17 resultados para Intelligent vehicle highway systems

em Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho"


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In this work we propose procedures for the identification of structure of group associate lattices from fundamental region F4g of regular tessellations {4g; 4g} in the Euclidian plane and hyperbolic plane, where g denote genus of compact surface. © 2006 SBrT.

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In this paper we present a method for evaluating the center density of algebraic lattices from subfields of Q(xi n), where n is a positive integer. This method allows to reproduce rotated versions of dense lattices in some dimensions. Constellations on algebraic lattices with high packing density have been proposed for use in communications in Gaussian channels and also in Rayleigh fading channels in case they have high diversity.

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This study discuss the use of the geotechnologies to aid the planners on the terrain aptness definition for highways implantation and on the different trace evaluation to the extention of the Governador Carvalho Pinto highway, between Taubaté-SP and Aparecida do Norte-SP. Fratures on the area were mapping using Landsat ETM+, band Pan. In order to elaborate the phisical aptness chart for highway implantation was used the Analitical Hierarchy Process (AHP) operation, in on geographical information system (GIS). Using GIS were realized a ponderate middle with the soils, rocks, relief, slope, fractures and land use/cover maps. Using this physical aptness chart and joinning it with urban and restrict areas (that was inserted by the Boolean operation), were obtained a viability chart for highway implantation. Based on it were proposed three aleatory traces for the Carvalho Pinto highway. This traces were evaluated with the cross tabulation operation. The integration of the restrict areas, land use and phisical aptness in digital media can offer for the planners the cartography of the viability for the highway implantation. The evaliation of these three traces, based on the viability chart, can subsidyse the decision by the planners.

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In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Outsourcing logistics has established itself in the area of the LSP (Logistics Service Provider), which offers a range of services to its customers. In this line, transportation is characterized as one of the most important services, and therefore efficient fleet management is essential for establishing a high level of customer service. With advances in technology and vehicle tracking systems, this approach of management has gained new possibilities for the improvement of logistics services. By studying the specific case of an LSP, this paper investigates the use of these technologies in the management of their business and services. The results indicate that the LSP seeks to increase its services and to streamline information in order to respond to customer needs in real time. It is also evident in this case under study that the combination of the technology available together with the fleet management system has become a distinguishing feature for this LSP, one which increases their skills and important information for both customers and business.

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The advantages offered by the electronic component light emitting diode ( LED) have caused a quick and wide application of this device in replacement of incandescent lights. However, in its combined application, the relationship between the design variables and the desired effect or result is very complex and it becomes difficult to model by conventional techniques. This work consists of the development of a technique, through artificial neural networks, to make possible to obtain the luminous intensity values of brake lights using LEDs from design data. (C) 2005 Elsevier B.V. All rights reserved.

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This paper deals with the design of a network-on-chip reconfigurable pseudorandom number generation unit that can map and execute meta-heuristic algorithms in hardware. The unit can be configured to implement one of the following five linear generator algorithms: a multiplicative congruential, a mixed congruential, a standard multiple recursive, a mixed multiple recursive, and a multiply-with-carry. The generation unit can be used both as a pseudorandom and a message passing-based server, which is able to produce pseudorandom numbers on demand, sending them to the network-on-chip blocks that originate the service request. The generator architecture has been mapped to a field programmable gate array, and showed that millions of numbers in 32-, 64-, 96-, or 128-bit formats can be produced in tens of milliseconds. (C) 2011 Elsevier B.V. All rights reserved.

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This paper presents the control strategies of nonlinear vehicle suspension using a magnetorheological (MR) damper. We used two different approaches for modeling and control of the mechanical and electrical parts of the suspension systems with the MR damper. First, we have formulated and resolved the control problem in order to design the linear feedback dumping force controller for a nonlinear suspension system. Then the values of the control dumping force functions were transformed into electrical control signals by the application of a fuzzy logic control method. The numerical simulations were provided in order to show the effectiveness of this method for the semi-active control of the quarter-car suspension.

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Robotic vehicle navigation in unstructured and uncertain environments is still a challenge. This paper presents the implementation of a multivalued neurofuzzy controller for autonomous ground vehicle (AGVs) in indoor environments. The control system consists of a hierarchy of mobile robot using multivalued adaptive neuro-fuzzy inference system behaviors.

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The advantages offered by the electronic component LED (Light Emitting Diode) have caused a quick and wide application of this device in replacement of incandescent lights. However, in its combined application, the relationship between the design variables and the desired effect or result is very complex and it becomes difficult to model by conventional techniques. This work consists of the development of a technique, through artificial neural networks, to make possible to obtain the luminous intensity values of brake lights using LEDs from design data.

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Domains where knowledge representation is too complex to be described analytically and in a deterministic way is very common in the petroleum industry, particularly in the field of exploration and production. In these domains, applications of artificial intelligence techniques are very suitable, especially in cases where the preservation of corporate and technical knowledge is important. The Laboratory for Research on Artificial Intelligence Applied to Petroleum Engineering (LIAP) at Unicamp, has, during the last 10 years, dedicated research efforts to build intelligent systems in well drilling and petroleum production fields. In the following sections, recent advances in intelligent systems, under development in the research laboratory, are described. (C) 2001 Published by Elsevier B.V. B.V.

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The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder. © 2006 IEEE.

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Nowadays, drives that use a combination of induction motors and frequency inverters are very common, a fact due to the financial practicality and viability in purchasing and operating that system. This system modeling and simulation becomes important when it wants to evaluate the performance, to calculate and correct parameters, and it has a fundamental role in functionality and viability analysis for application of new configurations and technologies. This work is about to elaborate a simple induction motor model based in the torque versus speed characteristic, using the linearization method for application in a specific operation range to be controlled by a frequency inverter. © 2011 Springer-Verlag Berlin Heidelberg.