| Issue |
E3S Web of Conf.
Volume 389, 2023
Ural Environmental Science Forum “Sustainable Development of Industrial Region” (UESF-2023)
|
|
|---|---|---|
| Article Number | 01035 | |
| Number of page(s) | 15 | |
| Section | Materials Science Innovations, Green Chemistry and Emission Reduction | |
| DOI | https://doi.org/10.1051/e3sconf/202338901035 | |
| Published online | 31 May 2023 | |
Implementation of a neural network in overhead crane control
1 Saint Petersburg Mining University, Saint Petersburg, Russia
2 Peter the Great St.Petersburg Polytechnic University, Saint Petersburg, Russia
* Corresponding author: y.n.kozhubaev@gmail.com
This work is aimed at proecting an automated control system for an overhead crane based on a neural network, which will replace the operator, but it is possible to transfer this control back to the operator. The paper considers an approach to the development of the necessary software for the correct operation of the crane, the choice of equipment suitable for the task. The final part describes the program blocks necessary for operation, as well as locks.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.