@article{MTMT:34110809,
	title = {Resilient Multipath Routing Protocol to Enable Hazardous event Monitoring with Wireless Sensor Network},
	url = {https://m2.mtmt.hu/api/publication/34110809},
	author = {Üveges, Bálint Áron and Lőrincz, Máté and Oláh, András},
	doi = {10.5937/telfor2301020Q},
	journal-iso = {TELFOR J},
	journal = {TELFOR JOURNAL},
	volume = {15},
	unique-id = {34110809},
	issn = {1821-3251},
	abstract = {With the growing impact of climate change, the occurrence of hazardous spatial events increases. Wireless sensor networks are suitable to sense, monitor, and report such events in remote or inaccessible locations. Hazardous events are rare compared to the network's lifetime, thus maintaining its consistency must be realized energy efficiently. During the impact, the network must monitor the event with precision, and report the incidence, while mitigating the loss of perishing nodes. 
		To fulfill these requirements, we propose the Self-healing Multipath Routing Protocol that is based on the Heterogeneous Disjoint Multipath Routing Protocol and introduces application-specific extensions to improve network stability, resiliency, and failover. To realize the monitoring of spatially extended hazardous events we introduce an event-based, application-level protocol. 
		To evaluate the routing protocol, we perform simulations utilizing a cellular automaton-based wildfire model as the spatial event and provide measurement results including delivery ratio, consumed energy, and protocol-specific metrics.},
	keywords = {Wireless sensor networks and applications; failover; Adaptive multipath routing},
	year = {2023},
	eissn = {2334-9905},
	pages = {20-25}
}

@inproceedings{MTMT:33536003,
	title = {Self-healing Multipath Routing Protocol to assist Wireless Sensor Network based Hazardous Event Monitoring},
	url = {https://m2.mtmt.hu/api/publication/33536003},
	author = {Üveges, Bálint Áron and Lőrincz, Máté and Oláh, András},
	booktitle = {2022 30th Telecommunications Forum (TELFOR)},
	doi = {10.1109/TELFOR56187.2022.9983752},
	unique-id = {33536003},
	year = {2022},
	pages = {1-4}
}

@article{MTMT:30866059,
	title = {PPCU Sam: Open-source face recognition framework},
	url = {https://m2.mtmt.hu/api/publication/30866059},
	author = {Botos, Csaba and Hakkel, Tamás and Horváth, András and Oláh, András and Reguly, István Zoltán},
	doi = {10.1016/j.procs.2019.09.367},
	journal-iso = {PROC COMPUTER SCI},
	journal = {PROCEDIA COMPUTER SCIENCE},
	volume = {159},
	unique-id = {30866059},
	issn = {1877-0509},
	abstract = {In recent years by the popularization of AI, an increasing number of enterprises deployed machine learning algorithms in real life settings. This trend shed light on leaking spots of the Deep Learning bubble, namely the catastrophic decrease in quality when the distribution of the test data shifts from the training data. It is of utmost importance that we treat the promises of novel algorithms with caution and discourage reporting near perfect experimental results by fine-tuning on fixed test sets and finding metrics that hide weak points of the proposed methods. To support the wider acceptance of computer vision solutions we share our findings through a case-study in which we built a face-recognition system from scratch using consumer grade devices only, collected a database of 100k images from 150 subjects and carried out extensive validation of the most prominent approaches in single-frame face recognition literature. We show that the reported worst-case score, 74.3% true-positive ratio drops below 46.8% on real data. To overcome this barrier, after careful error analysis of the single-frame baselines we propose a low complexity solution to cover the failure cases of the single-frame recognition methods which yields an increased stability in multi-frame recognition during test time. We validate the effectiveness of the proposal by an extensive survey among our users which evaluates to 89.5% true-positive ratio.},
	year = {2019},
	pages = {1947-1956},
	orcid-numbers = {Oláh, András/0009-0003-4796-8932; Reguly, István Zoltán/0000-0002-4385-4204}
}

@article{MTMT:3252486,
	title = {Low Complexity Algorithmic Trading by Feedforward Neural Networks},
	url = {https://m2.mtmt.hu/api/publication/3252486},
	author = {Levendovszky, János and Reguly, István Zoltán and Oláh, András and Ceffer, Attila},
	doi = {10.1007/s10614-017-9720-6},
	journal-iso = {COMPUT ECON},
	journal = {COMPUTATIONAL ECONOMICS},
	volume = {54},
	unique-id = {3252486},
	issn = {0927-7099},
	year = {2019},
	eissn = {1572-9974},
	pages = {267-279},
	orcid-numbers = {Levendovszky, János/0000-0003-1406-442X; Reguly, István Zoltán/0000-0002-4385-4204; Oláh, András/0009-0003-4796-8932}
}

@inproceedings{MTMT:3369208,
	title = {An effective neuron based method for process control in industrial environment},
	url = {https://m2.mtmt.hu/api/publication/3369208},
	author = {Lőrincz, Máté and Oláh, András and Tornai, Kálmán},
	booktitle = {2018 IEEE International Conference on Industrial Technology (ICIT)},
	doi = {10.1109/ICIT.2018.8352154},
	unique-id = {3369208},
	keywords = {NEURONS; training; Control systems; Process control; VALVES; Meters; Industries},
	year = {2018},
	pages = {69-74},
	orcid-numbers = {Oláh, András/0009-0003-4796-8932; Tornai, Kálmán/0000-0003-1852-0816}
}

@article{MTMT:3320488,
	title = {Bottom-up modeling of domestic appliances with Markov chains and semi-Markov processes},
	url = {https://m2.mtmt.hu/api/publication/3320488},
	author = {Drenyovszki, Rajmund and Kovács, Lóránt and Tornai, Kálmán and Oláh, András and Pintér, István},
	doi = {10.14736/kyb-2017-6-1100},
	journal-iso = {KYBERNETIKA},
	journal = {KYBERNETIKA},
	volume = {53},
	unique-id = {3320488},
	issn = {0023-5954},
	year = {2017},
	pages = {1100-1117},
	orcid-numbers = {Drenyovszki, Rajmund/0000-0002-9462-2729; Tornai, Kálmán/0000-0003-1852-0816; Oláh, András/0009-0003-4796-8932}
}

@inproceedings{MTMT:3314043,
	title = {Recurrent neural network based user classification for smart grids},
	url = {https://m2.mtmt.hu/api/publication/3314043},
	author = {Tornai, Kálmán and Oláh, András and Drenyovszki, Rajmund and Kovács, Lóránt and Pintér, István and J, Levendovszky},
	booktitle = {2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)},
	doi = {10.1109/ISGT.2017.8086043},
	unique-id = {3314043},
	keywords = {PATTERN CLASSIFICATION; training; time series analysis; Power consumption; NEURAL NETS; hidden Markov models; Power transmission; Data models; Buildings; Recurrent neural networks; Smart grids; Smart power grids; learning (artificial intelligence); power engineering computing; recurrent neural nets; Consumer classification; unsupervised categorization; smart power transmission systems; recurrent neural network-based user classification; power consumption patterns; power consuming users; power consumers; nonlinear forecast techniques; measured power consumption data; consumption forecast based scheme; behavior forecast; load forecasting},
	year = {2017},
	orcid-numbers = {Tornai, Kálmán/0000-0003-1852-0816; Oláh, András/0009-0003-4796-8932; Drenyovszki, Rajmund/0000-0002-9462-2729}
}

@inproceedings{MTMT:3256354,
	title = {Deep learning based consumer classification for smart grid},
	url = {https://m2.mtmt.hu/api/publication/3256354},
	author = {Tornai, Kálmán and Oláh, András and Drenyovszki, Rajmund and Kovács, Lóránt and Pintér, István and Levendovszky, J},
	booktitle = {Smart grid inspired future technologies},
	doi = {10.1007/978-3-319-61813-5_13},
	unique-id = {3256354},
	year = {2017},
	pages = {132-141},
	orcid-numbers = {Tornai, Kálmán/0000-0003-1852-0816; Oláh, András/0009-0003-4796-8932; Drenyovszki, Rajmund/0000-0002-9462-2729}
}

@article{MTMT:3183546,
	title = {A probabilistic demand side management approach by consumption admission control},
	url = {https://m2.mtmt.hu/api/publication/3183546},
	author = {Kovács, Lóránt and Drenyovszki, Rajmund and Oláh, András and Levendovszky, János and Tornai, Kálmán and Pintér, István},
	doi = {10.17559/TV-20151021201400},
	journal-iso = {TEH VJESN},
	journal = {TEHNICKI VJESNIK-TECHNICAL GAZETTE},
	volume = {24},
	unique-id = {3183546},
	issn = {1330-3651},
	year = {2017},
	eissn = {1848-6339},
	pages = {199-207},
	orcid-numbers = {Drenyovszki, Rajmund/0000-0002-9462-2729; Oláh, András/0009-0003-4796-8932; Levendovszky, János/0000-0003-1406-442X; Tornai, Kálmán/0000-0003-1852-0816}
}

@inproceedings{MTMT:3213513,
	title = {Jensen-Shannon divergence based algorithm for adaptive segmentation and labelling of household's electricity power consumption data series},
	url = {https://m2.mtmt.hu/api/publication/3213513},
	author = {Pintér, István and Kovács, Lóránt and Drenyovszki, Rajmund and Oláh, András and Tornai, Kálmán},
	booktitle = {2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)},
	doi = {10.1109/SMC.2016.7844518},
	unique-id = {3213513},
	year = {2016},
	pages = {1912-1916},
	orcid-numbers = {Drenyovszki, Rajmund/0000-0002-9462-2729; Oláh, András/0009-0003-4796-8932; Tornai, Kálmán/0000-0003-1852-0816}
}