873 resultados para multi-feature control


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El objetivo principal de este proyecto es crear un sistema capaz de controlar varios UAVs y hacer experimentos con ellos de manera coordinada. El UAV utilizado será la plataforma robótica aérea AR.Drone. Estos drones son cuadricópteros con sus cuatro servomotores eléctricos que permiten un control muy robusto de sus maniobras en el aire. El sistema completo estará compuesto por varios drones y un controlador, que en este caso será un ordenador. A partir de la creación de la red compuesta por los drones y el controlador, se detallarán los programas que se han utilizado para controlar los drones, ya sea en los vuelos autónomos o vuelos controlados. El objetivo de estos vuelos será transportar objetos lineales, como pueden ser mangueras o cuerdas, mediante el vuelo coordinado de los drones dotados de sistemas de jación para que los puedan transportar. Esto es, gracias a un sistema de sujeción que le añadiremos a cada drone probaremos el transporte de varios tipos de cuerdas por dos o más drones a la vez guiados por un solo controlador. Los programas creados tendrán el objetivo de corregir el vuelo e intentar conseguir la estabilidad necesaria para que los drones puedan transportar las cuerdas o mangueras sin perder el control debido a su peso o algún efecto producido por los otros drones, como corrientes de aire inducidas por los rotores. Este proyecto tiene dos partes importantes: La primera es la creación del sistema que nos permite transportar las cuerdas y la segunda es observar y analizar el comportamiento del sistema durante diferentes experimentos. A la hora de explicar las pruebas experimentales se detallará la situación del sistema con el número de drones y el objeto a transportar. Además, se resumirán los resultados de los experimentos re ejados en valores de parámetros de vuelo recibidos de los drones. Para acabar, se detallarán las diferentes conclusiones a las que se ha llegado mediante los experimentos y que nos han servido también para escribir las lineas futuras de trabajo que se detallan al nal.

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The dissertation studies the general area of complex networked systems that consist of interconnected and active heterogeneous components and usually operate in uncertain environments and with incomplete information. Problems associated with those systems are typically large-scale and computationally intractable, yet they are also very well-structured and have features that can be exploited by appropriate modeling and computational methods. The goal of this thesis is to develop foundational theories and tools to exploit those structures that can lead to computationally-efficient and distributed solutions, and apply them to improve systems operations and architecture.

Specifically, the thesis focuses on two concrete areas. The first one is to design distributed rules to manage distributed energy resources in the power network. The power network is undergoing a fundamental transformation. The future smart grid, especially on the distribution system, will be a large-scale network of distributed energy resources (DERs), each introducing random and rapid fluctuations in power supply, demand, voltage and frequency. These DERs provide a tremendous opportunity for sustainability, efficiency, and power reliability. However, there are daunting technical challenges in managing these DERs and optimizing their operation. The focus of this dissertation is to develop scalable, distributed, and real-time control and optimization to achieve system-wide efficiency, reliability, and robustness for the future power grid. In particular, we will present how to explore the power network structure to design efficient and distributed market and algorithms for the energy management. We will also show how to connect the algorithms with physical dynamics and existing control mechanisms for real-time control in power networks.

The second focus is to develop distributed optimization rules for general multi-agent engineering systems. A central goal in multiagent systems is to design local control laws for the individual agents to ensure that the emergent global behavior is desirable with respect to the given system level objective. Ideally, a system designer seeks to satisfy this goal while conditioning each agent’s control on the least amount of information possible. Our work focused on achieving this goal using the framework of game theory. In particular, we derived a systematic methodology for designing local agent objective functions that guarantees (i) an equivalence between the resulting game-theoretic equilibria and the system level design objective and (ii) that the resulting game possesses an inherent structure that can be exploited for distributed learning, e.g., potential games. The control design can then be completed by applying any distributed learning algorithm that guarantees convergence to the game-theoretic equilibrium. One main advantage of this game theoretic approach is that it provides a hierarchical decomposition between the decomposition of the systemic objective (game design) and the specific local decision rules (distributed learning algorithms). This decomposition provides the system designer with tremendous flexibility to meet the design objectives and constraints inherent in a broad class of multiagent systems. Furthermore, in many settings the resulting controllers will be inherently robust to a host of uncertainties including asynchronous clock rates, delays in information, and component failures.

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A novel technique of controlling the evolution of the filamentation was experimentally demonstrated in an argon gas-filled tube. The entrance of the filament was heated by a furnace and the other end was cooled with air, which resulted in the temperature gradient distribution along the tube. The experimental results show that multiple filaments are merged into a single filament and then no filament by only increasing the temperature at the entrance of the filament. Also, the filament can appear and disappear after increasing the local temperature and input pulse energy in turn. This technique offers another degree of freedom to control the filamentation and opens a new way for multi-mJ level monocycle pulse generation through filamentation in the noble gas.

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This thesis presents a civil engineering approach to active control for civil structures. The proposed control technique, termed Active Interaction Control (AIC), utilizes dynamic interactions between different structures, or components of the same structure, to reduce the resonance response of the controlled or primary structure under earthquake excitations. The primary control objective of AIC is to minimize the maximum story drift of the primary structure. This is accomplished by timing the controlled interactions so as to withdraw the maximum possible vibrational energy from the primary structure to an auxiliary structure, where the energy is stored and eventually dissipated as the external excitation decreases. One of the important advantages of AIC over most conventional active control approaches is the very low external power required.

In this thesis, the AIC concept is introduced and a new AIC algorithm, termed Optimal Connection Strategy (OCS) algorithm, is proposed. The efficiency of the OCS algorithm is demonstrated and compared with two previously existing AIC algorithms, the Active Interface Damping (AID) and Active Variable Stiffness (AVS) algorithms, through idealized examples and numerical simulations of Single- and Multi-Degree-of Freedom systems under earthquake excitations. It is found that the OCS algorithm is capable of significantly reducing the story drift response of the primary structure. The effects of the mass, damping, and stiffness of the auxiliary structure on the system performance are investigated in parametric studies. Practical issues such as the sampling interval and time delay are also examined. A simple but effective predictive time delay compensation scheme is developed.

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The Hamilton Jacobi Bellman (HJB) equation is central to stochastic optimal control (SOC) theory, yielding the optimal solution to general problems specified by known dynamics and a specified cost functional. Given the assumption of quadratic cost on the control input, it is well known that the HJB reduces to a particular partial differential equation (PDE). While powerful, this reduction is not commonly used as the PDE is of second order, is nonlinear, and examples exist where the problem may not have a solution in a classical sense. Furthermore, each state of the system appears as another dimension of the PDE, giving rise to the curse of dimensionality. Since the number of degrees of freedom required to solve the optimal control problem grows exponentially with dimension, the problem becomes intractable for systems with all but modest dimension.

In the last decade researchers have found that under certain, fairly non-restrictive structural assumptions, the HJB may be transformed into a linear PDE, with an interesting analogue in the discretized domain of Markov Decision Processes (MDP). The work presented in this thesis uses the linearity of this particular form of the HJB PDE to push the computational boundaries of stochastic optimal control.

This is done by crafting together previously disjoint lines of research in computation. The first of these is the use of Sum of Squares (SOS) techniques for synthesis of control policies. A candidate polynomial with variable coefficients is proposed as the solution to the stochastic optimal control problem. An SOS relaxation is then taken to the partial differential constraints, leading to a hierarchy of semidefinite relaxations with improving sub-optimality gap. The resulting approximate solutions are shown to be guaranteed over- and under-approximations for the optimal value function. It is shown that these results extend to arbitrary parabolic and elliptic PDEs, yielding a novel method for Uncertainty Quantification (UQ) of systems governed by partial differential constraints. Domain decomposition techniques are also made available, allowing for such problems to be solved via parallelization and low-order polynomials.

The optimization-based SOS technique is then contrasted with the Separated Representation (SR) approach from the applied mathematics community. The technique allows for systems of equations to be solved through a low-rank decomposition that results in algorithms that scale linearly with dimensionality. Its application in stochastic optimal control allows for previously uncomputable problems to be solved quickly, scaling to such complex systems as the Quadcopter and VTOL aircraft. This technique may be combined with the SOS approach, yielding not only a numerical technique, but also an analytical one that allows for entirely new classes of systems to be studied and for stability properties to be guaranteed.

The analysis of the linear HJB is completed by the study of its implications in application. It is shown that the HJB and a popular technique in robotics, the use of navigation functions, sit on opposite ends of a spectrum of optimization problems, upon which tradeoffs may be made in problem complexity. Analytical solutions to the HJB in these settings are available in simplified domains, yielding guidance towards optimality for approximation schemes. Finally, the use of HJB equations in temporal multi-task planning problems is investigated. It is demonstrated that such problems are reducible to a sequence of SOC problems linked via boundary conditions. The linearity of the PDE allows us to pre-compute control policy primitives and then compose them, at essentially zero cost, to satisfy a complex temporal logic specification.

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Many applications in cosmology and astrophysics at millimeter wavelengths including CMB polarization, studies of galaxy clusters using the Sunyaev-Zeldovich effect (SZE), and studies of star formation at high redshift and in our local universe and our galaxy, require large-format arrays of millimeter-wave detectors. Feedhorn and phased-array antenna architectures for receiving mm-wave light present numerous advantages for control of systematics, for simultaneous coverage of both polarizations and/or multiple spectral bands, and for preserving the coherent nature of the incoming light. This enables the application of many traditional "RF" structures such as hybrids, switches, and lumped-element or microstrip band-defining filters.

Simultaneously, kinetic inductance detectors (KIDs) using high-resistivity materials like titanium nitride are an attractive sensor option for large-format arrays because they are highly multiplexable and because they can have sensitivities reaching the condition of background-limited detection. A KID is a LC resonator. Its inductance includes the geometric inductance and kinetic inductance of the inductor in the superconducting phase. A photon absorbed by the superconductor breaks a Cooper pair into normal-state electrons and perturbs its kinetic inductance, rendering it a detector of light. The responsivity of KID is given by the fractional frequency shift of the LC resonator per unit optical power.

However, coupling these types of optical reception elements to KIDs is a challenge because of the impedance mismatch between the microstrip transmission line exiting these architectures and the high resistivity of titanium nitride. Mitigating direct absorption of light through free space coupling to the inductor of KID is another challenge. We present a detailed titanium nitride KID design that addresses these challenges. The KID inductor is capacitively coupled to the microstrip in such a way as to form a lossy termination without creating an impedance mismatch. A parallel plate capacitor design mitigates direct absorption, uses hydrogenated amorphous silicon, and yields acceptable noise. We show that the optimized design can yield expected sensitivities very close to the fundamental limit for a long wavelength imager (LWCam) that covers six spectral bands from 90 to 400 GHz for SZE studies.

Excess phase (frequency) noise has been observed in KID and is very likely caused by two-level systems (TLS) in dielectric materials. The TLS hypothesis is supported by the measured dependence of the noise on resonator internal power and temperature. However, there is still a lack of a unified microscopic theory which can quantitatively model the properties of the TLS noise. In this thesis we derive the noise power spectral density due to the coupling of TLS with phonon bath based on an existing model and compare the theoretical predictions about power and temperature dependences with experimental data. We discuss the limitation of such a model and propose the direction for future study.

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We are at the cusp of a historic transformation of both communication system and electricity system. This creates challenges as well as opportunities for the study of networked systems. Problems of these systems typically involve a huge number of end points that require intelligent coordination in a distributed manner. In this thesis, we develop models, theories, and scalable distributed optimization and control algorithms to overcome these challenges.

This thesis focuses on two specific areas: multi-path TCP (Transmission Control Protocol) and electricity distribution system operation and control. Multi-path TCP (MP-TCP) is a TCP extension that allows a single data stream to be split across multiple paths. MP-TCP has the potential to greatly improve reliability as well as efficiency of communication devices. We propose a fluid model for a large class of MP-TCP algorithms and identify design criteria that guarantee the existence, uniqueness, and stability of system equilibrium. We clarify how algorithm parameters impact TCP-friendliness, responsiveness, and window oscillation and demonstrate an inevitable tradeoff among these properties. We discuss the implications of these properties on the behavior of existing algorithms and motivate a new algorithm Balia (balanced linked adaptation) which generalizes existing algorithms and strikes a good balance among TCP-friendliness, responsiveness, and window oscillation. We have implemented Balia in the Linux kernel. We use our prototype to compare the new proposed algorithm Balia with existing MP-TCP algorithms.

Our second focus is on designing computationally efficient algorithms for electricity distribution system operation and control. First, we develop efficient algorithms for feeder reconfiguration in distribution networks. The feeder reconfiguration problem chooses the on/off status of the switches in a distribution network in order to minimize a certain cost such as power loss. It is a mixed integer nonlinear program and hence hard to solve. We propose a heuristic algorithm that is based on the recently developed convex relaxation of the optimal power flow problem. The algorithm is efficient and can successfully computes an optimal configuration on all networks that we have tested. Moreover we prove that the algorithm solves the feeder reconfiguration problem optimally under certain conditions. We also propose a more efficient algorithm and it incurs a loss in optimality of less than 3% on the test networks.

Second, we develop efficient distributed algorithms that solve the optimal power flow (OPF) problem on distribution networks. The OPF problem determines a network operating point that minimizes a certain objective such as generation cost or power loss. Traditionally OPF is solved in a centralized manner. With increasing penetration of volatile renewable energy resources in distribution systems, we need faster and distributed solutions for real-time feedback control. This is difficult because power flow equations are nonlinear and kirchhoff's law is global. We propose solutions for both balanced and unbalanced radial distribution networks. They exploit recent results that suggest solving for a globally optimal solution of OPF over a radial network through a second-order cone program (SOCP) or semi-definite program (SDP) relaxation. Our distributed algorithms are based on the alternating direction method of multiplier (ADMM), but unlike standard ADMM-based distributed OPF algorithms that require solving optimization subproblems using iterative methods, the proposed solutions exploit the problem structure that greatly reduce the computation time. Specifically, for balanced networks, our decomposition allows us to derive closed form solutions for these subproblems and it speeds up the convergence by 1000x times in simulations. For unbalanced networks, the subproblems reduce to either closed form solutions or eigenvalue problems whose size remains constant as the network scales up and computation time is reduced by 100x compared with iterative methods.

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The common 2652 6N del variant in the CASP8 promoter (rs3834129) has been described as a putative low-penetrance risk factor for different cancer types. In particular, some studies suggested that the deleted allele (del) was inversely associated with CRC risk while other analyses failed to confirm this. Hence, to better understand the role of this variant in the risk of developing CRC, we performed a multi-centric case-control study. In the study, the variant 2652 6N del was genotyped in a total of 6,733 CRC cases and 7,576 controls recruited by six different centers located in Spain, Italy, USA, England, Czech Republic and the Netherlands collaborating to the international consortium COGENT (COlorectal cancer GENeTics). Our analysis indicated that rs3834129 was not associated with CRC risk in the full data set. However, the del allele was under-represented in one set of cases with a family history of CRC (per allele model OR = 0.79, 95% CI = 0.69-0.90) suggesting this allele might be a protective factor versus familial CRC. Since this multi-centric case-control study was performed on a very large sample size, it provided robust clarification of the effect of rs3834129 on the risk of developing CRC in Caucasians.

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The manufacturing industry is currently facing unprecedented challenges from changes and disturbances. The sources of these changes and disturbances are of different scope and magnitude. They can be of a commercial nature, or linked to fast product development and design, or purely operational (e.g. rush order, machine breakdown, material shortage etc.). In order to meet these requirements it is increasingly important that a production operation be flexible and is able to adapt to new and more suitable ways of operating. This paper focuses on a new strategy for enabling manufacturing control systems to adapt to changing conditions both in terms of product variation and production system upgrades. The approach proposed is based on two key concepts: (1) An autonomous and distributed approach to manufacturing control based on multi-agent methods in which so called operational agents represent the key physical and logical elements in the production environment to be controlled - for example, products and machines and the control strategies that drive them and (2) An adaptation mechanism based around the evolutionary concept of replicator dynamics which updates the behaviour of newly formed operational agents based on historical performance records in order to be better suited to the production environment. An application of this approach for route selection of similar products in manufacturing flow shops is developed and is illustrated in this paper using an example based on the control of an automobile paint shop.

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O objetivo deste estudo foi avaliar ex vivo a extrusão bacteriana apical após instrumentação mecanizada com sistemas reciprocantes de instrumento único e movimento reciprocante (WaveOne and Reciproc) comparados a um sistema multi-instrumentos (BioRaCe). Quarenta e cinco incisivos inferiores humanos unirradiculares, ovais e de anatomia semelhante foram utilizados. Os dentes foram acessados e seus canais radiculares foram contaminados com uma suspensão de Enterococcus faecalis e incubados por 30 dias possibilitando crescimento bacteriano em biofilme. Os dentes contaminados foram divididos em três grupos com 15 espécimes cada (RE - Reciproc, WO -WaveOne e BR - BioRaCe). Foram utilizados oito dentes para grupos controle de crescimento bacteriano positivo e negativo. As bactérias extruídas apicalmente durante a instrumentação foram coletadas em frascos de vidro contendo 0,9% de NaCl. As amostras microbiológicas foram retiradas dos frascos e incubadas em meio BHI ágar, durante 24 horas. O crescimento bacteriano foi contado e os resultados foram expressos em unidades formadoras de colônia (UFC). Os dados foram analisados pelos testes estatísticos de Wilcoxon e Kruskal-Wallis. Não houve diferença estatisticamente significante no número de UFC entre os dois sistemas reciprocantes (p>0,05). Em contrapartida, o sistema de instrumentos rotatórios mostrou uma quantidade de UFC significativamente maior do que os dois outros grupos (p <0,05). A partir da análise dos resultados e dentro das limitações deste estudo foi possível concluir que todos os sistemas de instrumentação testados extruem bactérias apicalmente. No entanto, ambos os sistemas de instrumento único e movimento reciprocante extruem menos bactérias apicalmente do que o sistema rotatório multi-instrumentos de referência.

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As biometrias vêm sendo utilizadas como solução de controle de acesso a diversos sistemas há anos, mas o simples uso da biometria não pode ser considerado como solução final e perfeita. Muitos riscos existem e não devem ser ignorados. A maioria dos problemas está relacionada ao caminho de transmissão entre o local onde os usuários requerem seus acessos e os servidores onde são guardados os dados biométricos capturados em seu cadastro. Vários tipos de ataques podem ser efetuados por impostores que desejam usar o sistema indevidamente. Além dos aspectos técnicos, existe o aspecto social. É crescente a preocupação do usuário tanto com o armazenamento quanto o uso indevido de suas biometrias, pois é um identificador único e, por ser invariável no tempo, pode ser perdido para sempre caso seja comprometido. O fato de que várias empresas com seus diferentes servidores guardarem as biometrias está causando incomodo aos usuários, pois as torna mais suscetíveis à ataques. Nesta dissertação, o uso de cartões inteligentes é adotado como possível solução para os problemas supracitados. Os cartões inteligentes preparados para multi-aplicações são usados para realizar as comparações biométricas internamente. Dessa forma, não seria mais necessário utilizar diversos servidores, pois as características biométricas estarão sempre em um único cartão em posse do dono. Foram desenvolvidas e implementadas três diferentes algoritmos de identificação biométrica utilizando diferentes características: impressão digital, impressão da palma da mão e íris. Considerando a memória utilizada, tempo médio de execução e acurácia, a biometria da impressão da palma da mão obteve os melhores resultados, alcançando taxas de erro mínimas e tempos de execução inferiores a meio segundo.

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Social interactions in classic cognitive games like the ultimatum game or the prisoner's dilemma typically lead to Nash equilibria when multiple competitive decision makers with perfect knowledge select optimal strategies. However, in evolutionary game theory it has been shown that Nash equilibria can also arise as attractors in dynamical systems that can describe, for example, the population dynamics of microorganisms. Similar to such evolutionary dynamics, we find that Nash equilibria arise naturally in motor interactions in which players vie for control and try to minimize effort. When confronted with sensorimotor interaction tasks that correspond to the classical prisoner's dilemma and the rope-pulling game, two-player motor interactions led predominantly to Nash solutions. In contrast, when a single player took both roles, playing the sensorimotor game bimanually, cooperative solutions were found. Our methodology opens up a new avenue for the study of human motor interactions within a game theoretic framework, suggesting that the coupling of motor systems can lead to game theoretic solutions.