Monitoramento e aprendizado estratégico: o caso Furnas

Este estudo investigou como é o processo de monitoramento e aprendizado estratégico, identificando os fatores que influenciam o alcance dos seus resultados. Realizou-se pesquisa bibliográfica, através da qual foi possível obter a descrição de como o processo deve ocorrer, quais são os resultados que deve produzir e quais são os fatores potenciais que os influenciam. Em seguida, realizou-se pesquisa documental na empresa estudada, o que permitiu descrever como o processo de monitoramento e aprendizado estratégico ocorre in loco. Para a identificação dos fatores que influenciam o alcance dos resultados do processo na empresa, realizou-se pesquisa de campo qualitativa, junto a executivos de suas áreas finalísticas. Os resultados obtidos nessas etapas de pesquisa permitiram a compreensão de que o processo se dá através da execução de dois ciclos: o de uso e o de aprendizado e que seus elementos centrais são as reuniões estratégicas. Foi possível atestar que, em Furnas, empresa selecionada como campo de análise, o processo está sendo executado dentro dos padrões prescritos pela literatura. No entanto, como a sua implantação é recente, isso somente configura-se como uma tendência. Concluiu-se ainda que a maior parte dos fatores apontados pelos executivos da empresa coincide com os mapeados na literatura. Além disso, dentre os fatores identificados em Furnas, os que mais afetam o funcionamento do processo de monitoramento e aprendizado estratégico concentram-se na dimensão organizacional “estilo gerencial”, sendo eles: “fraca cultura de desempenho”, “patrocínio da alta direção” e “interferências operacionais na pauta das reuniões estratégicas”.

Análise do comportamento das expectativas de inflação no Brasil sob uma regra de aprendizado

Os estudos sobre as expectativas de inflação no Brasil rejeitam a hipótese de racionalidade. Essa rejeição se dá por meio de testes estatísticos que identificam a existência de um viés sistemático quando comparamos a expectativa de inflação e a inflação realizada. Atualizamos alguns destes testes com o tamanho de amostra disponível atualmente. No presente trabalho, realizamos um experimento de Monte Carlo que simula o comportamento da inflação e da sua expectativa em um modelo DSGE. Esse modelo inclui uma regra monetária sujeita a choques transitórios e permanentes (que representam uma mudança de regime). A partir das séries simuladas com esses modelos, realizamos testes estatísticos para verificar se os resultados são semelhantes aos observados na prática. O exercício de simulação realizado não foi capaz de gerar séries com essas mesmas características, não trazendo evidência que esse mecanismo de aprendizado possa explicar o viés encontrado nas expectativas de inflação.

Gibiteca escolar: um instrumento para o aprendizado

OLIVEIRA, Marta Raquel Santos de; SOUZA, Patrícia Severiano Barbosa de. Gibiteca escolar: um recurso para o aprendizado. In: SEMINÁRIO DE PESQUISA DO CCSA, XVI., 2010, Natal. Anais eletrônicos... Natal: UFRN, 2010. Disponível em: .

Estudo e implementação de algoritmos inteligentes para detecção e classificação de falhas na medição de gás natural

This master dissertation presents the study and implementation of inteligent algorithms to monitor the measurement of sensors involved in natural gas custody transfer processes. To create these algoritmhs Artificial Neural Networks are investigated because they have some particular properties, such as: learning, adaptation, prediction. A neural predictor is developed to reproduce the sensor output dynamic behavior, in such a way that its output is compared to the real sensor output. A recurrent neural network is used for this purpose, because of its ability to deal with dynamic information. The real sensor output and the estimated predictor output work as the basis for the creation of possible sensor fault detection and diagnosis strategies. Two competitive neural network architectures are investigated and their capabilities are used to classify different kinds of faults. The prediction algorithm and the fault detection classification strategies, as well as the obtained results, are presented

Avaliação de desempenho na perspectiva do balanced scorecard :um estudo da dimensão do aprendizado e crescimento nos restaurantes de Natal

This work treats of a field research in restaurants of Natal. The principal objective of the research was to verify the companies they would be using some type of acting evaluation with base in no-financial perspectives, that if they assimilated to Balanced Scorecard proposal, in the dimension of the Learning and Growth. In the statistical treatment, the descriptive analysis was accomplished with part of the Descriptive Statistics. The crossed analysis was made with Cluster Analysis employment. It was reached the conclusion that would not be careful to affirm the exact percentile of the ones that they use them referred practices, because there is not an uniform use on the part of the establishments. It is admitted that, even in an informal way, intentionally or not, partly, the companies are been worth of some investigated methods. It is also concluded that the adoption of instruments of that nature can take the companies they advance her/it in competitiveness, strengthening to your continuity possibilities and of growth. The word-key of this healthy work Balanced Scorecard, Knowledge Organizacional, Evaluation of Acting, Strategy and Competitiveness

Classificação de imagens de ambientes coralinos: uma abordagem empregando uma combinação de classificadores e máquina de vetor de suporte

The use of the maps obtained from remote sensing orbital images submitted to digital processing became fundamental to optimize conservation and monitoring actions of the coral reefs. However, the accuracy reached in the mapping of submerged areas is limited by variation of the water column that degrades the signal received by the orbital sensor and introduces errors in the final result of the classification. The limited capacity of the traditional methods based on conventional statistical techniques to solve the problems related to the inter-classes took the search of alternative strategies in the area of the Computational Intelligence. In this work an ensemble classifiers was built based on the combination of Support Vector Machines and Minimum Distance Classifier with the objective of classifying remotely sensed images of coral reefs ecosystem. The system is composed by three stages, through which the progressive refinement of the classification process happens. The patterns that received an ambiguous classification in a certain stage of the process were revalued in the subsequent stage. The prediction non ambiguous for all the data happened through the reduction or elimination of the false positive. The images were classified into five bottom-types: deep water; under-water corals; inter-tidal corals; algal and sandy bottom. The highest overall accuracy (89%) was obtained from SVM with polynomial kernel. The accuracy of the classified image was compared through the use of error matrix to the results obtained by the application of other classification methods based on a single classifier (neural network and the k-means algorithm). In the final, the comparison of results achieved demonstrated the potential of the ensemble classifiers as a tool of classification of images from submerged areas subject to the noise caused by atmospheric effects and the water column

RoboEduc: uma metodologia de aprendizado com robótica educacional

In this work, we propose a methodology for teaching robotics in elementary schools, based on the socio-historical Vygotsky theory. This methodology in conjunction with the Lego Mindstoms kit (R) and an educational software (an interface for control and programming of prototypes) are part of an educational robotics system named RoboEduc. For the practical development of this work, we have used the action-research strategy, being realized robotics activities with participation of children with age between 8 and 10 years, students of the elementary school level of Municipal School Ascendino de Almeida. This school is located at the city zone of Pitimbu, at the periphery of Natal, in Rio Grande do Norte state. The activities have focused on understanding the construction of robotic prototypes, their programming and control. At constructing prototypes, children develop zone of proximal development (ZPDs) that are learning spaces that, when well used, allow the construction not only of scientific concepts by the individuals but also of abilities and capabilities that are important for the social and cultural interactiond of each one and of the group. With the development of these practical workshops, it was possible to analyse the use of the Robot as the mediator element of the teaching-learning process and the contributions that the use of robotics may bring to teaching since elementary levels

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 contribuição ao estudo das categorias internas e de sua proliferação em redes ARTMAP

ART networks present some advantages: online learning; convergence in a few epochs of training; incremental learning, etc. Even though, some problems exist, such as: categories proliferation, sensitivity to the presentation order of training patterns, the choice of a good vigilance parameter, etc. Among the problems, the most important is the category proliferation that is probably the most critical. This problem makes the network create too many categories, consuming resources to store unnecessarily a large number of categories, impacting negatively or even making the processing time unfeasible, without contributing to the quality of the representation problem, i. e., in many cases, the excessive amount of categories generated by ART networks makes the quality of generation inferior to the one it could reach. Another factor that leads to the category proliferation of ART networks is the difficulty of approximating regions that have non-rectangular geometry, causing a generalization inferior to the one obtained by other methods of classification. From the observation of these problems, three methodologies were proposed, being two of them focused on using a most flexible geometry than the one used by traditional ART networks, which minimize the problem of categories proliferation. The third methodology minimizes the problem of the presentation order of training patterns. To validate these new approaches, many tests were performed, where these results demonstrate that these new methodologies can improve the quality of generalization for ART networks