Probabilistic sonar scan matching for an AUV

This paper proposes MSISpIC, a probabilistic sonar scan matching algorithm for the localization of an autonomous underwater vehicle (AUV). The technique uses range scans gathered with a Mechanical Scanning Imaging Sonar (MSIS), the robot displacement estimated through dead-reckoning using a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method is an extension of the pIC algorithm. An extended Kalman filter (EKF) is used to estimate the robot-path during the scan in order to reference all the range and bearing measurements as well as their uncertainty to a scan fixed frame before registering. The major contribution consists of experimentally proving that probabilistic sonar scan matching techniques have the potential to improve the DVL-based navigation. The algorithm has been tested on an AUV guided along a 600 m path within an abandoned marina underwater environment with satisfactory results

Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper proposes a pose-based algorithm to solve the full SLAM problem for an autonomous underwater vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a mechanical scanning imaging sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method utilizes two extended Kalman filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach

Ranking de 2009 en investigaci??n de las universidades p??blicas espa??olas

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Probabilistic models to assist maintenance of multiple instruments

The paper discusses maintenance challenges of organisations with a huge number of devices and proposes the use of probabilistic models to assist monitoring and maintenance planning. The proposal assumes connectivity of instruments to report relevant features for monitoring. Also, the existence of enough historical registers with diagnosed breakdowns is required to make probabilistic models reliable and useful for predictive maintenance strategies based on them. Regular Markov models based on estimated failure and repair rates are proposed to calculate the availability of the instruments and Dynamic Bayesian Networks are proposed to model cause-effect relationships to trigger predictive maintenance services based on the influence between observed features and previously documented diagnostics

Aproximación al proceso de internacionalización del sector de hidrocarburos: ¿Cómo Ecopetrol logra hacer parte del ranking “Fortune Global 500”?

Este documento analiza algunas estrategias de internacionalización aplicadas a casos de estudio de la empresa Colombiana de Petróleos, Ecopetrol con el fin de encontrar las razones que llevan a dicha compañía a ser un referente de crecimiento. Para ello, en primer lugar se explica el contexto en el cual la empresa se desarrolla; y pasa de ser una empresa estatal, para ocupar el puesto 280 dentro del Ranking Global Fortune 500. En segundo lugar el trabajo se centra en las diferentes estrategias y teorías de internacionalización en las cuales Ecopetrol se basa para lograr su éxito. Finalmente se realiza un análisis financiero con base en datos presentados por la herramienta bloomberg y entrevistas realizadas a especialistas en temas bursátiles y de internacionalización.

Divulgación on-line de los códigos de buen gobierno en las IES y su determinación para el posicionamiento en el ranking QS

Dado el interés que se presenta con los temas de gobierno corporativo, este trabajo busca describir si la divulgación on-line de los contenidos de los códigos de buen gobierno, es determinante en el posicionamiento que tienen las Instituciones de Educación Superior (IES) en el ranking QS. Partiendo de una muestra de 20 IES, se recolectaron un conjunto de datos dicotómicos para 30 variables independientes y se relacionaron con la variable dependiente denominada posicionamiento en el ranking. A partir de lo anterior, se elaboró un trabajo descriptivo y correlacional con el fin de probar las hipótesis de investigación. Este estudio reveló que la divulgación on-line de los contenidos de los códigos de buen gobierno en las IES, no es determinante para el posicionamiento en el ranking QS.

Ranking global del 2010 de las universidades públicas españolas

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El gobierno corporativo de las universidades : estudio de las cien primeras universidades del ranking de Shangh??i

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Ranking 2012 de investigaci??n de las universidades p??blicas espa??olas

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Ranking 2013 de investigaci??n de las universidades p??blicas espa??olas

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xhRank: ranking entities on the Semantic Web

In general, ranking entities (resources) on the Semantic Web (SW) is subject to importance, relevance, and query length. Few existing SW search systems cover all of these aspects. Moreover, many existing efforts simply reuse the technologies from conventional Information Retrieval (IR), which are not designed for SW data. This paper proposes a ranking mechanism, which includes all three categories of rankings and are tailored to SW data.

Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles

Process-based integrated modelling of weather and crop yield over large areas is becoming an important research topic. The production of the DEMETER ensemble hindcasts of weather allows this work to be carried out in a probabilistic framework. In this study, ensembles of crop yield (groundnut, Arachis hypogaea L.) were produced for 10 2.5 degrees x 2.5 degrees grid cells in western India using the DEMETER ensembles and the general large-area model (GLAM) for annual crops. Four key issues are addressed by this study. First, crop model calibration methods for use with weather ensemble data are assessed. Calibration using yield ensembles was more successful than calibration using reanalysis data (the European Centre for Medium-Range Weather Forecasts 40-yr reanalysis, ERA40). Secondly, the potential for probabilistic forecasting of crop failure is examined. The hindcasts show skill in the prediction of crop failure, with more severe failures being more predictable. Thirdly, the use of yield ensemble means to predict interannual variability in crop yield is examined and their skill assessed relative to baseline simulations using ERA40. The accuracy of multi-model yield ensemble means is equal to or greater than the accuracy using ERA40. Fourthly, the impact of two key uncertainties, sowing window and spatial scale, is briefly examined. The impact of uncertainty in the sowing window is greater with ERA40 than with the multi-model yield ensemble mean. Subgrid heterogeneity affects model accuracy: where correlations are low on the grid scale, they may be significantly positive on the subgrid scale. The implications of the results of this study for yield forecasting on seasonal time-scales are as follows. (i) There is the potential for probabilistic forecasting of crop failure (defined by a threshold yield value); forecasting of yield terciles shows less potential. (ii) Any improvement in the skill of climate models has the potential to translate into improved deterministic yield prediction. (iii) Whilst model input uncertainties are important, uncertainty in the sowing window may not require specific modelling. The implications of the results of this study for yield forecasting on multidecadal (climate change) time-scales are as follows. (i) The skill in the ensemble mean suggests that the perturbation, within uncertainty bounds, of crop and climate parameters, could potentially average out some of the errors associated with mean yield prediction. (ii) For a given technology trend, decadal fluctuations in the yield-gap parameter used by GLAM may be relatively small, implying some predictability on those time-scales.