Machine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

中文翻译：

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使用基于远程学习的归纳保形预测对支持学习的网络物理系统进行保证监控

深度神经网络等机器学习组件广泛用于网络物理系统 (CPS)。但是，此类组件可能会引入新类型的危害，这些危害可能会造成灾难性后果，需要为工程可信系统解决。尽管深度神经网络提供了高级功能，但它们必须辅以能够有效集成到 CPS 中的工程方法和实践。在本文中，我们提出了一种基于保形预测框架的学习型 CPS 保证监控方法。为了允许实时保证监控，该方法采用远程学习将高维输入转换为较小尺寸的嵌入表示。通过利用保形预测，该方法提供了经过良好校准的置信度，并确保了有界的小错误率，同时限制了无法做出准确预测的输入数量。我们使用三个数据集来演示该方法，其中移动机器人跟随墙壁、说话者识别和交通标志识别。实验结果表明，错误率得到了很好的校准，而警报的数量非常少。此外，该方法计算效率高，并允许对 CPS 进行实时保证监控。实验结果表明，错误率得到了很好的校准，而警报的数量非常少。此外，该方法计算效率高，并允许对 CPS 进行实时保证监控。实验结果表明，错误率得到了很好的校准，而警报的数量非常少。此外，该方法计算效率高，并允许对 CPS 进行实时保证监控。