Les structures de conversion multi-niveaux parallèles permettent de faire transiter de fortscourants tout en gardant une bonne puissance massique ; celles-ci sont réalisées en parallélisantdes cellules de commutation. Cette parallélisation permet de réduire le courant dans chaquecellule et ainsi de revenir dans des gammes plus standard de composants de puissance. Laparallélisation, en utilisant une commande adaptée, améliore les formes d’onde en sortie duconvertisseur. Ce manuscrit se focalisera sur une structure de conversion multiniveaux parallèlespécifique constituée de bras de hacheur dévolteur en parallèles couplés magnétiquement. Eneffet du fait de la commande entrelacée mise en place, l’ondulation du courant de sortie se voitréduite mais en contrepartie l’utilisation d’inductances séparées sur chaque bras entraine uneaugmentation de l’ondulation des courants de bras, directement liée au nombre de cellules decommutation, en fonction de l’ondulation du courant de sortie. Afin de palier à ce problème cesinductances sont remplacées par un (ou plusieurs) coupleur(s) magnétique(s) qui permet(tent) deréduire l’ondulation de courant dans chaque bras. Cependant dans le but de garantir la nonsaturation ainsi qu’une bonne intégration des coupleurs il est nécessaire de s’assurer del’équilibrage des courants de chaque bras malgré une différence entre les paramètres. Ainsi cemanuscrit s’est axé vers la détermination de différentes méthodes de modélisation découplant lesystème permettant le maintien de l’égale répartition des courants en utilisant des différences derapports cycliques. Ces méthodes de modélisation ont été généralisées afin de réaliser unalgorithme permettant de générer des lois de commande quel que soit le nombre de cellules enparallèle. Dans une dernière partie ces lois de commande ont été testées sur un prototype en lesimplémentant sur FPGA afin de procéder à une vérification expérimentale / The parallel multilevel converters allow high current with a high power-weight ratio by associatingcommutation cells in parallel. This parallelization reduces the current in each cells and so onpermits to use standard range of components. With an adapted command the quality of the outputwaveforms is improved. This report will focus on a specific structure made off Buck converter withmagnetic coupling. Indeed thanks to the interleaved command, the output current ripple is reducedbut in return using separated inductances on each leg leads an increasing of the leg current ripple,directly linked to the number of leg and the ripple of the output current. In order to avoid thisproblem those inductances are replaced by one or more intercell transformers (ICT) that reducethe ripple of each leg current. However in a way to ensure unsaturated ICTs and good integrationit is necessary to balance the current of each leg despite parameter variation. Thus this report isfocused on modeling uncoupling methods for the system ensuring an equal distribution of thecurrents with duty cycles differences. Those modeling methods were generalized to achieve to analgorithm which generate control law whatever the number of leg. In the last part those controllaws are tested on a test bench by implementing them on a FPGA board to validate experimentallythe results

Made available in DSpace on 2016-04-27T16:58:58Z (GMT). No. of bitstreams: 1 OTAVIO YOSHIO YAMANAKA.pdf: 13120911 bytes, checksum: ee8005677f56e81cb7fa2fb8137d7e07 (MD5) Previous issue date: 2009-10-15 / Secretaria da Educação do Estado de São Paulo / The purpose of this study was to investigate the way the teacher conceives a transition between the developed arithmetical concepts to an introduction of algebraic representation in the first grades of Elementary School and also which are the actions that he or she will unleash in order for this transition or passage to happen. So, the teacher s conceptions were researched as for the problem elaboration of additive and multiplicative structures, as well as competences related to the solving of certain problem and its algebraic representation as basis in a simple linear equation. For such, the theoretical foundation used was the Conceptual Fields Theory from Gerard Vernaud, focusing specifically on the Additive and Multiplicative Conceptual Fields, and also the ideas from Ponte (1992) and Tall & Vinner (1981). The research methodological approach is qualitative, making use of statistical instruments to show the data analyzed. As for the purpose it is descriptive with diagnostic outlining, having as a diagnosing tool an inquiry. For the data survey, two kinds of inquiries, being one for the teachers working in the first grades of Elementary School and other for students of the teaching credential course in Pedagogy. These inquiries correspond to three different parts, being, (1) Profile, (2) Conceptions and (3) Competences. The analysis of the obtained result was done quantitative and qualitatively and following the same order in which the tools were composed. The results showed that the teachers conceive problems related to additive structures to groups of Composition and Transformation of two measures. For the multiplicative structures the problems were elaborated basically of quaternary structures. In relation to the competences, the subjects are more familiar and have more familiarity when dealing with arithmetical representations, having a complex competence in relation to the algebraic focus that might be classified as an elementary competence / Este estudo teve por objetivo investigar a maneira pela qual o professor concebe uma transição entre os conceitos aritméticos desenvolvidos para uma introdução da representação algébrica nas séries iniciais do Ensino Fundamental e também quais são as ações que este desencadeará para que tal transição ou passagem seja efetuada. Neste sentido, foram pesquisadas as concepções dos professores quanto à elaboração de problemas de estruturas aditivas e multiplicativas, bem como as competências relacionadas à resolução de certos problemas e sua representação algébrica com base em uma equação linear simples. Para tal, a fundamentação teórica utilizada foi a Teoria dos Campos Conceituais de Gerard Vergnaud, focando especificamente os Campos Conceituais Aditivos e Multiplicativos, e também as idéias de Ponte (1992) e de Tall e Vinner (1981). O enfoque metodológico da pesquisa apresenta uma abordagem quanti-qualitativa, valendo-se de instrumentos estatísticos para retratar os dados analisados. Quanto ao objetivo é descritiva com um delineamento diagnóstico, tendo como instrumento desse diagnóstico foram utilizados dois questionários. Esses questionários compreenderam três partes distintas, a saber, (1) Perfil, (2) Concepções e (3) Competências. A análise dos resultados obtidos foi realizada quantitativa e qualitativamente e seguindo a mesma ordem em que foram compostos estes instrumentos. Os resultados mostraram que os sujeitos concebem problemas relacionados às situações prototípicas e às primeiras extensões de problemas de estruturas aditivas relacionadas às classes de Composição e Transformação de duas medidas. Para as estruturas multiplicativas foram elaborados problemas basicamente de estruturas quaternárias. Em relação às competências, os sujeitos estão mais familiarizados e têm maior desenvoltura quanto ao trato das representações aritméticas, possuindo uma competência em relação ao enfoque algébrico que se pode classificar como competência elementar

Dans le domaine de l'apprentissage machine, les caractéristiques des données varient généralement dans l'espace des entrées : la distribution globale pourrait être multimodale et contenir des non-linéarités. Afin d'obtenir de bonnes performances, l'algorithme d'apprentissage devrait alors être capable de capturer et de s'adapter à ces changements. Même si les modèles linéaires ne parviennent pas à décrire des distributions complexes, ils sont réputés pour leur passage à l'échelle, en entraînement et en test, aux grands ensembles de données en termes de nombre d'exemples et de nombre de fonctionnalités. Plusieurs méthodes ont été proposées pour tirer parti du passage à l'échelle et de la simplicité des hypothèses linéaires afin de construire des modèles aux grandes capacités discriminatoires. Ces méthodes améliorent les modèles linéaires, dans le sens où elles renforcent leur expressivité grâce à différentes techniques. Cette thèse porte sur l'amélioration des approches d'apprentissage locales, une famille de techniques qui infère des modèles en capturant les caractéristiques locales de l'espace dans lequel les observations sont intégrées.L'hypothèse fondatrice de ces techniques est que le modèle appris doit se comporter de manière cohérente sur des exemples qui sont proches, ce qui implique que ses résultats doivent aussi changer de façon continue dans l'espace des entrées. La localité peut être définie sur la base de critères spatiaux (par exemple, la proximité en fonction d'une métrique choisie) ou d'autres relations fournies, telles que l'association à la même catégorie d'exemples ou un attribut commun. On sait que les approches locales d'apprentissage sont efficaces pour capturer des distributions complexes de données, évitant de recourir à la sélection d'un modèle spécifique pour la tâche. Cependant, les techniques de pointe souffrent de trois inconvénients majeurs :ils mémorisent facilement l'ensemble d'entraînement, ce qui se traduit par des performances médiocres sur de nouvelles données ; leurs prédictions manquent de continuité dans des endroits particuliers de l'espace ; elles évoluent mal avec la taille des ensembles des données. Les contributions de cette thèse examinent les problèmes susmentionnés dans deux directions : nous proposons d'introduire des informations secondaires dans la formulation du problème pour renforcer la continuité de la prédiction et atténuer le phénomène de la mémorisation ; nous fournissons une nouvelle représentation de l'ensemble de données qui tient compte de ses spécificités locales et améliore son évolutivité. Des études approfondies sont menées pour mettre en évidence l'efficacité de ces contributions pour confirmer le bien-fondé de leurs intuitions. Nous étudions empiriquement les performances des méthodes proposées tant sur des jeux de données synthétiques que sur des tâches réelles, en termes de précision et de temps d'exécution, et les comparons aux résultats de l'état de l'art. Nous analysons également nos approches d'un point de vue théorique, en étudiant leurs complexités de calcul et de mémoire et en dérivant des bornes de généralisation serrées. / In Machine Learning field, data characteristics usually vary over the space: the overall distribution might be multi-modal and contain non-linearities.In order to achieve good performance, the learning algorithm should then be able to capture and adapt to these changes. Even though linear models fail to describe complex distributions, they are renowned for their scalability, at training and at testing, to datasets big in terms of number of examples and of number of features. Several methods have been proposed to take advantage of the scalability and the simplicity of linear hypotheses to build models with great discriminatory capabilities. These methods empower linear models, in the sense that they enhance their expressive power through different techniques. This dissertation focuses on enhancing local learning approaches, a family of techniques that infers models by capturing the local characteristics of the space in which the observations are embedded. The founding assumption of these techniques is that the learned model should behave consistently on examples that are close, implying that its results should also change smoothly over the space. The locality can be defined on spatial criteria (e.g. closeness according to a selected metric) or other provided relations, such as the association to the same category of examples or a shared attribute. Local learning approaches are known to be effective in capturing complex distributions of the data, avoiding to resort to selecting a model specific for the task. However, state of the art techniques suffer from three major drawbacks: they easily memorize the training set, resulting in poor performance on unseen data; their predictions lack of smoothness in particular locations of the space;they scale poorly with the size of the datasets. The contributions of this dissertation investigate the aforementioned pitfalls in two directions: we propose to introduce side information in the problem formulation to enforce smoothness in prediction and attenuate the memorization phenomenon; we provide a new representation for the dataset which takes into account its local specificities and improves scalability. Thorough studies are conducted to highlight the effectiveness of the said contributions which confirmed the soundness of their intuitions. We empirically study the performance of the proposed methods both on toy and real tasks, in terms of accuracy and execution time, and compare it to state of the art results. We also analyze our approaches from a theoretical standpoint, by studying their computational and memory complexities and by deriving tight generalization bounds.

Artificial neural networks have been widely used for machine learning and optimization. A neuro ensemble is a collection of neural networks that works cooperatively on a problem. In the literature, it has been shown that by combining several neural networks, the generalization of the overall system could be enhanced over the separate generalization ability of the individuals. Evolutionary computation can be used to search for a suitable architecture and weights for neural networks. When evolutionary computation is used to evolve a neuro ensemble, it is usually known as evolutionary neuro ensemble. In most real-world problems, we either know little about these problems or the problems are too complex to have a clear vision on how to decompose them by hand. Thus, it is usually desirable to have a method to automatically decompose a complex problem into a set of overlapping or non-overlapping sub-problems and assign one or more specialists (i.e. experts, learning machines) to each of these sub-problems. An important feature of neuro ensemble is automatic problem decomposition. Some neuro ensemble methods are able to generate networks, where each individual network is specialized on a unique sub-task such as mapping a subspace of the feature space. In real world problems, this is usually an important feature for a number of reasons including: (1) it provides an understanding of the decomposition nature of a problem; (2) if a problem changes, one can replace the network associated with the sub-space where the change occurs without affecting the overall ensemble; (3) if one network fails, the rest of the ensemble can still function in their sub-spaces; (4) if one learn the structure of one problem, it can potentially be transferred to other similar problems. In this thesis, I focus on classification problems and present a systematic study of a novel evolutionary neuro ensemble approach which I call cooperative coevolutionary mixture of experts (CCME). Cooperative coevolution (CC) is a branch of evolutionary computation where individuals in different populations cooperate to solve a problem and their fitness function is calculated based on their reciprocal interaction. The mixture of expert model (ME) is a neuro ensemble approach which can generate networks that are specialized on different sub-spaces in the feature space. By combining CC and ME, I have a powerful framework whereby it is able to automatically form the experts and train each of them. I show that the CCME method produces competitive results in terms of generalization ability without increasing the computational cost when compared to traditional training approaches. I also propose two different mechanisms for visualizing the resultant decomposition in high-dimensional feature spaces. The first mechanism is a simple one where data are grouped based on the specialization of each expert and a color-map of the data records is visualized. The second mechanism relies on principal component analysis to project the feature space onto lower dimensions, whereby decision boundaries generated by each expert are visualized through convex approximations. I also investigate the regularization effect of learning by forgetting on the proposed CCME. I show that learning by forgetting helps CCME to generate neuro ensembles of low structural complexity while maintaining their generalization abilities. Overall, the thesis presents an evolutionary neuro ensemble method whereby (1) the generated ensemble generalizes well; (2) it is able to automatically decompose the classification problem; and (3) it generates networks with small architectures.

Several modifications have been suggested to extend binary classifiers to multiclass classification, for instance the One Against All technique, the One Against One technique, or Directed Acyclic Graphs. A recent approach for multiclass classification is the pairwise classification, which relies on two input examples instead of one and predicts whether the two input examples belong to the same class or to different classes. A Support Vector Machine (SVM), which is able to handle pairwise classification tasks, is called pairwise SVM. A common pairwise classification task is face recognition. In this area, a set of images is given for training and another set of images is given for testing. Often, one is interested in the interclass setting. The latter means that any person which is represented by an image in the training set is not represented by any image in the test set. From the mentioned multiclass classification techniques only the pairwise classification technique provides meaningful results in the interclass setting. For a pairwise classifier the order of the two examples should not influence the classification result. A common approach to enforce this symmetry is the use of selected kernels. Relations between such kernels and certain projections are provided. It is shown, that those projections can lead to an information loss. For pairwise SVMs another approach for enforcing symmetry is the symmetrization of the training sets. In other words, if the pair (a,b) of examples is a training pair then (b,a) is a training pair, too. It is proven that both approaches do lead to the same decision function for selected parameters. Empirical tests show that the approach using selected kernels is three to four times faster. For a good interclass generalization of pairwise SVMs training sets with several million training pairs are needed. A technique is presented which further speeds up the training time of pairwise SVMs by a factor of up to 130 and thus enables the learning of training sets with several million pairs. Another element affecting time is the need to select several parameters. Even with the applied speed up techniques a grid search over the set of parameters would be very expensive. Therefore, a model selection technique is introduced that is much less computationally expensive. In machine learning, the training set and the test set are created by using some data generating process. Several pairwise data generating processes are derived from a given non pairwise data generating process. Advantages and disadvantages of the different pairwise data generating processes are evaluated. Pairwise Bayes' Classifiers are introduced and their properties are discussed. It is shown that pairwise Bayes' Classifiers for interclass generalization tasks can differ from pairwise Bayes' Classifiers for interexample generalization tasks. In face recognition the interexample task implies that each person which is represented by an image in the test set is also represented by at least one image in the training set. Moreover, the set of images of the training set and the set of images of the test set are disjoint. Pairwise SVMs are applied to four synthetic and to two real world datasets. One of the real world datasets is the Labeled Faces in the Wild (LFW) database while the other one is provided by Cognitec Systems GmbH. Empirical evidence for the presented model selection heuristic, the discussion about the loss of information and the provided speed up techniques is given by the synthetic databases and it is shown that classifiers of pairwise SVMs lead to a similar quality as pairwise Bayes' classifiers. Additionally, a pairwise classifier is identified for the LFW database which leads to an average equal error rate (EER) of 0.0947 with a standard error of the mean (SEM) of 0.0057. This result is better than the result of the current state of the art classifier, namely the combined probabilistic linear discriminant analysis classifier, which leads to an average EER of 0.0993 and a SEM of 0.0051. / Es gibt verschiedene Ansätze, um binäre Klassifikatoren zur Mehrklassenklassifikation zu nutzen, zum Beispiel die One Against All Technik, die One Against One Technik oder Directed Acyclic Graphs. Paarweise Klassifikation ist ein neuerer Ansatz zur Mehrklassenklassifikation. Dieser Ansatz basiert auf der Verwendung von zwei Input Examples anstelle von einem und bestimmt, ob diese beiden Examples zur gleichen Klasse oder zu unterschiedlichen Klassen gehören. Eine Support Vector Machine (SVM), die für paarweise Klassifikationsaufgaben genutzt wird, heißt paarweise SVM. Beispielsweise werden Probleme der Gesichtserkennung als paarweise Klassifikationsaufgabe gestellt. Dazu nutzt man eine Menge von Bildern zum Training und ein andere Menge von Bildern zum Testen. Häufig ist man dabei an der Interclass Generalization interessiert. Das bedeutet, dass jede Person, die auf wenigstens einem Bild der Trainingsmenge dargestellt ist, auf keinem Bild der Testmenge vorkommt. Von allen erwähnten Mehrklassenklassifikationstechniken liefert nur die paarweise Klassifikationstechnik sinnvolle Ergebnisse für die Interclass Generalization. Die Entscheidung eines paarweisen Klassifikators sollte nicht von der Reihenfolge der zwei Input Examples abhängen. Diese Symmetrie wird häufig durch die Verwendung spezieller Kerne gesichert. Es werden Beziehungen zwischen solchen Kernen und bestimmten Projektionen hergeleitet. Zudem wird gezeigt, dass diese Projektionen zu einem Informationsverlust führen können. Für paarweise SVMs ist die Symmetrisierung der Trainingsmengen ein weiter Ansatz zur Sicherung der Symmetrie. Das bedeutet, wenn das Paar (a,b) von Input Examples zur Trainingsmenge gehört, dann muss das Paar (b,a) ebenfalls zur Trainingsmenge gehören. Es wird bewiesen, dass für bestimmte Parameter beide Ansätze zur gleichen Entscheidungsfunktion führen. Empirische Messungen zeigen, dass der Ansatz mittels spezieller Kerne drei bis viermal schneller ist. Um eine gute Interclass Generalization zu erreichen, werden bei paarweisen SVMs Trainingsmengen mit mehreren Millionen Paaren benötigt. Es wird eine Technik eingeführt, die die Trainingszeit von paarweisen SVMs um bis zum 130-fachen beschleunigt und es somit ermöglicht, Trainingsmengen mit mehreren Millionen Paaren zu verwenden. Auch die Auswahl guter Parameter für paarweise SVMs ist im Allgemeinen sehr zeitaufwendig. Selbst mit den beschriebenen Beschleunigungen ist eine Gittersuche in der Menge der Parameter sehr teuer. Daher wird eine Model Selection Technik eingeführt, die deutlich geringeren Aufwand erfordert. Im maschinellen Lernen werden die Trainingsmenge und die Testmenge von einem Datengenerierungsprozess erzeugt. Ausgehend von einem nicht paarweisen Datengenerierungsprozess werden unterschiedliche paarweise Datengenerierungsprozesse abgeleitet und ihre Vor- und Nachteile bewertet. Es werden paarweise Bayes-Klassifikatoren eingeführt und ihre Eigenschaften diskutiert. Es wird gezeigt, dass sich diese Bayes-Klassifikatoren für Interclass Generalization Aufgaben und für Interexample Generalization Aufgaben im Allgemeinen unterscheiden. Bei der Gesichtserkennung bedeutet die Interexample Generalization, dass jede Person, die auf einem Bild der Testmenge dargestellt ist, auch auf mindestens einem Bild der Trainingsmenge vorkommt. Außerdem ist der Durchschnitt der Menge der Bilder der Trainingsmenge mit der Menge der Bilder der Testmenge leer. Paarweise SVMs werden an vier synthetischen und an zwei Real World Datenbanken getestet. Eine der verwendeten Real World Datenbanken ist die Labeled Faces in the Wild (LFW) Datenbank. Die andere wurde von Cognitec Systems GmbH bereitgestellt. Die Annahmen der Model Selection Technik, die Diskussion über den Informationsverlust, sowie die präsentierten Beschleunigungstechniken werden durch empirische Messungen mit den synthetischen Datenbanken belegt. Zudem wird mittels dieser Datenbanken gezeigt, dass Klassifikatoren von paarweisen SVMs zu ähnlich guten Ergebnissen wie paarweise Bayes-Klassifikatoren führen. Für die LFW Datenbank wird ein paarweiser Klassifikator bestimmt, der zu einer durchschnittlichen Equal Error Rate (EER) von 0.0947 und einem Standard Error of The Mean (SEM) von 0.0057 führt. Dieses Ergebnis ist besser als das des aktuellen State of the Art Klassifikators, dem Combined Probabilistic Linear Discriminant Analysis Klassifikator. Dieser führt zu einer durchschnittlichen EER von 0.0993 und einem SEM von 0.0051.

This thesis addresses the problem of the development of Natural Language Processing techniques for the extraction and generalization of compositional and functional relations from specialized written texts and, in particular, from patent claims. One of the most demanding tasks tackled in the thesis is, according to the state of the art, the semantic generalization of linguistic denominations of relations between object components and processes described in the texts. These denominations are usually verbal expressions or nominalizations that are too concrete to be used as standard labels in knowledge representation forms -as, for example, “A leads to B”, and “C provokes D”, where “leads to” and “provokes” both express, in abstract terms, a cause, such that in both cases “A CAUSE B” and “C CAUSE D” would be more appropriate. A semantic generalization of the relations allows us to achieve a higher degree of abstraction of the relationships between objects and processes described in the claims and reduces their number to a limited set that is oriented towards relations as commonly used in the generic field of knowledge representation. / Esta tesis se centra en el del desarrollo de tecnologías del Procesamiento del Lenguage Natural para la extracción y generalización de relaciones encontradas en textos especializados; concretamente en las reivindicaciones de patentes. Una de las tareas más demandadas de nuestro trabajo, desde el punto vista del estado de la cuestión, es la generalización de las denominaciones lingüísticas de las relaciones. Estas denominaciones, usualmente verbos, son demasiado concretas para ser usadas como etiquetas de relaciones en el contexto de la representación del conocimiento; por ejemplo, “A lleva a B”, “B es el resultado de A” están mejor representadas por “A causa B”. La generalización de relaciones permite reducir el n\'umero de relaciones a un conjunto limitado, orientado al tipo de relaciones utilizadas en el campo de la representación del conocimiento.

No description available.

Made available in DSpace on 2014-06-11T19:22:25Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-04-29Bitstream added on 2014-06-13T18:08:12Z : No. of bitstreams: 1 marques_aps_me_prud.pdf: 1844754 bytes, checksum: 98269ab519565c997b4f261950db8198 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O objetivo desta pesquisa consiste na elaboração de mapas áudio-dinâmicos em múltiplas escalas automáticas, para um Sistema de Navegação e Guia de Rota em Automóvel (SINGRA). O projeto das representações cartográficas foi dividido em duas fases: projeto de composição geral e projeto áudio-gráfico. Os mapas visuais dinâmicos foram elaborados com base nos princípios da comunicação cartográfica e da percepção visual, com ênfase nas operações de generalização. A área de estudo apresenta uma malha urbana com diferentes tipos de vias, cruzamentos e limites de velocidade. Os mapas foram projetados para serem exibidos em um monitor de pequeno formato (sete polegadas), com alta resolução, e um total de quatro escalas de representação foi determinado: 1/10.000, 1/5.000, 1/2.500 e 1/1.000. Tais escalas foram definidas em função do tamanho da mídia de apresentação e do tipo de tarefa tática. Os mapas generalizados foram obtidos pela aplicação das operações de simplificação, exagero e deslocamento, sobre uma base cartográfica na escala 1/1.000. As representações áudio-dinâmicas foram produzidas a partir de variáveis áudio-dinâmicas. As mensagens de voz foram pré-gravadas na voz feminina, executadas em sincronia com as informações visuais. O projeto foi implementado em um SINGRA disponível na FCT-UNESP, a partir do compilador Visual Basic e da biblioteca MapObjects. Ao comparar o sistema de múltiplas escalas com o de escala única, observa-se que os novos mapas adaptados ao contexto de direção do motorista, podem permitir que o usuário receba a informação de acordo com a tarefa de navegação desenvolvida ao longo da rota... / The aim of this research is to design and implement an automatics multi-scale and audio-dynamic map for an In-Car Route Guidance and Navigation System (RGNS). The design was organized in two stages: general composition and auditory-graphic design. The visual-dynamic maps were designed based on cartographic communication principles and visual perception, especially on the generalization operators. The area of study presents an urban network with different types of roads, nodes, and speed limits. The maps were designed for a small-screen display, and a total of four different scales were employed: 1:10.000, 1:5.000, 1:2.500 and 1:1.000. These scales were chosen according to the media size and type of tactical task. The maps were derived from an accurate cartographic database at scale of 1:1000, by applying generalization techniques, such as simplification, displacement, and enhancement. The audio-dynamic representations were produced by taking account a set of audio-dynamic variables. The voice messages were recorded in a female voice, and they were presented with visual information, simultaneously. The design was implemented in a navigation system, which is available in the Faculty of Sciences and Technology, by using Visual Basic compiler and MapObjects library. The results of comparison between the automatic multiple-scale and single scale system show that the new system, enhanced driver's context, can allow the user receiving information according to the tasks performed along of the route. From the employment of generalization technique it was possible to present in a properly way the amount of information in the display, in which it can contribute for reducing navigational errors and visual demand, when compared with single-scale map ... (Complete abstract click electronic access below)

Made available in DSpace on 2016-06-02T19:44:10Z (GMT). No. of bitstreams: 1 4006.pdf: 1538521 bytes, checksum: a98a7359983f1a339fbb240a7c38e199 (MD5) Previous issue date: 2011-07-08 / Universidade Federal de Minas Gerais / An important issue in the field of verbal behavior is how a person understands and learns to behave according to verbal commands or instructions. The stimulus equivalence paradigm, as a model of symbolic behavior, may explain the origins of the comprehension of instructions. Following new instructions can result from the recombination of subunits of previously learned instructions. This work reports three studies that investigated questions related to instructional control. In the first two studies, the general objective was to establish pseudo-phrases (action-object) as members of equivalence classes with actions, objects and abstract pictures; and to verify whether, when employed with an instructional function, the pseudo-phrases and the abstract pictures would control the participants responding. Additionally, the studies asked whether participants would follow new (recombined) instructions. The third study investigated whether the overlapping of elements of pseudo-phrases in teaching phase would favor generalized instruction-following. In the studies 1 and 2, twelve of the thirteen participants learned the auditory-visual conditional discriminations among spoken pseudo-phrases and actions presented in videotapes and among the same sentences and abstract pictures. Probes for class formation showed that the same twelve children comprehended the sentences, relating, through equivalence, the pseudo-phrases, the actions and the abstract pictures. Similar performances were observed with the pictures, suggesting that they had been comprehended and that they could work as substitutes for (or equivalent to) oral instructions. However, none of the children followed new (recombined) instructions, although all children responded under partial control of what was previously taught (object or action). In Study 3, four participants learned auditory-visual conditional discriminations (Condition 1) among spoken pseudophrases and videotapes (each showing action-object) and followed oral instructions in the tests of the instructional control, but only one participant followed recombined sentences. Four other participants learned to follow the experimental instructions via execution of the action related to object in the simultaneous presence of the auditory stimulus and of the corresponding videotape (Condition 2), but did not present recombinative generalization. Seven of the eight participants followed new instructions in the pre-test of new training matrixes with overlapping of the elements of the sentences previously learned (that is, their responding was under the control of elements of the compound). As a whole, the results represent a systematic replication of previous results indicating that class formation could promote the comprehension of sentences and facilitate the instruction-following behavior when the sentences are used with instructional function. Regarding the development of stimulus control by subunits of these complex stimuli, the evidences were very fragile, but when it occurred, the recombination was clearly related to systematic training with overlapping of elements in different sentences, thus suggesting the relevance of this procedure as an effective teaching condition. / Uma das questões de interesse no campo do comportamento verbal diz respeito a como as pessoas entendem e passam a se comportar de acordo com comandos ou instruções verbais. O paradigma de equivalência de estímulos, como um modelo do comportamento simbólico, pode contribuir para esclarecer a origem da compreensão de instruções. O seguimento de instruções novas, por sua vez, pode resultar da recombinação de subunidades de instruções previamente aprendidas. Este trabalho relata três estudos que investigaram questões relativas ao controle instrucional. Nos dois primeiros o objetivo geral foi estabelecer pseudofrases (ação-objeto) como membros de classes de equivalência com ações, objetos e figuras abstratas e verificar se quando empregadas com função instrucional, as pseudossentenças e as figuras abstratas controlariam o responder dos participantes. Adicionalmente, os estudos perguntaram se os participantes seguiriam novas instruções (recombinadas). O terceiro estudo investigou se a sobreposição de elementos de pseudofrases durante o ensino favoreceria o seguimento de instrução generalizado. Nos Estudos 1 e 2, 12 dos 13 participantes aprenderam discriminações condicionais auditivo-visuais entre pseudofrases faladas e ações filmadas em videoteipe e entre as mesmas sentenças e figuras abstratas. Sondas de formação de classes mostraram que as mesmas 12 crianças compreenderam as sentenças, relacionando, por equivalência, as pseudofrases, as ações e as figuras abstratas. Desempenhos similares foram observados diante das figuras, o que sugere que passaram a ser compreendidas e que podiam funcionar como substitutos (equivalentes) das instruções orais. Entretanto, nenhuma criança seguiu novas instruções (recombinadas), embora todas responderam sob controle parcial do que foi previamente ensinado (o objeto ou a ação). No Estudo 3, quatro participantes aprenderam discriminações condicionais auditivo-visuais (Condição 1) entre pseudossentenças faladas e videoteipes (ação-objeto) e seguiram as instruções orais nos testes de controle instrucional, mas apenas um participante seguiu sentenças recombinadas. Outros quatro participantes aprenderam a seguir as instruções experimentais via execução da ação em relação ao objeto diante da apresentação simultânea do estímulo auditivo e do videoteipe correspondente (Condição 2), mas não apresentaram generalização recombinativa. Sete dos oito participantes seguiram novas instruções nos pré-testes de novas matrizes de ensino com sobreposição dos elementos das sentenças previamente aprendidas (o responder estava sob controle de elementos do composto). No conjunto, os resultados constituem uma replicação sistemática de resultados prévios indicando que a formação de classes pode promover a compreensão de sentenças e favorecer seu seguimento, quando usadas com função instrucional. Quanto ao desenvolvimento de controle por subunidades dos estímulos complexos, as evidências foram bastante frágeis, mas quando ocorreu, a recombinação esteve claramente relacionada ao treino sistemático com sobreposição de elementos em diferentes sentenças, sugerindo a relevância dos procedimentos como uma condição de ensino eficaz.

Made available in DSpace on 2016-06-02T19:46:25Z (GMT). No. of bitstreams: 1 DissEMV.pdf: 791702 bytes, checksum: aec417790823990d5caeefebc08c7079 (MD5) Previous issue date: 2005-02-24 / Social Skills Training (SST) can be used to increase teachers social competence, contributing to the development of interactive contexts in the classroom as well as to the student s learning. The purpose of this research was to elaborate and describe intervention procedures used in a group SST program with teachers who work with children that present learning difficulties, consisting of 15 sessions for the development of social skills. The effects of the SST were analysed in terms of: a) acquisition of social skills classes and b) generalization of social skills learned in the sessions for the classroom context. The sample included 10 teachers, in the 26-54 age range. The data gathering procedure involved the use of the Interpersonal Relations Questionnaire (IRQ) before the intervention, the Social Skills Inventory (SSI) and the filming of each teacher with his/her students, before and after the intervention. The IRQ data showed that at the beginning the teachers had some difficulties in dealing with interpersonal conflicts in the classroom, demonstrating lack of social skills. It was also observed a certain incoherence in the verbal report related to some questions, highlighting flaws in the self-observation and self-knowledge repertoire. Based on the survey of the behavioral classes presented by the facilitator by means of the tape transcriptions, it was possible to classify the intervention procedures that had the following purposes: a) to investigate interpersonal difficulties; b) to analyse previous-consequent relations; c) to train specific social skills; d) presentation of positive results and; e) to sound out generalization which contributed to the learning of social skills and generalization for the classroom context. The SSI data showed that after the intervention there was an increase in the scores average of the group of teachers in all the social skills classes that were assessed. In the individual analysis some teachers showed much bigger changes than others and for some of them there was a slight decrease in the scores. As for the filming data in the classroom, there was no increase in all the social skills that were assessed, but in those that increased, the generalization was evidenced. The results obtained allow us to state that the improvement of the teachers social skills repertoire enables them to foster interactive contexts in the classroom, contributing to the student s academic and interpersonal development. / O Treinamento de Habilidades Sociais (THS) pode ser utilizado para aumentar a competência social de professores, contribuindo para o arranjo de contextos interativos em sala de aula e para a aprendizagem do aluno. O objetivo dessa pesquisa foi elaborar e descrever procedimentos de intervenção utilizados em um programa de THS em grupo, com professores de crianças com dificuldades de aprendizagem, composto por 15 sessões para o desenvolvimento de habilidades sociais. Analisaram-se os efeitos do THS em termos de: a) aquisição de classes de habilidades sociais; e b) generalização das habilidades sociais aprendidas nas sessões para o contexto de sala de aula. A amostra incluiu dez professores, de 26 a 54 anos. O procedimento de coleta de dados envolveu a aplicação do Questionário de Relações Interpessoais (QRI) antes da intervenção, do Inventário de Habilidades Sociais (IHS) e realização da filmagem de cada professora com seus alunos, pré e pós intervenção. Os dados do QRI mostraram que, inicialmente, as professoras apresentavam dificuldades de lidar com conflitos interpessoais em sala de aula, demonstrando um repertório deficitário de habilidades sociais. Observou-se, também, certa incoerência no relato verbal com relação a algumas questões, evidenciando falhas no repertório de auto-observação e de autoconhecimento. A partir do levantamento das classes comportamentais apresentadas pela facilitadora, por meio das transcrições de fitas cassetes, houve a possibilidade de classificar os procedimentos de intervenção que apresentavam os objetivos de: a) investigar dificuldades interpessoais; b) analisar relações antecedentes-conseqüentes; c) treinar habilidades sociais específicas; d) usar conseqüenciação positiva; e e) sondar generalização, os quais contribuíram para a aprendizagem das habilidades sociais e generalização para o contexto de sala de aula. Os dados do IHS mostraram que, após a intervenção, houve aumento nas médias dos escores do grupo de professoras em todas as classes de habilidades sociais avaliadas. Na análise individual, algumas professoras apresentaram mudanças bem maiores do que outras e, para algumas, houve uma pequena diminuição nos escores. Com relação aos dados da filmagem em sala de aula, não ocorreu aumento em todas as habilidades sociais avaliadas, mas naquelas que aumentaram, evidenciou-se generalização. Os resultados obtidos permitem afirmar que o aprimoramento do repertório de habilidades sociais de professores possibilita capacitá-los para a promoção de contextos interativos em sala de aula, contribuindo para o desenvolvimento acadêmico e interpessoal do aluno.