@article{MTMT:34818876,
	title = {Investigation of the modified Ballast Breakage Index for laboratory test series using the Proctor compactor machine},
	url = {https://m2.mtmt.hu/api/publication/34818876},
	author = {Juhász, Erika and Fischer, Szabolcs},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	unique-id = {34818876},
	issn = {1785-8860},
	year = {2025},
	eissn = {1785-8860},
	orcid-numbers = {Fischer, Szabolcs/0000-0001-7298-9960}
}

@article{MTMT:34777287,
	title = {Laboratory investigation on seams between rails and hardened fine-grained as well as Hadfield steel plates prepared by manual arc welding},
	url = {https://m2.mtmt.hu/api/publication/34777287},
	author = {Brautigam, András and Szalai, Szabolcs and Légmán, Nikoletta and Fischer, Szabolcs},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	volume = {22},
	unique-id = {34777287},
	issn = {1785-8860},
	year = {2025},
	eissn = {1785-8860},
	orcid-numbers = {Fischer, Szabolcs/0000-0001-7298-9960}
}

@article{MTMT:34825488,
	title = {An Improved IEHO Super-Twisting Sliding Mode Control Algorithm for Trajectory Tracking of a Mobile Robot},
	url = {https://m2.mtmt.hu/api/publication/34825488},
	author = {OULTILIGH, Ahmed and AYAD, Hassan and EL KARI, Abdeljalil and MJAHED, Mostafa and EL GMILI, Nada and Horváth, Ernő and POZNA, Claudiu},
	doi = {10.24846/v33i1y202405},
	journal-iso = {STUD INFORM CONTROL},
	journal = {STUDIES IN INFORMATICS AND CONTROL},
	volume = {33},
	unique-id = {34825488},
	issn = {1220-1766},
	year = {2024},
	eissn = {1841-429X},
	pages = {49-60},
	orcid-numbers = {Horváth, Ernő/0000-0001-5083-2073}
}

@article{MTMT:34778056,
	title = {Silt erosion and cavitation impact on hydraulic turbines performance: An in-depth analysis and preventative strategies},
	url = {https://m2.mtmt.hu/api/publication/34778056},
	author = {Kashyap, Tanish and Thakur, Robin and Ngo, Gia Huy and Lee, Daeho and Fekete, Gusztáv and Kumar, Raj and Singh, Tej},
	doi = {10.1016/j.heliyon.2024.e28998},
	journal-iso = {HELIYON},
	journal = {HELIYON},
	volume = {10},
	unique-id = {34778056},
	year = {2024},
	eissn = {2405-8440},
	orcid-numbers = {Fekete, Gusztáv/0000-0002-6138-8382; Singh, Tej/0000-0003-2316-4107}
}

@article{MTMT:34777903,
	title = {Rethinking running biomechanics: a critical review of ground reaction forces, tibial bone loading, and the role of wearable sensors},
	url = {https://m2.mtmt.hu/api/publication/34777903},
	author = {Xiang, Liangliang and Gao, Zixiang and Wang, Alan and Shim, Vickie and Fekete, Gusztáv and Gu, Yaodong and Fernandez, Justin},
	doi = {10.3389/fbioe.2024.1377383},
	journal-iso = {FRONT BIOENG BIOTECHNOL},
	journal = {FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY},
	volume = {12},
	unique-id = {34777903},
	issn = {2296-4185},
	abstract = {This study presents a comprehensive review of the correlation between tibial acceleration (TA), ground reaction forces (GRF), and tibial bone loading, emphasizing the critical role of wearable sensor technology in accurately measuring these biomechanical forces in the context of running. This systematic review and meta-analysis searched various electronic databases (PubMed, SPORTDiscus, Scopus, IEEE Xplore, and ScienceDirect) to identify relevant studies. It critically evaluates existing research on GRF and tibial acceleration (TA) as indicators of running-related injuries, revealing mixed findings. Intriguingly, recent empirical data indicate only a marginal link between GRF, TA, and tibial bone stress, thus challenging the conventional understanding in this field. The study also highlights the limitations of current biomechanical models and methodologies, proposing a paradigm shift towards more holistic and integrated approaches. The study underscores wearable sensors’ potential, enhanced by machine learning, in transforming the monitoring, prevention, and rehabilitation of running-related injuries.},
	year = {2024},
	eissn = {2296-4185},
	orcid-numbers = {Fekete, Gusztáv/0000-0002-6138-8382}
}

@article{MTMT:34758553,
	title = {Brake Disc Deformation Detection Using Intuitive Feature Extraction and Machine Learning},
	url = {https://m2.mtmt.hu/api/publication/34758553},
	author = {Dózsa, Tamás and Őri, P and Szabari, M and Simonyi, Ernő and Soumelidis, Alexandros and Lakatos, István},
	doi = {10.3390/machines12040214},
	journal-iso = {MACHINES},
	journal = {MACHINES},
	volume = {12},
	unique-id = {34758553},
	abstract = {In this work we propose proof-of-concept methods to detect malfunctions of the braking system in passenger vehicles. In particular, we investigate the problem of detecting deformations of the brake disc based on data recorded by acceleration sensors mounted on the suspension of the vehicle. Our core hypothesis is that these signals contain vibrations caused by brake disc deformation. Since faults of this kind are typically monitored by the driver of the vehicle, the development of automatic fault-detection systems becomes more important with the rise of autonomous driving. In addition, the new brake boosters separate the brake pedal from the hydraulic system which results in less significant effects on the brake pedal force. Our paper offers two important contributions. Firstly, we provide a detailed description of our novel measurement scheme, the type and placement of the used sensors, signal acquisition and data characteristics. Then, in the second part of our paper we detail mathematically justified signal representations and different algorithms to distinguish between deformed and normal brake discs. For the proper understanding of the phenomenon, different brake discs were used with measured runout values. Since, in addition to brake disc deformation, the vibrations recorded by our accelerometers are nonlinearly dependent on a number of factors (such as the velocity, suspension, tire pressure, etc.), data-driven models are considered. Through experiments, we show that the proposed methods can be used to recognize faults in the braking system caused by brake disc deformation.},
	year = {2024},
	eissn = {2075-1702},
	orcid-numbers = {Őri, P/0000-0002-4706-6432; Soumelidis, Alexandros/0000-0002-0067-7746; Lakatos, István/0000-0002-3855-7379}
}

@article{MTMT:34722701,
	title = {Investigating the Effect of Gear Ratio in the Case of Joint Multi-Objective Optimization of Electric Motor and Gearbox},
	url = {https://m2.mtmt.hu/api/publication/34722701},
	author = {Istenes, György and Polák, József},
	doi = {10.3390/en17051203},
	journal-iso = {ENERGIES},
	journal = {ENERGIES},
	volume = {17},
	unique-id = {34722701},
	issn = {1996-1073},
	abstract = {In this paper, a software framework is presented through an application that is able to jointly optimize an electric motor and a gearbox for the design of a drive system for electric vehicles. The framework employs a global optimization method and uses both analytical and finite element method (FEM) models to evaluate the objective functions. The optimization process is supported by a statistical surrogate model, which allows a large reduction of runtime. An earlier version of this framework was only suitable for electric motor optimization. In the application presented in a previous paper, the motor of a belt-driven electric drive system was optimized. In this paper, the optimization of the same drive system is shown, but now with a combined optimization of a gear drive and motor. The objective functions of optimization are minimizing the total loss energy and the weight of the drive system. The optimization results are compared with previous results to demonstrate the further potential of joint optimization.},
	year = {2024},
	eissn = {1996-1073},
	orcid-numbers = {Polák, József/0000-0003-3445-1515}
}

@article{MTMT:34568996,
	title = {A new method applied for explaining the landing patterns: Interpretability analysis of machine learning},
	url = {https://m2.mtmt.hu/api/publication/34568996},
	author = {XU, DATAO and Zhou, Huiyu and Quan, Wenjing and Ugbolue, Ukadike Chris and Fekete, Gusztáv and Gu, Yaodong},
	doi = {10.1016/j.heliyon.2024.e26052},
	journal-iso = {HELIYON},
	journal = {HELIYON},
	volume = {10},
	unique-id = {34568996},
	year = {2024},
	eissn = {2405-8440},
	orcid-numbers = {Quan, Wenjing/0000-0002-3881-518X; Ugbolue, Ukadike Chris/0000-0002-6640-4126; Fekete, Gusztáv/0000-0002-6138-8382; Gu, Yaodong/0000-0003-2187-9440}
}

@article{MTMT:34524770,
	title = {Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles},
	url = {https://m2.mtmt.hu/api/publication/34524770},
	author = {Ignéczi, Gergő Ferenc and Horváth, Ernő and Tóth, Roland and Nyilas, K},
	doi = {10.1007/s42154-023-00259-8},
	journal-iso = {Automot. Innov.},
	journal = {Automotive Innovation},
	volume = {7},
	unique-id = {34524770},
	issn = {2096-4250},
	abstract = {Automated driving systems are often used for lane keeping tasks. By these systems, a local path is planned ahead of the vehicle. However, these paths are often found unnatural by human drivers. In response to this, this paper proposes a linear driver model, which can calculate node points reflective of human driver preferences and based on these node points a human driver preferred motion path can be designed for autonomous driving. The model input is the road curvature, effectively harnessed through a self-developed Euler-curve-based curve fitting algorithm. A comprehensive case study is undertaken to empirically validate the efficacy of the proposed model, demonstrating its capacity to emulate the average behavioral patterns observed in human curve path selection. Statistical analyses further underscore the model's robustness, affirming the authenticity of the established relationships. This paradigm shift in trajectory planning holds promising implications for the seamless integration of autonomous driving systems with human driving preferences.},
	year = {2024},
	eissn = {2522-8765},
	pages = {59-70},
	orcid-numbers = {Horváth, Ernő/0000-0001-5083-2073}
}

@article{MTMT:34231272,
	title = {A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis},
	url = {https://m2.mtmt.hu/api/publication/34231272},
	author = {XU, DATAO and Zhou, Huiyu and QUAN, WENJING and Jiang, Xinyan and Liang, Minjun and Li, Shudong and Ugbolue, Ukadike Chris and Baker, Julien S. and Fekete, Gusztáv and Ma, Xin and Chen, Li and Gu, Yaodong},
	doi = {10.1016/j.gaitpost.2023.10.019},
	journal-iso = {GAIT POSTURE},
	journal = {GAIT & POSTURE},
	volume = {107},
	unique-id = {34231272},
	issn = {0966-6362},
	year = {2024},
	eissn = {1879-2219},
	pages = {293-305},
	orcid-numbers = {Fekete, Gusztáv/0000-0002-6138-8382; Gu, Yaodong/0000-0003-2187-9440}
}