974 resultados para Simulation Environments


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El auge y penetración de las nuevas tecnologías junto con la llamada Web Social están cambiando la forma en la que accedemos a la medicina. Cada vez más pacientes y profesionales de la medicina están creando y consumiendo recursos digitales de contenido clínico a través de Internet, surgiendo el problema de cómo asegurar la fiabilidad de estos recursos. Además, un nuevo concepto está apareciendo, el de pervasive healthcare o sanidad ubicua, motivado por pacientes que demandan un acceso a los servicios sanitarios en todo momento y en todo lugar. Este nuevo escenario lleva aparejado un problema de confianza en los proveedores de servicios sanitarios. Las plataformas de eLearning se están erigiendo como paradigma de esta nueva Medicina 2.0 ya que proveen un servicio abierto a la vez que controlado/supervisado a recursos digitales, y facilitan las interacciones y consultas entre usuarios, suponiendo una buena aproximación para esta sanidad ubicua. En estos entornos los problemas de fiabilidad y confianza pueden ser solventados mediante la implementación de mecanismos de recomendación de recursos y personas de manera confiable. Tradicionalmente las plataformas de eLearning ya cuentan con mecanismos de recomendación, si bien están más enfocados a la recomendación de recursos. Para la recomendación de usuarios es necesario acudir a mecanismos más elaborados como son los sistemas de confianza y reputación (trust and reputation) En ambos casos, tanto la recomendación de recursos como el cálculo de la reputación de los usuarios se realiza teniendo en cuenta criterios principalmente subjetivos como son las opiniones de los usuarios. En esta tesis doctoral proponemos un nuevo modelo de confianza y reputación que combina evaluaciones automáticas de los recursos digitales en una plataforma de eLearning, con las opiniones vertidas por los usuarios como resultado de las interacciones con otros usuarios o después de consumir un recurso. El enfoque seguido presenta la novedad de la combinación de una parte objetiva con otra subjetiva, persiguiendo mitigar el efecto de posibles castigos subjetivos por parte de usuarios malintencionados, a la vez que enriquecer las evaluaciones objetivas con información adicional acerca de la capacidad pedagógica del recurso o de la persona. El resultado son recomendaciones siempre adaptadas a los requisitos de los usuarios, y de la máxima calidad tanto técnica como educativa. Esta nueva aproximación requiere una nueva herramienta para su validación in-silico, al no existir ninguna aplicación que permita la simulación de plataformas de eLearning con mecanismos de recomendación de recursos y personas, donde además los recursos sean evaluados objetivamente. Este trabajo de investigación propone pues una nueva herramienta, basada en el paradigma de programación orientada a agentes inteligentes para el modelado de comportamientos complejos de usuarios en plataformas de eLearning. Además, la herramienta permite también la simulación del funcionamiento de este tipo de entornos dedicados al intercambio de conocimiento. La evaluación del trabajo propuesto en este documento de tesis se ha realizado de manera iterativa a lo largo de diferentes escenarios en los que se ha situado al sistema frente a una amplia gama de comportamientos de usuarios. Se ha comparado el rendimiento del modelo de confianza y reputación propuesto frente a dos modos de recomendación tradicionales: a) utilizando sólo las opiniones subjetivas de los usuarios para el cálculo de la reputación y por extensión la recomendación; y b) teniendo en cuenta sólo la calidad objetiva del recurso sin hacer ningún cálculo de reputación. Los resultados obtenidos nos permiten afirmar que el modelo desarrollado mejora la recomendación ofrecida por las aproximaciones tradicionales, mostrando una mayor flexibilidad y capacidad de adaptación a diferentes situaciones. Además, el modelo propuesto es capaz de asegurar la recomendación de nuevos usuarios entrando al sistema frente a la nula recomendación para estos usuarios presentada por el modo de recomendación predominante en otras plataformas que basan la recomendación sólo en las opiniones de otros usuarios. Por último, el paradigma de agentes inteligentes ha probado su valía a la hora de modelar plataformas virtuales complejas orientadas al intercambio de conocimiento, especialmente a la hora de modelar y simular el comportamiento de los usuarios de estos entornos. La herramienta de simulación desarrollada ha permitido la evaluación del modelo de confianza y reputación propuesto en esta tesis en una amplia gama de situaciones diferentes. ABSTRACT Internet is changing everything, and this revolution is especially present in traditionally offline spaces such as medicine. In recent years health consumers and health service providers are actively creating and consuming Web contents stimulated by the emergence of the Social Web. Reliability stands out as the main concern when accessing the overwhelming amount of information available online. Along with this new way of accessing the medicine, new concepts like ubiquitous or pervasive healthcare are appearing. Trustworthiness assessment is gaining relevance: open health provisioning systems require mechanisms that help evaluating individuals’ reputation in pursuit of introducing safety to these open and dynamic environments. Technical Enhanced Learning (TEL) -commonly known as eLearning- platforms arise as a paradigm of this Medicine 2.0. They provide an open while controlled/supervised access to resources generated and shared by users, enhancing what it is being called informal learning. TEL systems also facilitate direct interactions amongst users for consultation, resulting in a good approach to ubiquitous healthcare. The aforementioned reliability and trustworthiness problems can be faced by the implementation of mechanisms for the trusted recommendation of both resources and healthcare services providers. Traditionally, eLearning platforms already integrate recommendation mechanisms, although this recommendations are basically focused on providing an ordered classifications of resources. For users’ recommendation, the implementation of trust and reputation systems appears as the best solution. Nevertheless, both approaches base the recommendation on the information from the subjective opinions of other users of the platform regarding the resources or the users. In this PhD work a novel approach is presented for the recommendation of both resources and users within open environments focused on knowledge exchange, as it is the case of TEL systems for ubiquitous healthcare. The proposed solution adds the objective evaluation of the resources to the traditional subjective personal opinions to estimate the reputation of the resources and of the users of the system. This combined measure, along with the reliability of that calculation, is used to provide trusted recommendations. The integration of opinions and evaluations, subjective and objective, allows the model to defend itself against misbehaviours. Furthermore, it also allows ‘colouring’ cold evaluation values by providing additional quality information such as the educational capacities of a digital resource in an eLearning system. As a result, the recommendations are always adapted to user requirements, and of the maximum technical and educational quality. To our knowledge, the combination of objective assessments and subjective opinions to provide recommendation has not been considered before in the literature. Therefore, for the evaluation of the trust and reputation model defined in this PhD thesis, a new simulation tool will be developed following the agent-oriented programming paradigm. The multi-agent approach allows an easy modelling of independent and proactive behaviours for the simulation of users of the system, conforming a faithful resemblance of real users of TEL platforms. For the evaluation of the proposed work, an iterative approach have been followed, testing the performance of the trust and reputation model while providing recommendation in a varied range of scenarios. A comparison with two traditional recommendation mechanisms was performed: a) using only users’ past opinions about a resource and/or other users; and b) not using any reputation assessment and providing the recommendation considering directly the objective quality of the resources. The results show that the developed model improves traditional approaches at providing recommendations in Technology Enhanced Learning (TEL) platforms, presenting a higher adaptability to different situations, whereas traditional approaches only have good results under favourable conditions. Furthermore the promotion period mechanism implemented successfully helps new users in the system to be recommended for direct interactions as well as the resources created by them. On the contrary OnlyOpinions fails completely and new users are never recommended, while traditional approaches only work partially. Finally, the agent-oriented programming (AOP) paradigm has proven its validity at modelling users’ behaviours in TEL platforms. Intelligent software agents’ characteristics matched the main requirements of the simulation tool. The proactivity, sociability and adaptability of the developed agents allowed reproducing real users’ actions and attitudes through the diverse situations defined in the evaluation framework. The result were independent users, accessing to different resources and communicating amongst them to fulfil their needs, basing these interactions on the recommendations provided by the reputation engine.

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Thesis (Master's)--University of Washington, 2016-06

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A novel strategy linking physiology with plant breeding, molecular biology and computer simulation modelling is outlined here which aims to enhance selection of high yielding wheats with superior performance under conditions of water scarcity for the northern, subtropical, winter cereals region of Australia. In previous research, a source of high yield and performance under dry conditions for the target region was identified in a drought resistant parent. A large population of fixed lines for molecular genetic studies has been developed using the drought resistant line and widely grown current Australian variety. A preliminary study comparing the parent varieties was conducted in the winter of 2003. The two varieties were similar in many aspects of phenology, morphology and physiology. However, several important traits were identified that likely contribute to higher grain mass and yield of the drought resistant parent, including differences in the number and dry mass of tillers and spikes during development and the ability of drought resistant line to retain green leaves longer during grain filling.

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This paper reports on a current research project in which virtual reality simulators are being investigated as a means of simulating hazardous Rail work conditions in order to allow train drivers to practice decision-making under stress. When working under high stress conditions train drivers need to move beyond procedural responses into a response activated through their own problem-solving and decision-making skills. This study focuses on the use of stress inoculation training which aims to build driver’s confidence in the use of new decision-making skills by being repeatedly required to respond to hazardous driving conditions. In particular, the study makes use of a train cab driving simulator to reproduce potentially stress inducing real-world scenarios. Initial pilot research has been undertaken in which drivers have experienced the training simulation and subsequently completed surveys on the level of immersion experienced. Concurrently drivers have also participated in a velocity perception experiment designed to objectively measure the fidelity of the virtual training environment. Baseline data, against which decision-making skills post training will be measured, is being gathered via cognitive task analysis designed to identify primary decision requirements for specific rail events. While considerable efforts have been invested in improving Virtual Reality technology, little is known about how to best use this technology for training personnel to respond to workplace conditions in the Rail Industry. To enable the best use of simulators for training in the Rail context the project aims to identify those factors within virtual reality that support required learning outcomes and use this information to design training simulations that reliably and safely train staff in required workplace accident response skills.

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The identification of disease clusters in space or space-time is of vital importance for public health policy and action. In the case of methicillin-resistant Staphylococcus aureus (MRSA), it is particularly important to distinguish between community and health care-associated infections, and to identify reservoirs of infection. 832 cases of MRSA in the West Midlands (UK) were tested for clustering and evidence of community transmission, after being geo-located to the centroids of UK unit postcodes (postal areas roughly equivalent to Zip+4 zip code areas). An age-stratified analysis was also carried out at the coarser spatial resolution of UK Census Output Areas. Stochastic simulation and kernel density estimation were combined to identify significant local clusters of MRSA (p<0.025), which were supported by SaTScan spatial and spatio-temporal scan. In order to investigate local sampling effort, a spatial 'random labelling' approach was used, with MRSA as cases and MSSA (methicillin-sensitive S. aureus) as controls. Heavy sampling in general was a response to MRSA outbreaks, which in turn appeared to be associated with medical care environments. The significance of clusters identified by kernel estimation was independently supported by information on the locations and client groups of nursing homes, and by preliminary molecular typing of isolates. In the absence of occupational/ lifestyle data on patients, the assumption was made that an individual's location and consequent risk is adequately represented by their residential postcode. The problems of this assumption are discussed, with recommendations for future data collection.

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This thesis has two aims. First, it sets out to develop an alternative methodology for the investigation of risk homeostasis theory (RHT). It is argued that the current methodologies of the pseudo-experimental design and post hoc analysis of road-traffic accident data both have their limitations, and that the newer 'game' type simulation exercises are also, but for different reasons, incapable of testing RHT predictions. The alternative methodology described here is based on the simulation of physical risk with intrinsic reward rather than a 'points pay-off'. The second aim of the thesis is to examine a number of predictions made by RHT through the use of this alternative methodology. Since the pseudo-experimental design and post hoc analysis of road-traffic data are both ill-suited to the investigation of that part of RHT which deals with the role of utility in determining risk-taking behaviour in response to a change in environmental risk, and since the concept of utility is critical to RHT, the methodology reported here is applied to the specific investigation of utility. Attention too is given to the question of which behavioural pathways carry the homeostasis effect, and whether those pathways are 'local' to the nature of the change in environmental risk. It is suggested that investigating RHT through this new methodology holds a number of advantages and should be developed further in an attempt to answer the RHT question. It is suggested too that the methodology allows RHT to be seen in a psychological context, rather than the statistical context that has so far characterised its investigation. The experimental findings reported here are in support of hypotheses derived from RHT and would therefore seem to argue for the importance of the individual and collective target level of risk, as opposed to the level of environmental risk, as the major determinant of accident loss.

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Self-awareness and self-expression are promising architectural concepts for embedded systems to be equipped with to match them with dedicated application scenarios and constraints in the avionic and space-flight industry. Typically, these systems operate in largely undefined environments and are not reachable after deployment for a long time or even never ever again. This paper introduces a reference architecture as well as a novel modelling and simulation environment for self-aware and self-expressive systems with transaction level modelling, simulation and detailed modelling capabilities for hardware aspects, precise process chronology execution as well as fine timing resolutions. Furthermore, industrial relevant system sizes with several self-aware and self-expressive nodes can be handled by the modelling and simulation environment.

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Electrically excited synchronous machines with brushes and slip rings are popular but hardly used in inflammable and explosive environments. This paper proposes a new brushless electrically excited synchronous motor with a hybrid rotor. It eliminates the use of brushes and slip rings so as to improve the reliability and cost-effectiveness of the traction drive. The proposed motor is characterized with two sets of stator windings with two different pole numbers to provide excitation and drive torque independently. This paper introduces the structure and operating principle of the machine, followed by the analysis of the air-gap magnetic field using the finite-element method. The influence of the excitation winding's pole number on the coupling capability is studied and the operating characteristics of the machine are simulated. These are further examined by the experimental tests on a 16 kW prototype motor. The machine is proved to have good static and dynamic performance, which meets the stringent requirements for traction applications.

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The population of English Language Learners (ELLs) globally has been increasing substantially every year. In the United States alone, adult ELLs are the fastest growing portion of learners in adult education programs (Yang, 2005). There is a significant need to improve the teaching of English to ELLs in the United States and other English-speaking dominant countries. However, for many ELLs, speaking, especially to Native English Speakers (NESs), causes considerable language anxiety, which in turn plays a vital role in hindering their language development and academic progress (Pichette, 2009; Woodrow, 2006). ^ Task-based Language Teaching (TBLT), such as simulation activities, has long been viewed as an effective approach for second-language development. The current advances in technology and rapid emergence of Multi-User Virtual Environments (MUVEs) have provided an opportunity for educators to consider conducting simulations online for ELLs to practice speaking English to NESs. Yet to date, empirical research on the effects of MUVEs on ELLs' language development and speaking is limited (Garcia-Ruiz, Edwards, & Aquino-Santos, 2007). ^ This study used a true experimental treatment control group repeated measures design to compare the perceived speaking anxiety levels (as measured by an anxiety scale administered per simulation activity) of 11 ELLs (5 in the control group, 6 in the experimental group) when speaking to Native English Speakers (NESs) during 10 simulation activities. Simulations in the control group were done face-to-face, while those in the experimental group were done in the MUVE of Second Life. ^ The results of the repeated measures ANOVA revealed after the Huynh-Feldt epsilon correction, demonstrated for both groups a significant decrease in anxiety levels over time from the first simulation to the tenth and final simulation. When comparing the two groups, the results revealed a statistically significant difference, with the experimental group demonstrating a greater anxiety reduction. These results suggests that language instructors should consider including face-to-face and MUVE simulations with ELLs paired with NESs as part of their language instruction. Future investigations should investigate the use of other multi-user virtual environments and/or measure other dimensions of the ELL/NES interactions.^

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In geotechnical engineering, the stability of rock excavations and walls is estimated by using tools that include a map of the orientations of exposed rock faces. However, measuring these orientations by using conventional methods can be time consuming, sometimes dangerous, and is limited to regions of the exposed rock that are reachable by a human. This thesis introduces a 2D, simulated, quadcopter-based rock wall mapping algorithm for GPS denied environments such as underground mines or near high walls on surface. The proposed algorithm employs techniques from the field of robotics known as simultaneous localization and mapping (SLAM) and is a step towards 3D rock wall mapping. Not only are quadcopters agile, but they can hover. This is very useful for confined spaces such as underground or near rock walls. The quadcopter requires sensors to enable self localization and mapping in dark, confined and GPS denied environments. However, these sensors are limited by the quadcopter payload and power restrictions. Because of these restrictions, a light weight 2D laser scanner is proposed. As a first step towards a 3D mapping algorithm, this thesis proposes a simplified scenario in which a simulated 1D laser range finder and 2D IMU are mounted on a quadcopter that is moving on a plane. Because the 1D laser does not provide enough information to map the 2D world from a single measurement, many measurements are combined over the trajectory of the quadcopter. Least Squares Optimization (LSO) is used to optimize the estimated trajectory and rock face for all data collected over the length of a light. Simulation results show that the mapping algorithm developed is a good first step. It shows that by combining measurements over a trajectory, the scanned rock face can be estimated using a lower-dimensional range sensor. A swathing manoeuvre is introduced as a way to promote loop closures within a short time period, thus reducing accumulated error. Some suggestions on how to improve the algorithm are also provided.

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Providing high levels of product variety and product customization is challenging for many companies. This paper presents a new classification of production and order fulfillment approaches available to manufacturing companies that offer high variety and/or product customization. Six categories of approaches are identified and described. An important emerging approach - open pipeline planning – is highlighted for high variety manufacturing environments. It allows a customer order to be fulfilled from anywhere in the system, enabling greater responsiveness in Build-to-Forecast systems. The links between the open pipeline approach, decoupling concepts and postponement strategies are discussed and the relevance of the approach to the volume automotive sector is highlighted. Results from a simulation study are presented illustrating the potential benefits when products can be reconfigured in an open pipeline system. The application of open pipeline concepts to different manufacturing domains is discussed and the operating characteristics of most relevance are highlighted. In addition to the automotive, sectors such as machinery and instrumentation, computer servers, telecommunications and electronic equipment may benefit from an open pipeline planning approach. When properly designed these systems can significantly enhance order fulfillment performance.

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Part 18: Optimization in Collaborative Networks

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Part 13: Virtual Reality and Simulation