TY - JOUR AU - Juhász, Erika AU - Fischer, Szabolcs TI - Investigation of the modified Ballast Breakage Index for laboratory test series using the Proctor compactor machine JF - ACTA POLYTECHNICA HUNGARICA J2 - ACTA POLYTECH HUNG PY - 2025 SN - 1785-8860 UR - https://m2.mtmt.hu/api/publication/34818876 ID - 34818876 LA - English DB - MTMT ER - TY - JOUR AU - Brautigam, András AU - Szalai, Szabolcs AU - Légmán, Nikoletta AU - Fischer, Szabolcs TI - Laboratory investigation on seams between rails and hardened fine-grained as well as Hadfield steel plates prepared by manual arc welding JF - ACTA POLYTECHNICA HUNGARICA J2 - ACTA POLYTECH HUNG VL - 22 PY - 2025 SN - 1785-8860 UR - https://m2.mtmt.hu/api/publication/34777287 ID - 34777287 LA - English DB - MTMT ER - TY - JOUR AU - OULTILIGH, Ahmed AU - AYAD, Hassan AU - EL KARI, Abdeljalil AU - MJAHED, Mostafa AU - EL GMILI, Nada AU - Horváth, Ernő AU - POZNA, Claudiu TI - An Improved IEHO Super-Twisting Sliding Mode Control Algorithm for Trajectory Tracking of a Mobile Robot JF - STUDIES IN INFORMATICS AND CONTROL J2 - STUD INFORM CONTROL VL - 33 PY - 2024 IS - 1 SP - 49 EP - 60 PG - 12 SN - 1220-1766 DO - 10.24846/v33i1y202405 UR - https://m2.mtmt.hu/api/publication/34825488 ID - 34825488 LA - English DB - MTMT ER - TY - JOUR AU - Kashyap, Tanish AU - Thakur, Robin AU - Ngo, Gia Huy AU - Lee, Daeho AU - Fekete, Gusztáv AU - Kumar, Raj AU - Singh, Tej TI - Silt erosion and cavitation impact on hydraulic turbines performance: An in-depth analysis and preventative strategies JF - HELIYON J2 - HELIYON VL - 10 PY - 2024 IS - 8 SN - 2405-8440 DO - 10.1016/j.heliyon.2024.e28998 UR - https://m2.mtmt.hu/api/publication/34778056 ID - 34778056 LA - English DB - MTMT ER - TY - JOUR AU - Xiang, Liangliang AU - Gao, Zixiang AU - Wang, Alan AU - Shim, Vickie AU - Fekete, Gusztáv AU - Gu, Yaodong AU - Fernandez, Justin TI - Rethinking running biomechanics: a critical review of ground reaction forces, tibial bone loading, and the role of wearable sensors JF - FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY J2 - FRONT BIOENG BIOTECHNOL VL - 12 PY - 2024 SN - 2296-4185 DO - 10.3389/fbioe.2024.1377383 UR - https://m2.mtmt.hu/api/publication/34777903 ID - 34777903 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Dózsa, Tamás AU - Őri, P AU - Szabari, M AU - Simonyi, Ernő AU - Soumelidis, Alexandros AU - Lakatos, István TI - Brake Disc Deformation Detection Using Intuitive Feature Extraction and Machine Learning JF - MACHINES J2 - MACHINES VL - 12 PY - 2024 IS - 4 PG - 21 SN - 2075-1702 DO - 10.3390/machines12040214 UR - https://m2.mtmt.hu/api/publication/34758553 ID - 34758553 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Istenes, György AU - Polák, József TI - Investigating the Effect of Gear Ratio in the Case of Joint Multi-Objective Optimization of Electric Motor and Gearbox JF - ENERGIES J2 - ENERGIES VL - 17 PY - 2024 IS - 5 SN - 1996-1073 DO - 10.3390/en17051203 UR - https://m2.mtmt.hu/api/publication/34722701 ID - 34722701 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - XU, DATAO AU - Zhou, Huiyu AU - Quan, Wenjing AU - Ugbolue, Ukadike Chris AU - Fekete, Gusztáv AU - Gu, Yaodong TI - A new method applied for explaining the landing patterns: Interpretability analysis of machine learning JF - HELIYON J2 - HELIYON VL - 10 PY - 2024 IS - 4 SN - 2405-8440 DO - 10.1016/j.heliyon.2024.e26052 UR - https://m2.mtmt.hu/api/publication/34568996 ID - 34568996 LA - English DB - MTMT ER - TY - JOUR AU - Ignéczi, Gergő Ferenc AU - Horváth, Ernő AU - Tóth, Roland AU - Nyilas, K TI - Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles JF - Automotive Innovation J2 - Automot. Innov. VL - 7 PY - 2024 SP - 59 EP - 70 PG - 12 SN - 2096-4250 DO - 10.1007/s42154-023-00259-8 UR - https://m2.mtmt.hu/api/publication/34524770 ID - 34524770 N1 - Vehicle Research Center, Szechenyi Istvan University, Egyetem ter 1, Gyor, 9026, Hungary Institute for Computer Science and Control, Kende str. 13-17., Budapest, 1111, Hungary Robert Bosch Kft, Gyomroi str. 104-120, Budapest, 1103, Hungary Export Date: 5 February 2024 Correspondence Address: Igneczi, G.F.; Vehicle Research Center, Egyetem ter 1, Hungary; email: gergo.igneczi@ga.sze.hu AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - XU, DATAO AU - Zhou, Huiyu AU - QUAN, WENJING AU - Jiang, Xinyan AU - Liang, Minjun AU - Li, Shudong AU - Ugbolue, Ukadike Chris AU - Baker, Julien S. AU - Fekete, Gusztáv AU - Ma, Xin AU - Chen, Li AU - Gu, Yaodong TI - A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis JF - GAIT & POSTURE J2 - GAIT POSTURE VL - 107 PY - 2024 SP - 293 EP - 305 PG - 13 SN - 0966-6362 DO - 10.1016/j.gaitpost.2023.10.019 UR - https://m2.mtmt.hu/api/publication/34231272 ID - 34231272 LA - English DB - MTMT ER -