One of the critical tasks of Systems Engineering MBSE methodology is execution of Trade Studies. Trade Studies are key to identifying the best‐in‐class architectural and realization alternatives. These studies often rely on physical characteristics of the available system elements represented by the “P” in the core system elements called RFLP (Requirements‐Functional‐Logical‐Physical). Such realization alternatives are not always easy to find for the Systems Engineers and often require tedious manual definition of the implementation elements in a system model. That information, however, often already resides within the enterprise design data and product configuration platforms – most likely a PLM (Product Lifecycle Management) system. This paper discusses how a proper integration between system model authoring tools (e.g. SysML) and PLM, can interactively and in real‐time deliver all relevant “P” information of the RFLP core used in trade studies and therefore save Systems Engineers a significant amount of time, eliminate incorrect assumptions, and maximize reuse.
This article presents a new machine learning (ML) development lifecycle which will constitute the core of the new aeronautical standard on ML called AS6983, jointly being developed by working group WG-114/G34 of EUROCAE and SAE. The article also presents a survey of several existing standards and guidelines related to ML in aeronautics, automotive, and industrial domains by comparing and contrasting their scope, purpose, and results. Standards and guidelines reviewed include the European Union Aviation Safety Agency (EASA) Concept Paper, the DEEL (DEpendable and Explainable Learning) white paper “Machine Learning in Certified Systems”, Aerospace Vehicle System Institute (AVSI) Authorization for Expenditure (AFE) 87 report on Machine Learning, Guidance on the Assurance of Machine Learning for use in Autonomous Systems (AMLAS), Laboratoire National de Metrologie et d’Essais (LNE) Certification Standard of Processes for AI, the Underwriters Laboratories (UL) 4600 Safety Standard for Autonomous Vehicles, and the paper on Assuring the Machine Learning Lifecycle. These standards and guidelines are examined from the perspective of the learning assurance objectives they propose, and the means of evaluation and compliance for achieving these learning objectives. The reference used for comparison is the list of learning assurance objectives defined within the framework of AS6983 development. From this comparative analysis, and based on a coverage criterion defined in this article, only three (3) standards and guidelines exceed 50% coverage of the Machine Learning Development Lifecycle (MLDL) learning assurance objectives baseline. The next steps of this work are to update the AS6983 learning assurance objectives and improve the associated means of compliance to approach a coverage score of 100%, and offer a certification-based process to other domains that could benefit from the AS6983 standard.
As we evolve artificial and machine intelligence concepts, and consider their extension into intelligent systems, it becomes important to be able to assess the system's intelligence level. This assessment serves several important needs in systems engineering. First, it enables the tradeoff of system design alternatives based on a system's intelligence, along with other factors including cost and performance. Second, it influences system verification and validation methods. Lastly, it will help stakeholders specify the required system intelligence. Assessing or specifying the system's intelligence presents many challenges and difficulties similar to those faced by psychologists and neurologists in measuring the intelligence of human beings and animals. This article explores some human and animal intelligence assessment concepts and shows their application to assessing a system's intelligence.
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