Integration of cooperation for development in Survey Engineering curricula at the European Higher Education Area (EHEA) framework

Survey Engineering curricula involves the integration of many formal disciplines at a high level of proficiency. The Escuela de Ingenieros en Topografía, Cartografía y Geodesia at Universidad Politécnica de Madrid (Survey Engineering) has developed an intense and deep teaching on so-called Applied Land Sciences and Technologies or Land Engineering. However, new approaches are encouraged by the European Higher Education Area (EHEA). This fact requires a review of traditional teaching and methods. Furthermore, the new globalization and international approach gives new ways to this discipline to teach and learn about how to bridge gap between cultures and regions. This work is based in two main needs. On one hand, it is based on integration of basic knowledge and disciplines involved in typical Survey Engineering within Land Management. On the other, there is an urgent need to consider territory on a social and ethical basis, as far as a part of the society, culture, idiosyncrasy or economy. The integration of appropriate knowledge of the Land Management is typically dominated by civil engineers and urban planners. It would be very possible to integrate Survey Engineering and Cooperation for Development in the framework of Land Management disciplines. Cooperation for Development is a concept that has changed since beginning of its use until now. Development projects leave an impact on society in response to their beneficiaries and are directed towards self-sustainability. Furthermore, it is the true bridge to reduce gap between societies when differences are immeasurable. The concept of development has also been changing and nowadays it is not a purely economic concept. Education, science and technology are increasingly taking a larger role in what is meant by development. Moreover, it is commonly accepted that Universities should transfer knowledge to society, and the transfer of knowledge should be open to countries most in need for developing. If the importance of the country development is given by education, science and technology, knowledge transfer would be one of the most clear of ways of Cooperation for Development. Therefore, university cooperation is one of the most powerful tools to achieve it, placing universities as agents of development. In Spain, the role of universities as agents of development and cooperation has been largely strengthened. All about this work deals to how to implement both Cooperation for Development and Land Management within Survey Engineering at the EHEA framework.

Scheduling Ontology Engineering Projects Using gOntt

In order to manage properly ontology development projects in complex settings and to apply correctly the NeOn Methodology, it is crucial to have knowledge of the entire ontology development life cycle before starting the development projects. The ontology project plan and scheduling helps the ontology development team to have this knowledge and to monitor the project execution. To facilitate the planning and scheduling of ontology development projects, the NeOn Toolkit plugin called gOntt has been developed. gOntt is a tool that supports the scheduling of ontology network development projects and helps to execute them. In addition, prescriptive methodological guidelines for scheduling ontology development projects using gOntt are provided.

The Innovation Sources in Software Engineering: A Systematic Literature Review

During the last century many researches on the business, marketing and technology fields have developed the innovation research line and large amount of knowledge can be found in the literature. Currently, the importance of systematic and openness approaches to manage the available innovation sources is well established in many knowledge fields. Also in the software engineering sector, where the organizations need to absorb and to exploit as much innovative ideas as possible to get success in the current competitive environment. This Master Thesis presents an study related with the innovation sources in the software engineering eld. The main research goals of this work are the identication and the relevance assessment of the available innovation sources and the understanding of the trends on the innovation sources usage. Firstly, a general review of the literature have been conducted in order to define the research area and to identify research gaps. Secondly, the Systematic Literature Review (SLR) has been proposed as the research method in this work to report reliable conclusions collecting systematically quality evidences about the innovation sources in software engineering field. This contribution provides resources, built-on empirical studies included in the SLR, to support a systematic identication and an adequate exploitation of the innovation sources most suitable in the software engineering field. Several artefacts such as lists, taxonomies and relevance assessments of the innovation sources most suitable for software engineering have been built, and their usage trends in the last decades and their particularities on some countries and knowledge fields, especially on the software engineering, have been researched. This work can facilitate to researchers, managers and practitioners of innovative software organizations the systematization of critical activities on innovation processes like the identication and exploitation of the most suitable opportunities. Innovation researchers can use the results of this work to conduct research studies involving the innovation sources research area. Whereas, organization managers and software practitioners can use the provided outcomes in a systematic way to improve their innovation capability, increasing consequently the value creation in the processes that they run to provide products and services useful to their environment. In summary, this Master Thesis research the innovation sources in the software engineering field, providing useful resources to support an effective innovation sources management. Moreover, several aspects should be deeply study to increase the accuracy of the presented results and to obtain more resources built-on empirical knowledge. It can be supported by the INno- vation SOurces MAnagement (InSoMa) framework, which is introduced in this work in order to encourage openness and systematic approaches to identify and to exploit the innovation sources in the software engineering field.

Dynamic Response Optimization of Vehicles through Efficient Multibody Formulations and Automatic Differentiation Techniques

El diseño y desarrollo de sistemas de suspensión para vehículos se basa cada día más en el diseño por ordenador y en herramientas de análisis por ordenador, las cuales permiten anticipar problemas y resolverlos por adelantado. El comportamiento y las características dinámicas se calculan con precisión, bajo coste, y recursos y tiempos de cálculo reducidos. Sin embargo, existe una componente iterativa en el proceso, que requiere la definición manual de diseños a través de técnicas “prueba y error”. Esta Tesis da un paso hacia el desarrollo de un entorno de simulación eficiente capaz de simular, analizar y evaluar diseños de suspensiones vehiculares, y de mejorarlos hacia la solución optima mediante la modificación de los parámetros de diseño. La modelización mediante sistemas multicuerpo se utiliza aquí para desarrollar un modelo de autocar con 18 grados de libertad, de manera detallada y eficiente. La geometría y demás características de la suspensión se ajustan a las del vehículo real, así como los demás parámetros del modelo. Para simular la dinámica vehicular, se utiliza una formulación multicuerpo moderna y eficiente basada en las ecuaciones de Maggi, a la que se ha incorporado un visor 3D. Así, se consigue simular maniobras vehiculares en tiempos inferiores al tiempo real. Una vez que la dinámica está disponible, los análisis de sensibilidad son cruciales para una optimización robusta y eficiente. Para ello, se presenta una técnica matemática que permite derivar las variables dinámicas dentro de la formulación, de forma algorítmica, general, con la precisión de la maquina, y razonablemente eficiente: la diferenciación automática. Este método propaga las derivadas con respecto a las variables de diseño a través del código informático y con poca intervención del usuario. En contraste con otros enfoques en la bibliografía, generalmente particulares y limitados, se realiza una comparación de librerías, se desarrolla una formulación híbrida directa-automática para el cálculo de sensibilidades, y se presentan varios ejemplos reales. Finalmente, se lleva a cabo la optimización de la respuesta dinámica del vehículo citado. Se analizan cuatro tipos distintos de optimización: identificación de parámetros, optimización de la maniobrabilidad, optimización del confort y optimización multi-objetivo, todos ellos aplicados al diseño del autocar. Además de resultados analíticos y gráficos, se incluyen algunas consideraciones acerca de la eficiencia. En resumen, se mejora el comportamiento dinámico de vehículos por medio de modelos multicuerpo y de técnicas de diferenciación automática y optimización avanzadas, posibilitando un ajuste automático, preciso y eficiente de los parámetros de diseño. ABSTRACT Each day, the design and development of vehicle suspension systems relies more on computer-aided design and computer-aided engineering tools, which allow anticipating the problems and solving them ahead of time. Dynamic behavior and characteristics are thus simulated accurately and inexpensively with moderate computational times and resources. There is, however, an iterative component in the process, which involves the manual definition of designs in a trialand-error manner. This Thesis takes a step towards the development of an efficient simulation framework capable of simulating, analyzing and evaluating vehicle suspension designs, and automatically improving them by varying the design parameters towards the optimal solution. The multibody systems approach is hereby used to model a three-dimensional 18-degrees-of-freedom coach in a comprehensive yet efficient way. The suspension geometry and characteristics resemble the ones from the real vehicle, as do the rest of vehicle parameters. In order to simulate vehicle dynamics, an efficient, state-of-the-art multibody formulation based on Maggi’s equations is employed, and a three-dimensional graphics viewer is developed. As a result, vehicle maneuvers can be simulated faster than real-time. Once the dynamics are ready, a sensitivity analysis is crucial for a robust optimization. To that end, a mathematical technique is introduced, which allows differentiating the dynamic variables within the multibody formulation in a general, algorithmic, accurate to machine precision, and reasonably efficient way: automatic differentiation. This method propagates the derivatives with respect to the design parameters throughout the computer code, with little user interaction. In contrast with other attempts in the literature, mostly not generalpurpose, a benchmarking of libraries is carried out, a hybrid direct-automatic differentiation approach for the computation of sensitivities is developed, and several real-life examples are analyzed. Finally, a design optimization process of the aforementioned vehicle is carried out. Four different types of dynamic response optimization are presented: parameter identification, handling optimization, ride comfort optimization and multi-objective optimization; all of which are applied to the design of the coach example. Together with analytical and visual proof of the results, efficiency considerations are made. In summary, the dynamic behavior of vehicles is improved by using the multibody systems approach, along with advanced differentiation and optimization techniques, enabling an automatic, accurate and efficient tuning of design parameters.

CloudRoom: una plataforma universitaria social, semántica y ubicua

Estamos viviendo la era de la Internetificación. A día de hoy, las conexiones a Internet se asumen presentes en nuestro entorno como una necesidad más. La Web, se ha convertido en un lugar de generación de contenido por los usuarios. Una información generada, que sobrepasa la idea con la que surgió esta, ya que en la mayoría de casos, su contenido no se ha diseñado más que para ser consumido por humanos, y no por máquinas. Esto supone un cambio de mentalidad en la forma en que diseñamos sistemas capaces de soportar una carga computacional y de almacenamiento que crece sin un fin aparente. Al mismo tiempo, vivimos un momento de crisis de la educación superior: los altos costes de una educación de calidad suponen una amenaza para el mundo académico. Mediante el uso de la tecnología, se puede lograr un incremento de la productividad, y una reducción en dichos costes en un campo, en el que apenas se ha avanzado desde el Renacimiento. En CloudRoom se ha diseñado una plataforma MOOC con una arquitectura ajustada a las últimas convenciones en Cloud Computing, que implica el uso de Servicios REST, bases de datos NoSQL, y que hace uso de las últimas recomendaciones del W3C en materia de desarrollo web y Linked Data. Para su construcción, se ha hecho uso de métodos ágiles de Ingeniería del Software, técnicas de Interacción Persona-Ordenador, y tecnologías de última generación como Neo4j, Redis, Node.js, AngularJS, Bootstrap, HTML5, CSS3 o Amazon Web Services. Se ha realizado un trabajo integral de Ingeniería Informática, combinando prácticamente la totalidad de aquellas áreas de conocimiento fundamentales en Informática. En definitiva se han ideado las bases de un sistema distribuido robusto, mantenible, con características sociales y semánticas, que puede ser ejecutado en múltiples dispositivos, y que es capaz de responder ante millones de usuarios. We are living through an age of Internetification. Nowadays, Internet connections are a utility whose presence one can simply assume. The web has become a place of generation of content by users. The information generated surpasses the notion with which the World Wide Web emerged because, in most cases, this content has been designed to be consumed by humans and not by machines. This fact implies a change of mindset in the way that we design systems; these systems should be able to support a computational and storage capacity that apparently grows endlessly. At the same time, our education system is in a state of crisis: the high costs of high-quality education threaten the academic world. With the use of technology, we could achieve an increase of productivity and quality, and a reduction of these costs in this field, which has remained largely unchanged since the Renaissance. In CloudRoom, a MOOC platform has been designed with an architecture that satisfies the last conventions on Cloud Computing; which involves the use of REST services, NoSQL databases, and uses the last recommendations from W3C in terms of web development and Linked Data. For its building process, agile methods of Software Engineering, Human-Computer Interaction techniques, and state of the art technologies such as Neo4j, Redis, Node.js, AngularJS, Bootstrap, HTML5, CSS3 or Amazon Web Services have been used. Furthermore, a comprehensive Informatics Engineering work has been performed, by combining virtually all of the areas of knowledge in Computer Science. Summarizing, the pillars of a robust, maintainable, and distributed system have been devised; a system with social and semantic capabilities, which runs in multiple devices, and scales to millions of users.

Learning and Assessing Competencies: New challenges for Mathematics in Engineering Degrees in Spain.

The introduction of new degrees adapted to the European Area of Higher Education (EAHE) has involved a radically different approach to the curriculum. The new programs are structured around competencies that should be acquired. Considering the competencies, teachers must define and develop learning objectives, design teaching methods and establish appropriate evaluation systems. While most Spanish universities have incorporated methodological innovations and evaluation systems different from traditional exams, there is enough confusion about how to teach and assess competencies and learning outcomes, as traditionally the teaching and assessment have focused on knowledge. In this paper we analyze the state-of-the-art in the mathematical courses of the new engineering degrees in some Spanish universities.

Mealiness assessment in fruits using MRI techniques.

Mealiness is a sensory attribute that cannot be defined by a single parameter but through a combination of variables (multidimensional structure). Previous studies propose the definition of mealiness as the lack of crispiness, of hardness and of juiciness. Current aims are focused on establishing non destructive tests for mealiness assessment. MultiSliceMultiEcho Magnetic resonance images (MRI, 64*64pixels) have been taken corresponding to a 3ms of Echo time. Small samples of Top Red apples stored 6 months at controlled atmosphere (expected to be non mealy) and 2°C (expected to be mealy) have been used for MRI imaging. Three out of four apples corresponding to the sample maintained at controlled atmosphere did not develop mealiness while three out of four fruits corresponding to the sample stored at 2°C became mealy after 6 month of storage. The minimum T2 values/image obtained for the mealy apples shows to be significantly lower when compared with non mealy apples pointing that a more dis-aggregated structure leads to a quicker loss of signal Also, there is a significant linear correlation (r=-0.76) between the number of pixels with a T2 value below 35ms within a fruit image and the deformation parameter registered during the Magness-Taylor firmness test. Finally, all the T2 images of the mealy apples show a regional variation of contrast which is not shown for non mealy apples. This variation of contrast is similar to the MRI images of water-cored apples indicating that in these cases there is a differential water movement that may precede the internal browning.

Knowledge representation tool for cognitiveprocesses modeling

In the last decades, neuropsychological theories tend to consider cognitive functions as a result of the whole brainwork and not as individual local areas of its cortex. Studies based on neuroimaging techniques have increased in the last years, promoting an exponential growth of the body of knowledge about relations between cognitive functions and brain structures [1]. However, so fast evolution make complicated to integrate them in verifiable theories and, even more, translated in to cognitive rehabilitation. The aim of this research work is to develop a cognitive process-modeling tool. The purpose of this system is, in the first term, to represent multidimensional data, from structural and functional connectivity, neuroimaging, data from lesion studies and derived data from clinical intervention [2][3]. This will allow to identify consolidated knowledge, hypothesis, experimental designs, new data from ongoing studies and emerging results from clinical interventions. In the second term, we pursuit to use Artificial Intelligence to assist in decision making allowing to advance towards evidence based and personalized treatments in cognitive rehabilitation. This work presents the knowledge base design of the knowledge representation tool. It is compound of two different taxonomies (structure and function) and a set of tags linking both taxonomies at different levels of structural and functional organization. The remainder of the abstract is organized as follows: Section 2 presents the web application used for gathering necessary information for generating the knowledge base, Section 3 describes knowledge base structure and finally Section 4 expounds reached conclusions.

Clustering techniques for patients suffering acquired brain injury inneuro personal trainer

The study of the effectiveness of the cognitive rehabilitation processes and the identification of cognitive profiles, in order to define comparable populations, is a controversial area, but concurrently it is strongly needed in order to improve therapies. There is limited evidence about cognitive rehabilitation efficacy. Many of the trials conclude that in spite of an apparent clinical good response, differences do not show statistical significance. The common feature in all these trials is heterogeneity among populations. In this situation, observational studies on very well controlled cohort of studies, together with innovative methods in knowledge extraction, could provide methodological insights for the design of more accurate comparative trials. Some correlation studies between neuropsychological tests and patients capacities have been carried out -1---2- and also correlation between tests and morphological changes in the brain -3-. The procedures efficacy depends on three main factors: the affectation profile, the scheduled tasks and the execution results. The relationship between them makes up the cognitive rehabilitation as a discipline, but its structure is not properly defined. In this work we present a clustering method used in Neuro Personal Trainer (NPT) to group patients into cognitive profiles using data mining techniques. The system uses these clusters to personalize treatments, using the patients assigned cluster to select which tasks are more suitable for its concrete needs, by comparing the results obtained in the past by patients with the same profile.

Effectiveness for Ddtecting faults within and outside the scope of testing techniques: an independent replication

The verification and validation activity plays a fundamental role in improving software quality. Determining which the most effective techniques for carrying out this activity are has been an aspiration of experimental software engineering researchers for years. This paper reports a controlled experiment evaluating the effectiveness of two unit testing techniques (the functional testing technique known as equivalence partitioning (EP) and the control-flow structural testing technique known as branch testing (BT)). This experiment is a literal replication of Juristo et al. (2013).Both experiments serve the purpose of determining whether the effectiveness of BT and EP varies depending on whether or not the faults are visible for the technique (InScope or OutScope, respectively). We have used the materials, design and procedures of the original experiment, but in order to adapt the experiment to the context we have: (1) reduced the number of studied techniques from 3 to 2; (2) assigned subjects to experimental groups by means of stratified randomization to balance the influence of programming experience; (3) localized the experimental materials and (4) adapted the training duration. We ran the replication at the Escuela Politécnica del Ejército Sede Latacunga (ESPEL) as part of a software verification & validation course. The experimental subjects were 23 master?s degree students. EP is more effective than BT at detecting InScope faults. The session/program andgroup variables are found to have significant effects. BT is more effective than EP at detecting OutScope faults. The session/program and group variables have no effect in this case. The results of the replication and the original experiment are similar with respect to testing techniques. There are some inconsistencies with respect to the group factor. They can be explained by small sample effects. The results for the session/program factor are inconsistent for InScope faults.We believe that these differences are due to a combination of the fatigue effect and a technique x program interaction. Although we were able to reproduce the main effects, the changes to the design of the original experiment make it impossible to identify the causes of the discrepancies for sure. We believe that further replications closely resembling the original experiment should be conducted to improve our understanding of the phenomena under study.

A knowledge based application for industrialization design

After being designed, a product has to be manufactured, which means converting concepts and information into a real, physical object. This requires a big amount of resources and a careful planning. The product manufacturing must be designed too, and that is called Industrialization Design. An accepted methodology for this activity is starting defining simple structures and then progressively increasing the detail degree of the manufacturing solution. The impact of decisions taken at first stages of Industrialization Design is remarkable, and software tools to assist designers are required. In this paper a Knowledge Based Application prototype for the Industrialization Design is presented. The application is implemented within the environment CATIA V5/DELMIA. A case study with a simple Product from aerospace sector illustrates the prototype development.

Modelo de Gestión del Conocimiento para la Competitividad del Sector de las Construcciones Petroleras, Petroquímicas y Energéticas Venezolanas

La construcción es una de las actividades más valiosas para la sociedad debido a la naturaleza de los servicios que ofrece y por el volumen de empleos y movimiento económico que genera. Por ello es un elemento fundamental para el desarrollo sustentable. Es una industria compleja, cada vez más dependiente del conocimiento. Debido a su naturaleza fragmentaria y temporal y la alta rotación de personal presenta grandes retos y complicaciones particulares. Estas dificultades en oportunidades pueden transformarse en problemas por la complejidad, localización geográfica o los requisitos técnicos, financieros e innovaciones de los proyectos. Debido a sus características, las construcciones sufren cambios en las condiciones planificadas. Con frecuencia estos cambios conducen a retrasos en la ejecución de los proyectos, costes superiores a los presupuestados y conflictos entre los clientes y los ejecutores. Esto genera problemas de competitividad que afectan tanto a países desarrollados como países en vías de desarrollo. Los problemas de la construcción tienen perniciosos efectos para la sociedad, que pierde recursos que deberían permitir mejores resultados en términos de calidad de vida y beneficios sociales y económicos. Debido a la importancia del sector y los ingentes recursos que se invierten en cada proyecto se justifican los máximos esfuerzos para lograr los mejores desempeños de esta industria. Éste interés ha orientado el desarrollo de investigaciones, para apoyar el logro de los objetivos de mejoramiento continuo y construcción sustentable. Los estudios desarrollados han permitido demostrar el valor añadido del conocimiento en todos los sectores productivos. Para la construcción, los conocimientos ofrecen indicadores de desempeño, datos y lecciones aprendidas provenientes de aciertos y errores. Estos deben conducir a aprendizajes fundamentales para sustentar su competitividad. Sin embargo, a pesar de los conocimientos disponibles y los avances en las técnicas de control gerencial y de proyectos, es alarmante la recurrencia de los problemas de construcción. Esta problemática se manifiesta con severidad en los proyectos de construcción industrial que se desarrollan para el sector petrolero, petroquímico y energético venezolano. El sector presenta evidentes necesidades para un mejor desempeño competitivo por la alta incidencia de retrasos de los proyectos, que implican pérdidas de gran parte de los recursos humanos, financieros, técnicos y conocimientos invertidos. Esta investigación plantea como objetivos analizar la importancia de la construcción y su sustentabilidad, los principales problemas que afectan el sector, la gestión del conocimiento y algunos modelos disponibles para gestionarlos. Igualmente examina las lecciones aprendidas y la productividad y competitividad, con particular atención a los problemas de competitividad venezolanos. Adicionalmente se evalúan las implicaciones del conocimiento como activo estratégico y se caracterizan las empresas de construcción industrial venezolanas. Para ello se identifican las dimensiones que sustentan la gestión del conocimiento en estas empresas, para finalmente determinar las que resultan más idóneas para el nuevo modelo a ser propuesto. Con estos objetivos se desarrolló el estudio empírico. Para ello fueron invitados a participar representantes de 105 empresas y expertos de construcción distintos, todos con experiencias de construcción al sector industrial venezolano. Se obtuvieron 112 respuestas en representación de 41 organizaciones y expertos diferentes. El trabajo de campo inició en Junio de 2012 y culminó en Noviembre de 2012. Los datos obtenidos fueron analizados con apoyo de técnicas estadísticas descriptivas y multivariables. Los objetivos de la investigación se alcanzaron ya que se logró caracterizar el sector de las construcciones industriales y se propuso un nuevo modelo de gestión del conocimiento adecuado a sus características. El nuevo modelo fue formulado atendiendo a criterios de sencillez, bajos costes y facilidad de adaptación para motivar su utilización en organizaciones de construcción industrial variadas. Con ello se busca que resulte de utilidad aún para las organizaciones más pequeñas, con menores recursos o aquellas que enfrentan entornos constructivos complicados. Por último se presentan algunas sugerencias para motivar la comprensión de los fenómenos estudiados en los grupos de interés de la construcción. Se propone analizar estos problemas desde las etapas iniciales de los estudios de ingeniería, de arquitectura, de construcción, de economía y administración. Igualmente se propone desarrollar acciones conjuntas de parte de los sectores académicos, gubernamentales, industriales y asociaciones para el mejoramiento competitivo y desarrollo sustentable global. La propuesta aporta datos sobre el sector constructivo venezolano en un área que presenta grandes carencias y propone un modelo innovador por su sencillez y orientación hacia el uso diario e intuitivo de los conocimientos como recursos fundamentales para la competitividad. Esta orientación puede tener trascendencia más allá del sector descrito, para apoyar la solución de problemas de otras industrias en entornos globales. ABSTRACT Construction is one of the most valuable activities for society due to the nature of the services offered and the number of jobs and revenues generated. Therefore it is a key element for sustainable development. Construction is a complex industry increasingly dependent on knowledge. Its temporary and fragmentary nature and the high staff turnover present great challenges and particular complications to construction. In some cases these conditions may evolve to serious problems because of the complexity, geographic location or even technical, financial and innovative requirements of each project. Due to their characteristics, constructions frequently undergo changes in planned conditions. Often these changes lead to delays in project completion, costs higher than budgeted and conflicts between clients and performers. This creates problems of competitiveness affecting both developed and developing countries. The construction problems have harmful effects on society, since it loses resources that would otherwise allow better results in terms of quality of life and social and economic benefits. The importance and the enormous resources invested in each project justify the efforts to achieve the best performance of this industry. This interest has guided the development of multiple research efforts to support the achievement of construction performance improvements and sustainable construction. The studies carried out have demonstrated the added value of knowledge in all productive sectors. For construction, knowledge offers performance indicators, data and lessons learned from successes and failures. These should lead to fundamental learning to sustain sector competitiveness. However, despite the available knowledge and advances in techniques and project management control, the recurrence of construction problems is alarming. This problem shows itself severely in industrial construction projects that are developed for the Venezuelan oil, petrochemical and energy sectors. These sectors have evident needs for better competitive performance because of the high incidence of project delays, involving loss of much of the human, financial, technical and knowledge resources invested. This research analyzes the importance of construction and sustainability, the main problems affecting the sector, knowledge and some models available to manage them. It also examines the lessons learned and the productivity and competitiveness, with particular attention to the problems of Venezuelan competitiveness. Additionally, the Venezuelan industrial construction companies are characterized evaluating the implications of knowledge as an strategic asset for construction. Moreover, the research evaluates the dimensions that support knowledge management in these companies, to finally identify those that are the most suitable for the new model to be proposed. With these objectives in mind the empirical study was developed. 105 different companies and experts with Venezuelan industrial construction experiences were invited to participate on the survey. 112 responses were obtained representing 41 different organizations and experts. Fieldwork started in June 2012 and ended in November 2012. The data obtained was analyzed with descriptive and multivariate statistical techniques. The research objectives were achieved since the industrial construction sector was characterized and a new management model was proposed based on the particular characteristics of these companies. The new model was formulated according to the criteria of simplicity, low cost and ease of adaptation. This was performed to motivate the use of the new model in various industrial construction organizations, even in smaller companies, with limited resources or those facing complex construction environments. Finally some suggestions to encourage understanding of the phenomena studied among construction stakeholders were proposed. The importance of studying these problems at an early stage of the engineering, architectural, construction, economic and administration studies is highlighted. Additionally, academic, government, industrial organizations and associations are invited to join efforts to improve the competitive and sustainable global development. The proposal provides data on the Venezuelan construction sector in an area that has large gaps and proposes a model which is innovative for its simplicity and suggests the daily and intuitive use of knowledge resources as a key issue to competitiveness. This orientation may have implications beyond the described sector to support the solution of problems of other industries in a global environment.

Teaching automatic control in non-specialist engineering schools

Automatic Control Teaching in the new degree syllabus has reduced both, its contents and its implementation course, with regard to traditional engineering careers. On the other hand, where the qualification is not considered as automatic control specialist, it is required an adapted methodology to provide the minimum contents that the student needs to assimilate, even in the case that students do not perceive these contents as the most important in their future career. In this paper we present the contents of a small automatic course taught Naval Architecture and Marine Engineering Degrees at the School of Naval Engineering of the Polytechnic University of Madrid. We have included the contents covered using the proposed methodology which is based on practical work after lectures. Firstly, the students performed exercises by hand. Secondly, they solve the exercises using informatics support tools, and finally, they validate their previous results and their knowledge in the laboratory platforms.

Smart aeronautical structures: development and experimental validation of a structural health monitoring system for damage detection

En muchas áreas de la ingeniería, la integridad y confiabilidad de las estructuras son aspectos de extrema importancia. Estos son controlados mediante el adecuado conocimiento de danos existentes. Típicamente, alcanzar el nivel de conocimiento necesario que permita caracterizar la integridad estructural implica el uso de técnicas de ensayos no destructivos. Estas técnicas son a menudo costosas y consumen mucho tiempo. En la actualidad, muchas industrias buscan incrementar la confiabilidad de las estructuras que emplean. Mediante el uso de técnicas de última tecnología es posible monitorizar las estructuras y en algunos casos, es factible detectar daños incipientes que pueden desencadenar en fallos catastróficos. Desafortunadamente, a medida que la complejidad de las estructuras, los componentes y sistemas incrementa, el riesgo de la aparición de daños y fallas también incrementa. Al mismo tiempo, la detección de dichas fallas y defectos se torna más compleja. En años recientes, la industria aeroespacial ha realizado grandes esfuerzos para integrar los sensores dentro de las estructuras, además de desarrollar algoritmos que permitan determinar la integridad estructural en tiempo real. Esta filosofía ha sido llamada “Structural Health Monitoring” (o “Monitorización de Salud Estructural” en español) y este tipo de estructuras han recibido el nombre de “Smart Structures” (o “Estructuras Inteligentes” en español). Este nuevo tipo de estructuras integran materiales, sensores, actuadores y algoritmos para detectar, cuantificar y localizar daños dentro de ellas mismas. Una novedosa metodología para detección de daños en estructuras se propone en este trabajo. La metodología está basada en mediciones de deformación y consiste en desarrollar técnicas de reconocimiento de patrones en el campo de deformaciones. Estas últimas, basadas en PCA (Análisis de Componentes Principales) y otras técnicas de reducción dimensional. Se propone el uso de Redes de difracción de Bragg y medidas distribuidas como sensores de deformación. La metodología se validó mediante pruebas a escala de laboratorio y pruebas a escala real con estructuras complejas. Los efectos de las condiciones de carga variables fueron estudiados y diversos experimentos fueron realizados para condiciones de carga estáticas y dinámicas, demostrando que la metodología es robusta ante condiciones de carga desconocidas. ABSTRACT In many engineering fields, the integrity and reliability of the structures are extremely important aspects. They are controlled by the adequate knowledge of existing damages. Typically, achieving the level of knowledge necessary to characterize the structural integrity involves the usage of nondestructive testing techniques. These are often expensive and time consuming. Nowadays, many industries look to increase the reliability of the structures used. By using leading edge techniques it is possible to monitoring these structures and in some cases, detect incipient damage that could trigger catastrophic failures. Unfortunately, as the complexity of the structures, components and systems increases, the risk of damages and failures also increases. At the same time, the detection of such failures and defects becomes more difficult. In recent years, the aerospace industry has done great efforts to integrate the sensors within the structures and, to develop algorithms for determining the structural integrity in real time. The ‘philosophy’ has being called “Structural Health Monitoring” and these structures have been called “smart structures”. These new types of structures integrate materials, sensors, actuators and algorithms to detect, quantify and locate damage within itself. A novel methodology for damage detection in structures is proposed. The methodology is based on strain measurements and consists in the development of strain field pattern recognition techniques. The aforementioned are based on PCA (Principal Component Analysis) and other dimensional reduction techniques. The use of fiber Bragg gratings and distributed sensing as strain sensors is proposed. The methodology have been validated by using laboratory scale tests and real scale tests with complex structures. The effects of the variable load conditions were studied and several experiments were performed for static and dynamic load conditions, demonstrating that the methodology is robust under unknown load conditions.

A visual framework to accelerate knowledge discovery based on dimensionality reduction minimizing degradation of quality

Tradicionalmente, el uso de técnicas de análisis de datos ha sido una de las principales vías para el descubrimiento de conocimiento oculto en grandes cantidades de datos, recopilados por expertos en diferentes dominios. Por otra parte, las técnicas de visualización también se han usado para mejorar y facilitar este proceso. Sin embargo, existen limitaciones serias en la obtención de conocimiento, ya que suele ser un proceso lento, tedioso y en muchas ocasiones infructífero, debido a la dificultad de las personas para comprender conjuntos de datos de grandes dimensiones. Otro gran inconveniente, pocas veces tenido en cuenta por los expertos que analizan grandes conjuntos de datos, es la degradación involuntaria a la que someten a los datos durante las tareas de análisis, previas a la obtención final de conclusiones. Por degradación quiere decirse que los datos pueden perder sus propiedades originales, y suele producirse por una reducción inapropiada de los datos, alterando así su naturaleza original y llevando en muchos casos a interpretaciones y conclusiones erróneas que podrían tener serias implicaciones. Además, este hecho adquiere una importancia trascendental cuando los datos pertenecen al dominio médico o biológico, y la vida de diferentes personas depende de esta toma final de decisiones, en algunas ocasiones llevada a cabo de forma inapropiada. Ésta es la motivación de la presente tesis, la cual propone un nuevo framework visual, llamado MedVir, que combina la potencia de técnicas avanzadas de visualización y minería de datos para tratar de dar solución a estos grandes inconvenientes existentes en el proceso de descubrimiento de información válida. El objetivo principal es hacer más fácil, comprensible, intuitivo y rápido el proceso de adquisición de conocimiento al que se enfrentan los expertos cuando trabajan con grandes conjuntos de datos en diferentes dominios. Para ello, en primer lugar, se lleva a cabo una fuerte disminución en el tamaño de los datos con el objetivo de facilitar al experto su manejo, y a la vez preservando intactas, en la medida de lo posible, sus propiedades originales. Después, se hace uso de efectivas técnicas de visualización para representar los datos obtenidos, permitiendo al experto interactuar de forma sencilla e intuitiva con los datos, llevar a cabo diferentes tareas de análisis de datos y así estimular visualmente su capacidad de comprensión. De este modo, el objetivo subyacente se basa en abstraer al experto, en la medida de lo posible, de la complejidad de sus datos originales para presentarle una versión más comprensible, que facilite y acelere la tarea final de descubrimiento de conocimiento. MedVir se ha aplicado satisfactoriamente, entre otros, al campo de la magnetoencefalografía (MEG), que consiste en la predicción en la rehabilitación de lesiones cerebrales traumáticas (Traumatic Brain Injury (TBI) rehabilitation prediction). Los resultados obtenidos demuestran la efectividad del framework a la hora de acelerar y facilitar el proceso de descubrimiento de conocimiento sobre conjuntos de datos reales. ABSTRACT Traditionally, the use of data analysis techniques has been one of the main ways of discovering knowledge hidden in large amounts of data, collected by experts in different domains. Moreover, visualization techniques have also been used to enhance and facilitate this process. However, there are serious limitations in the process of knowledge acquisition, as it is often a slow, tedious and many times fruitless process, due to the difficulty for human beings to understand large datasets. Another major drawback, rarely considered by experts that analyze large datasets, is the involuntary degradation to which they subject the data during analysis tasks, prior to obtaining the final conclusions. Degradation means that data can lose part of their original properties, and it is usually caused by improper data reduction, thereby altering their original nature and often leading to erroneous interpretations and conclusions that could have serious implications. Furthermore, this fact gains a trascendental importance when the data belong to medical or biological domain, and the lives of people depends on the final decision-making, which is sometimes conducted improperly. This is the motivation of this thesis, which proposes a new visual framework, called MedVir, which combines the power of advanced visualization techniques and data mining to try to solve these major problems existing in the process of discovery of valid information. Thus, the main objective is to facilitate and to make more understandable, intuitive and fast the process of knowledge acquisition that experts face when working with large datasets in different domains. To achieve this, first, a strong reduction in the size of the data is carried out in order to make the management of the data easier to the expert, while preserving intact, as far as possible, the original properties of the data. Then, effective visualization techniques are used to represent the obtained data, allowing the expert to interact easily and intuitively with the data, to carry out different data analysis tasks, and so visually stimulating their comprehension capacity. Therefore, the underlying objective is based on abstracting the expert, as far as possible, from the complexity of the original data to present him a more understandable version, thus facilitating and accelerating the task of knowledge discovery. MedVir has been succesfully applied to, among others, the field of magnetoencephalography (MEG), which consists in predicting the rehabilitation of Traumatic Brain Injury (TBI). The results obtained successfully demonstrate the effectiveness of the framework to accelerate and facilitate the process of knowledge discovery on real world datasets.