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Aprendizagem e inovação nas pequenas empresas : um estudo multicasos no arranjo produtivo local do setor do vestuário de Cianorte-PR
(Universidade Federal de São Carlos, 2008-05-16) Oliveira, Márcia Freire de; Torkomian, Ana Lúcia Vitale; https://lattes.cnpq.br/5410927608982362
In company clusters, the proximity between the agents in a common socioeconomic context can facilitate relationships among companies and between those companies and other actors (clients, suppliers, universities, associations, etc.), creating spaces for the exchange of knowledge and information, and fomenting the learning process. This process is intertemporal and cumulative, helping to enable those companies to incorporate innovations. In view of this context, the main objective of this work was to ascertain whether the different forms of learning (learning by using, learning by doing, and learning by interacting) contribute to the introduction of innovations (commercial, organizational, institutional and technological) by the small companies inserted in production clusters. We hypothesize that learning by interaction is the form that most favors the introduction of innovations on the part of small companies inserted in production clusters. This research is qualitative; hence, it is an exploratory study. The focus of our analysis is the case of the garment sector s LPC (local production cluster) in Cianorte, state of Paraná, Brazil. To achieve the proposed objective, a multicase study was made of four garment producers established in this city. The data collection technique consisted of a bibliographic review, documental analyses, notes taken at meetings of the Cianorte LPC, and nonstructured interviews with employees of the four companies whose activities involved the adoption of innovations. Our findings do not confirm the initial hypothesis, indicating, instead, that learning by using contributes the most toward the introduction of innovations. Moreover, learning by interaction could represent a greater contribution if the local businessmen were to engage in joint actions aimed at achieving collective gains for the cluster.
Sistema baseado em redes neurais para composição musical assistida por computador
(Universidade Federal de São Carlos, 2008-05-16) Corrêa, Débora Cristina; Saito, José Hiroki; https://lattes.cnpq.br/7065615446493390; https://lattes.cnpq.br/7795579469691468
Several research studies have been realized in order to achieve a musical composition computational system that could, as much as possible, catch the human mind, skills, and creativity. More recently, artificial neural networks (ANNs), also have been deployed as auxiliary models for musical compositions. For musical computation, connectionist systems, as well as other systems that involve machine learning, are able to learn patterns and features available in the melodies of the training set and to generalize them to compose new melodies. Therefore, the use of neural networks in music learning and composition has attracted researchers and many approaches have been developed. The aim of this study is the proposal of a neural network based system for computer-aided musical composition. This system can be divided into four main processes: training, composition, evaluation and optimization. It is also proposed to complement the training and composition processes with a kind of inspiration, from Nature, using landscapes contours as additional information to the network. The neural networks used in the system are: BPTT (Back-Propagation Through Time) and LSTM (Long-Short Term Memory) networks. The results obtained are compared from both networks and it is observed that the LSTM network performs better. It is also proposed an approach that consists of optimizing the weight initialization process of the LSTM network in addition to an estimative of the ideal configuration of the hidden layer, that contributes to the obtained results.