@inproceedings{MTMT:34994521,
	title = {Reinforcement Learning based Control of Electrical Drives},
	url = {https://m2.mtmt.hu/api/publication/34994521},
	author = {Szécsényi, Nándor},
	booktitle = {Proceedings of the Automation and Applied Computer Science Workshop 2024 : AACS'24},
	unique-id = {34994521},
	year = {2024},
	pages = {1-13}
}

@{MTMT:34900557,
	title = {Useability Evaluation of Reinforcement Learning Toolboxes for Electrical Drives},
	url = {https://m2.mtmt.hu/api/publication/34900557},
	author = {Szécsényi, Nándor and Stumpf, Péter Pál},
	booktitle = {22nd International Conference on Renewable Energies and Power Quality (ICREPQ’24)},
	unique-id = {34900557},
	year = {2024}
}

@article{MTMT:34871088,
	title = {Useability Evaluation of Reinforcement Learning Toolboxes for Electrical Drives},
	url = {https://m2.mtmt.hu/api/publication/34871088},
	author = {Szécsényi, Nándor and Stumpf, Péter Pál},
	doi = {10.52152/3916},
	journal-iso = {RENEWABLE ENERGY & POWER QUALITY J},
	journal = {RENEWABLE ENERGY & POWER QUALITY JOURNAL},
	volume = {22},
	unique-id = {34871088},
	year = {2024},
	eissn = {2172-038X},
	pages = {89-94}
}

@inproceedings{MTMT:34608877,
	title = {Efficient Annotation Methods for Industrial Datasets using Artificial Intelligence},
	url = {https://m2.mtmt.hu/api/publication/34608877},
	author = {Szécsényi, Nándor},
	booktitle = {Proceedings of the Automation and Applied Computer Science Workshop 2023 (AACS'23)},
	unique-id = {34608877},
	abstract = {Due to the rapid development and growth of Artificial Intelligence in recent times, it is also being used more and more frequently in industrial environments, however there are many problems besides its advantages. One of them is the difficult process of Dataset construction which often involves manual sample annotation. This task is often performed by human labor at larger companies with relative ease, smaller projects or developments however usually lack the necessary resources to do so, thus making it seem advantageous to use Artificial Intelligence, more precisely Deep Learning, for this problem too. Another issue is the asymmetry of these Datasets, as with production lines, the chance of failure is kept very low, resulting in only a few error samples. To overcome these issues and further democratize AI, in this paper, we present 2 latent space-based sampling methods, which based on the acquired results can achieve reasonably good classification performance with only annotating a few 100s of samples. Moreover, to aid in the understanding of the results, the presented techniques are compared to a naive method representing the baseline approach.},
	keywords = {sample selection method; AI Artificial intelligence; latent space interpretation},
	year = {2023},
	pages = {64-75}
}