16 resultados para Centre for Infectious Disease Research and Biosafety Laboratories

em Cambridge University Engineering Department Publications Database


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This report presents the results from a survey of current practice in the use of design optimization conducted amongst UK companies. The survey was completed by the Design Optimization Group in the Department of Engineering at Cambridge University. The general aims of this research were to understand the current status of design optimization research and practice and to identify ways in which the use of design optimization methods and tools could be improved.

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This paper describes the design considerations for a proposed aerodynamic characterization facility (ACF) for micro aerial vehicles (MAVs). This is a collaborative effort between the Air Force Research Laboratory Munitions Directorate (AFRL/MN) and the University of Florida Research and Engineering Education Facility (UF/REEF). The ACF is expected to provide a capability for the characterization of the aerodynamic performance of future MAVs. This includes the ability to gather the data necessary to devise control strategies as well as the potential to investigate aerodynamic 'problem areas' or specific failings. Since it is likely that future MAVs will incorporate advanced control strategies, the facility must enable researchers to critically assess such novel methods. Furthermore, the aerodynamic issues should not be seen (and tested) in isolation, but rather the facility should be able to also provide information on structural responses (such as aeroelasticity) as well as integration issues (say, thrust integration or sensor integration). Therefore the mission for the proposed facility ranges form fairly basic investigations of individual technical issues encountered by MAVs (for example an evaluation of wing shapes or control effectiveness) all the way to testing a fully integrated vehicle in a flight configuration for performance evaluation throughout the mission envelope.

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Like the Research Assessment Exercise (RAE) that preceded it, the UK government's proposed Research Excellence Framework (REF) is a means of allocating funding in higher education to support research. As with any method for the competitive allocation of funds it creates winners and losers and inevitably generates a lot of emotion among those rewarded or penalised. More specifically, the 'winners' tend to approve of the method of allocation and the 'losers' denigrate it as biased against their activities and generally unfair. An extraordinary press campaign has been consistently waged against research assessment and its methods by those involved in architectural education, which I will track over a decade and a half. What follows will question whether this campaign demonstrates the sophistication and superior judgment of those who have gone into print, or conversely whether its mixture of misinformation and disinformation reveals not just disenchantment and prejudice, but a naivety and a depth of ignorance about the fundamentals of research that is deeply damaging to the credibility of architecture as a research-based discipline. With the recent consultation process towards a new cycle of research assessment, the REF, getting under way, I aim to draw attention to the risk of repeating past mistakes. Copyright © Cambridge University Press 2010.

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Compared with construction data sources that are usually stored and analyzed in spreadsheets and single data tables, data sources with more complicated structures, such as text documents, site images, web pages, and project schedules have been less intensively studied due to additional challenges in data preparation, representation, and analysis. In this paper, our definition and vision for advanced data analysis addressing such challenges are presented, together with related research results from previous work, as well as our recent developments of data analysis on text-based, image-based, web-based, and network-based construction sources. It is shown in this paper that particular data preparation, representation, and analysis operations should be identified, and integrated with careful problem investigations and scientific validation measures in order to provide general frameworks in support of information search and knowledge discovery from such information-abundant data sources.