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Titlebook: Commercial Vehicle Technology 2024; Proceedings of the 8 Karsten Berns,Klaus Dre?ler,Martin Thul Conference proceedings 2024 Der/die Heraus

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發(fā)表于 2025-3-23 11:20:36 | 只看該作者
12#
發(fā)表于 2025-3-23 16:02:56 | 只看該作者
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發(fā)表于 2025-3-23 20:38:21 | 只看該作者
Efficient HiL-Testing for Electric Heavy-Duty Drivetrains using Model-Based Systems Engineering, electronics, hydraulics, and alternative fuels, and present complex interdisciplinary challenges. Product testing is necessary to validate that these new concepts meet functional requirements and fulfill their intended purpose. Although virtual testing through simulation models offers cost-effecti
14#
發(fā)表于 2025-3-23 23:01:12 | 只看該作者
15#
發(fā)表于 2025-3-24 03:01:26 | 只看該作者
Cloud-Based Identification of Dynamic Trailer Statesions and autonomous transport processes in the future. In this contribution, we present the IdenT system concept, which has been developed for tractor-semitrailers within the research project of the same name, consisting of an intelligent trailer sensor network, a cloud-based data platform and metho
16#
發(fā)表于 2025-3-24 10:12:58 | 只看該作者
Ana M. Angulo,José M. Gil,Azucena Gracia two case studies on the development of characteristic systems within the vehicle and crane part of a mobile crane. Based on the results of process analysis and case studies, measures for an adaption of the current development process are proposed.
17#
發(fā)表于 2025-3-24 12:13:34 | 只看該作者
Aad Tilburg,Henk A. J. Moll,Arie Kuyvenhovenic data for training of AI for crop row detection, developing assets for the John Deere 612R self-driving sprayer as well as numerous assets of corn crops in multiple stages of growth. We show that, among other benefits for the business, this process is effective in improving performance of a crop row detection algorithm.
18#
發(fā)表于 2025-3-24 18:40:10 | 只看該作者
19#
發(fā)表于 2025-3-24 20:26:26 | 只看該作者
20#
發(fā)表于 2025-3-25 02:39:19 | 只看該作者
AI-Based Surrogate Modeling for Highly Efficient Soil-Tool Simulation loosing accuracy in the prediction of soil-tool interaction forces, would be highly beneficial. Here, we discuss an approach based on recurrent neural networks with the potential of combining real-time capability with accurate soil-tool interaction force prediction.
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