24,087 research outputs found
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Using a Log-normal Failure Rate Distribution for Worst Case Bound Reliability Prediction
Prior research has suggested that the failure rates of faults follow a log normal distribution. We propose a specific model where distributions close to a log normal arise naturally from the program structure. The log normal distribution presents a problem when used in reliability growth models as it is not mathematically tractable. However we demonstrate that a worst case bound can be estimated that is less pessimistic than our earlier worst case bound theory
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Confidence: Its role in dependability cases for risk assessment
Society is increasingly requiring quantitative assessment of risk and associated dependability cases. Informally, a dependability case comprises some reasoning, based on assumptions and evidence, that supports a dependability claim at a particular level of confidence. In this paper we argue that a quantitative assessment of claim confidence is necessary for proper assessment of risk. We discuss the way in which confidence depends upon uncertainty about the underpinnings of the dependability case (truth of assumptions, correctness of reasoning, strength of evidence), and propose that probability is the appropriate measure of uncertainty. We discuss some of the obstacles to quantitative assessment of confidence (issues of composability of subsystem claims; of the multi-dimensional, multi-attribute nature of dependability claims; of the difficult role played by dependence between different kinds of evidence, assumptions, etc). We show that, even in simple cases, the confidence in a claim arising from a dependability case can be surprisingly low
The application of structural reliability techniques to plume impingement loading of the Space Station Freedom Photovoltaic Array
A new aerospace application of structural reliability techniques is presented, where the applied forces depend on many probabilistic variables. This application is the plume impingement loading of the Space Station Freedom Photovoltaic Arrays. When the space shuttle berths with Space Station Freedom it must brake and maneuver towards the berthing point using its primary jets. The jet exhaust, or plume, may cause high loads on the photovoltaic arrays. The many parameters governing this problem are highly uncertain and random. An approach, using techniques from structural reliability, as opposed to the accepted deterministic methods, is presented which assesses the probability of failure of the array mast due to plume impingement loading. A Monte Carlo simulation of the berthing approach is used to determine the probability distribution of the loading. A probability distribution is also determined for the strength of the array. Structural reliability techniques are then used to assess the array mast design. These techniques are found to be superior to the standard deterministic dynamic transient analysis, for this class of problem. The results show that the probability of failure of the current array mast design, during its 15 year life, is minute
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Reliability modeling of a 1-out-of-2 system: Research with diverse Off-the-shelf SQL database servers
Fault tolerance via design diversity is often the only viable way of achieving sufficient dependability levels when using off-the-shelf components. We have reported previously on studies with bug reports of four open-source and commercial off-the-shelf database servers and later release of two of them. The results were very promising for designers of fault-tolerant solutions that wish to employ diverse servers: very few bugs caused failures in more than one server and none caused failure in more than two. In this paper we offer details of two approaches we have studied to construct reliability growth models for a 1-out-of-2 fault-tolerant server which utilize the bug reports. The models presented are of practical significance to system designers wishing to employ diversity with off-the-shelf components since often the bug reports are the only direct dependability evidence available to them
Uncertainty-Aware Attention for Reliable Interpretation and Prediction
Department of Computer Science and EngineeringAttention mechanism is effective in both focusing the deep learning models on relevant features and
interpreting them. However, attentions may be unreliable since the networks that generate them are
often trained in a weakly-supervised manner. To overcome this limitation, we introduce the notion of
input-dependent uncertainty to the attention mechanism, such that it generates attention for each
feature with varying degrees of noise based on the given input, to learn larger variance on instances it
is uncertain about. We learn this Uncertainty-aware Attention (UA) mechanism using variational
inference, and validate it on various risk prediction tasks from electronic health records on which our
model significantly outperforms existing attention models. The analysis of the learned attentions
shows that our model generates attentions that comply with clinicians' interpretation, and provide
richer interpretation via learned variance. Further evaluation of both the accuracy of the uncertainty
calibration and the prediction performance with "I don't know'' decision show that UA yields networks
with high reliability as well.ope
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