228 resultados para Multipliers


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This paper presents a study of kinematic and force singularities in parallel manipulators and closed-loop mechanisms and their relationship to accessibility and controllability of such manipulators and closed-loop mechanisms, Parallel manipulators and closed-loop mechanisms are classified according to their degrees of freedom, number of output Cartesian variables used to describe their motion and the number of actuated joint inputs. The singularities in the workspace are obtained by considering the force transformation matrix which maps the forces and torques in joint space to output forces and torques ill Cartesian space. The regions in the workspace which violate the small time local controllability (STLC) and small time local accessibility (STLA) condition are obtained by deriving the equations of motion in terms of Cartesian variables and by using techniques from Lie algebra.We show that for fully actuated manipulators when the number ofactuated joint inputs is equal to the number of output Cartesian variables, and the force transformation matrix loses rank, the parallel manipulator does not meet the STLC requirement. For the case where the number of joint inputs is less than the number of output Cartesian variables, if the constraint forces and torques (represented by the Lagrange multipliers) become infinite, the force transformation matrix loses rank. Finally, we show that the singular and non-STLC regions in the workspace of a parallel manipulator and closed-loop mechanism can be reduced by adding redundant joint actuators and links. The results are illustrated with the help of numerical examples where we plot the singular and non-STLC/non-STLA regions of parallel manipulators and closed-loop mechanisms belonging to the above mentioned classes. (C) 2000 Elsevier Science Ltd. All rights reserved.

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There are a number of large networks which occur in many problems dealing with the flow of power, communication signals, water, gas, transportable goods, etc. Both design and planning of these networks involve optimization problems. The first part of this paper introduces the common characteristics of a nonlinear network (the network may be linear, the objective function may be non linear, or both may be nonlinear). The second part develops a mathematical model trying to put together some important constraints based on the abstraction for a general network. The third part deals with solution procedures; it converts the network to a matrix based system of equations, gives the characteristics of the matrix and suggests two solution procedures, one of them being a new one. The fourth part handles spatially distributed networks and evolves a number of decomposition techniques so that we can solve the problem with the help of a distributed computer system. Algorithms for parallel processors and spatially distributed systems have been described.There are a number of common features that pertain to networks. A network consists of a set of nodes and arcs. In addition at every node, there is a possibility of an input (like power, water, message, goods etc) or an output or none. Normally, the network equations describe the flows amoungst nodes through the arcs. These network equations couple variables associated with nodes. Invariably, variables pertaining to arcs are constants; the result required will be flows through the arcs. To solve the normal base problem, we are given input flows at nodes, output flows at nodes and certain physical constraints on other variables at nodes and we should find out the flows through the network (variables at nodes will be referred to as across variables).The optimization problem involves in selecting inputs at nodes so as to optimise an objective function; the objective may be a cost function based on the inputs to be minimised or a loss function or an efficiency function. The above mathematical model can be solved using Lagrange Multiplier technique since the equalities are strong compared to inequalities. The Lagrange multiplier technique divides the solution procedure into two stages per iteration. Stage one calculates the problem variables % and stage two the multipliers lambda. It is shown that the Jacobian matrix used in stage one (for solving a nonlinear system of necessary conditions) occurs in the stage two also.A second solution procedure has also been imbedded into the first one. This is called total residue approach. It changes the equality constraints so that we can get faster convergence of the iterations.Both solution procedures are found to coverge in 3 to 7 iterations for a sample network.The availability of distributed computer systems — both LAN and WAN — suggest the need for algorithms to solve the optimization problems. Two types of algorithms have been proposed — one based on the physics of the network and the other on the property of the Jacobian matrix. Three algorithms have been deviced, one of them for the local area case. These algorithms are called as regional distributed algorithm, hierarchical regional distributed algorithm (both using the physics properties of the network), and locally distributed algorithm (a multiprocessor based approach with a local area network configuration). The approach used was to define an algorithm that is faster and uses minimum communications. These algorithms are found to converge at the same rate as the non distributed (unitary) case.

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We study the boundedness of Toeplitz operators on Segal-Bargmann spaces in various contexts. Using Gutzmer's formula as the main tool we identify symbols for which the Toeplitz operators correspond to Fourier multipliers on the underlying groups. The spaces considered include Fock spaces, Hermite and twisted Bergman spaces and Segal-Bargmann spaces associated to Riemannian symmetric spaces of compact type.

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Combinatorial exchanges are double sided marketplaces with multiple sellers and multiple buyers trading with the help of combinatorial bids. The allocation and other associated problems in such exchanges are known to be among the hardest to solve among all economic mechanisms. It has been shown that the problems of surplus maximization or volume maximization in combinatorial exchanges are inapproximable even with free disposal. In this paper, the surplus maximization problem is formulated as an integer linear programming problem and we propose a Lagrangian relaxation based heuristic to find a near optimal solution. We develop computationally efficient tâtonnement mechanisms for clearing combinatorial exchanges where the Lagrangian multipliers can be interpreted as the prices of the items set by the exchange in each iteration. Our mechanisms satisfy Individual-rationality and Budget-nonnegativity properties. The computational experiments performed on representative data sets show that the proposed heuristic produces a feasible solution with negligible optimality gap.

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In the present study, the mechanical behaviour of CSM (chopped strand mat)-based GFRC (glass fibre-reinforced composite) plates with single and multiple hemispheres under compressive loads has been investigated both experimentally and numerically. The basic stress-strain behaviours arc identified with quasi-static tests on two-ply coupon laminates and short cylinders, and these are followed up with compressive tests in a UTM (universal testing machine) on single- and multiple-hemisphere plates. The ability of an explicit LS-DYNA solver in predicting the complex material behaviour of composite hemispheres, including failure, is demonstrated. The relevance and scalability of the present class of structural components as `force-multipliers' and `energy-multipliers' have been justified by virtue of findings that as the number of hemispheres in a panel increased from one to four, peak load and average absorbed energy rose by factors of approximately four and six, respectively. The performance of a composite hemisphere has been compared to similar-sized steel and aluminium hemispheres, and the former is found to be of distinctly higher specific energy than the steel specimen. A simulation-based study has also been carried out on a composite 2 x 2-hemisphere panel under impact loads and its behaviour approaching that of an ideal energy absorber has been predicted. In summary, the present investigation has established the efficacy of composite plates with hemispherical force multipliers as potential energy-absorbing countermeasures and the suitability of CAE (computer-aided engineering) for their design.

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We characterise higher order Riesz transforms on the Heisenberg group and also show that they satisfy dimension-free bounds under some assumptions on the multipliers. Using transference theorems, we deduce boundedness theorems for Riesz transforms on the reduced Heisenberg group and hence also for the Riesz transforms associated to multiple Hermite and Laguerre expansions.

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We introduce the defect sequence for a contractive tuple of Hilbert space operators and investigate its properties. The defect sequence is a sequence of numbers, called defect dimensions associated with a contractive tuple. We show that there are upper bounds for the defect dimensions. The tuples for which these upper bounds are obtained, are called maximal contractive tuples. The upper bounds are different in the non-commutative and in the commutative case. We show that the creation operators on the full Fock space and the coordinate multipliers on the Drury-Arveson space are maximal. We also study pure tuples and see how the defect dimensions play a role in their irreducibility. (C) 2012 Elsevier Inc. All rights reserved.

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Service systems are labor intensive. Further, the workload tends to vary greatly with time. Adapting the staffing levels to the workloads in such systems is nontrivial due to a large number of parameters and operational variations, but crucial for business objectives such as minimal labor inventory. One of the central challenges is to optimize the staffing while maintaining system steady-state and compliance to aggregate SLA constraints. We formulate this problem as a parametrized constrained Markov process and propose a novel stochastic optimization algorithm for solving it. Our algorithm is a multi-timescale stochastic approximation scheme that incorporates a SPSA based algorithm for ‘primal descent' and couples it with a ‘dual ascent' scheme for the Lagrange multipliers. We validate this optimization scheme on five real-life service systems and compare it with a state-of-the-art optimization tool-kit OptQuest. Being two orders of magnitude faster than OptQuest, our scheme is particularly suitable for adaptive labor staffing. Also, we observe that it guarantees convergence and finds better solutions than OptQuest in many cases.

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We consider the problem of optimizing the workforce of a service system. Adapting the staffing levels in such systems is non-trivial due to large variations in workload and the large number of system parameters do not allow for a brute force search. Further, because these parameters change on a weekly basis, the optimization should not take longer than a few hours. Our aim is to find the optimum staffing levels from a discrete high-dimensional parameter set, that minimizes the long run average of the single-stage cost function, while adhering to the constraints relating to queue stability and service-level agreement (SLA) compliance. The single-stage cost function balances the conflicting objectives of utilizing workers better and attaining the target SLAs. We formulate this problem as a constrained parameterized Markov cost process parameterized by the (discrete) staffing levels. We propose novel simultaneous perturbation stochastic approximation (SPSA)-based algorithms for solving the above problem. The algorithms include both first-order as well as second-order methods and incorporate SPSA-based gradient/Hessian estimates for primal descent, while performing dual ascent for the Lagrange multipliers. Both algorithms are online and update the staffing levels in an incremental fashion. Further, they involve a certain generalized smooth projection operator, which is essential to project the continuous-valued worker parameter tuned by our algorithms onto the discrete set. The smoothness is necessary to ensure that the underlying transition dynamics of the constrained Markov cost process is itself smooth (as a function of the continuous-valued parameter): a critical requirement to prove the convergence of both algorithms. We validate our algorithms via performance simulations based on data from five real-life service systems. For the sake of comparison, we also implement a scatter search based algorithm using state-of-the-art optimization tool-kit OptQuest. From the experiments, we observe that both our algorithms converge empirically and consistently outperform OptQuest in most of the settings considered. This finding coupled with the computational advantage of our algorithms make them amenable for adaptive labor staffing in real-life service systems.

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This work deals with the transient analysis of flexible multibody systems within a hybrid finite element framework. Hybrid finite elements are based on a two-field variational formulation in which the displacements and stresses are interpolated separately yielding very good coarse mesh accuracy. Most of the literature on flexible multibody systems uses beam-theory-based formulations. In contrast, the use of hybrid finite elements uses continuum-based elements, thus avoiding the problems associated with rotational degrees of freedom. In particular, any given three-dimensional constitutive relations can be directly used within the framework of this formulation. Since the coarse mesh accuracy as compared to a conventional displacement-based formulation is very high, the scheme is cost effective as well. A general formulation is developed for the constrained motion of a given point on a line manifold, using a total Lagrangian method. The multipoint constraint equations are implemented using Lagrange multipliers. Various kinds of joints such as cylindrical, prismatic, and screw joints are implemented within this general framework. Hinge joints such as spherical, universal, and revolute joints are obtained simply by using shared nodes between the bodies. In addition to joints, the formulation and implementation details for a DC motor actuator and for prescribed relative rotation are also presented. Several example problems illustrate the efficacy of the developed formulation.

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The question of finding variational principles for coupled systems of first order partial differential equations is considered. Using a potential representation for solutions of the first order system a higher order system is obtained. Existence of a variational principle follows if the original system can be transformed to a self-adjoint higher order system. Existence of variational principles for all linear wave equations with constant coefficients having real dispersion relations is established. The method of adjoining some of the equations of the original system to a suitable Lagrangian function by the method of Lagrange multipliers is used to construct new variational principles for a class of linear systems. The equations used as side conditions must satisfy highly-restrictive integrability conditions. In the more difficult nonlinear case the system of two equations in two independent variables can be analyzed completely. For systems determined by two conservation laws the side condition must be a conservation law in addition to satisfying the integrability conditions.

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This thesis discusses various methods for learning and optimization in adaptive systems. Overall, it emphasizes the relationship between optimization, learning, and adaptive systems; and it illustrates the influence of underlying hardware upon the construction of efficient algorithms for learning and optimization. Chapter 1 provides a summary and an overview.

Chapter 2 discusses a method for using feed-forward neural networks to filter the noise out of noise-corrupted signals. The networks use back-propagation learning, but they use it in a way that qualifies as unsupervised learning. The networks adapt based only on the raw input data-there are no external teachers providing information on correct operation during training. The chapter contains an analysis of the learning and develops a simple expression that, based only on the geometry of the network, predicts performance.

Chapter 3 explains a simple model of the piriform cortex, an area in the brain involved in the processing of olfactory information. The model was used to explore the possible effect of acetylcholine on learning and on odor classification. According to the model, the piriform cortex can classify odors better when acetylcholine is present during learning but not present during recall. This is interesting since it suggests that learning and recall might be separate neurochemical modes (corresponding to whether or not acetylcholine is present). When acetylcholine is turned off at all times, even during learning, the model exhibits behavior somewhat similar to Alzheimer's disease, a disease associated with the degeneration of cells that distribute acetylcholine.

Chapters 4, 5, and 6 discuss algorithms appropriate for adaptive systems implemented entirely in analog hardware. The algorithms inject noise into the systems and correlate the noise with the outputs of the systems. This allows them to estimate gradients and to implement noisy versions of gradient descent, without having to calculate gradients explicitly. The methods require only noise generators, adders, multipliers, integrators, and differentiators; and the number of devices needed scales linearly with the number of adjustable parameters in the adaptive systems. With the exception of one global signal, the algorithms require only local information exchange.

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Moving mesh methods (also called r-adaptive methods) are space-adaptive strategies used for the numerical simulation of time-dependent partial differential equations. These methods keep the total number of mesh points fixed during the simulation, but redistribute them over time to follow the areas where a higher mesh point density is required. There are a very limited number of moving mesh methods designed for solving field-theoretic partial differential equations, and the numerical analysis of the resulting schemes is challenging. In this thesis we present two ways to construct r-adaptive variational and multisymplectic integrators for (1+1)-dimensional Lagrangian field theories. The first method uses a variational discretization of the physical equations and the mesh equations are then coupled in a way typical of the existing r-adaptive schemes. The second method treats the mesh points as pseudo-particles and incorporates their dynamics directly into the variational principle. A user-specified adaptation strategy is then enforced through Lagrange multipliers as a constraint on the dynamics of both the physical field and the mesh points. We discuss the advantages and limitations of our methods. The proposed methods are readily applicable to (weakly) non-degenerate field theories---numerical results for the Sine-Gordon equation are presented.

In an attempt to extend our approach to degenerate field theories, in the last part of this thesis we construct higher-order variational integrators for a class of degenerate systems described by Lagrangians that are linear in velocities. We analyze the geometry underlying such systems and develop the appropriate theory for variational integration. Our main observation is that the evolution takes place on the primary constraint and the 'Hamiltonian' equations of motion can be formulated as an index 1 differential-algebraic system. We then proceed to construct variational Runge-Kutta methods and analyze their properties. The general properties of Runge-Kutta methods depend on the 'velocity' part of the Lagrangian. If the 'velocity' part is also linear in the position coordinate, then we show that non-partitioned variational Runge-Kutta methods are equivalent to integration of the corresponding first-order Euler-Lagrange equations, which have the form of a Poisson system with a constant structure matrix, and the classical properties of the Runge-Kutta method are retained. If the 'velocity' part is nonlinear in the position coordinate, we observe a reduction of the order of convergence, which is typical of numerical integration of DAEs. We also apply our methods to several models and present the results of our numerical experiments.

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A Atenção Primária à Saúde - APS é reconhecida como o nível fundamental e porta de entrada do sistema de atenção à saúde, sendo o lugar adequado onde pode ser atendida e resolvida a maior parte dos problemas de saúde. É considerada pela OMS como a principal proposta de modelo assistencial. Essa importância da APS leva a necessidade de pesquisas avaliativas dos seus resultados para adequação e melhoria de políticas e planos de ação delineados em relação à mesma. Pesquisas internacionais e nacionais são realizadas, nas quais indicadores relativos às atividades hospitalares estão sendo empregados com o objetivo de medir resultados como efetividade e acesso da APS. Um desses indicadores, desenvolvido por John Billings da Universidade de Nova York, na década de 90, consiste nas condições pelas quais as internações hospitalares por Condições Sensíveis à Atenção Ambulatorial (CSAA) deveriam ser evitadas caso os serviços da APS fossem efetivos e acessíveis. Utilizando-se o SIH-AIH/2008 e a lista brasileira de Internações por Condições Sensíveis a Atenção Primária, publicada em 2008, a proposta do presente trabalho é a de estudar os cuidados primários à saúde baseando-se nas ICSAA, na área urbana da cidade de Juiz de Fora-MG. Buscou-se responder sobre os efeitos que ocorrem nessas internações a partir das características individuais dos pacientes, das características das Unidades Básicas de Saúde-UBS (infraestrutura, produção e modelos assistenciais) e das condições sócio-econômicas/ambientais das áreas cobertas por UAPS e descobertas (sem UAPS), com a utilização de modelos multiníveis logísticos com intercepto aleatório. Buscou-se conhecer, também, a distribuição espacial das taxas padronizadas por idade das ICSAA nessas áreas e suas associações com as variáveis contextuais, utilizando-se ferramentas da análise espacial. Os resultados do presente trabalho mostraram que a porcentagem de internações por CSAA, foi de 4,1%. Os modelos assistenciais ESF e o Modelo Tradicional, base da organização da atenção primária no Brasil, não apresentaram no município, impacto significativo nas ICSAA, somente na forma de áreas descobertas tendo como referência as áreas cobertas. Também não foram significativas as variáveis de infraestrutura e produção das UAPS. Os efeitos individuais (idade e sexo) nas ICSAA foram significativos, apresentando probabilidades de significância menores que 1%, o mesmo acontecendo com o Índice de Desenvolvimento Social-IDS, que contempla as condições sociais, econômicas e ambientais das áreas analisadas. A distribuição espacial das taxas padronizadas por idade apresentou padrão aleatório e os testes dos Multiplicadores de Lagrange não foram significativos indicando o modelo de regressão clássico (MQO) como adequado para explicar as taxas em função das variáveis contextuais. Para a análise conjunta das áreas cobertas e descobertas foram fatores de risco: a variável econômica (% dos domicílios com renda até 2 SM), áreas descobertas tendo como referência as áreas cobertas e a região nordeste do município. Para as áreas cobertas as variáveis de produção das UAPS, econômica e a região nordeste apresentaram como fator de risco para as taxas de internação por CSAA.

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Este trabalho estuda a política de humanização implementada no estado do Amazonas. Para fazer este percurso utiliza-se uma abordagem ampla, acerca dos processos sociais, econômicos e políticos ocorridos no sistema capitalista, que mudam a tônica do discurso sobre o papel do Estado na provisão de bens e serviços sociais para a sociedade e que impactam diretamente na seguridade social brasileira e em especial, a política de saúde. Ao mesmo tempo, se faz uma breve discussão sobre as desigualdades regionais engendradas no país, à luz da teoria do desenvolvimento desigual e combinado, trazendo alguns elementos sobre o federalismo a fim de enriquecer o debate. Então, o panorama da saúde no Amazonas é abordado, pensando-o em suas várias dimensões e procurando dar ênfase ao orçamento da saúde, para analisar a política de humanização que implementada no estado. A pesquisa de campo foi realizada por meio de dezoito entrevistas semi-estruturadas com pessoas consideradas chave no processo, como Apoiadores, Multiplicadores da Política Nacional de Humanização (PNH), os Conselheiros de Saúde dos níveis Estadual e Municipal, além de trabalhadores ligados ao projeto Amigos da Saúde. Também foram utilizados relatórios do Tribunal de Contas do Estado (TCE), para a análise do orçamento estadual da saúde, os relatórios de gestão da Secretaria Executiva do Ministério da Saúde, o Plano Estadual de Saúde do Amazonas e os relatórios do município, sobre a política de humanização, e do projeto Amigos da Saúde. Desta forma, a questão que permeia este trabalho é: que tipo de política de humanização está sendo implementada, visando que tipo de política de saúde?