CyberPhysical systems (CPS) must be closely monitored to identify and
potentially mitigate emergent problems that arise during their routine
operations. However, the multivariate time-series data which they typically
produce can be complex to understand and analyze. While formal product
documentation often provides example data plots with diagnostic suggestions,
the sheer diversity of attributes, critical thresholds, and data interactions
can be overwhelming to non-experts who subsequently seek help from discussion
forums to interpret their data logs. Deep learning models, such as Long
Short-term memory (LSTM) networks can be used to automate these tasks and to
provide clear explanations of diverse anomalies detected in real-time
multivariate data-streams. In this paper we present RESAM, a requirements
process that integrates knowledge from domain experts, discussion forums, and
formal product documentation, to discover and specify requirements and design
definitions in the form of time-series attributes that contribute to the
construction of effective deep learning anomaly detectors. We present a
case-study based on a flight control system for small Uncrewed Aerial Systems
and demonstrate that its use guides the construction of effective anomaly
detection models whilst also providing underlying support for explainability.
RESAM is relevant to domains in which open or closed online forums provide
discussion support for log analysis.

该研究提出了 RESAM，它是一种集成领域专家知识、讨论论坛和正式产品文档的需求过程，用于发现和指定构建有效深度学习异常检测器的时间序列属性的需求和设计定义。以小型无人机飞行控制系统为案例进行了研究，并演示了 RESAM 的使用在构建有效异常检测模型的同时，提供了可解释性的支持。

深度学习异常模型的需求获取和规范化：以无人机飞行控制器应用为例

RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers

This article shows how the recent breakthroughs in Reinforcement Learning
(RL) that have enabled robots to learn to play arcade video games, walk or
assemble colored bricks, can be used to perform other tasks that are currently
at the core of engineering cyberphysical systems. We present the first use of
RL for the control of systems modeled by discretized non-linear Partial
Differential Equations (PDEs) and devise a novel algorithm to use
non-parametric control techniques for large multi-agent systems. We show how
neural network based RL enables the control of discretized PDEs whose
parameters are unknown, random, and time-varying. We introduce an algorithm of
Mutual Weight Regularization (MWR) which alleviates the curse of dimensionality
of multi-agent control schemes by sharing experience between agents while
giving each agent the opportunity to specialize its action policy so as to
tailor it to the local parameters of the part of the system it is located in.

本文介绍了如何运用强化学习技术控制非线性偏微分方程模型的工程实体，以及设计了一种新颖算法，用于管理大型多智能体系统，并通过神经网络建模解决未知、随机、时变参数的偏微分方程控制问题，提出了一种相互加权规则 (MWR) 算法，用于解决多智能体控制方案的高维度问题。

基于多任务深度强化学习的匝道收费口专家级控制

Expert Level control of Ramp Metering based on Multi-task Deep  Reinforcement Learning

To effectively analyze and design cyberphysical systems (CPS), designers
today have to combat the data deluge problem, i.e., the burden of processing
intractably large amounts of data produced by complex models and experiments.
In this work, we utilize monotonic Parametric Signal Temporal Logic (PSTL) to
design features for unsupervised classification of time series data. This
enables using off-the-shelf machine learning tools to automatically cluster
similar traces with respect to a given PSTL formula. We demonstrate how this
technique produces interpretable formulas that are amenable to analysis and
understanding using a few representative examples. We illustrate this with case
studies related to automotive engine testing, highway traffic analysis, and
auto-grading massively open online courses.

本文研究利用 Parametric Signal Temporal Logic 设计无监督的时间序列数据特征以解决数据过载问题，以实现对复杂模型和实验产生的大量数据进行自动分类。作者通过几个示例说明了这种技术如何生产可解释性的公式，适合进行分析和理解。