18 resultados para Sensory motor gating
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Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.
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This paper presents the design and the prototype implementation of a three-phase power inverter developed to drive a motor-in-wheel. The control system is implemented in a FPGA (Field Programmable Gate Array) device. The paper describes the Field Oriented Control (FOC) algorithm and the Space Vector Modulation (SVM) technique that were implemented. The control platform uses a Spartan-3E FPGA board, programmed with Verilog language. Simulation and experimental results are presented to validate the developed system operation under different load conditions. Finally are presented conclusions based on the experimental results.
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Electric Vehicles (EVs) are increasingly used nowadays, and different powertrain solutions can be adopted. This paper describes the control system of an axial flux Permanent Magnet Synchronous Motor (PMSM) for EVs powertrain. It is described the implemented Field Oriented Control (FOC) algorithm and the Space Vector Modulation (SVM) technique. Also, the mathematical model of the PMSM is presented. Both, simulation and experimental, results with different types of mechanical load are presented. The experimental results were obtained using a laboratory test bench. The obtained results are discussed.
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Relatório de estágio de mestrado em Ensino de Educação Física nos Ensinos Básico e Secundário
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Relatório de estágio de mestrado em Ensino de Educação Física nos Ensinos Básico e Secundário
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Relatório de estágio de mestre em Ensino de Educação Física nos Ensinos Básico e Secundário
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Relatório de estágio de mestrado em Ensino de Educação Física nos Ensinos Básico e Secundário
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O objetivo do estudo é verificar e compreender a relação entre o desenvolvimento motor da criança e suas características empreendedoras. A relevância do estudo resulta do carácter inovador devido à escassez de trabalhos científicos que relacionam, em crianças, o desenvolvimento motor e o empreendedorismo. Não será relevante compreender como ocorre o desenvolvimento de competências empreendedoras nas crianças e como promovê-las? Será possível, a longo prazo, melhorar a qualidade de vida dos cidadãos evitando comportamentos sedentários adquiridos na infância, bem como promover a autonomia e responsabilidade perante uma sociedade que se tornará mais sustentável do ponto de vista social, económica e financeira.
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O objetivo foi analisar a relação entre desempenho motor (DM) e variáveis antropométricas de crianças e adolescentes de escolas públicas de Florianópolis. Metodologia: 300 alunos do 3º ao 5º ano e da 4ª a 6ª série do Ensino Fundamental, entre os 8 e 16 anos. Os resultados vão ao encontro à literatura, que indicam correlações negativas entre DM e variáveis antropométricas em crianças e adolescentes.
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Relatório de estágio de mestrado em Ensino de Educação Física nos Ensinos Básico e Secundário
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Dissertação de mestrado integrado em Psicologia
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Olive oils may be commercialized as intense, medium or light, according to the intensity perception of fruitiness, bitterness and pungency attributes, assessed by a sensory panel. In this work, the capability of an electronic tongue to correctly classify olive oils according to the sensory intensity perception levels was evaluated. Cross-sensitivity and non-specific lipid polymeric membranes were used as sensors. The sensor device was firstly tested using quinine monohydrochloride standard solutions. Mean sensitivities of 14±2 to 25±6 mV/decade, depending on the type of plasticizer used in the lipid membranes, were obtained showing the device capability for evaluating bitterness. Then, linear discriminant models based on sub-sets of sensors, selected by a meta-heuristic simulated annealing algorithm, were established enabling to correctly classify 91% of olive oils according to their intensity sensory grade (leave-one-out cross-validation procedure). This capability was further evaluated using a repeated K-fold cross-validation procedure, showing that the electronic tongue allowed an average correct classification of 80% of the olive oils used for internal-validation. So, the electronic tongue can be seen as a taste sensor, allowing differentiating olive oils with different sensory intensities, and could be used as a preliminary, complementary and practical tool for panelists during olive oil sensory analysis.
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Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.
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Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores
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Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e de Computadores