938 resultados para Comitês de máquinas
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[T. I: Texto -- t. II]: Atlas.
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Ilustraciones de aplicaciones de fuerza mecánica a las máquinas.
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t.1. Assemblée Constituante (première partie)--t.2. Assemblée Constituante (deuxière partie)--t.3. Convention Nationale (première partie)--t.4. Convention Nationale (deuxière partie)--[t.5.] Tables.
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Mode of access: Internet.
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Mode of access: Internet.
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Mode of access: Internet.
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O principal objetivo desta dissertação foi analisar por meio de estudo comparativo, o posicionamento competitivo de três máquinas ferramentas multitarefas. As hipóteses iniciais partiram das seguintes suposições: a) a indústria nacional de máquinas ferramentas esta sendo penalizada pela política industrial praticada pelo governo federal; e b) a importância relativa atribuída aos elementos multicriteriais das especificações das máquinas nacionais quando demonstram tendências elevadas, nem sempre alcançam as concorrentes importadas devido aos recursos tecnológicos agregados para se atingir a competitividade plena. Assim, indaga-se: até que ponto as máquinas ferramentas multitarefas selecionadas para o estudo, estão alinhadas com os critérios escolhidos e com suas importâncias relativas avaliadas por dois usuários desse equipamento. Como metodologia adotou-se estudo de caso múltiplo de duas empresas de médio porte do mesmo ramo. Utilizou-se o método multicritério de apoio à decisão por meio de Analytic Hierarchy Process (AHP), para a escolha da melhor alternativa entre máquinas ferramentas multitarefas similares, nacionais e importadas. Os resultados identificam que, para essas duas empresas usuárias pesquisadas, existe vantagem na aquisição da máquina importada, embora seja notório o avanço tecnológico da indústria nacional. Estas máquinas ainda carecem de algumas inovações, perdendo em competitividade, bem como em critérios importantes como versatilidade e rendimento. Com base nos trabalhos, conclui-se que, as máquinas ferramentas do tipo multitarefas nacionais das duas empresas fornecedoras analisadas não são competitivas em comparação as importadas.
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Significant advances have emerged in research related to the topic of Classifier Committees. The models that receive the most attention in the literature are those of the static nature, also known as ensembles. The algorithms that are part of this class, we highlight the methods that using techniques of resampling of the training data: Bagging, Boosting and Multiboosting. The choice of the architecture and base components to be recruited is not a trivial task and has motivated new proposals in an attempt to build such models automatically, and many of them are based on optimization methods. Many of these contributions have not shown satisfactory results when applied to more complex problems with different nature. In contrast, the thesis presented here, proposes three new hybrid approaches for automatic construction for ensembles: Increment of Diversity, Adaptive-fitness Function and Meta-learning for the development of systems for automatic configuration of parameters for models of ensemble. In the first one approach, we propose a solution that combines different diversity techniques in a single conceptual framework, in attempt to achieve higher levels of diversity in ensembles, and with it, the better the performance of such systems. In the second one approach, using a genetic algorithm for automatic design of ensembles. The contribution is to combine the techniques of filter and wrapper adaptively to evolve a better distribution of the feature space to be presented for the components of ensemble. Finally, the last one approach, which proposes new techniques for recommendation of architecture and based components on ensemble, by techniques of traditional meta-learning and multi-label meta-learning. In general, the results are encouraging and corroborate with the thesis that hybrid tools are a powerful solution in building effective ensembles for pattern classification problems.
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Educational Data Mining is an application domain in artificial intelligence area that has been extensively explored nowadays. Technological advances and in particular, the increasing use of virtual learning environments have allowed the generation of considerable amounts of data to be investigated. Among the activities to be treated in this context exists the prediction of school performance of the students, which can be accomplished through the use of machine learning techniques. Such techniques may be used for student’s classification in predefined labels. One of the strategies to apply these techniques consists in their combination to design multi-classifier systems, which efficiency can be proven by results achieved in other studies conducted in several areas, such as medicine, commerce and biometrics. The data used in the experiments were obtained from the interactions between students in one of the most used virtual learning environments called Moodle. In this context, this paper presents the results of several experiments that include the use of specific multi-classifier systems systems, called ensembles, aiming to reach better results in school performance prediction that is, searching for highest accuracy percentage in the student’s classification. Therefore, this paper presents a significant exploration of educational data and it shows analyzes of relevant results about these experiments.
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Dissertação (Mestrado)
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Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.
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Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.
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Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.