24 resultados para Data dissemination and sharing

em Universidad Politécnica de Madrid


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We present the data structures and algorithms used in the approach for building domain ontologies from folksonomies and linked data. In this approach we extracts domain terms from folksonomies and enrich them with semantic information from the Linked Open Data cloud. As a result, we obtain a domain ontology that combines the emergent knowledge of social tagging systems with formal knowledge from Ontologies.

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PART I:Cross-section uncertainties under differentneutron spectra. PART II: Processing uncertainty libraries

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El objetivo de este proyecto es diseñar un sistema capaz de controlar la velocidad de rotación de un motor DC en función del valor de temperatura obtenido de un sensor. Para ello se generará con un microcontrolador una señal PWM, cuyo ciclo de trabajo estará en función de la temperatura medida. En lo que respecta a la fase de diseño, hay dos partes claramente diferenciadas, relativas al hardware y al software. En cuanto al diseño del hardware puede hacerse a su vez una división en dos partes. En primer lugar, hubo que diseñar la circuitería necesaria para adaptar los niveles de tensión entregados por el sensor de temperatura a los niveles requeridos por ADC, requerido para digitalizar la información para su posterior procesamiento por parte del microcontrolador. Por tanto hubo que diseñar capaz de corregir el offset y la pendiente de la función tensión-temperatura del sensor, a fin de adaptarlo al rango de tensión requerido por el ADC. Por otro lado, hubo que diseñar el circuito encargado de controlar la velocidad de rotación del motor. Este circuito estará basado en un transistor MOSFET en conmutación, controlado mediante una señal PWM como se mencionó anteriormente. De esta manera, al variar el ciclo de trabajo de la señal PWM, variará de manera proporcional la tensión que cae en el motor, y por tanto su velocidad de rotación. En cuanto al diseño del software, se programó el microcontrolador para que generase una señal PWM en uno de sus pines en función del valor entregado por el ADC, a cuya entrada está conectada la tensión obtenida del circuito creado para adaptar la tensión generada por el sensor. Así mismo, se utiliza el microcontrolador para representar el valor de temperatura obtenido en una pantalla LCD. Para este proyecto se eligió una placa de desarrollo mbed, que incluye el microcontrolador integrado, debido a que facilita la tarea del prototipado. Posteriormente se procedió a la integración de ambas partes, y testeado del sistema para comprobar su correcto funcionamiento. Puesto que el resultado depende de la temperatura medida, fue necesario simular variaciones en ésta, para así comprobar los resultados obtenidos a distintas temperaturas. Para este propósito se empleó una bomba de aire caliente. Una vez comprobado el funcionamiento, como último paso se diseñó la placa de circuito impreso. Como conclusión, se consiguió desarrollar un sistema con un nivel de exactitud y precisión aceptable, en base a las limitaciones del sistema. SUMMARY: It is obvious that day by day people’s daily life depends more on technology and science. Tasks tend to be done automatically, making them simpler and as a result, user life is more comfortable. Every single task that can be controlled has an electronic system behind. In this project, a control system based on a microcontroller was designed for a fan, allowing it to go faster when temperature rises or slowing down as the environment gets colder. For this purpose, a microcontroller was programmed to generate a signal, to control the rotation speed of the fan depending on the data acquired from a temperature sensor. After testing the whole design developed in the laboratory, the next step taken was to build a prototype, which allows future improvements in the system that are discussed in the corresponding section of the thesis.

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The uptake of Linked Data (LD) has promoted the proliferation of datasets and their associated ontologies for describing different domains. Par-ticular LD development characteristics such as agility and web-based architec-ture necessitate the revision, adaption, and lightening of existing methodologies for ontology development. This thesis proposes a lightweight method for ontol-ogy development in an LD context which will be based in data-driven agile de-velopments, existing resources to be reused, and the evaluation of the obtained products considering both classical ontological engineering principles and LD characteristics.

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Abstract is not available.

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In this position paper, we claim that the need for time consuming data preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for ontology building can be reduced through exploiting the interplay of data mining and ontology engineering. The aim is to obtain in a semi-automatic way new knowledge from distributed data sources that can be used for inference and reasoning, as well as to guide the extraction of further knowledge from these data sources. The proposed approach is based on the creation of a novel knowledge discovery method relying on the combination, through an iterative ?feedbackloop?, of (a) data mining techniques to make emerge implicit models from data and (b) pattern-based ontology engineering to capture these models in reusable, conceptual and inferable artefacts.

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While a number of virtual data-gloves have been used in stroke, there is little evidence about their use in spinal cord injury (SCI). A pilot clinical experience with nine SCI subjects was performed comparing two groups: one carried out a virtual rehabilitation training based on the use of a data glove, CyberTouch combined with traditional rehabilitation, during 30 minutes a day twice a week along two weeks; while the other made only conventional rehabilitation. Furthermore, two functional indexes were developed in order to assess the patient’s performance of the sessions: normalized trajectory lengths and repeatability. While differences between groups were not statistically significant, the data-glove group seemed to obtain better results in the muscle balance and functional parameters, and in the dexterity, coordination and fine grip tests. Related to the indexes that we implemented, normalized trajectory lengths and repeatability, every patient showed an improvement in at least one of the indexes, either along Y-axis trajectory or Z-axis trajectory. This study might be a step in investigating new ways of treatments and objective measures in order to obtain more accurate data about the patient’s evolution, allowing the clinicians to develop rehabilitation treatments, adapted to the abilities and needs of the patients.

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Linked Data is the key paradigm of the Semantic Web, a new generation of the World Wide Web that promises to bring meaning (semantics) to data. A large number of both public and private organizations have published their data following the Linked Data principles, or have done so with data from other organizations. To this extent, since the generation and publication of Linked Data are intensive engineering processes that require high attention in order to achieve high quality, and since experience has shown that existing general guidelines are not always sufficient to be applied to every domain, this paper presents a set of guidelines for generating and publishing Linked Data in the context of energy consumption in buildings (one aspect of Building Information Models). These guidelines offer a comprehensive description of the tasks to perform, including a list of steps, tools that help in achieving the task, various alternatives for performing the task, and best practices and recommendations. Furthermore, this paper presents a complete example on the generation and publication of Linked Data about energy consumption in buildings, following the presented guidelines, in which the energy consumption data of council sites (e.g., buildings and lights) belonging to the Leeds City Council jurisdiction have been generated and published as Linked Data.

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Current methods and tools that support Linked Data publication have mainly focused so far on static data, without considering the growing amount of streaming data available on the Web. In this paper we describe a case study that involves the publication of static and streaming Linked Data for bike sharing systems and related entities. We describe some of the challenges that we have faced, the solutions that we have explored, the lessons that we have learned, and the opportunities that lie in the future for exploiting Linked Stream Data.

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Current “Internet of Things” concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3C’s Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where drivers’ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is sound

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In a series of attempts to research and document relevant sloshing type phenomena, a series of experiments have been conducted. The aim of this paper is to describe the setup and data processing of such experiments. A sloshing tank is subjected to angular motion. As a result pressure registers are obtained at several locations, together with the motion data, torque and a collection of image and video information. The experimental rig and the data acquisition systems are described. Useful information for experimental sloshing research practitioners is provided. This information is related to the liquids used in the experiments, the dying techniques, tank building processes, synchronization of acquisition systems, etc. A new procedure for reconstructing experimental data, that takes into account experimental uncertainties, is presented. This procedure is based on a least squares spline approximation of the data. Based on a deterministic approach to the first sloshing wave impact event in a sloshing experiment, an uncertainty analysis procedure of the associated first pressure peak value is described.

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In this paper, abstract interpretation algorithms are described for computing the sharmg as well as the freeness information about the run-time instantiations of program variables. An abstract domain is proposed which accurately and concisely represents combined freeness and sharing information for program variables. Abstract unification and all other domain-specific functions for an abstract interpreter working on this domain are presented. These functions are illustrated with an example. The importance of inferring freeness is stressed by showing (1) the central role it plays in non-strict goal independence, and (2) the improved accuracy it brings to the analysis of sharing information when both are computed together. Conversely, it is shown that keeping accurate track of sharing allows more precise inference of freeness, thus resulting in an overall much more powerful abstract interpreter.

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This paper presents improved unification algorithms, an implementation, and an analysis of the effectiveness of an abstract interpreter based on the sharing + freeness domain presented in a previous paper, which was designed to accurately and concisely represent combined freeness and sharing information for program variables. We first briefly review this domain and the unification algorithms previously proposed. We then improve these algorithms and correct them to deal with some cases which were not well analyzed previously, illustrating the improvement with an example. We then present the implementation of the improved algorithm and evaluate its performance by comparing the effectiveness of the information inferred to that of other interpreters available to us for an application (program parallelization) that is common to all these interpreters. All these systems have been embedded in a real parallelizing compiler. Effectiveness of the analysis is measured in terms of actual final performance of the system: i.e. in terms of the actual speedups obtained. The results show good performance for the combined domain in that it improves the accuracy of both types of information and also in that the analyzer using the combined domain is more effective in the application than any of the other analyzers it is compared to.

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The development of new-generation intelligent vehicle technologies will lead to a better level of road safety and CO2 emission reductions. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) infrastructure sensors and 2) floating vehicles. The former consists of a set of fixed point detectors installed in the roads, and the latter consists of the use of mobile probe vehicles as mobile sensors. However, both systems still have some deficiencies. The infrastructure sensors retrieve information fromstatic points of the road, which are spaced, in some cases, kilometers apart. This means that the picture of the actual traffic situation is not a real one. This deficiency is corrected by floating cars, which retrieve dynamic information on the traffic situation. Unfortunately, the number of floating data vehicles currently available is too small and insufficient to give a complete picture of the road traffic. In this paper, we present a floating car data (FCD) augmentation system that combines information fromfloating data vehicles and infrastructure sensors, and that, by using neural networks, is capable of incrementing the amount of FCD with virtual information. This system has been implemented and tested on actual roads, and the results show little difference between the data supplied by the floating vehicles and the virtual vehicles.