815 resultados para Problem solving method
Resumo:
Este artigo apresenta uma breve revisão de alguns dos mais recentes métodos bioinspirados baseados no comportamento de populações para o desenvolvimento de técnicas de solução de problemas. As metaheurísticas tratadas aqui correspondem às estratégias de otimização por colônia de formigas, otimização por enxame de partículas, algoritmo shuffled frog-leaping, coleta de alimentos por bactérias e colônia de abelhas. Os princípios biológicos que motivaram o desenvolvimento de cada uma dessas estratégias, assim como seus respectivos algoritmos computacionais, são introduzidos. Duas aplicações diferentes foram conduzidas para exemplificar o desempenho de tais algoritmos. A finalidade é enfatizar perspectivas de aplicação destas abordagens em diferentes problemas da área de engenharia.
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No presente artigo, além de lançar mão de informações da literatura, como comumente se faz, peço licença e me autorizo a emitir sobre avaliação escolar, minhas próprias idéias e opiniões, fruto de muitos anos de investigação, vivência em salas de aula, problemas e reflexão.
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O estudo avaliou a formação em enfermagem ancorada na problematização e na aprendizagem baseada em problemas a partir da percepção dos egressos. Trata-se de estudo transversal de natureza ex-post-facto. Os resultados foram analisados na triangulação das abordagens quantitativa e qualitativa e na perspectiva hermenêutica dialética. Na abordagem quantitativa 180 egressos responderam um questionário. Na qualitativa, 14 participaram das entrevistas, que buscaram sentidos relacionados ao cuidado ideal, para construção dos indicadores que revelassem a lógica da avaliação. Esses indicadores nortearam a escolha das questões para triangulação. Os resultados apontaram que 85,1% dos egressos estão inseridos no mercado de trabalho, 92,1% cursaram pós-graduação e 99,1% acreditam apresentar formação necessária ao cuidado ético, humanizado e fundamentado. A análise dos dados aponta para formação comprometida com a construção da autonomia e do conhecimento, bem como articulada aos princípios do Sistema Único de Saúde e do mundo do trabalho em enfermagem.
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In this paper, a loads transportation system in platforms or suspended by cables is considered. It is a monorail device and is modeled as an inverted pendulum built on a car driven by a dc motor the governing equations of motion were derived via Lagrange's equations. In the mathematical model we consider the interaction between the dc motor and the dynamical system, that is, we have a so called nonideal periodic problem. The problem is analyzed, qualitatively, through the comparison of the stability diagrams, numerically obtained, for several motor torque constants. Furthermore, we also analyze the problem quantitatively using the Floquet multipliers technique. Finally, we devise a control for the studied nonideal problem. The method that was used for analysis and control of this nonideal periodic system is based on the Chebyshev polynomial exponsion, the Picard iterative method, and the Lyapunov-Floquet transformation (L-F transformation). We call it Sinha's theory.
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This paper investigates properties of integer programming models for a class of production planning problems. The models are developed within a decision support system to advise a sales team of the products on which to focus their efforts in gaining new orders in the short term. The products generally require processing on several manufacturing cells and involve precedence relationships. The cells are already (partially) committed with products for stock and to satisfy existing orders and therefore only the residual capacities of each cell in each time period of the planning horizon are considered. The determination of production recommendations to the sales team that make use of residual capacities is a nontrivial optimization problem. Solving such models is computationally demanding and techniques for speeding up solution times are highly desirable. An integer programming model is developed and various preprocessing techniques are investigated and evaluated. In addition, a number of cutting plane approaches have been applied. The performance of these approaches which are both general and application specific is examined.
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The objective of this study is to present the trajectory of a research project (ALLEVATO, 2005) whose phenomenon of interest is the teaching of mathematics using problem solving with computers. The text is an attempt to portray this trajectory, from the point of view of the methodological route followed by the researcher, which was based on two main axes: the guidance of the educator Thomas A. Romberg (1992), and the guidelines provided by the foundations of qualitative research. The study was developed during a doctoral course offered by the Graduate Program in Mathematics Education at the State University of São Paulo (UNESP), Rio Claro campus.
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Analog networks for solving convex nonlinear unconstrained programming problems without using gradient information of the objective function are proposed. The one-dimensional net can be used as a building block in multi-dimensional networks for optimizing objective functions of several variables.
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This article describes a methodological approach to conditional reasoning in online asynchronous learning environments such as Virtual-U VGroups, developed by SFU, BC, Canada, consistent with the notion of meaning implication: If part of a meaning C is embedded in B and a part of a meaning B is embedded in A, then A implies C in terms of meaning [Piaget 91]. A new transcript analysis technique was developed to assess the flows of conditional meaning implications and to identify the occurrence of hypotheses and connections among them in two human science graduate mixed-mode online courses offered in the summer/spring session of 1997 by SFU. Flows of conditional meaning implications were confronted with Virtual-U VGroups threads and results of the two courses were compared. Findings suggest that Virtual-U VGroups is a knowledge-building environment although the tree-like Virtual-U VGroups threads should be transformed into neuronal-like threads. Findings also suggest that formulating hypotheses together triggers a collaboratively problem-solving process that scaffolds knowledge-building in asynchronous learning environments: A pedagogical technique and an built-in tool for formulating hypotheses together are proposed. © Springer Pub. Co.
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A branch and bound algorithm is proposed to solve the H2-norm model reduction problem and the H2-norm controller reduction problem, with conditions assuring convergence to the global optimum in finite time. The lower and upper bounds used in the optimization procedure are obtained through linear matrix inequalities formulations. Examples illustrate the results.
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Mathematical programming problems with equilibrium constraints (MPEC) are nonlinear programming problems where the constraints have a form that is analogous to first-order optimality conditions of constrained optimization. We prove that, under reasonable sufficient conditions, stationary points of the sum of squares of the constraints are feasible points of the MPEC. In usual formulations of MPEC all the feasible points are nonregular in the sense that they do not satisfy the Mangasarian-Fromovitz constraint qualification of nonlinear programming. Therefore, all the feasible points satisfy the classical Fritz-John necessary optimality conditions. In principle, this can cause serious difficulties for nonlinear programming algorithms applied to MPEC. However, we show that most feasible points do not satisfy a recently introduced stronger optimality condition for nonlinear programming. This is the reason why, in general, nonlinear programming algorithms are successful when applied to MPEC.
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This article addresses the problem of stability of impulsive control systems whose dynamics are given by measure driven differential inclusions. One important feature concerns the adopted solution which allows the consideration of systems whose singular dynamics do not satisfy the so-called Frobenius condition. After extending the conventional notion of control Lyapounov pair for impulsive systems, some stability conditions of the Lyapounov type are given. Some conclusions follow the outline of the proof of the main result.
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Tuberculosis (TB), an infectious disease readily transmitted between animals and humans, is an great threat to public and animal health a like and has been declared an international emergency. According to the WHO, animal TB is a source of much concern, especially in the poorer countries where there is little awareness of the problem. Thus, improvements in veterinary health are essential, with regard to infection by Mycobacterium bovis, particularly in areas where the public is at risk. The prevalence of infected animals in Brazil is about 1%, whereas, in the dairy cattle region of São Carlos, the average proportion of positive tests was found to be 3. 1% from 1987 to 1996. The current research was carried out to investigate the prevalence, in the region, of dairy cattle reacting positively to the tuberculin test, from 1997 to the first half of 2001. The results were obtained by searching through the data collected by the Federal Inspection Authority, and they indicated an average prevalence of 1.3% over the period, similar to the official national average. It was concluded that bovine TB continues to be present in the region, posing a potential threat of infection to the human population.
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This paper presents a new methodology for the adjustment of fuzzy inference systems. A novel approach, which uses unconstrained optimization techniques, is developed in order to adjust the free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through an estimation of time series. More specifically, the Mackey-Glass chaotic time series estimation is used for the validation of the proposed methodology.
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In this work the problem of defects location in power systems is formulated through a binary linear programming (BLP) model based on alarms historical database of control and protection devices from the system control center, sets theory of minimal coverage (AI) and protection philosophy adopted by the electric utility. In this model, circuit breaker operations are compared to their expected states in a strictly mathematical manner. For solving this BLP problem, which presents a great number of decision variables, a dedicated Genetic Algorithm (GA), is proposed. Control parameters of the GA, such as crossing over and mutation rates, population size, iterations number and population diversification, are calibrated in order to obtain efficiency and robustness. Results for a test system found in literature, are presented and discussed. © 2004 IEEE.
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An analysis of the performances of three important methods for generators and loads loss allocation is presented. The discussed methods are: based on pro-rata technique; based on the incremental technique; and based on matrices of circuit. The algorithms are tested considering different generation conditions, using a known electric power system: IEEE 14 bus. Presented and discussed results verify: the location and the magnitude of generators and loads; the possibility to have agents well or poorly located in each network configuration; the discriminatory behavior considering variations in the power flow in the transmission lines. © 2004 IEEE.