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<span title="2020-07-06">2020</span> <i title="University of Pannonia"> <a target="_blank" rel="noopener" href="" style="color: black;">Hungarian Journal of Industry and Chemistry</a> </i> &nbsp;
At Széchenyi István University, an autonomous racing car for the Shell Eco-marathon is being developed. One of the main tasks is to create a neural network which segments the road surface, protective barriers and other components of the racing track. The difficulty with this task is that no suitable dataset for special objects, e.g. protective barriers, exists. Only a dataset limited in terms of its size is available, therefore, computer-generated virtual images from a virtual city environment
more &raquo; ... re used to expand this dataset. In this work, the effect of computer-generated virtual images on the efficiency of different neural network architectures is examined. In the training process, real images and computer-generated virtual images are mixed in several ways. Subsequently, three different neural network architectures for road surfaces and the detection of protective barriers are trained. Past experiences determine how to mix datasets and how they can improve efficiency. Autonomous and self-driving technology is a rapidly emerging field among automotive-related companies and academic research institutes. The main challenges include sensory perception, prediction, trajectory planning and trajectory execution. The current paper introduces a design strategy and the mathematical background of the optimization problem with regard to the multiple goal pure pursuit algorithm. The aim of the algorithm is to provide a low degree of computational complexity and, therefore, a fast trajectory-tracking approach. Finally, in terms of our approach, not only the theoretical questions but the application challenges will be described as well. This paper presents the development process of a high-power modular inverter system. The goal was to develop a universal inverter system (motor controller + power electronics) in which the performance of the power stage is scalable. The design concepts, hardware architecture, system components, built-in features and protections are described regarding the power stage. As autonomous technologies flourish within the vehicle industry, an increasing number of academic autonomous competitions are appearing. One of them is the Shell Eco-marathon Autonomous Urban Concept competition (SEM AUC) which seeks to provide hands-on experience for the academic community to design, build and test their own driverless vehicles within a realistic infrastructure. The team at our university participates in this competition and our concept is to rely on simple and robust algorithms. This paper presents a simple collision-free space estimation algorithm for the LiDAR sensor. The aim of this paper is to present an application of the generalized Warburg element and Constant Phase Element (CPE) for non-Fickian diffusion modeling. These distributed elements are intended to provide a better fit of low-frequency impedance data than the standard finite-length Warburg element in the case of most batteries. In addition, the current study demonstrates the ambiguity of the finite-length Warburg element if impedance data is insufficient within the verylow-frequency impedance spectrum. In order to select the appropriate Randles circuit for non-Fickian diffusion modeling, several configurations have been investigated. Based on the best fit of impedance data, the State-of-Charge (SoC) dependency of the Randles circuit parameters has also been analyzed. This study concerns a Samsung ICR18650-26F 2600 mAh battery cell which was subjected to Electrochemical Impedance Spectroscopy (EIS) measurements between 10 mHz and 100 kHz as a function of SoC. The results were plotted and compared in the form of Nyquist plots. The Randles circuit parameters such as the resistances Rs and Rct, double-layer C dl , leaky capacitance CPE and Warburg coefficients were estimated using ZView software. The present paper shows that CPE -and its QPE form -is a recommended choice to yield the best fit in terms of non-Fickian diffusion impedance. In addition, using CPE is a better alternative to avoid problems with initial values and multiple local solutions, which may exist in the case of the Warburg element. The resultant Randles circuit parameters and their SoC characteristics can be effectively used in further electrochemical modeling. Abstract -Since the energy consumption of electric drivetrains can be optimized in an automatically controlled system, the driving ranges and efficiency of driverless electric cars can be enhanced. The analysis of a model of the electric power conversion system provides the opportunity to consider different driving circumstances, moreover, it is possible to evaluate the performance of a power conversion system when the vehicle is driven along different routes. The results provide detailed information on the transient operation of all the power modules as well as their components, and on the overall performance of the power conversion system. In this study, a permanent-magnet synchronous machine (PMSM) acts as the traction motor in the autonomously driven electric car. Besides the PMSM, the power electronics and battery are also modeled in an OrCAD PSpice circuit-simulation environment that serves as a model of an electric power conversion system for the simulation and testing of a battery management system algorithm. The battery management system and control algorithm are modeled in Simulink and can be tested together with the PSpice-modeled circuits utilizing an interconnected simulation environment. The input of the power conversion system model was a driving scenario that included uphill and downhill sections. The performance of the implemented battery management system algorithm was analyzed and evaluated. The external magnetic field required to activate a magnetic fluid in an industrial application is sufficiently large that magnetization is no longer a linear function of the external field strength, i.e. magnetic fluids exhibit nonlinear characteristics. The aim of our research was to develop a measuring system which is capable of determining the nonlinear AC susceptibility of magnetic fluids at discrete frequencies and in the presence of a high-intensity driving magnetic field. The measurement of susceptibility is based on the determination of the change in frequency of a low-intensity field, which is generated by an LC oscillator. The application of sinusoidal excitation to the material results in a variation in the susceptibility that modulates the frequency of the measured low-intensity field and in the appearance of higher-order harmonics of the driving field. The higher-order components of the nonlinear AC susceptibility are extracted from the measured response by Fourier analysis. By applying the measuring system, the nonlinear susceptibility of water-based ferrofluids (Ferrotec's EMG 700) and its dependence on the magnetic field strength were investigated. A low-cost vibration generator device based on industrial vibrators was designed. The control software was implemented in LabVIEW Environment. The device is able to generate an oscillating force of 8 kN and an amplitude of up to 4 mm at a frequency of 50 Hz to model low-amplitude, high-frequency vehicle vibrations. A National Instruments myRIO device was responsible for data acquisition, with which a signal of a piezoelectric accelerometer was detected. The test results show that the device is able to generate a sinusoidal harmonic acceleration. This article describes the design of a vibration data acquisition system which can be mounted on the undercarriage of a vehicle to acquire information about the quality of and defects in road surfaces. It is important to be able to deduce the condition of a road section from its data. For practical reasons, a microcontroller-based control unit was used and a separate power supply created. Bumps in the road were detected by a piezoelectric accelerometer. Once the system was completed, different measurements were made and the results analyzed. According to the results, it can be stated that the whole system worked well since they are identical to reality. The bumps in the road were clearly visible on the diagrams. It was concluded that the completed vibration data acquisition system is more than capable of detecting bumps in roads. The advantage of the system is that it can be easily mounted on any car which does not need to be driven at low speeds. In this paper, the torque transmission time-constant of a simple disk-type magnetorheological (MR) clutch is investigated. By using MR fluid, controlled torque transmission can be easily implemented, facilitating the widespread use of similar systems. In order to describe the dynamical properties of the system, time constants were measured at different speeds and magnetic inductions. The time constants were derived by fitting an exponential function to the data. The main aim of this paper is to create an energy harvesting system, which can convert vibrational energy into electrical energy efficiently. Our research was carried out in the field of electromagnetic energy conversion using the principles of linear generator construction for both low and high frequency vibrations. Energy can be recovered efficiently. During the measurements, how the induced voltage is dependent on the impulsive frequency and the amplitude of impulses was investigated. In this paper, different factors of fuel consumption are examined. Driveload equitation is used as a basis and the parts that handle energy consumption in particular are analyzed. For the purposes of visibility, it was implemented using MAT-LAB. In statistical works, fuel consumption data require that the energy consumption of vehicles be analyzed correctly. Variables which affect fuel consumption during a given drive are defined. Research is analyzed in the second part of the paper where vehicle diagnostics are combined with global positioning. Examinations are necessary to create on-board diagnostics-based positioning. Márk u. 18/A, Zalaegerszeg, 8900, HUNGARY Brownian dynamics (BD) simulations based on a novel Langevin integrator algorithm are used to simulate the dynamics of chain formation in electrorheological (ER) fluids that are non-conducting solid particles suspended in a liquid that has a dielectric constant different from that of the ER particles. An external electric field induces polarization charge distributions on the spheres' surfaces that can be modeled as point dipoles in the centers of the spheres. The interaction of these aligned dipoles leads to formation of chains and other aggregates in the ER fluid. In this work, we introduce our methodology and report results for various quantities characterizing the structure of the ER system as obtained with BD simulations. These quantities include the potential energy, diffusion constant, average chain length, chain length distributions, and pair correlation functions. Their behavior as a function of time is presented as the electric field is switched on. The properties of the ER fluid change considerably making this system a potential basic material of many applications. Advanced driver assistance systems and autonomous vehicles rely heavily on position information, therefore, enhancing localization algorithms is an actively researched field. Novel algorithms fuse the signals of common vehicle sensors, the inertial measurement unit and global positioning system. This paper presents a localization algorithm for vehicle position estimation that integrates vehicle sensors (steering angle encoder, wheel speed sensors and a yaw-rate sensor) and GPS signals. The estimation algorithm uses an extended Kalman filter designed for a simplified version of the single track model. The vehicle dynamics-based model only includes calculation of the lateral force and planar motion of the vehicle resulting in the minimal state-space model and filter algorithm. A TESIS veDYNA vehicle dynamics and MathWorks Simulink-based simulation environment was used in the development and validation process. The presented results include different low-and high-speed maneuvers as well as filter estimates of the position and heading of the vehicle. A lot of highly detailed geospatial information (obtained by mobile mapping and spatial data processing) is available that can be used to describe the exact road parameters for simulation and modeling. A gap between the freely available geospatial information and the descriptive standards of the road is present that is used in driving/traffic simulation as well as the systems of test vehicles. The toolset of GIS (Geographic Information Systems) provides wide-ranging functionality for spatial data processing, but is yet to offer support for standard formats of road description (OpenDRIVE data). This paper describes a method for gathering and converting road-network information from OpenStreetMap to OpenDRIVE data format. Using a similar conversion tool, the scenario generation and synthesis of realistic road networks for driving simulator applications can be more convenient and faster.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.33927/931</a> <a target="_blank" rel="external noopener" href="">fatcat:pgjn46w74vb5djx3koev7lnsty</a> </span>
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