Estimación de la peligrosidad sísmica mediante protocolos de juicios de expertos

Este trabajo estudia la aportación que los métodos de agregación de juicios de expertos pueden realizar en el cálculo de la peligrosidad sísmica de emplazamientos. Se han realizado cálculos en dos emplazamientos de la Península Ibérica: Mugardos (La Coruña) y Cofrentes (Valencia) que están sometidos a regímenes tectónicos distintos y que, además, alojan instalaciones industriales de gran responsabilidad. Las zonas de estudio, de 320 Km de radio, son independientes. Se ha aplicado un planteamiento probabilista a la estimación de la tasa anual de superación de valores de la aceleración horizontal de pico y se ha utilizado el Método de Montecarlo para incorporar a los resultados la incertidumbre presente en los datos relativos a la definición de cada fuente sismogenética y de su sismicidad. Los cálculos se han operado mediante un programa de ordenador, desarrollado para este trabajo, que utiliza la metodología propuesta por el Senior Seismic Hazard Analysis Commitee (1997) para la NRC. La primera conclusión de los resultados ha sido que la Atenuación es la fuente principal de incertidumbre en las estimaciones de peligrosidad en ambos casos. Dada la dificultad de completar los datos históricos disponibles de esta variable se ha estudiado el comportamiento de cuatro métodos matemáticos de agregación de juicios de expertos a la hora de estimar una ley de atenuación en un emplazamiento. Los datos de partida se han obtenido del Catálogo de Isosistas del IGN. Los sismos utilizados como variables raíz se han elegido con el criterio de cubrir uniformemente la serie histórica disponible y los valores de magnitud observados. Se ha asignado un panel de expertos particular a cada uno de los dos emplazamientos y se han aplicado a sus juicios los métodos de Cooke, equipesos, Apostolakis_Mosleh y Morris. Sus propuestas se han comparado con los datos reales para juzgar su eficacia y su facilidad de operación. A partir de los resultados se ha concluido que el método de Cooke ha mostrado el comportamiento más eficiente y robusto para ambos emplazamientos. Este método, además, ha permitido identificar, razonadamente, a aquellos expertos que no deberían haberse introducido en un panel. The present work analyses the possible contribution of the mathematical methods of aggregation in the assessment of Seismic Hazzard. Two sites, in the Iberian Peninsula, have been considered: Mugardos ( La Coruña) and Cofrentes (Valencia).Both of them are subjected to different tectonic regimes an both accommodate high value industrial plants. Their areas of concern, with radius of 320 Km, are not overlapping. A probabilistic approach has been applied in the assessment the annual probability of exceedence of the horizontal peak acceleration. The Montecarlo Method has allowed to transfer the uncertainty in the models and parameters to the final results. A computer program has been developed for this purpose. The methodology proposed by the Senior Seismic Analysis Committee (1997) for the NRC has been considered. Attenuation in Ground motion has been proved to be the main source of uncertainty in seismic hazard for both sites. Taking into account the difficulties to complete existing historical data in this subject the performance of four mathematical methods of aggregation has been studied. Original data have been obtained from the catalogs of the Spanish National Institute of Geography. The seismic events considered were chosen to cover evenly the historical records and the observed values of magnitude. A panel of experts have been applied to each site and four aggregation methods have been developed : equal weights, Cooke, Apostolakis-Mosleh and Morris The four proposals have been compaired with the actual data to judge their performance and ease of application. The results have shown that the Method of Cooke have proved the most efficient and robust for both sites. This method, besides, allow the reasoned identification of those experts who should be rejected from the panel

Comparative analysis of meta-analysis methods: when to use which?

Background: Several meta-analysis methods can be used to quantitatively combine the results of a group of experiments, including the weighted mean difference, statistical vote counting, the parametric response ratio and the non-parametric response ratio. The software engineering community has focused on the weighted mean difference method. However, other meta-analysis methods have distinct strengths, such as being able to be used when variances are not reported. There are as yet no guidelines to indicate which method is best for use in each case. Aim: Compile a set of rules that SE researchers can use to ascertain which aggregation method is best for use in the synthesis phase of a systematic review. Method: Monte Carlo simulation varying the number of experiments in the meta analyses, the number of subjects that they include, their variance and effect size. We empirically calculated the reliability and statistical power in each case Results: WMD is generally reliable if the variance is low, whereas its power depends on the effect size and number of subjects per meta-analysis; the reliability of RR is generally unaffected by changes in variance, but it does require more subjects than WMD to be powerful; NPRR is the most reliable method, but it is not very powerful; SVC behaves well when the effect size is moderate, but is less reliable with other effect sizes. Detailed tables of results are annexed. Conclusions: Before undertaking statistical aggregation in software engineering, it is worthwhile checking whether there is any appreciable difference in the reliability and power of the methods. If there is, software engineers should select the method that optimizes both parameters.

Gaussian Multiscale Aggregation oriented to Hand Biometric Segmentation in Mobile Devices

New trends in biometrics are oriented to mobile devices in order to increase the overall security in daily actions like bank account access, e-commerce or even document protection within the mobile. However, applying biometrics to mobile devices imply challenging aspects in biometric data acquisition, feature extraction or private data storage. Concretely, this paper attempts to deal with the problem of hand segmentation given a picture of the hand in an unknown background, requiring an accurate result in terms of hand isolation. For the sake of user acceptability, no restrictions are done on background, and therefore, hand images can be taken without any constraint, resulting segmentation in an exigent task. Multiscale aggregation strategies are proposed in order to solve this problem due to their accurate results in unconstrained and complicated scenarios, together with their properties in time performance. This method is evaluated with a public synthetic database with 480000 images considering different backgrounds and illumination environments. The results obtained in terms of accuracy and time performance highlight their capability of being a suitable solution for the problem of hand segmentation in contact-less environments, outperforming competitive methods in literature like Lossy Data Compression image segmentation (LDC).

Gaussian Multiscale Aggregation Applied to Segmentation in Hand Biometrics

This paper presents an image segmentation algorithm based on Gaussian multiscale aggregation oriented to hand biometric applications. The method is able to isolate the hand from a wide variety of background textures such as carpets, fabric, glass, grass, soil or stones. The evaluation was carried out by using a publicly available synthetic database with 408,000 hand images in different backgrounds, comparing the performance in terms of accuracy and computational cost to two competitive segmentation methods existing in literature, namely Lossy Data Compression (LDC) and Normalized Cuts (NCuts). The results highlight that the proposed method outperforms current competitive segmentation methods with regard to computational cost, time performance, accuracy and memory usage.

Structuring research methods and data with the research object model: genomics workflows as a case study

Background: One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e.g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows. Results: We present the application of the workflow-centric RO model for our bioinformatics case study. Three workflows were produced following recently defined Best Practices for workflow design. By modelling the experiment as an RO, we were able to automatically query the experiment and answer questions such as “which particular data was input to a particular workflow to test a particular hypothesis?”, and “which particular conclusions were drawn from a particular workflow?”. Conclusions: Applying a workflow-centric RO model to aggregate and annotate the resources used in a bioinformatics experiment, allowed us to retrieve the conclusions of the experiment in the context of the driving hypothesis, the executed workflows and their input data. The RO model is an extendable reference model that can be used by other systems as well.