Caracterização de atapulgita visando aplicação para reforço de materiais poloméricos

The clay mineral attapulgite is a group of hormitas, which has its structures formed by microchannels, which give superior technological properties classified the industrial clays, clays of this group has a very versatile range of applications, ranging from the drilling fluid for wells oil has applications in the pharmaceutical industry. Such properties can be improved by activating acid and / or thermal activation. The attapulgite when activated can improve by up to 5-8 times some of its properties. The clay was characterized by X-ray diffraction, fluorescence, thermogravimetric analysis, differential thermal analysis, scanning electron microscopy and transmission electron microscopy before and after chemical activation. It can be seen through the results the efficiency of chemical treatment, which modified the clay without damaging its structure, as well as production of polymer matrix composites with particles dispersed atapugita

Uma aplicação da apredizagem por reforço na otimização da produção em um campo de petróleo

The objective of reservoir engineering is to manage fields of oil production in order to maximize the production of hydrocarbons according to economic and physical restrictions. The deciding of a production strategy is a complex activity involving several variables in the process. Thus, a smart system, which assists in the optimization of the options for developing of the field, is very useful in day-to-day of reservoir engineers. This paper proposes the development of an intelligent system to aid decision making, regarding the optimization of strategies of production in oil fields. The intelligence of this system will be implemented through the use of the technique of reinforcement learning, which is presented as a powerful tool in problems of multi-stage decision. The proposed system will allow the specialist to obtain, in time, a great alternative (or near-optimal) for the development of an oil field known

A intermediação da noção de probabilidade na construção de conceitos relacionados à cinética química

Este trabalho procurou identificar como estudantes do Ensino Médio se apropriam de conceitos e elaboram determinados modelos inseridos em cinética química, especificamente o modelo cinético de colisão de partículas numa reação (Teoria das Colisões). Esta análise e as reflexões que a seguiram foram baseadas, sobretudo, nos estudos realizados por Piaget, Piaget e Inhelder, Jun e Fischbein. Utilizamos como documentos as transcrições das entrevistas (pré e pós-testes) realizadas individualmente com cada aluno. Inicialmente, os estudantes foram entrevistados (pré-testes) com o intuito de identificar a familiaridade com a noção de evento probabilístico ou aleatório. Numa segunda etapa (pós-testes), esse conhecimento (ou a ausência parcial/total dele) foi posto à prova numa tentativa de se estabelecerem relações com um conteúdo específico da Química (Teoria das Colisões). Os resultados obtidos apontam para grandes discrepâncias entre o modelo cinético de colisões elaborado pelos estudantes e aquele cientificamente aceito.

Efeito da densidade e proporção de plantas de tomate industrial e de maria-pretinha em competição

O presente trabalho foi realizado com o objetivo de quantificar as interações competitivas e os índices de competitividade entre plantas de tomate industrial (Lycopersicon esculentum cv. Heinz 9553) e maria-pretinha (Solanum americanum). Usou-se como método um experimento substitutivo, com densidade total de 40 plantas m-2 e 11 proporções, além das monoculturas em densidades, que variaram de 20 a 100 plantas m-2, em intervalos de 20 plantas, conduzidos no delineamento de blocos casualizados com três repetições. Os resultados obtidos foram analisados pelo método convencional de análise de experimentos substitutivos e pela produção recíproca total. A maria-pretinha mostrou ser um competidor mais agressivo que o tomate, sendo mais importante a competição interespecífica para a planta cultivada. Para a biomassa seca total, as duas espécies não competiram pelos mesmos fatores de crescimento. Já para a área foliar, as duas espécies se mostraram competidoras pelos mesmos fatores de crescimento, mostrando ser essa a característica mais sensível à interferência.

Interações competitivas de triticale (Triticum turgidosecale) e nabiça (Raphanus raphanistrum) em função da população e proporção de plantas

Este trabalho foi realizado com o objetivo de quantificar as interações competitivas e os índices de competitividade entre plantas de triticale e nabiça. A metodologia utilizada foi a de um experimento em monocultura, que variou de 25 a 500 plantas m-2 para determinar o valor a partir do qual a produção se torna independente do aumento da densidade para cada espécie, e um experimento substitutivo, com população total de 300 plantas m-2 e sete proporções de nabiça: triticale (0:300, 50:250, 100:200, 150:150, 200:100, 250:50 e 300:0), sendo conduzidos em delineamento experimental de blocos casualizados, com cinco repetições. Os resultados foram analisados pelo método convencional de análise de experimentos substitutivos e pelo método da produção recíproca total e por planta. Os índices calculados, a partir da massa seca das plantas, indicaram o triticale como competidor superior à nabiça.

Fatores genéticos e ambientais sobre a probabilidade de prenhez precoce em bovinos Caracu

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Distribuição de probabilidade de ocorrência de Orthezia praelonga Douglas (Hemiptera: Sternorrhyncha: Ortheziidae) na cultura de citros

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aprendizado por Reforço com Valores deInfluência em Sistemas Multi-Agente

We propose a new paradigm for collective learning in multi-agent systems (MAS) as a solution to the problem in which several agents acting over the same environment must learn how to perform tasks, simultaneously, based on feedbacks given by each one of the other agents. We introduce the proposed paradigm in the form of a reinforcement learning algorithm, nominating it as reinforcement learning with influence values. While learning by rewards, each agent evaluates the relation between the current state and/or action executed at this state (actual believe) together with the reward obtained after all agents that are interacting perform their actions. The reward is a result of the interference of others. The agent considers the opinions of all its colleagues in order to attempt to change the values of its states and/or actions. The idea is that the system, as a whole, must reach an equilibrium, where all agents get satisfied with the obtained results. This means that the values of the state/actions pairs match the reward obtained by each agent. This dynamical way of setting the values for states and/or actions makes this new reinforcement learning paradigm the first to include, naturally, the fact that the presence of other agents in the environment turns it a dynamical model. As a direct result, we implicitly include the internal state, the actions and the rewards obtained by all the other agents in the internal state of each agent. This makes our proposal the first complete solution to the conceptual problem that rises when applying reinforcement learning in multi-agent systems, which is caused by the difference existent between the environment and agent models. With basis on the proposed model, we create the IVQ-learning algorithm that is exhaustive tested in repetitive games with two, three and four agents and in stochastic games that need cooperation and in games that need collaboration. This algorithm shows to be a good option for obtaining solutions that guarantee convergence to the Nash optimum equilibrium in cooperative problems. Experiments performed clear shows that the proposed paradigm is theoretical and experimentally superior to the traditional approaches. Yet, with the creation of this new paradigm the set of reinforcement learning applications in MAS grows up. That is, besides the possibility of applying the algorithm in traditional learning problems in MAS, as for example coordination of tasks in multi-robot systems, it is possible to apply reinforcement learning in problems that are essentially collaborative

Uma implementação paralela híbrida para o problema do caixeiro viajante usando algoritmos genéticos, GRASP e aprendizagem por reforço

The metaheuristics techiniques are known to solve optimization problems classified as NP-complete and are successful in obtaining good quality solutions. They use non-deterministic approaches to generate solutions that are close to the optimal, without the guarantee of finding the global optimum. Motivated by the difficulties in the resolution of these problems, this work proposes the development of parallel hybrid methods using the reinforcement learning, the metaheuristics GRASP and Genetic Algorithms. With the use of these techniques, we aim to contribute to improved efficiency in obtaining efficient solutions. In this case, instead of using the Q-learning algorithm by reinforcement learning, just as a technique for generating the initial solutions of metaheuristics, we use it in a cooperative and competitive approach with the Genetic Algorithm and GRASP, in an parallel implementation. In this context, was possible to verify that the implementations in this study showed satisfactory results, in both strategies, that is, in cooperation and competition between them and the cooperation and competition between groups. In some instances were found the global optimum, in others theses implementations reach close to it. In this sense was an analyze of the performance for this proposed approach was done and it shows a good performance on the requeriments that prove the efficiency and speedup (gain in speed with the parallel processing) of the implementations performed

Gráficos de probabilidade normal para avaliação de mecanismos de distribuição de sementes em semeadoras

Currently great emphasis is given for seed metering that assist rigorous demands in relation to longitudinal distribution of seeds, as well as to the index of fails in spacing laws, breaks and double seeds. The evaluation of these variable demands much time and work of attainment of data and processing. The objective of this work went propose to use of graphs of normal probability, facilitating the treatment of the data and decreasing the time of processing. The evaluation methodology consists in the counting of broken seeds, fail spacing and double seeds through the measure of the spacing among seeds, preliminary experiments through combinations of treatments had been carried through whose factors of variation were the level of the reservoir of seeds, the leveling of the seed metering, the speed of displacement and dosage of seeds. The evaluation was carried through in two parts, first through preliminary experiments for elaboration of the graphs of normal probability and later in experiments with bigger sampling for evaluation of the influence of the factors most important. It was done the evaluation of seed metering of rotating internal ring, and the amount of necessary data for the evaluation was very decreased through of the graphs of normal probability that facilitated to prioritize only the significant factors. The dosage of seeds was factor that more important because factor (D) have greater significance.

Uma contribuição à solução do problema dos k-servos usando aprendizagem por reforço

Neste trabalho é proposto um novo algoritmo online para o resolver o Problema dos k-Servos (PKS). O desempenho desta solução é comparado com o de outros algoritmos existentes na literatura, a saber, os algoritmos Harmonic e Work Function, que mostraram ser competitivos, tornando-os parâmetros de comparação significativos. Um algoritmo que apresente desempenho eficiente em relação aos mesmos tende a ser competitivo também, devendo, obviamente, se provar o referido fato. Tal prova, entretanto, foge aos objetivos do presente trabalho. O algoritmo apresentado para a solução do PKS é baseado em técnicas de aprendizagem por reforço. Para tanto, o problema foi modelado como um processo de decisão em múltiplas etapas, ao qual é aplicado o algoritmo Q-Learning, um dos métodos de solução mais populares para o estabelecimento de políticas ótimas neste tipo de problema de decisão. Entretanto, deve-se observar que a dimensão da estrutura de armazenamento utilizada pela aprendizagem por reforço para se obter a política ótima cresce em função do número de estados e de ações, que por sua vez é proporcional ao número n de nós e k de servos. Ao se analisar esse crescimento (matematicamente, ) percebe-se que o mesmo ocorre de maneira exponencial, limitando a aplicação do método a problemas de menor porte, onde o número de nós e de servos é reduzido. Este problema, denominado maldição da dimensionalidade, foi introduzido por Belmann e implica na impossibilidade de execução de um algoritmo para certas instâncias de um problema pelo esgotamento de recursos computacionais para obtenção de sua saída. De modo a evitar que a solução proposta, baseada exclusivamente na aprendizagem por reforço, seja restrita a aplicações de menor porte, propõe-se uma solução alternativa para problemas mais realistas, que envolvam um número maior de nós e de servos. Esta solução alternativa é hierarquizada e utiliza dois métodos de solução do PKS: a aprendizagem por reforço, aplicada a um número reduzido de nós obtidos a partir de um processo de agregação, e um método guloso, aplicado aos subconjuntos de nós resultantes do processo de agregação, onde o critério de escolha do agendamento dos servos é baseado na menor distância ao local de demanda

Classificação de padrões através de um comitê de máquinas aprimorado por aprendizagem por reforço

Reinforcement learning is a machine learning technique that, although finding a large number of applications, maybe is yet to reach its full potential. One of the inadequately tested possibilities is the use of reinforcement learning in combination with other methods for the solution of pattern classification problems. It is well documented in the literature the problems that support vector machine ensembles face in terms of generalization capacity. Algorithms such as Adaboost do not deal appropriately with the imbalances that arise in those situations. Several alternatives have been proposed, with varying degrees of success. This dissertation presents a new approach to building committees of support vector machines. The presented algorithm combines Adaboost algorithm with a layer of reinforcement learning to adjust committee parameters in order to avoid that imbalances on the committee components affect the generalization performance of the final hypothesis. Comparisons were made with ensembles using and not using the reinforcement learning layer, testing benchmark data sets widely known in area of pattern classification

Roteamento em Redes de Sensores Sem Fios Com Base Em Aprendizagem Por Reforço

The use of wireless sensor and actuator networks in industry has been increasing past few years, bringing multiple benefits compared to wired systems, like network flexibility and manageability. Such networks consists of a possibly large number of small and autonomous sensor and actuator devices with wireless communication capabilities. The data collected by sensors are sent directly or through intermediary nodes along the network to a base station called sink node. The data routing in this environment is an essential matter since it is strictly bounded to the energy efficiency, thus the network lifetime. This work investigates the application of a routing technique based on Reinforcement Learning s Q-Learning algorithm to a wireless sensor network by using an NS-2 simulated environment. Several metrics like energy consumption, data packet delivery rates and delays are used to validate de proposal comparing it with another solutions existing in the literature

Fibras de licuri: um reforço vegetal alternativo de compósitos poliméricos

This research is based, at first, on the seeking of alternatives naturals reinforced in place of polymeric composites, also named reinforced plastics. Therein, this work starts with a whole licuri fiber micro structural characterization, as alternative proposal to polymeric composites. Licuri fiber is abundant on the Bahia state flora, native from a palm tree called Syagrus Coronata (Martius) Beccari. After, it was done only licuri fiber laminar composite developing studies, in order to know its behavior when impregnated with thermofix resin. The composite was developed in laminar structure shape (plate with a single layer of reinforcement) and produced industrially. The layer of reinforcement is a fabric-fiber unidirectional of licuri up in a manual loom. Their structure was made of polyester resin ortofitálica (unsaturated) only reinforced with licuri fibers. Fiber characterization studies were based on physical chemistry properties and their constitution. It was made by tension, scanning electron microscopy (SEM), x-ray diffraction (RDX) and thermal analyses (TG and DTA) tests, besides fiber chemistry analyses. Relating their mechanical properties of strength and hardness testing, they were determined through unit axial tension test and flexion in three points. A study in order to know fiber/matrix interface effects, in the final composites results, was required. To better understand the mechanical behavior of the composite, macroscopic and microscopic optical analysis of the fracture was performed

Utilização de RNA´s na construção do diagrama de vida constante de probabilidade de materiais compósitos

The static and cyclic assays are common to test materials in structures.. For cycling assays to assess the fatigue behavior of the material and thereby obtain the S-N curves and these are used to construct the diagrams of living constant. However, these diagrams, when constructed with small amounts of S-N curves underestimate or overestimate the actual behavior of the composite, there is increasing need for more testing to obtain more accurate results. Therewith, , a way of reducing costs is the statistical analysis of the fatigue behavior. The aim of this research was evaluate the probabilistic fatigue behavior of composite materials. The research was conducted in three parts. The first part consists of associating the equation of probability Weilbull equations commonly used in modeling of composite materials S-N curve, namely the exponential equation and power law and their generalizations. The second part was used the results obtained by the equation which best represents the S-N curves of probability and trained a network to the modular 5% failure. In the third part, we carried out a comparative study of the results obtained using the nonlinear model by parts (PNL) with the results of a modular network architecture (MN) in the analysis of fatigue behavior. For this we used a database of ten materials obtained from the literature to assess the ability of generalization of the modular network as well as its robustness. From the results it was found that the power law of probability generalized probabilistic behavior better represents the fatigue and composites that although the generalization ability of the MN that was not robust training with 5% failure rate, but for values mean the MN showed more accurate results than the PNL model