@article{MTMT:34774286,
	title = {Demonstrating a new evaluation method on ReLU based Neural Networks for classification problems},
	url = {https://m2.mtmt.hu/api/publication/34774286},
	author = {Tollner, Dávid and Ziyu, Wang and Zöldy, Máté and Török, Árpád},
	doi = {10.1016/j.eswa.2024.123905},
	journal-iso = {EXPERT SYST APPL},
	journal = {EXPERT SYSTEMS WITH APPLICATIONS},
	volume = {250},
	unique-id = {34774286},
	issn = {0957-4174},
	abstract = {Deep neural networks, which have proven to be effective methods to solve complex problems, can even be applied in decision systems controlling critical processes. However, in such applications the outcomes of the neural network must be checked if it we have a clear understanding regarding the operation of network at given input intervals. The most straightforward, though often computationally expensive approach for this checking process evaluates the network at discrete input points and estimates the expected outputs at the given interval. The present research aims to develop a novel approach that can identify in the case of specific input intervals whether the operation process and the output of a neural network can be considered as known or unknown. During the presented case study, we investigated the ReLU (Rectified Linear Unit) and Sigmoid activation functions, using the double moon and the Banknote Authentication classification problems for demonstration. Our method can be applied to identify certain input intervals where the given neural network cannot support critical decisions. The evaluation is performed based on a nonlinear system of equations and inequalities built on arbitrary continuous activation functions. To define the critical intervals of the input variables (i.e. where the decision-making system should not be relied on), those input variable combinations are identified which result in a non-expected output value. This inverse logic is intended to identify intervals of the input variables where the response of the system and the correct decision are not identical. The presented demonstration examples supported our assumption that the number of the neurons and the dimension of the decision space have a significant impact on the complexity of the evaluation process. © 2024 The Author(s)},
	year = {2024},
	eissn = {1873-6793},
	orcid-numbers = {Ziyu, Wang/0000-0002-1267-9319; Zöldy, Máté/0000-0003-1271-840X; Török, Árpád/0000-0002-1985-4095}
}

@article{MTMT:34774194,
	title = {Autonomous Vehicle and Pedestrian Interaction. Leveraging The Use of Model Predictive Control & Genetic Algorithm},
	url = {https://m2.mtmt.hu/api/publication/34774194},
	author = {Elaa, Elgharbi and Zöldy, Máté and Safa, Bhar Layeb},
	doi = {10.55343/cogsust.90},
	journal-iso = {COGSUST},
	journal = {COGNITIVE SUSTAINABILITY},
	volume = {3},
	unique-id = {34774194},
	year = {2024},
	eissn = {2939-5240},
	pages = {15-31},
	orcid-numbers = {Zöldy, Máté/0000-0003-1271-840X}
}

@article{MTMT:34746683,
	title = {Neural Network-based Multi-Class Traffic-Sign Classification with the German Traffic Sign Recognition Benchmark},
	url = {https://m2.mtmt.hu/api/publication/34746683},
	author = {Ferencz, Csanád and Zöldy, Máté},
	doi = {10.12700/APH.21.7.2024.7.12},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	volume = {21},
	unique-id = {34746683},
	issn = {1785-8860},
	year = {2024},
	eissn = {1785-8860},
	pages = {203-220},
	orcid-numbers = {Zöldy, Máté/0000-0003-1271-840X}
}

@article{MTMT:34746682,
	title = {Trends in Cognitive Mobility in 2022},
	url = {https://m2.mtmt.hu/api/publication/34746682},
	author = {Zöldy, Máté and Baranyi, Péter Zoltán and Török, Ádám},
	doi = {10.12700/APH.21.7.2024.7.11},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	volume = {21},
	unique-id = {34746682},
	issn = {1785-8860},
	year = {2024},
	eissn = {1785-8860},
	pages = {189-202},
	orcid-numbers = {Zöldy, Máté/0000-0003-1271-840X; Baranyi, Péter Zoltán/0000-0002-8265-5849; Török, Ádám/0000-0001-6727-1540}
}

@article{MTMT:34734773,
	title = {Enhancing the Viability of a Promising E-Fuel: Oxymethylene Ether–Decanol Mixtures},
	url = {https://m2.mtmt.hu/api/publication/34734773},
	author = {Virt, Márton and Zöldy, Máté},
	doi = {10.3390/en17061348},
	journal-iso = {ENERGIES},
	journal = {ENERGIES},
	volume = {17},
	unique-id = {34734773},
	issn = {1996-1073},
	abstract = {Achieving sustainable mobility is a crucial factor in maintaining long-term economic growth without adverse effects on human health and the environment. E-fuels, such as the promising oxymethylene ether (OME), can contribute to sustainable road transport. However, this compound does not meet the requirements of EN590; thus, it is unsuitable for unmodified diesel engines. This work aims to improve the applicability of OME by blending it with n-decanol, which can also be produced sustainably. Combustion and emissions were investigated in a medium-duty commercial diesel engine with different binary and ternary mixtures of oxymethylene ether, n-decanol, and B7 diesel. Laboratory analysis of six key mixture parameters revealed that the formulated blends met the EN590 requirements, with the exception of that of density. The results demonstrated that the created mixtures, including one without any diesel fuel, can be efficiently utilized in unmodified diesel engines. OME’s beneficial effects on combustion and emission were preserved while its viability was improved; a maximum increase of 7.6% in brake thermal efficiency was observed, alongside a potential decrease of nearly 70% in PM emissions at unaltered NOx levels.},
	year = {2024},
	eissn = {1996-1073},
	orcid-numbers = {Zöldy, Máté/0000-0003-1271-840X}
}

@article{MTMT:34730296,
	title = {Cost Efficient Training Method for Artificial Neural Networks based on Engine Measurements},
	url = {https://m2.mtmt.hu/api/publication/34730296},
	author = {Virt, Márton and Zöldy, Máté},
	doi = {10.12700/APH.21.7.2024.7.8},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	volume = {21},
	unique-id = {34730296},
	issn = {1785-8860},
	year = {2024},
	eissn = {1785-8860},
	pages = {123-145},
	orcid-numbers = {Zöldy, Máté/0000-0003-1271-840X}
}

@article{MTMT:34498268,
	title = {Exhaustive Investigation of the Promises and Perils of Autonomous Mobility Technology},
	url = {https://m2.mtmt.hu/api/publication/34498268},
	author = {Ferencz, Csanád and Zöldy, Máté},
	doi = {10.3311/PPTR.22573},
	journal-iso = {PERIOD POLYTECH TRANSP ENG},
	journal = {PERIODICA POLYTECHNICA TRANSPORTATION ENGINEERING},
	volume = {52},
	unique-id = {34498268},
	issn = {0303-7800},
	abstract = {The technology of autonomous driving systems has the potential to bring significant benefits in terms of safety, mobility, and congestion, as well as land use and energy consumption. In this paper, the authors present both the promises and possible dangers of the large-scale implementation of autonomous vehicle technologies in the current road traffic networks. The effects of autonomous mobility technology are complex and require careful consideration. While there is great potential to transform transportation and offer significant improvements in many areas, there are also risks and challenges that need to be addressed to ensure safe, efficient, and equitable deployment of the technology. © 2024 Budapest University of Technology and Economics. All rights reserved.},
	keywords = {Roads and streets; Energy utilization; Land use; Motor transportation; Intelligent transportation; Intelligent transportation; Autonomous Vehicles; Autonomous Vehicles; Autonomous Vehicles; vehicle safety; vehicle safety; vehicle safety; sustainable mobility; sustainable mobility; autonomous driving; Energy-consumption; Large-scales; Road traffic management; Road traffic management; Driving systems; Autonomous mobilities},
	year = {2024},
	eissn = {1587-3811},
	pages = {86-95},
	orcid-numbers = {Zöldy, Máté/0000-0003-1271-840X}
}

@inproceedings{MTMT:34414615,
	title = {Dynamic Data extraction from vehicles through OBD connector},
	url = {https://m2.mtmt.hu/api/publication/34414615},
	author = {Tollner, Dávid and Zöldy, Máté},
	booktitle = {2023 IEEE 2nd International Conference on Cognitive Mobility (CogMob)},
	unique-id = {34414615},
	year = {2023},
	pages = {189-194},
	orcid-numbers = {Zöldy, Máté/0000-0003-1271-840X}
}

@inproceedings{MTMT:34402144,
	title = {Failure rate analysis of driverless car reliability using different confidence intervals},
	url = {https://m2.mtmt.hu/api/publication/34402144},
	author = {Ferencz, Csanád and Zöldy, Máté},
	booktitle = {2023 IEEE 2nd International Conference on Cognitive Mobility (CogMob)},
	unique-id = {34402144},
	year = {2023},
	orcid-numbers = {Zöldy, Máté/0000-0003-1271-840X}
}

@misc{MTMT:34220816,
	title = {Speed profile generation for an electric race vehicle},
	url = {https://m2.mtmt.hu/api/publication/34220816},
	author = {Nyerges, László Ádám and Zöldy, Máté},
	unique-id = {34220816},
	year = {2023},
	orcid-numbers = {Zöldy, Máté/0000-0003-1271-840X}
}