Optimization-Based Architecture for Managing Complex Integrated Product Development Projects

By the mid-1990\u27s, the importance of early introduction of new products to both market share and profitability became fully understood. Thus, reducing product time-to-market became an essential requirement for continuous competition. Integrated Product Development (IPD) is a holistic approach that helps to overcome problems that arise in a complex product development project. IPD emphasis is to provide a framework for an effective planning and managing of engineering projects. Coupled with the fact that about 70% of the life cycle cost of a product is committed at early design phases, the motivation for developing and implementing more effective methodologies for managing the design process of IPD projects became very strong. The main objective of this dissertation is to develop an optimization-based architecture that helps guiding the project manager efforts for managing the design process of complex integrated product development projects. The proposed architecture consists of three major phases: system decomposition, process re-engineering, and project scheduling and time-cost trade-off analysis. The presented research contributes to five areas of research: (1) Improving system performance through efficient re-engineering of its structure. The Dependency Structure Matrix (DSM) provides an effective tool for system structure understanding. An optimization algorithm called Simulated Annealing (SA) was implemented to find an optimal activity sequence of the DSM representing a design project. (2) A simulation-based optimization framework that integrates simulated annealing with a commercial risk analysis software called Crystal Ball was developed to optimally re-sequence the DSM activities given stochastic activity data. (3) Since SA was originally developed to handle deterministic objective functions, a modified SA algorithm able to handle stochastic objective functions was presented. (4) A methodology for the conversion of the optimally sequenced DSM into an equivalent DSM, and then into a project schedule was proposed. (5) Finally, a new hybrid time-cost trade-off model based on the trade-off of resources for project networks was presented. These areas of research were further implemented through a developed excel add-in called “optDSM”. The tool was developed by the author using Visual Basic for Application (VBA) programming language

Modular product platform design

Modular product platforms, sets of common modules that are shared among a product family, can bring cost savings and enable introduction of multiple product variants quicker than without platforms. This thesis describes the current state of modular platform design and identifies gaps in the current state. The gaps were identified through application of three existing methods and by testing their usability and reliability on engineers and engineering students. Existing platform or modular design methods either are meant for (a) single products, (b) identify only module "cores" leaving the final module boundary definition to the designer, and (c) use only a limited set of evaluation criteria. I introduce a clustering algorithm for common module identification that takes into account possible degrees of commonality. This new algorithm can be applied both at physical and functional domains and at any, and even mixed, levels of hierarchy. Furthermore, the algorithm is not limited to a single measure for commonality analysis. To select the candidate modules for the algorithm, a key discriminator is how difficult the interfaces become. I developed an interface complexity metric based on minimizing redesign in case of a design change. The metric is based on multiple expert interviews during two case studies. The new approach was to look at the interface complexity as described by the material, energy, and information flows flowing through the interface. Finally, I introduce a multi criteria platform scorecard for improved evaluation of modular platforms. It helps a company focus on their strategy and benchmark one's own platform to the competitors'. These tools add to the modular platform development process by filling in the gaps identified. The tools are described in the context of the entire platform design process, and the validity of the methods and applicability to platform design is shown through industrial case studies and examples.reviewe

Load Shifting in the Smart Grid: To Participate or Not?

Demand-side management (DSM) has emerged as an important smart grid feature that allows utility companies to maintain desirable grid loads. However, the success of DSM is contingent on active customer participation. Indeed, most existing DSM studies are based on game-theoretic models that assume customers will act rationally and will voluntarily participate in DSM. In contrast, in this paper, the impact of customers' subjective behavior on each other's DSM decisions is explicitly accounted for. In particular, a noncooperative game is formulated between grid customers in which each customer can decide on whether to participate in DSM or not. In this game, customers seek to minimize a cost function that reflects their total payment for electricity. Unlike classical game-theoretic DSM studies which assume that customers are rational in their decision-making, a novel approach is proposed, based on the framework of prospect theory (PT), to explicitly incorporate the impact of customer behavior on DSM decisions. To solve the proposed game under both conventional game theory and PT, a new algorithm based on fictitious player is proposed using which the game will reach an epsilon-mixed Nash equilibrium. Simulation results assess the impact of customer behavior on demand-side management. In particular, the overall participation level and grid load can depend significantly on the rationality level of the players and their risk aversion tendency.Comment: 9 pages, 7 figures, journal, accepte

MOTOR SEQUENCE LEARNING IN ADULTS WITH ADHD

A healthy motor system is able to switch and adapt to different environmental contexts and select the most suitable strategy, thus maximizing the efficiency of the movements and save time or energy. Motor hyperactivity in individuals with ADHD is clinically well recognized and can be understood as abnormal motor inhibition. Individuals with ADHD often have problems with responding effectively to a situation that requires a mobilization of complex motor programs. This deficient flexibility of the motor system in ADHD suggests hypofunctioning of the nigral-striatal dopaminergic system. This study used the motor sequence learning paradigm to examine the selection of movement kinematics and force production and modulation in adults with ADHD. A two-by-three mixed design ANOVA, post-hoc independent measure t-tests and Pearson\u27s correlations were performed. Our results found significantly greater reaction time variability in ADHD as compared to controls. Moreover, subjects with ADHD showed a decreased ability to optimize force production when reacting to different contexts despite intact learning. Thus, participants with ADHD seemed to not be able to integrate the new information and feedback from the environment to inform ongoing motor behavior. Our study provides additional support for the notion that individuals with ADHD have basal ganglia abnormalities and has clinical implications for the diagnosis of ADHD. The findings strongly suggest that motor indices should be further explored as possible biomarkers for ADHD and that the neurophysiological networks underlying motor dysfunctions in ADHD warrant further study

New Challenges on Web Architectures for the Homogenization of the Heterogeneity of Smart Objects in the Internet of Things

Aquesta tesi tracta de dues de les noves tecnologies relacionades amb la Internet of Things (IoT) i la seva integració amb el camp de les Smart Grids (SGs); aquestes tecnologies son la Web of Things (WoT) i la Social Internet of Things (SIoT). La WoT és una tecnologia que s’espera que proveeixi d’un entorn escalable i interoperable a la IoT usant la infraestructura web existent, els protocols web y la web semàntica. També s’espera que la SIoT contribueixi a solucionar els reptes d’escalabilitat i capacitat de descobriment creant una xarxa social d’agents (objectes i humans). Per explorar la sinergia entre aquestes tecnologies, l’objectiu és el de proporcionar evidència pràctica i empírica, generalment en forma de prototips d’implementació i experimentació empírica. En relació amb la WoT i les SGs, s’ha creat un prototip per al Web of Energy (WoE) que té com a objectiu abordar els desafiaments presents en el domini les SGs. El prototip és capaç de proporcionar interoperabilitat i homogeneïtat entre diversos protocols. El disseny d’implementació es basa en el Model d’Actors, que també proporciona escalabilitat del prototip. L’experimentació mostra que el prototip pot gestionar la transmissió de missatges per a aplicacions de les SGs que requereixen que la comunicació es realitzi sota llindars de temps crítics. També es pren una altra direcció d’investigació similar, menys centrada en les SGs, però per a una gamma més àmplia de dominis d’aplicació. S’integra la descripció dels fluxos d’execució com a màquines d’estats finits utilitzant ontologies web (Resource Description Framework (RDF)) i metodologies de la WoT (les accions es realitzen basant-se en peticions Hyper-Text Transfer Protocol/Secure (HTTP/S) a Uniform Resource Locators (URLs)). Aquest flux d’execució, que també pot ser un plantilla per a permetre una configuració flexible en temps d’execució, s’implementa i interpreta com si fos (i mitjançant) un Virtual Object (VO). L’objectiu de la plantilla és ser reutilitzable i poder-se compartir entre múltiples desplegaments de la IoT dins el mateix domini d’aplicació. A causa de les tecnologies utilitzades, la solució no és adequada per a aplicacions de temps crític (llindar de temps relativament baix i rígid). No obstant això, és adequat per a aplicacions que no demanden resposta en un temps crític i que requereixen el desplegament de VOs similars en el que fa referència al flux d’execució. Finalment, el treball s’enfoca en una altra tecnologia destinada a millorar l’escalabilitat i la capacitat de descobriment en la IoT. La SIoT està sorgint com una nova estructura de la IoT que uneix els nodes a través de relacions significatives. Aquestes relacions tenen com a objectiu millorar la capacitat de descobriment; en conseqüència, millora la escalabilitat d’una xarxa de la IoT. En aquest treball s’aplica aquest nou paradigma per optimitzar la gestió de l’energia en el costat de la demanda a les SGs. L’objectiu és aprofitar les característiques de la SIoT per ajudar a la creació de Prosumer Community Groups (PCGs) (grups d’usuaris que consumeixen o produeixen energia) amb el mateix objectiu d’optimització en l’ús de l’energia. La sinergia entre la SIoT i les SGs s’ha anomenat Social Internet of Energy (SIoE). Per tant, amb la SIoE i amb el focus en un desafiament específic, s’estableix la base conceptual per a la integració entre la SIoT i les SGs. Els experiments inicials mostren resultats prometedors i aplanen el camí per a futures investigacions i avaluacions de la proposta. Es conclou que el WoT i la SIoT són dos paradigmes complementaris que nodreixen l’evolució de la propera generació de la IoT. S’espera que la propera generació de la IoT sigui un Multi-Agent System (MAS) generalitzat. Alguns investigadors ja estan apuntant a la Web i les seves tecnologies (per exemple, Web Semàntica, HTTP/S)—i més concretamente a la WoT — com a l’entorn que nodreixi a aquests agents. La SIoT pot millorar tant l’entorn com les relacions entre els agents en aquesta fusió. Les SGs també poden beneficiar-se dels avenços de la IoT, ja que es poden considerar com una aplicació específica d’aquesta última.  Esta tesis trata de dos de las novedosas tecnologías relacionadas con la Internet of Things (IoT) y su integración con el campo de las Smart Grids (SGs); estas tecnologías son laWeb of Things (WoT) y la Social Internet of Things (SIoT). La WoT es una tecnología que se espera que provea de un entorno escalable e interoperable a la IoT usando la infraestructura web existente, los protocolos web y la web semántica. También se espera que la SIoT contribuya a solucionar los retos de escalabilidad y capacidad de descubrimiento creando una red social de agentes (objetos y humanos). Para explorar la sinergia entre estas tecnologías, el objetivo es el de proporcionar evidencia práctica y empírica, generalmente en forma de prototipos de implementación y experimentación empírica. En relación con la WoT y las SGs, se ha creado un prototipo para la Web of Energy (WoE) que tiene como objetivo abordar los desafíos presentes en el dominio las SGs. El prototipo es capaz de proporcionar interoperabilidad y homogeneidad entre diversos protocolos. El diseño de implementación se basa en el Modelo de Actores, que también proporciona escalabilidad del prototipo. La experimentación muestra que el prototipo puede manejar la transmisión de mensajes para aplicaciones de las SGs que requieran que la comunicación se realice bajo umbrales de tiempo críticos. También se toma otra dirección de investigación similar, menos centrada en las SGs, pero para una gama más amplia de dominios de aplicación. Se integra la descripción de los flujos de ejecución como máquinas de estados finitos utilizando ontologías web (Resource Description Framework (RDF)) y metodologías de la WoT (las acciones se realizan basándose en peticiones Hyper-Text Transfer Protocol/Secure (HTTP/S) a Uniform Resource Locators (URLs)). Este flujo de ejecución, que también puede ser una plantilla para permitir una configuración flexible en tiempo de ejecución, se implementa e interpreta como si fuera (y a través de) un Virtual Object (VO). El objetivo de la plantilla es que sea reutilizable y se pueda compartir entre múltiples despliegues de la IoT dentro del mismo dominio de aplicación. Debido a las tecnologías utilizadas, la solución no es adecuada para aplicaciones de tiempo crítico (umbral de tiempo relativamente bajo y rígido). Sin embargo, es adecuado para aplicaciones que no demandan respuesta en un tiempo crítico y que requieren el despliegue de VOs similares en cuanto al flujo de ejecución. Finalmente, el trabajo se enfoca en otra tecnología destinada a mejorar la escalabilidad y la capacidad de descubrimiento en la IoT. La SIoT está emergiendo como una nueva estructura de la IoT que une los nodos a través de relaciones significativas. Estas relaciones tienen como objetivo mejorar la capacidad de descubrimiento; en consecuencia, mejora la escalabilidad de una red de la IoT. En este trabajo se aplica este nuevo paradigma para optimizar la gestión de la energía en el lado de la demanda en las SGs. El objetivo es aprovechar las características de la SIoT para ayudar en la creación de Prosumer Community Groups (PCGs) (grupos de usuarios que consumen o producen energía) con el mismo objetivo de optimización en el uso de la energía. La sinergia entre la SIoT y las SGs ha sido denominada Social Internet of Energy (SIoE). Por lo tanto, con la SIoE y con el foco en un desafío específico, se establece la base conceptual para la integración entre la SIoT y las SG. Los experimentos iniciales muestran resultados prometedores y allanan el camino para futuras investigaciones y evaluaciones de la propuesta. Se concluye que la WoT y la SIoT son dos paradigmas complementarios que nutren la evolución de la próxima generación de la IoT. Se espera que la próxima generación de la IoT sea un Multi-Agent System (MAS) generalizado. Algunos investigadores ya están apuntando a la Web y sus tecnologías (por ejemplo,Web Semántica, HTTP/S)—y más concretamente a la WoT — como el entorno que nutra a estos agentes. La SIoT puede mejorar tanto el entorno como las relaciones entre los agentes en esta fusión. Como un campo específico de la IoT, las SGs también pueden beneficiarse de los avances de la IoT.This thesis deals with two novel Internet of Things (IoT) technologies and their integration to the field of the Smart Grid (SG); these technologies are the Web of Things (WoT) and the Social Internet of Things (SIoT). The WoT is an enabling technology expected to provide a scalable and interoperable environment to the IoT using the existing web infrastructure, web protocols and the semantic web. The SIoT is expected to expand further and contribute to scalability and discoverability challenges by creating a social network of agents (objects and humans). When exploring the synergy between those technologies, we aim at providing practical and empirical evidence, usually in the form of prototype implementations and empirical experimentation. In relation to the WoT and SG, we create a prototype for the Web of Energy (WoE), that aims at addressing challenges present in the SG domain. The prototype is capable of providing interoperability and homogeneity among diverse protocols. The implementation design is based on the Actor Model, which also provides scalability in regards to the prototype. Experimentation shows that the prototype can handle the transmission of messages for time-critical SG applications. We also take another similar research direction less focused on the SG, but for a broader range of application domains. We integrate the description of flows of execution as Finite-State Machines (FSMs) using web ontologies (Resource Description Framework (RDF)) and WoT methodologies (actions are performed on the basis of calls Hyper Text Transfer Protocol/ Secure (HTTP/S) to a Uniform Resource Locator (URL)). This execution flow, which can also be a template to allow flexible configuration at runtime, is deployed and interpreted as (and through) a Virtual Object (VO). The template aims to be reusable and shareable among multiple IoT deployments within the same application domain. Due to the technologies used, the solution is not suitable for time-critical applications. Nevertheless, it is suitable for non-time-critical applications that require the deployment of similar VOs. Finally, we focus on another technology aimed at improving scalability and discoverability in IoT. The SIoT is emerging as a new IoT structure that links nodes through meaningful relationships. These relationships aim at improving discoverability; consequently, improving the scalability of an IoT network. We apply this new paradigm to optimize energy management at the demand side in a SG. Our objective is to harness the features of the SIoT to aid in the creation of Prosumer Community Group (PCG) (groups of energy users that consume or produce energy) with the same Demand Side Management (DSM) goal. We refer to the synergy between SIoT and SG as Social Internet of Energy (SIoE). Therefore, with the SIoE and focusing on a specific challenge, we set the conceptual basis for the integration between SIoT and SG. Initial experiments show promising results and pave the way for further research and evaluation of the proposal. We conclude that the WoT and the SIoT are two complementary paradigms that nourish the evolution of the next generation IoT. The next generation IoT is expected to be a pervasive Multi-Agent System (MAS). Some researchers are already pointing at the Web and its technologies (e.g. Semantic Web, HTTP/S) — and more concretely at the WoT — as the environment nourishing the agents. The SIoT can enhance both the environment and the relationships between agents in this fusion. As a specific field of the IoT, the SG can also benefit from IoT advancements

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

New Challenges on Web Architectures for the Homogenization of the Heterogeneity of Smart Objects in the Internet of Things

Aquesta tesi tracta de dues de les noves tecnologies relacionades amb la Internet of Things (IoT) i la seva integració amb el camp de les Smart Grids (SGs); aquestes tecnologies son la Web of Things (WoT) i la Social Internet of Things (SIoT). La WoT és una tecnologia que s’espera que proveeixi d’un entorn escalable i interoperable a la IoT usant la infraestructura web existent, els protocols web y la web semàntica. També s’espera que la SIoT contribueixi a solucionar els reptes d’escalabilitat i capacitat de descobriment creant una xarxa social d’agents (objectes i humans). Per explorar la sinergia entre aquestes tecnologies, l’objectiu és el de proporcionar evidència pràctica i empírica, generalment en forma de prototips d’implementació i experimentació empírica. En relació amb la WoT i les SGs, s’ha creat un prototip per al Web of Energy (WoE) que té com a objectiu abordar els desafiaments presents en el domini les SGs. El prototip és capaç de proporcionar interoperabilitat i homogeneïtat entre diversos protocols. El disseny d’implementació es basa en el Model d’Actors, que també proporciona escalabilitat del prototip. L’experimentació mostra que el prototip pot gestionar la transmissió de missatges per a aplicacions de les SGs que requereixen que la comunicació es realitzi sota llindars de temps crítics. També es pren una altra direcció d’investigació similar, menys centrada en les SGs, però per a una gamma més àmplia de dominis d’aplicació. S’integra la descripció dels fluxos d’execució com a màquines d’estats finits utilitzant ontologies web (Resource Description Framework (RDF)) i metodologies de la WoT (les accions es realitzen basant-se en peticions Hyper-Text Transfer Protocol/Secure (HTTP/S) a Uniform Resource Locators (URLs)). Aquest flux d’execució, que també pot ser un plantilla per a permetre una configuració flexible en temps d’execució, s’implementa i interpreta com si fos (i mitjançant) un Virtual Object (VO). L’objectiu de la plantilla és ser reutilitzable i poder-se compartir entre múltiples desplegaments de la IoT dins el mateix domini d’aplicació. A causa de les tecnologies utilitzades, la solució no és adequada per a aplicacions de temps crític (llindar de temps relativament baix i rígid). No obstant això, és adequat per a aplicacions que no demanden resposta en un temps crític i que requereixen el desplegament de VOs similars en el que fa referència al flux d’execució. Finalment, el treball s’enfoca en una altra tecnologia destinada a millorar l’escalabilitat i la capacitat de descobriment en la IoT. La SIoT està sorgint com una nova estructura de la IoT que uneix els nodes a través de relacions significatives. Aquestes relacions tenen com a objectiu millorar la capacitat de descobriment; en conseqüència, millora la escalabilitat d’una xarxa de la IoT. En aquest treball s’aplica aquest nou paradigma per optimitzar la gestió de l’energia en el costat de la demanda a les SGs. L’objectiu és aprofitar les característiques de la SIoT per ajudar a la creació de Prosumer Community Groups (PCGs) (grups d’usuaris que consumeixen o produeixen energia) amb el mateix objectiu d’optimització en l’ús de l’energia. La sinergia entre la SIoT i les SGs s’ha anomenat Social Internet of Energy (SIoE). Per tant, amb la SIoE i amb el focus en un desafiament específic, s’estableix la base conceptual per a la integració entre la SIoT i les SGs. Els experiments inicials mostren resultats prometedors i aplanen el camí per a futures investigacions i avaluacions de la proposta. Es conclou que el WoT i la SIoT són dos paradigmes complementaris que nodreixen l’evolució de la propera generació de la IoT. S’espera que la propera generació de la IoT sigui un Multi-Agent System (MAS) generalitzat. Alguns investigadors ja estan apuntant a la Web i les seves tecnologies (per exemple, Web Semàntica, HTTP/S)—i més concretamente a la WoT — com a l’entorn que nodreixi a aquests agents. La SIoT pot millorar tant l’entorn com les relacions entre els agents en aquesta fusió. Les SGs també poden beneficiar-se dels avenços de la IoT, ja que es poden considerar com una aplicació específica d’aquesta última.  Esta tesis trata de dos de las novedosas tecnologías relacionadas con la Internet of Things (IoT) y su integración con el campo de las Smart Grids (SGs); estas tecnologías son laWeb of Things (WoT) y la Social Internet of Things (SIoT). La WoT es una tecnología que se espera que provea de un entorno escalable e interoperable a la IoT usando la infraestructura web existente, los protocolos web y la web semántica. También se espera que la SIoT contribuya a solucionar los retos de escalabilidad y capacidad de descubrimiento creando una red social de agentes (objetos y humanos). Para explorar la sinergia entre estas tecnologías, el objetivo es el de proporcionar evidencia práctica y empírica, generalmente en forma de prototipos de implementación y experimentación empírica. En relación con la WoT y las SGs, se ha creado un prototipo para la Web of Energy (WoE) que tiene como objetivo abordar los desafíos presentes en el dominio las SGs. El prototipo es capaz de proporcionar interoperabilidad y homogeneidad entre diversos protocolos. El diseño de implementación se basa en el Modelo de Actores, que también proporciona escalabilidad del prototipo. La experimentación muestra que el prototipo puede manejar la transmisión de mensajes para aplicaciones de las SGs que requieran que la comunicación se realice bajo umbrales de tiempo críticos. También se toma otra dirección de investigación similar, menos centrada en las SGs, pero para una gama más amplia de dominios de aplicación. Se integra la descripción de los flujos de ejecución como máquinas de estados finitos utilizando ontologías web (Resource Description Framework (RDF)) y metodologías de la WoT (las acciones se realizan basándose en peticiones Hyper-Text Transfer Protocol/Secure (HTTP/S) a Uniform Resource Locators (URLs)). Este flujo de ejecución, que también puede ser una plantilla para permitir una configuración flexible en tiempo de ejecución, se implementa e interpreta como si fuera (y a través de) un Virtual Object (VO). El objetivo de la plantilla es que sea reutilizable y se pueda compartir entre múltiples despliegues de la IoT dentro del mismo dominio de aplicación. Debido a las tecnologías utilizadas, la solución no es adecuada para aplicaciones de tiempo crítico (umbral de tiempo relativamente bajo y rígido). Sin embargo, es adecuado para aplicaciones que no demandan respuesta en un tiempo crítico y que requieren el despliegue de VOs similares en cuanto al flujo de ejecución. Finalmente, el trabajo se enfoca en otra tecnología destinada a mejorar la escalabilidad y la capacidad de descubrimiento en la IoT. La SIoT está emergiendo como una nueva estructura de la IoT que une los nodos a través de relaciones significativas. Estas relaciones tienen como objetivo mejorar la capacidad de descubrimiento; en consecuencia, mejora la escalabilidad de una red de la IoT. En este trabajo se aplica este nuevo paradigma para optimizar la gestión de la energía en el lado de la demanda en las SGs. El objetivo es aprovechar las características de la SIoT para ayudar en la creación de Prosumer Community Groups (PCGs) (grupos de usuarios que consumen o producen energía) con el mismo objetivo de optimización en el uso de la energía. La sinergia entre la SIoT y las SGs ha sido denominada Social Internet of Energy (SIoE). Por lo tanto, con la SIoE y con el foco en un desafío específico, se establece la base conceptual para la integración entre la SIoT y las SG. Los experimentos iniciales muestran resultados prometedores y allanan el camino para futuras investigaciones y evaluaciones de la propuesta. Se concluye que la WoT y la SIoT son dos paradigmas complementarios que nutren la evolución de la próxima generación de la IoT. Se espera que la próxima generación de la IoT sea un Multi-Agent System (MAS) generalizado. Algunos investigadores ya están apuntando a la Web y sus tecnologías (por ejemplo,Web Semántica, HTTP/S)—y más concretamente a la WoT — como el entorno que nutra a estos agentes. La SIoT puede mejorar tanto el entorno como las relaciones entre los agentes en esta fusión. Como un campo específico de la IoT, las SGs también pueden beneficiarse de los avances de la IoT.This thesis deals with two novel Internet of Things (IoT) technologies and their integration to the field of the Smart Grid (SG); these technologies are the Web of Things (WoT) and the Social Internet of Things (SIoT). The WoT is an enabling technology expected to provide a scalable and interoperable environment to the IoT using the existing web infrastructure, web protocols and the semantic web. The SIoT is expected to expand further and contribute to scalability and discoverability challenges by creating a social network of agents (objects and humans). When exploring the synergy between those technologies, we aim at providing practical and empirical evidence, usually in the form of prototype implementations and empirical experimentation. In relation to the WoT and SG, we create a prototype for the Web of Energy (WoE), that aims at addressing challenges present in the SG domain. The prototype is capable of providing interoperability and homogeneity among diverse protocols. The implementation design is based on the Actor Model, which also provides scalability in regards to the prototype. Experimentation shows that the prototype can handle the transmission of messages for time-critical SG applications. We also take another similar research direction less focused on the SG, but for a broader range of application domains. We integrate the description of flows of execution as Finite-State Machines (FSMs) using web ontologies (Resource Description Framework (RDF)) and WoT methodologies (actions are performed on the basis of calls Hyper Text Transfer Protocol/ Secure (HTTP/S) to a Uniform Resource Locator (URL)). This execution flow, which can also be a template to allow flexible configuration at runtime, is deployed and interpreted as (and through) a Virtual Object (VO). The template aims to be reusable and shareable among multiple IoT deployments within the same application domain. Due to the technologies used, the solution is not suitable for time-critical applications. Nevertheless, it is suitable for non-time-critical applications that require the deployment of similar VOs. Finally, we focus on another technology aimed at improving scalability and discoverability in IoT. The SIoT is emerging as a new IoT structure that links nodes through meaningful relationships. These relationships aim at improving discoverability; consequently, improving the scalability of an IoT network. We apply this new paradigm to optimize energy management at the demand side in a SG. Our objective is to harness the features of the SIoT to aid in the creation of Prosumer Community Group (PCG) (groups of energy users that consume or produce energy) with the same Demand Side Management (DSM) goal. We refer to the synergy between SIoT and SG as Social Internet of Energy (SIoE). Therefore, with the SIoE and focusing on a specific challenge, we set the conceptual basis for the integration between SIoT and SG. Initial experiments show promising results and pave the way for further research and evaluation of the proposal. We conclude that the WoT and the SIoT are two complementary paradigms that nourish the evolution of the next generation IoT. The next generation IoT is expected to be a pervasive Multi-Agent System (MAS). Some researchers are already pointing at the Web and its technologies (e.g. Semantic Web, HTTP/S) — and more concretely at the WoT — as the environment nourishing the agents. The SIoT can enhance both the environment and the relationships between agents in this fusion. As a specific field of the IoT, the SG can also benefit from IoT advancements

Development of Wireless Techniques in Data and Power Transmission - Application for Particle Physics Detectors

Wireless techniques have developed extremely fast over the last decade and using them for data and power transmission in particle physics detectors is not science- fiction any more. During the last years several research groups have independently thought of making it a reality. Wireless techniques became a mature field for research and new developments might have impact on future particle physics experiments. The Instrumentation Frontier was set up as a part of the SnowMass 2013 Community Summer Study [1] to examine the instrumentation R&D for the particle physics research over the coming decades: {\guillemotleft} To succeed we need to make technical and scientific innovation a priority in the field {\guillemotright}. Wireless data transmission was identified as one of the innovations that could revolutionize the transmission of data out of the detector. Power delivery was another challenge mentioned in the same report. We propose a collaboration to identify the specific needs of different projects that might benefit from wireless techniques. The objective is to provide a common platform for research and development in order to optimize effectiveness and cost, with the aim of designing and testing wireless demonstrators for large instrumentation systems

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Lessons Learned and Next Steps in Energy Efficiency Measurement and Attribution: Energy Savings, Net to Gross, Non-Energy Benefits, and Persistence of Energy Efficiency Behavior

This white paper examines four topics addressing evaluation, measurement, and attribution of direct and indirect effects to energy efficiency and behavioral programs: Estimates of program savings (gross); Net savings derivation through free ridership / net to gross analyses; Indirect non-energy benefits / impacts (e.g., comfort, convenience, emissions, jobs); and, Persistence of savings