920 resultados para automação


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Frequency selective surfaces (Frequency Selective Surface - FSS) are often used in various applications in telecommunications. Some of these applications may require that these structures have response with multiple resonance bands. Other applications require that the FSS response have large frequency range, to meet the necessary requirements. FSS to design with these features there are numerous techniques cited in the scientific literature. Thus, the purpose of this paper is to examine some common techniques such as: Overlap of FSS; Elements combined; Elements Elements convolucionados and fractals. And designing multiband FSS and / or broadband selecting simple ways in terms of construction and occupy the smallest possible space, aiming at practical applications. Given these requirements, three projects FSS were performed: a technology applied to IEEE 802.11 a/b/g/n and two projects for application in UWB. In project development, commercial software Ansoft DesignerTM and experimental results were satisfactory was used

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We propose in this work a software architecture for robotic boats intended to act in diverse aquatic environments, fully autonomously, performing telemetry to a base station and getting this mission to be accomplished. This proposal aims to apply within the project N-Boat Lab NatalNet DCA, which aims to empower a sailboat navigating autonomously. The constituent components of this architecture are the memory modules, strategy, communication, sensing, actuation, energy, security and surveillance, making these systems the boat and base station. To validate the simulator was developed in C language and implemented using the graphics API OpenGL resources, whose main results were obtained in the implementation of memory, performance and strategy modules, more specifically data sharing, control of sails and rudder and planning short routes based on an algorithm for navigation, respectively. The experimental results, shown in this study indicate the feasibility of the actual use of the software architecture developed and their application in the area of autonomous mobile robotics

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There is a growing need to develop new tools to help end users in tasks related to the design, monitoring, maintenance and commissioning of critical infrastructures. The complexity of the industrial environment, for example, requires that these tools have flexible features in order to provide valuable data for the designers at the design phases. Furthermore, it is known that industrial processes have stringent requirements for dependability, since failures can cause economic losses, environmental damages and danger to people. The lack of tools that enable the evaluation of faults in critical infrastructures could mitigate these problems. Accordingly, the said work presents developing a framework for analyzing of dependability for critical infrastructures. The proposal allows the modeling of critical infrastructure, mapping its components to a Fault Tree. Then the mathematical model generated is used for dependability analysis of infrastructure, relying on the equipment and its interconnections failures. Finally, typical scenarios of industrial environments are used to validate the proposal

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This work presents the specification and the implementation of a language of Transformations in definite Models specification MOF (Meta Object Facility) of OMG (Object Management Group). The specification uses a boarding based on rules ECA (Event-Condition-Action) and was made on the basis of a set of scenes of use previously defined. The Parser Responsible parser for guaranteeing that the syntactic structure of the language is correct was constructed with the tool JavaCC (Java Compiler Compiler) and the description of the syntax of the language was made with EBNF (Extended Backus-Naur Form). The implementation is divided in three parts: the creation of the interpretative program properly said in Java, the creation of an executor of the actions specified in the language and its integration with the type of considered repository (generated for tool DSTC dMOF). A final prototype was developed and tested in the scenes previously defined

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Wireless sensor networks are reality nowadays. The growing necessity of connectivity between existing industrial plant equipments pushes the research and development of several technologies. The IEEE 802.15.4 LR-WPAN comes as a low-cost and powersaving viable solution, which are important concerns while making decisions on remote sensoring projects. This study intends to propose a wireless communication system which makes possible the monitoring of analogic and/or digital variables (i. e., the pressure studied) involved on the artificial methods for oil and gas lifting. The main issues are: To develop a software based on SMAC Standard in order to create a wireless network to monitoring analogic and/or digital variables; To evaluate the communication link based on the number of lost packets tested in different environments (indoor and outdoor) and To propose an instrumentation system consisting of wireless devices

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Bayesian networks are powerful tools as they represent probability distributions as graphs. They work with uncertainties of real systems. Since last decade there is a special interest in learning network structures from data. However learning the best network structure is a NP-Hard problem, so many heuristics algorithms to generate network structures from data were created. Many of these algorithms use score metrics to generate the network model. This thesis compare three of most used score metrics. The K-2 algorithm and two pattern benchmarks, ASIA and ALARM, were used to carry out the comparison. Results show that score metrics with hyperparameters that strength the tendency to select simpler network structures are better than score metrics with weaker tendency to select simpler network structures for both metrics (Heckerman-Geiger and modified MDL). Heckerman-Geiger Bayesian score metric works better than MDL with large datasets and MDL works better than Heckerman-Geiger with small datasets. The modified MDL gives similar results to Heckerman-Geiger for large datasets and close results to MDL for small datasets with stronger tendency to select simpler network structures

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Recently, an amazing development has been observed in telecommunication systems. Two good examples of this development are observed in mobile communication and aerospace systems. This impressive development is related to the increasing need for receiving and transmitting communication signals. Particularly, this development has required the study of new antennas and filters. This work presents a fullwave analysis of reflectarrays. The considered structures are composed by arrays of rectangular conducting patches printed on multilayer dieletric substrates, that are mounted on a ground plane. The analysis is developed in the spectral domain, using an equivalent transmission line method in combination with Galerkin method. Results for the reflection coefficient of these structures are presented and compared to those available in the literature. A good agreement was observed. Particularly, the developed analysis uses the transmission lines theory in combination with the incident potentials and the field continuity equations, at the structures interfaces, for obtaining the scattered field components expressions as function of the patch surface currents and of the incident field. Galerkin method is used to determine the unknown coefficients in the boundary value problem. Curves for the reflection coefficient of several reflectarray geometries are presented as function of frequency and of the structural parameters

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This work considers the development of a filtering system composed of an intelligent algorithm, that separates information and noise coming from sensors interconnected by Foundation Fieldbus (FF) network. The algorithm implementation will be made through FF standard function blocks, with on-line training through OPC (OLE for Process Control), and embedded technology in a DSP (Digital Signal Processor) that interacts with the fieldbus devices. The technique ICA (Independent Component Analysis), that explores the possibility of separating mixed signals based on the fact that they are statistically independent, was chosen to this Blind Source Separation (BSS) process. The algorithm and its implementations will be Presented, as well as the results

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Oil spill on the sea, accidental or not, generates enormous negative consequences for the affected area. The damages are ambient and economic, mainly with the proximity of these spots of preservation areas and/or coastal zones. The development of automatic techniques for identification of oil spots on the sea surface, captured through Radar images, assist in a complete monitoring of the oceans and seas. However spots of different origins can be visualized in this type of imaging, which is a very difficult task. The system proposed in this work, based on techniques of digital image processing and artificial neural network, has the objective to identify the analyzed spot and to discern between oil and other generating phenomena of spot. Tests in functional blocks that compose the proposed system allow the implementation of different algorithms, as well as its detailed and prompt analysis. The algorithms of digital image processing (speckle filtering and gradient), as well as classifier algorithms (Multilayer Perceptron, Radial Basis Function, Support Vector Machine and Committe Machine) are presented and commented.The final performance of the system, with different kind of classifiers, is presented by ROC curve. The true positive rates are considered agreed with the literature about oil slick detection through SAR images presents

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This work has as main objective the application of Artificial Neural Networks, ANN, in the resolution of problems of RF /microwaves devices, as for example the prediction of the frequency response of some structures in an interest region. Artificial Neural Networks, are presently a alternative to the current methods of analysis of microwaves structures. Therefore they are capable to learn, and the more important to generalize the acquired knowledge, from any type of available data, keeping the precision of the original technique and adding the low computational cost of the neural models. For this reason, artificial neural networks are being increasily used for modeling microwaves devices. Multilayer Perceptron and Radial Base Functions models are used in this work. The advantages/disadvantages of these models and the referring algorithms of training of each one are described. Microwave planar devices, as Frequency Selective Surfaces and microstrip antennas, are in evidence due the increasing necessities of filtering and separation of eletromagnetic waves and the miniaturization of RF devices. Therefore, it is of fundamental importance the study of the structural parameters of these devices in a fast and accurate way. The presented results, show to the capacities of the neural techniques for modeling both Frequency Selective Surfaces and antennas

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The continuous gas lift method is the main artificial lifting method used in the oil industry for submarine wells, due to its robustness and the large range of flow rate that the well might operate. Nowadays, there is a huge amount of wells producing under this mechanism. This method of elevation has a slow dynamics due to the transients and a correlation between the injected gas rate and the of produced oil rate. Electronics controllers have been used to adjust many parameters of the oil wells and also to improve the efficiency of the gas lift injection system. This paper presents a intelligent control system applied to continuous gas injection in wells, based in production s rules, that has the target of keeping the wells producing during the maximum period of time, in its best operational condition, and doing automatically all necessary adjustments when occurs some disturbance in the system. The author also describes the application of the intelligent control system as a tool to control the flow pressure in the botton of the well (Pwf). In this case, the control system actuates in the surface control valve

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Navigation based on visual feedback for robots, working in a closed environment, can be obtained settling a camera in each robot (local vision system). However, this solution requests a camera and capacity of local processing for each robot. When possible, a global vision system is a cheapest solution for this problem. In this case, one or a little amount of cameras, covering all the workspace, can be shared by the entire team of robots, saving the cost of a great amount of cameras and the associated processing hardware needed in a local vision system. This work presents the implementation and experimental results of a global vision system for mobile mini-robots, using robot soccer as test platform. The proposed vision system consists of a camera, a frame grabber and a computer (PC) for image processing. The PC is responsible for the team motion control, based on the visual feedback, sending commands to the robots through a radio link. In order for the system to be able to unequivocally recognize each robot, each one has a label on its top, consisting of two colored circles. Image processing algorithms were developed for the eficient computation, in real time, of all objects position (robot and ball) and orientation (robot). A great problem found was to label the color, in real time, of each colored point of the image, in time-varying illumination conditions. To overcome this problem, an automatic camera calibration, based on clustering K-means algorithm, was implemented. This method guarantees that similar pixels will be clustered around a unique color class. The obtained experimental results shown that the position and orientation of each robot can be obtained with a precision of few millimeters. The updating of the position and orientation was attained in real time, analyzing 30 frames per second

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There has been an increasing tendency on the use of selective image compression, since several applications make use of digital images and the loss of information in certain regions is not allowed in some cases. However, there are applications in which these images are captured and stored automatically making it impossible to the user to select the regions of interest to be compressed in a lossless manner. A possible solution for this matter would be the automatic selection of these regions, a very difficult problem to solve in general cases. Nevertheless, it is possible to use intelligent techniques to detect these regions in specific cases. This work proposes a selective color image compression method in which regions of interest, previously chosen, are compressed in a lossless manner. This method uses the wavelet transform to decorrelate the pixels of the image, competitive neural network to make a vectorial quantization, mathematical morphology, and Huffman adaptive coding. There are two options for automatic detection in addition to the manual one: a method of texture segmentation, in which the highest frequency texture is selected to be the region of interest, and a new face detection method where the region of the face will be lossless compressed. The results show that both can be successfully used with the compression method, giving the map of the region of interest as an input

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The hardness test is thoroughly used in research and evaluation of materials for quality control. However, this test results are subject to uncertainties caused by the process operator in the moment of the mensuration impression diagonals make by the indenter in the sample. With this mind, an automated equipment of hardness mensuration was developed. The hardness value was obtained starting from the mensuration of plastic deformation suffered by the material to a well-known load. The material deformation was calculated through the mensuration of the difference between the progress and retreat of a diamond indenter on the used sample. It was not necessary, therefore, the manual mensuration of the diagonals, decreasing the mistake source caused by the operator. Tension graphs of versus deformation could be analyzed from data obtained by the accomplished analysis, as well as you became possible a complete observation of the whole process. Following, the hardness results calculated by the experimental apparatus were compared with the results calculated by a commercial microhardness machine with the intention of testing its efficiency. All things considered, it became possible the materials hardness mensuration through an automated method, which minimized the mistakes caused by the operator and increased the analysis reliability

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Due to advances in the manufacturing process of orthopedic prostheses, the need for better quality shape reading techniques (i.e. with less uncertainty) of the residual limb of amputees became a challenge. To overcome these problems means to be able in obtaining accurate geometry information of the limb and, consequently, better manufacturing processes of both transfemural and transtibial prosthetic sockets. The key point for this task is to customize these readings trying to be as faithful as possible to the real profile of each patient. Within this context, firstly two prototype versions (α and β) of a 3D mechanical scanner for reading residual limbs shape based on reverse engineering techniques were designed. Prototype β is an improved version of prototype α, despite remaining working in analogical mode. Both prototypes are capable of producing a CAD representation of the limb via appropriated graphical sheets and were conceived to work purely by mechanical means. The first results were encouraging as they were able to achieve a great decrease concerning the degree of uncertainty of measurements when compared to traditional methods that are very inaccurate and outdated. For instance, it's not unusual to see these archaic methods in action by making use of ordinary home kind measure-tapes for exploring the limb's shape. Although prototype β improved the readings, it still required someone to input the plotted points (i.e. those marked in disk shape graphical sheets) to an academic CAD software called OrtoCAD. This task is performed by manual typing which is time consuming and carries very limited reliability. Furthermore, the number of coordinates obtained from the purely mechanical system is limited to sub-divisions of the graphical sheet (it records a point every 10 degrees with a resolution of one millimeter). These drawbacks were overcome by designing the second release of prototype β in which it was developed an electronic variation of the reading table components now capable of performing an automatic reading (i.e. no human intervention in digital mode). An interface software (i.e. drive) was built to facilitate data transfer. Much better results were obtained meaning less degree of uncertainty (it records a point every 2 degrees with a resolution of 1/10 mm). Additionally, it was proposed an algorithm to convert the CAD geometry, used by OrtoCAD, to an appropriate format and enabling the use of rapid prototyping equipment aiming future automation of the manufacturing process of prosthetic sockets.