2 resultados para Arthritis, Reactive

em Universitat de Girona, Spain


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The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

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Selenium (Se) is an element with important health implications that is emitted in significant amounts from volcanoes. Attracted by the fertility of volcanic soils, around 10% of the world population lives within 100 km of an active volcano. Nevertheless, the behaviour of Se in volcanic environments is poorly understood. Therefore, the main aim of this thesis is to investigate the role of soils in the Se cycling in volcanic environments. Prior to the geochemical studies, precise and accurate methods for the determination of Se contents, speciation and isotopic signatures were developed. Afterwards, a combination of field studies and lab controlled experiments were performed with soils from two contrasting European volcanic settings: Mount Etna in Sicily (Italy) and Mount Teide in Tenerife (Spain). The results showed a strong link between Se behaviour and soil development, indicating that Se mobility in volcanic soils is controlled by sorption processes and soil mineralogy.