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.
The PCs co-operating into the Distributed Measurement System (DMS) can work on synchronized modality on the basis of the standard IEEE 1588. Nevertheless, the hardware and software architecture of the path involved in the communication PC-Measurement Instrument (MI) can delay the command. The research given in the paper aims to investigate about the time delay introduced by the software in the communication between the PC and the MI, by referring to the previous established hardware connection PC-MI offering the minimum delay. The analyzed solution concerns with the modification of the Linux software driver of the WiFi receiver so as to make fast the software processing of the message received at the interface PC-DMS and transmitted to the interface PC-MI. This solution is compared with another two solutions based on particular setting up of non predictive software Linux OS, and employment of Linux OS RT.
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