翻訳待ち：Towards On-Board Implementation of ML-Based Helicopter Weight Estimator

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

--> [Submitted on 12 Jun 2026] Title:Towards On-Board Implementation of ML-Based Helicopter Weight Estimator View a PDF of the paper titled Towards On-Board Implementation of ML-Based Helicopter Weight Estimator, by Nicolas Valot and 4 other authors View PDF Abstract:This paper focuses on the implementation of a novel supervised Machine Learning model for estimating helicopter weight during takeoff, utilizing extensive datasets from Airbus's global in-service fleet. The study details a learning assurance process aligned with the EASA concept paper for machine learning application, and with the on-going Eurocae ED-324. We propose a set of Machine Learning Requirements, a Machine Learning Model Description, and its implementation for a long short-term memory recurrent neural network. Finally, we verify the requirements on the implementation. Demonstrated on legacy avionics computers, the implementation is suitable for the deployment of the developed Machine Learning Model weight estimator on airborne targets for critical functions such as on-board alerting. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.19210 [cs.LG] (or arXiv:2608.19210v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.19210 arXiv-issued DOI via DataCite Journal reference: The 2026 Annual Forum and Technology Display (Forum 82), The Future of Vertical Flight (VFS), May 2026, Palm Beach Florida, USA, United States Submission history From: Claire Pagetti [view email] [via CCSD proxy] [v1] Fri, 12 Jun 2026 11:42:28 UTC (1,376 KB) Full-text links: Access Paper: View a PDF of the paper titled Towards On-Board Implementation of ML-Based Helicopter Weight Estimator, by Nicolas Valot and 4 other authors View PDF TeX Source view license Current browse context: cs.LG new | recent | 2026-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)