52,117 research outputs found

    Risk Analysis for Autonomous Underwater Vehicle Operations in Extreme Environments

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    Autonomous underwater vehicles (AUVs) are used increasingly to explore hazardous marine environments. Risk assessment for such complex systems is based on subjective judgment and expert knowledge as much as on hard statistics. Here, we describe the use of a risk management process tailored to AUV operations, the implementation of which requires the elicitation of expert judgment. We conducted a formal judgment elicitation process where eight world experts in AUV design and operation were asked to assign a probability of AUV loss given the emergence of each fault or incident from the vehicle's life history of 63 faults and incidents. After discussing methods of aggregation and analysis, we show how the aggregated risk estimates obtained from the expert judgments were used to create a risk model. To estimate AUV survival with mission distance, we adopted a statistical survival function based on the nonparametric Kaplan-Meier estimator. We present theoretical formulations for the estimator, its variance, and confidence limits. We also present a numerical example where the approach is applied to estimate the probability that the Autosub3 AUV would survive a set of missions under Pine Island Glacier, Antarctica in January–March 2009

    Eliciting expert judgement for the probability of AUV loss in contrasting operational environments

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    Each time an autonomous underwater vehicle (AUV) is used in the sea there is a non-zero probability of loss. Quantifying probability of loss is not an exact science; therefore much depends on the fault history of the vehicle, the operational environment and the complex relationships between the consequences of faults or incidents and the environment. While this problem may be stated in scientific terms, in practice, there is no solution through scientific means alone. This is an example of ‘trans-science’. We suggest that an approach based on the formal process of eliciting expert judgement may be an effective means of approaching this problem, as the process has been used successfully for other trans-scientific questions. The paper provides an introduction to the process of eliciting expert judgement, outlines four exemplar environments: coastal, open water, under sea ice and under shelf ice, and gives a worked example of one expert’s judgement on the probability of loss in the four environments arising from a real fault with the Autosub1 AUV. Using the fault history of the Autosub3 AUV, included in the Annex, we ask experts from among UUST attendees (and others) to take part in this expert judgement elicitation. Based on the results of this elicitation we aim to publish a paper in the peer-reviewed literature

    The productive ward: releasing time to care - learning and impact review

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    The Productive Ward: Releasing time to care™ programme aims to empower ward teams to identify areas for improvement by giving staff the information, skills and time they need to regain control of their ward and the care they provide.The NHS Institute for Innovation and Improvement commissioned Kings College London to undertake a review of the Productive Ward programme. This review (undertaken February-June 2009) set out to establish the overall learning from and impact of The Productive Ward programme since its conception in 2005, and to suggest how this can be spread and sustained.The review applies an evidence-based Diffusion of Innovation framework to The Productive Ward programme to examine multi-level perspectives (national, regional, local) of learning and impact. The findings are informed by in-depth interviews with national and regional stakeholders, a national online-survey of frontline staff, and case studies of implementation within five NHS acute Trusts.Overall, this review finds The Productive Ward programme has been successfully framed and communicated in a way that connects with frontline NHS staffs’ need and will for change, and that it thrives where local leadership and ownership are strong. The review suggests 15 ‘top tips’, which comprise of key lessons from the programme to date that will assist trusts in local implementation in the future

    Engineering geomorphology- theory and practice

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    The article reviews the book "Engineering Geomorphology- Theory and Practice," edited by P. G. Fookes, E. M. Lee, and J. S. Griffiths

    Results of expert judgments on the faults and risks with Autosub3 and an analysis of its campaign to Pine Island Bay, Antarctica, 2009

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    Probabilistic risk assessment is a methodology that can be systematically applied to estimate the risk associated with the design and operation of complex systems. The National Oceanography Centre, Southampton, UK has developed a risk management process tailored to the operation of autonomous underwater vehicles. Central to the application of the risk management process is a probabilistic risk assessment. The risk management process was applied to estimate the risk associated with an Autosub3 science campaign in the Pine Island Glacier, Antarctica, and to support decision making. The campaign was successful. In this paper we present the Autosub3 risk model and we show how this model was used to assess the campaign risk

    The Griffiths bundle is generated by groups

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    First the Griffiths line bundle of a Q-VHS V is generalized to a Griffiths character grif(G, mu, r) associated to any triple (G, mu, r), where G is a connected reductive group over an arbitrary field F, mu is an element of X-*(G) is a cocharacter (over (F) over bar) and r : G -&gt; GL(V) is an F-representation; the classical bundle studied by Griffiths is recovered by taking F = Q, G the Mumford-Tate group of V, r : G -&gt; GL(V) the tautological representation afforded by a very general fiber and pulling back along the period map the line bundle associated to grif(G, mu, r). The more general setting also gives rise to the Griffiths bundle in the analogous situation in characteristic p given by a scheme mapping to a stack of G-Zips. When G is F-simple, we show that, up to positive multiples, the Griffiths character grif(G, mu, r) (and thus also the Griffiths line bundle) is essentially independent of r with central kernel, and up to some identifications is given explicitly by -mu. As an application, we show that the Griffiths line bundle of a projective G-Zip(mu)-scheme is nef.</p

    On the reliability of the Autosub autonomous underwater vehicle

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    As autonomous underwater vehicles (AUVs) enter operational service an assessment of their reliability is timely. Using the Autosub AUV as an example, several design issues affecting reliability are discussed, followed by an analysis of recorded faults. Perhaps contrary to expectations, failures rarely involved the autonomous nature of the vehicle. Rather, faults were typical of those that occur with any complex item of marine electromechanical equipment. A statistical analysis showed that the failure rate decreased with distance travelled- an indicator that an AUV underway, submerged, is at less risk of a fault developing than during other phases of a mission. 1
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