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- LiDAR sensor; rain; fog; sunlight; advanced driver-assistance system; backscattering; Mie theory; open simulation interface; functional mock-up interface; functional mock-up unit (1)
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Die professionelle Entwicklung elektronischer Schaltungen ist heute ohne computer-gestützte Simulationen nicht mehr denkbar. Das Simulationsprogramm PSpice setzt hierbei den Industriestandard und wird von einem großen Anwenderbereich genutzt.Das Lehrbuch besteht aus überschaubaren, in sich abgeschlossenen Abschnitten mitsamt Beispielschaltungen, die typische Anwendungen aus beruflicher Ausbildung, Studium und industrieller Praxis zeigen. Der erste Teil des Buches vermittelt Grundlagen und richtet sich an Einsteiger:innen. In diesem Teil wird durch viele Schritt-für-Schritt-Aktionen die Vorgehensweise transparent und einfach nachvollziehbar. Im zweiten Teil werden Anwendungen in der analogen und digitalen Schaltungstechnik, Leistungselektronik und Regelungstechnik simuliert und die erweiterten Analyseoptionen von PSpice angewendet. Auf Zuverlässigkeitsanalysen wie Monte-Carlo- und Worst-Case-Verfahren sowie die elektrische Stressanalyse (Smoke Analysis) wird im Detail eingegangen.
Piezomaterialien finden in der Mikrosystemtechnik ein breites Anwendungsspektrum. Die Machbarkeitsstudie untersucht die Möglichkeiten Piezoplatten in den Herstellungsprozess von modernen Leiterplatten-Multilayer zu integrieren. Die glasfaserverstärkte Epoxidstruktur des PCBs wirkt zum einen als Träger für die spröde Piezokeramik. Des Weiteren kann durch die internen Leiterbahnen eine Kontaktierung der Keramik realisiert werden. Im Vorfeld wurden der Lagenaufbau sowie geometrischen Voraussetzungen für eine Verbindung mittels Lotpaste experimentell überprüft. Als Anwendungsbeispiel wird ein Vibrations-Harvesters aufgebaut und getestet. Die maximale Leistung des Harvesters liegt bei 7,86 mW mit einer Eingangsbeschleunigung von 4g. Der positive Funktionstest demonstriert die grundsätzliche Anwendbarkeit des Ansatzes, auch wenn die elektrische Kontaktierung mittels Lotpaste kritisch zu bewerten ist.
Die Entwicklung, Erprobung und Validierung von Fahrerassistenzsystemen und automatisierten Fahrfunktionen ist im realen Fahrversuch aufgrund mangelnder Skalierbarkeit nur eingeschränkt möglich. IPG Automotive und die Hochschule Kempten beschreiben eine effziente Simulations-Toolchain, die eine nahtlose Integration und Austauschbarkeit verschiedener Sensormodelle und Systemkomponenten ermöglicht.
Developing, testing and validating advanced driver assistance systems and automated driving functions can only be realized to a limited extent in real-world test drives due to a lack of scalability. IPG Automotive and the Kempten University describe an efficient simulation toolchain that enables the seamless integration and exchange of different sensor models and system components.
Electronics Reliability Prediction by using Physics of Failures SHERLOCK Automated Design Analysis
(2018)
In this modern era as the electronics technology is progressing systems and products are becoming more complex. Rapid progress in electronics technology increases its complexity too. These rapid changes in electronics technology leads to new failure modes and standard reliability tools have to tackle with all these challenges. New technologies required the adapted approaches that should be cost effective in order to make sure product will be meet its desired reliability goals. Since so many years lot of approaches have been used in order to predict the reliability of Avionics and on ground electronics but physics of failure (POF) is one the most reliable approach for this purpose. This paper will provide insight to a process used to predict reliability of avionics electronics by using Sherlock ™ Automated Design.
Phase noise (PN) is one of the most significant impairments adversely affecting the detection performance of frequency-modulated continuous wave (FMCW) radar systems. Due to the rapid advance of advanced driver assistance systems (ADAS), virtual testing and the evaluation of highlyautomated driving (HAD) functions became indispensable. In this work, the impact of PN on the performance of automotive radar sensors is demonstrated on HAD functions in a virtual driving simulator. Therefore, a PN model initially developed for static objects is applied to dynamic scenarios including moving objects. By implementing a real world scenario in the virtual environment the influence of PN on the detection performance of the radar sensor is demonstrated. The virtual test scenario is implemented using the CarMaker test driving software, which is commonly accepted as an accurate and reliable tool by the automotive industry. The radar sensor model including PN is implemented as a functional mock-up unit (FMU) using the standardized functional mock-up interface (FMI) 2.0 and the open simulation interface (OSI) 3.0.0. Finally, the radar FMU model simulations are compared with hardware measurements.
Fullerene solar cells are becoming a feasible choice due to advanced developments in donor materials and improved fabrication techniques of devices. Recently, sufficient optimization and improvements in processing techniques like incorporation of solvent vapor annealing (SVA) with additives in solvents has become a major reason for prominent improvements in the performance of organic solar cell-based devices . On the other hand, the challenge of reduced open circuit voltage (Voc) remains. This study presents an approach for significant performance improvement of overall device based on organic small molecular solar cells (SMSCs) by following a two step technique that comprises thermal annealing (TA) and SVA (abbreviated as SVA+TA). In case of exclusive use of SVA, reduction in Voc can be eliminated in an effective way. The characteristics of charge carriers can be determined by the measurement of transient photo-voltage (TPV) and transient photo-current (TPC) that determines the scope for improvement in the performance of device by two step annealing. The recovery of reduced Voc is linked with the necessary change in the dynamics of charge that lead to increased overall performance of device. Moreover, SVA and TA complement each other; therefore, two step annealing technique is an appropriate way to simultaneously improve the parameters such as Voc, fill factor (FF), short circuit current density (Jsc) and PCE of small molecular solar cells.
In this paper, we derive intermediate frequency (IF) level analytical formulation of radio frequency (RF) group delay for automotive frequency-modulated continuous-wave (FMCW) radar waveform under quasi-static approximation. To the best of our knowledge, this paper is the first to develop and simulate an IF-level analytical form ulation of RF group delay, including random and deterministic variation for the FMCW radar waveform. Theoretical limitation for the tolerable RF group delay can be derived based on the proposed model. We demonstrated the impact of RF group delay on the FMCW radar sensor's range spectrum in dynamic virtual traffic scenarios. The proposed model is integrated into a virtual FMCW radar sensor model implemented as a functional mock-up unit (FMU) using the standardized interfaces functional mock-up interface (FMI) 2 .0 and the open simulation interface (OSI) 3. 0. 0. A virtual test scenario is implemented in an industry-standard simulation tool, CarMaker, to demonstrate the effect.
In this article, the optimization of the control circuit and path planning of a delta kinematic with the help of machine learning is presented. The described delta kinematic is primarily used for pick-and-place applications in the field of packaging machines. The optimization of the path planning procedure aims to make the workflow more efficent and flexible for commissioning the delta kinematic. By optimizing the control circuit using machine learning, mechanical oscillations and the deviation of the specified path are to be minimized. The possible use of a simulator for training, the prediction quality and the implementation on the robot controller are discussed. Furthermore, the path planning procedure was optimized. For this purpose, an environment was implemented in which a reinforcement learning agent plans the path of the robot between a starting point and a target point in a time-optimized manner, considering interference contours e.g. from the machine. The obtained results show the optimization of the robot by machine learning with a root mean squared error of the predicted torques of 0.06025 Nm in a prediction time of around 0.125 ms and the possibility of path planning with different criteria.
Many modern automated vehicle sensor systems use light detection and ranging (LiDAR) sensors. The prevailing technology is scanning LiDAR, where a collimated laser beam illuminates objects sequentially point-by-point to capture 3D range data. In current systems, the point clouds from the LiDAR sensors are mainly used for object detection. To estimate the velocity of an object of interest (OoI) in the point cloud, the tracking of the object or sensor data fusion is needed. Scanning LiDAR sensors show the motion distortion effect, which occurs when objects have a relative velocity to the sensor. Often, this effect is filtered, by using sensor data fusion, to use an undistorted point cloud for object detection. In this study, we developed a method using an artificial neural network to estimate an object’s velocity and direction of motion in the sensor’s field of view (FoV) based on the motion distortion effect without any sensor data fusion. This network was trained and evaluated with a synthetic dataset featuring the motion distortion effect. With the method presented in this paper, one can estimate the velocity and direction of an OoI that moves independently from the sensor from a single point cloud using only one single sensor. The method achieves a root mean squared error (RMSE) of 0.1187 m s−1 and a two-sigma confidence interval of [−0.0008 m s−1, 0.0017 m s−1] for the axis-wise estimation of an object’s relative velocity, and an RMSE of 0.0815 m s−1 and a two-sigma confidence interval of [0.0138 m s−1, 0.0170 m s−1] for the estimation of the resultant velocity. The extracted velocity information (4D-LiDAR) is available for motion prediction and object tracking and can lead to more reliable velocity data due to more redundancy for sensor data fusion.