Inspección técnica de la presa Santa Marta, mediante medidas subacuáticas de potencial espontáneo

Las filtraciones de agua, con la consecuente erosión interna en presas de materiales sueltos, es una de las causas principales de fallos y accidentes. Las consecuencias del fallo de estas estructuras, pueden ser, pérdidas tanto económicas como de vidas humanas. Por lo cual en este proyecto se describe la aplicación de un método de prospección geofísica no invasiva, medidas de potencial espontáneo, para detectar posibles filtraciones de agua en el cuerpo de la presa. El flujo de agua a través de un material poroso y permeable crea un campo de potencial eléctrico de una magnitud de decenas o centenas de milivoltios, el cual puede ser medido y así detectar infiltraciones de agua en presas de materiales sueltos. Se ha aplicado esta técnica en la Presa Santa Marta, y mediante una interpretación cualitativa de los datos medidos, tomados en la cara aguas arriba de la presa (medidas subacuáticas), se logró identificar un flujo de agua vertical y otro subhorizontal, que estaban ingresando en el cuerpo de la presa, los cuales estaban causando erosión interna y la formación de una tubificación. ABSTRACT Water leakages and internal erosion in embankment dams is one of the main causes of failures and accidents. The consequences of the failure of these structures may cause losses both, economical and of human lives. Therefore, this project describes the application of a noninvasive geophysical prospecting method, self potential measurements, to detect water leakages in the body of the dam. Water flow through a porous and pervious medium creates an electric potential field with a magnitude of tens or hundreds of milivolts, which can be measured and thus detect water leakage in embankment dams. This technique has been applied to the Santa Marta dam, and through a qualitative self potential data interpretation, of the measurements obtained in an upstream direction (underwater measurements), a vertical and sub horizontal water flows entering in the body dam were identified, which were causing internal erosion and developing a piping

Aplicación de herramientas gis para la cartografía y correlación de datos de ensayos no destructivos en el diagnóstico de edificios históricos

La presente tesis propone un nuevo método de cartografía de ensayos no destructivos en edificios históricos mediante el uso de técnicas basadas en SIG. Primeramente, se define el método por el cual es posible elaborar y convertir una cartografía 3D basada en nubes de puntos de un elemento arquitectónico obtenida mediante fotogrametría, en cartografía raster y vectorial, legible por los sistemas SIG mediante un sistema de coordenadas particular que referencian cada punto de la nube obtenida por fotogrametría. A esta cartografía inicial la denominaremos cartografía base. Después, se define el método por el cual los puntos donde se realiza un ensayo NDT se referencian al sistema de coordenadas del plano base, lo que permite la generación de cartografías de los ensayos referenciadas y la posibilidad de obtener sobre un mismo plano base diferentes datos de múltiples ensayos. Estas nuevas cartografías las denominaremos cartografías de datos, y se demostrará la utilidad de las mismas en el estudio del deterioro y la humedad. Se incluirá el factor tiempo en las cartografías, y se mostrará cómo este nuevo hecho posibilita el trabajo interdisciplinar en la elaboración del diagnóstico. Finalmente, se generarán nuevas cartografías inéditas hasta entonces consistentes en la combinación de diferentes cartografías de datos con la misma planimetría base. Estas nuevas cartografías, darán pie a la obtención de lo que se ha definido como mapas de isograma de humedad, mapa de isograma de salinidad, factor de humedad, factor de evaporación, factor de salinidad y factor de degradación del material. Mediante este sistema se facilitará una mejor visión del conjunto de los datos obtenidos en el estudio del edificio histórico, lo que favorecerá la correcta y rigurosa interpretación de los datos para su posterior restauración. ABSTRACT This research work proposes a new mapping method of non-destructive testing in historical buildings, by using techniques based on GIS. First of all, the method that makes it possible to produce and convert a 3D map based on cloud points from an architectural element obtained by photogrammetry, are defined, as raster and vector, legible by GIS mapping systems using a particular coordinate system that will refer each cloud point obtained by photogrammetry. This initial mapping will be named base planimetry. Afterwards, the method by which the points where the NDT test is performed are referenced to the coordinate system of the base plane , which allows the generation of maps of the referenced tests and the possibility of obtaining different data from multiple tests on the same base plane. These new maps will be named mapping data and their usefulness will be demonstrated in the deterioration and moisture study. The time factor in maps will be included, and how this new fact will enable the interdisciplinary work in the elaboration of the diagnosis will be proved. Finally, new maps (unpublished until now) will be generated by combining different mapping from the same planimetry data base. These new maps will enable us to obtain what have been called isograma moisture maps, isograma salinity- maps, humidity factor, evaporation factor, salinity factor and the material degradation factor. This system will provide a better vision of all data obtained in the study of historical buildings , and will ease the proper and rigorous data interpretation for its subsequent restoration.

New data on the ophiolitic VMS deposits of Moeche (Cabo Ortegal Complex, NW Spain)

As a result of the variscan collision, several allochtonous complexes were emplaced on the Iberian margin in Devonian times, among them the Cabo Ortegal Complex comprising the Moeche ophiolitic sequence. Copper has been won from several mines (Piquitos I & II, Barqueira, Maruxa) from disseminated ores and thin massive sulphide layers in the Moeche Unit, a strongly deformed meta-volcanic sequence comprising mainly quartz-chlorite schists and mylonites, which defines the top of the ophiolite. The ores were metamorphosed and strongly deformed under brittle conditions (for pyrite), but their textures are often apparently post-deformational, due to very common solution-transfer processes; they are composed mostly of pyrite and chalcopyrite, with minor sphalerite, pyrrhotite, etc., and with traces of native gold and PGE. The geology, mineralogy, and geochemistry of the orebodies relate closely to VMS of the Cu-Zn (Cyprus) type. Fluid inclusion studies allowed an estimation of metamorphic conditions at pressures of 2/2’5 kb and T 325/350ºC. New determinations using the chlorite geothermometer yield temperatures around 320 ºC, corresponding to pressures near 2 kb according to the isochores deduced from the fluid inclusion study, although in the Barqueira mine higher temperatures, up to 350 ºC, are found, corresponding to presssures up to 2’5 kb. Pb isotopic compositions of pyrite point to a double source of Pb, i.e. a main mantle and a subordinate crustal source. The values for 87SR/86Sr in pyrite support this interpretation, but some results suggest later mobilization in an open system, corresponding to solution-transfer. Age determinations of pyrite deduced from the Pb isotope uranogenic graph, ≈ 480 Ma, do not fit with the metamorphic ages published for the Moeche Unit, and might point to the age of Pb extraction from the mantle.

Integrated program debugging, verification, and optimization using abstract interpretation (and the Ciao system preprocessor)

The technique of Abstract Interpretation has allowed the development of very sophisticated global program analyses which are at the same time provably correct and practical. We present in a tutorial fashion a novel program development framework which uses abstract interpretation as a fundamental tool. The framework uses modular, incremental abstract interpretation to obtain information about the program. This information is used to validate programs, to detect bugs with respect to partial specifications written using assertions (in the program itself and/or in system libraries), to generate and simplify run-time tests, and to perform high-level program transformations such as multiple abstract specialization, parallelization, and resource usage control, all in a provably correct way. In the case of validation and debugging, the assertions can refer to a variety of program points such as procedure entry, procedure exit, points within procedures, or global computations. The system can reason with much richer information than, for example, traditional types. This includes data structure shape (including pointer sharing), bounds on data structure sizes, and other operational variable instantiation properties, as well as procedure-level properties such as determinacy, termination, nonfailure, and bounds on resource consumption (time or space cost). CiaoPP, the preprocessor of the Ciao multi-paradigm programming system, which implements the described functionality, will be used to illustrate the fundamental ideas.

Effectiveness of abstract interpretation in automatic parallelization: a case study in logic programming

We report on a detailed study of the application and effectiveness of program analysis based on abstract interpretation to automatic program parallelization. We study the case of parallelizing logic programs using the notion of strict independence. We first propose and prove correct a methodology for the application in the parallelization task of the information inferred by abstract interpretation, using a parametric domain. The methodology is generic in the sense of allowing the use of different analysis domains. A number of well-known approximation domains are then studied and the transformation into the parametric domain defined. The transformation directly illustrates the relevance and applicability of each abstract domain for the application. Both local and global analyzers are then built using these domains and embedded in a complete parallelizing compiler. Then, the performance of the domains in this context is assessed through a number of experiments. A comparatively wide range of aspects is studied, from the resources needed by the analyzers in terms of time and memory to the actual benefits obtained from the information inferred. Such benefits are evaluated both in terms of the characteristics of the parallelized code and of the actual speedups obtained from it. The results show that data flow analysis plays an important role in achieving efficient parallelizations, and that the cost of such analysis can be reasonable even for quite sophisticated abstract domains. Furthermore, the results also offer significant insight into the characteristics of the domains, the demands of the application, and the trade-offs involved.

Efficient negation using abstract interpretation

While negation has been a very active área of research in logic programming, comparatively few papers have been devoted to implementation issues. Furthermore, the negation-related capabilities of current Prolog systems are limited. We recently presented a novel method for incorporating negation in a Prolog compiler which takes a number of existing methods (some modified and improved by us) and uses them in a combined fashion. The method makes use of information provided by a global analysis of the source code. Our previous work focused on the systematic description of the techniques and the reasoning about correctness and completeness of the method, but provided no experimental evidence to evalúate the proposal. In this paper, we report on an implementation, using the Ciao Prolog system preprocessor, and provide experimental data which indicates that the method is not only feasible but also quite promising from the efficiency point of view. In addition, the tests have provided new insight as to how to improve the proposal further. Abstract interpretation techniques are shown to offer important improvements in this application.

Program development using abstract interpretation (and the ciao system preprocessor)

The technique of Abstract Interpretation has allowed the development of very sophisticated global program analyses which are at the same time provably correct and practical. We present in a tutorial fashion a novel program development framework which uses abstract interpretation as a fundamental tool. The framework uses modular, incremental abstract interpretation to obtain information about the program. This information is used to validate programs, to detect bugs with respect to partial specifications written using assertions (in the program itself and/or in system librarles), to genérate and simplify run-time tests, and to perform high-level program transformations such as múltiple abstract specialization, parallelization, and resource usage control, all in a provably correct way. In the case of validation and debugging, the assertions can refer to a variety of program points such as procedure entry, procedure exit, points within procedures, or global computations. The system can reason with much richer information than, for example, traditional types. This includes data structure shape (including pointer sharing), bounds on data structure sizes, and other operational variable instantiation properties, as well as procedure-level properties such as determinacy, termination, non-failure, and bounds on resource consumption (time or space cost). CiaoPP, the preprocessor of the Ciao multi-paradigm programming system, which implements the described functionality, will be used to illustrate the fundamental ideas.

Efficient implementation of general negation using abstract interpretation

While negation has been a very active área of research in logic programming, comparatively few papers have been devoted to implementation issues. Furthermore, the negation-related capabilities of current Prolog systems are limited. We recently presented a novel method for incorporating negation in a Prolog compiler which takes a number of existing methods (some modified and improved) and uses them in a combined fashion. The method makes use of information provided by a global analysis of the source code. Our previous work focused on the systematic description of the techniques and the reasoning about correctness and completeness of the method, but provided no experimental evidence to evalúate the proposal. In this paper, after proposing some extensions to the method, we provide experimental data which indicates that the method is not only feasible but also quite promising from the efficiency point of view. In addition, the tests have provided new insight as to how to improve the proposal further. Abstract interpretation techniques (in particular those included in the Ciao Prolog system preprocessor) have had a significant role in the success of the technique.

Data-Flow analysis of prolog programs with extra-logical features

Abstract interpretation-based data-flow analysis of logic programs is at this point relatively well understood from the point of view of general frameworks and abstract domains. On the other hand, comparatively little attention has been given to the problems which arise when analysis of a full, practical dialect of the Prolog language is attempted, and only few solutions to these problems have been proposed to date. Such problems relate to dealing correctly with all builtins, including meta-logical and extra-logical predicates, with dynamic predicates (where the program is modified during execution), and with the absence of certain program text during compilation. Existing proposals for dealing with such issues generally restrict in one way or another the classes of programs which can be analyzed if the information from analysis is to be used for program optimization. This paper attempts to fill this gap by considering a full dialect of Prolog, essentially following the recently proposed ISO standard, pointing out the problems that may arise in the analysis of such a dialect, and proposing a combination of known and novel solutions that together allow the correct analysis of arbitrary programs using the full power of the language.

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50–100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

Combining data mining and ontology engineering to enrich ontologies and linked data

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.

Air pollution data classification by SOM Neural Network

Over the last ten years, Salamanca has been considered among the most polluted cities in México. This paper presents a Self-Organizing Maps (SOM) Neural Network application to classify pollution data and automatize the air pollution level determination for Sulphur Dioxide (SO2) in Salamanca. Meteorological parameters are well known to be important factors contributing to air quality estimation and prediction. In order to observe the behavior and clarify the influence of wind parameters on the SO2 concentrations a SOM Neural Network have been implemented along a year. The main advantages of the SOM is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. The results show a significative correlation between pollutant concentrations and some environmental variables.

Data mining for the diagnosis of type 2 diabetes

Diabetes is the most common disease nowadays in all populations and in all age groups. diabetes contributing to heart disease, increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. Different techniques of artificial intelligence has been applied to diabetes problem. The purpose of this study is apply the artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining (DM) technique for the diabetes disease diagnosis. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with decision tree (DT), Bayesian classifier (BC) and other algorithms, recently proposed by other researchers, that were applied to the same database. The robustness of the algorithms are examined using classification accuracy, analysis of sensitivity and specificity, confusion matrix. The results obtained by AMMLP are superior to obtained by DT and BC.

Reachability-based acyclicity analysis by Abstract Interpretation

In programming languages with dynamic use of memory, such as Java, knowing that a reference variable x points to an acyclic data structure is valuable for the analysis of termination and resource usage (e.g., execution time or memory consumption). For instance, this information guarantees that the depth of the data structure to which x points is greater than the depth of the data structure pointed to by x.f for any field f of x. This, in turn, allows bounding the number of iterations of a loop which traverses the structure by its depth, which is essential in order to prove the termination or infer the resource usage of the loop. The present paper provides an Abstract-Interpretation-based formalization of a static analysis for inferring acyclicity, which works on the reduced product of two abstract domains: reachability, which models the property that the location pointed to by a variable w can be reached by dereferencing another variable v (in this case, v is said to reach w); and cyclicity, modeling the property that v can point to a cyclic data structure. The analysis is proven to be sound and optimal with respect to the chosen abstraction.