AI 中文总结
该研究针对现有设计结构矩阵（DSM）依赖专家知识易不完整的问题，提出用先进网络识别方法从数据中获取DSM，验证了其可行性并应用于聚变反应堆建模。
AI 中文摘要
设计结构矩阵（Design Structure Matrices, DSMs）用于全面表征复杂系统，可视化并描述各类变量、过程、状态与事件间的依赖关系，因此被应用于多项系统工程方法，如需求与接口管理、故障检测及监督控制。当前，DSMs通常由专家知识构建，这可能导致DSM存在不完整或不平衡的问题，例如元素与链接可能缺失或冗余。本文提出一种采用先进网络识别方法获取DSM的新方法，证明了从数据中识别DSM作为标准启发式方法补充工具的原理可行性。未来，计划将DSMs嵌入系统设计与监督控制器中。我们将该技术应用于识别由描述托卡马克输运的五室等离子体模型所建模的聚变反应堆的DSM。
英文摘要
Design structure matrices (DSMs) are used to comprehensively represent complex systems. They visualize and describe the dependencies between various variables, processes, states, and events. As such they are used in several system engineering approaches, such as requirement and interface management, fault detection, and supervisory control. Currently, a DSM is typically built from knowledge of experts. This may lead to an incomplete or imbalanced DSMs. For instance, elements and links might be missing or superfluous. In this article, we propose a novel method to acquire the DSM using state-of-the-art network identification methods. This demonstrates a proof-of-principle of identifying DSMs from data as an additional tool to the standard heuristic approach. In the future, we plan to embed DSMs in system design and supervisory controllers. We apply this technique to identify the DSM of a fusion reactor modelled by a five-chamber plasma model describing the transport in a tokamak.
Comments10 pages, 5 figures, 4 tables. Submitted to System engineering
