple et basse vitesse. Une classification est établie visant à identifier les structures intéressantes et innovantes. L’étude de la machine radiale est d’abord réalisée. Une étude comparative de différentes machines issues de l’étude bibliographique est effectuée. Cette étude a permis de choisir une structure originale à bobinage concentré sur dents et aimants en multi-V. Dans le but de calculer les performances du moteur avec un temps de calcul réduit, une modélisation analytique multi-physique de la structure est réalisée. Un premier dimensionnement de la machine a conduit à la définition d’un prototype qui a servi à la validation expérimentale du modèle multi-physique. Une approche de conception par optimisation multi-objectifs est adoptée pour obtenir les machines optimales répondant à un cahier des charges industriel. La suite de l’étude concerne une machine à flux axial à structure innovante. Il s’agit d’une machine à double rotor et simple stator avec plusieurs barrières de flux par pôle. Une étude par éléments finis est d’abord réalisée afin de valider le passage d’un modèle tridimensionnel à modèle bidimensionnel. L’analyse des pertes fer a permis de choisir les matériaux utilisés au stator et aux rotors. L’étape suivante consiste à établir un modèle analytique multiphysique de la machine à flux axial et de l’optimiser selon le même cahier des charges que celui de la machine radiale. Finalement, une comparaison entre les deux structures radiale et axiale est effectuée pour évaluer les avantages en termes de densité de couple. / The work presented in this thesis deals with the modeling and optimization of electrical machines for direct drive applications. The objective is to reduce the use of rare earth permanent magnets along with the improvement of the motor’s efficiency. A state of the art of electrical machines is realized. It focused on radial and axial flow structures for high torque and low speed applications. A classification is established to identify interesting and innovative structures. Firstly, the radial machine is studied. The choice of the structure is made after a finite element comparison of different machines resulting from the literature. This led to an original structure with concentrated winding and multi-V shape barriers. Then, a multi-physics analytical modeling of the structure is detailed in order to calculate the performances with a reduced calculation time. A preliminary design led to the definition of a prototype which was used to experimentally validate the multi-physics model. An optimization design approach is adopted to obtain optimal machines meeting industrial specifications. Secondly, a novel axial flux structure is studied. It is a machine with double rotor and single stator with several barriers per pole. A finite element study is carried out in order to validate the transition from a three dimensional to a two-dimensional model. The analysis of iron losses made it possible to choose the materials used in the stator and the rotors. Then, the development of a multi-physics analytical model for the axial machine is proposed. It is used to optimize the structure according to the same specifications defined for the radial machine. Finally, a comparison between the radial and axial structures is performed in order to evaluate the advantages in terms of torque density.

No description available.

Analytical models of wood-frame shear walls and diaphragms for use in monotonic, quasi-static (cyclic), and dynamic analyses are developed in this thesis. A new analytical model is developed to accurately represent connections between sheathing panels and wood framing members (sheathing-to-framing connections) in structural analysis computer programs. This new model represents sheathing–to–framing connections using an oriented pair of nonlinear springs. Unlike previous models, the new analytical model for sheathing-to-framing connections is suitable for both monotonic, cyclic, or dynamic analyses. Moreover, the new model does not need to be scaled or adjusted. The new analytical model may be implemented in a general purpose finite element program, such as ABAQUS, or in a specialized structural analysis program, such as CASHEW. The analytical responses of several shear walls and diaphragms employing this new model are validated against measured data from experimental testing. A less complex analytical model of shear walls and diaphragms, QUICK, is developed for routine use and for dynamic analysis. QUICK utilizes an equivalent single degree of freedom system that has been determined using either calibrated parameters from experimental or analytical data, or estimated sheathing-to-framing connection data. Application of the new analytical models is illustrated in two applications. In the first application, the advantages of diaphragms using glass fiber reinforced polymer (GFRP) panels in conjunction with plywood panels as sheathing (hybrid diaphragms) are presented. In the second application, the response of shear walls with improperly driven (overdriven)nails is determined along with a method to estimate strength reduction due to both the depth and the percentage of total nails overdriven.