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首页> 外文会议>Safety-critical Systems Symposium; 20060207-09; Bristol(GB) >Optimising Data-Driven Safety Related Systems
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Optimising Data-Driven Safety Related Systems

机译：优化数据驱动的安全相关系统

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摘要

The operation of many safety related systems is dependent upon a number of interacting parameters. Frequently these parameters must be 'tuned' to the particular operating environment to provide the best possible performance. We focus on the Short Term Conflict Alert (STCA) system, which warns of airspace infractions between aircraft, as an example of a safety related system that must raise an alert to dangerous situations, but should not raise false alarms. Current practice is to 'tune' by hand the many parameters governing the system in order to optimise the operating point in terms of the true positive and false positive rates, which are frequently associated with highly imbalanced costs. We regard the tuning of safety related systems as a multi-objective optimisation problem. We show how a region of the optimal receiver operating characteristic (ROC) curve may be obtained, permitting the system operators to select the operating point. We apply this methodology to the STCA system, showing that we can improve upon the current hand-tuned operating point, as well as providing the salient ROC curve describing the true positive versus false positive trade-off. We also address the robustness of the optimal ROC curve to perturbations of the data used to learn it. Bootstrap resampling is used to evaluate the uncertainty in the optimal operating curve and show how the probability of a particular operating point can be estimated.
机译：许多安全相关系统的操作取决于许多相互作用的参数。通常，必须将这些参数“调整”到特定的操作环境，以提供最佳的性能。我们将重点放在短期冲突警报（STCA）系统上，该系统警告飞机之间的空域违规，作为安全相关系统的示例，该系统必须对危险情况发出警报，但不应发出虚假警报。当前的实践是用手“调节”控制系统的许多参数，以便根据真实的肯定率和错误的肯定率来优化操作点，这经常与高度不平衡的成本相关。我们将安全相关系统的调整视为一个多目标优化问题。我们展示了如何获得最佳接收机工作特性（ROC）曲线的区域，从而允许系统操作员选择工作点。我们将此方法应用于STCA系统，表明我们可以改进当前的手动调整工作点，并提供显着的ROC曲线来描述真正的正与假正的权衡。我们还将解决最佳ROC曲线对于学习数据的鲁棒性问题。自举重采样用于评估最佳工作曲线中的不确定性，并显示如何估计特定工作点的概率。

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