A Digital Twin (DT) is a simulation of a physical system that provides information to make decisions that add economic, social or commercial value. The behaviour of a physical system changes over time, a DT must therefore be continually updated with data from the physical systems to reflect its changing behaviour. For resource-constrained systems, updating a DT is non-trivial because of challenges such as on-board learning and the off-board data transfer. This paper presents a framework for updating data-driven DTs of resource-constrained systems geared towards system health monitoring. The proposed solution consists of: (1) an on-board system running a light-weight DT allowing the prioritisation and parsimonious transfer of data generated by the physical system; and (2) off-board robust updating of the DT and detection of anomalous behaviours. Two case studies are considered using a production gas turbine engine system to demonstrate the digital representation accuracy for real-world, time-varying physical systems.
Trust-Aware Predictive Emissions Monitoring for Gas Turbine Fleets with Limited Labelled Data
School of Natural and Computing Sciences University of Aberdeen（自然与计算科学学院 布拉德福德大学）；School of Computing Sciences University of East Anglia（计算科学学院 东安格利亚大学）；Siemens Energy Industrial Turbomachinery Ltd.（西门子能源工业涡轮机有限公司）；Department of Physics and Technology UiT The Arctic University of Norway（物理与技术系 UiT 北极大学）
Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask Learning
The Alan Turing Institute（艾伦·图灵研究所）；University of Cambridge（剑桥大学）；Scania CV（斯堪尼亚商用车公司）；Scania AB (publ)（斯堪尼亚公司（上市））；Stockholm University（斯德哥尔摩大学）；Imperial College London（伦敦帝国理工学院）
Learning Citywide Patterns of Life from Trajectory Monitoring
Novateur Research Solutions（诺瓦图研究解决方案公司）
最近，真实世界人类移动数据集的激增促进了轨迹预测、需求预测、旅行时间估计和异常检测等方面的地理空间和交通研究。然而，这些数据集也可以更广泛地对复杂的人类流动系统进行描述性分析。我们将生命分析模式正式定义为在线无监督异常检测的自然，可解释的扩展，我们不仅监控数据流的异常，还明确提取正常模式。为了学习生活模式，我们将计算生物学和神经机器人学研究中的“需要时生长”（Grow When Required，GWR）情景记忆应用到地理空间分析的新领域。这种受生物启发的神经网络与自组织映射（SOM）相关，当它在GPS流上迭代时，会逐步构建一组“记忆”或原型交通模式。然后，它将每个新的观察结果与之前的经验进行比较，从而对数据进行在线、无监督的聚类和异常检测。我们从波尔图出租车数据集中挖掘感兴趣的模式，包括主要的公共假日和新发现的交通异常，如节日和音乐会，据我们所知，这些都是以前没有被承认或报告的。我们预计，逐步学习正常和异常道路交通行为的能力将在许多领域有用，包括智能城市、自动驾驶汽车以及城市规划和管理。
AI/ML Life Cycle Management for Interoperable AI Native RAN
Department of Electrical Engineering, National Taiwan University（国立台湾大学电子工程系）；Institute of Communications Engineering, National Sun Yat-sen University（国立成功大学通讯工程研究所）；Department of Electrical and Electronic Engineering, Imperial College London（伦敦帝国理工学院电子与电气工程系）
