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Frequenzumrichter speisen Antriebe mit nichtsinusförmigen Spannungen und Strömen. Diese dürfen den sicheren Betrieb von Anlagen nicht stören. Der Einfluss von Energieleitungen zwischen Frequenzumrichter und Motor auf parallelverlaufende Signalleitungen wurde untersucht. Das Ergebnis bestätigt die Empfehlung, in der Prozesstechnik unter Einhaltung bestimmter Vorgaben keine geschirmten Kabel einzusetzen.
Camera-based object detection is widely used in safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous vehicle research. Road infrastructure has been designed for human vision, so computer vision, with RGB cameras, is a vital source of semantic information from the environment. Sensors, such as LIDAR and RADAR, are also often utilized for these applications; however, cameras provide a higher spatial resolution and color information. The spatial frequency response (SFR), or sharpness of a camera, utilized in object detection systems must be sufficient to allow a detection algorithm to localize objects in the environment over its lifetime reliably. This study explores the relationship between object detection performance and SFR. Six state-of-the-art object detection models are evaluated with varying levels of lens defocus. A novel raw image dataset is created and utilized, containing pedestrians and cars over a range of distances up to 100-m from the sensor. Object detection performance for each defocused dataset is analyzed over a range of distances to determine the minimum SFR necessary in each case. Results show that the relationship between object detection performance and lens blur is much more complex than previous studies have found due to lens field curvature, chromatic aberration, and astigmatisms. We have found that smaller objects are disproportionately impacted by lens blur, and different object detection models have differing levels of robustness to lens blur
The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also, the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper, we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well-known MNIST data set based an handwritten digits.
With the increasing capabilities of machine learning systems and their potential use in safety-critical systems, ensuring high-quality data is becoming increasingly important. In this paper, we present a novel approach for the assurance of data quality. For this purpose, the mathematical basics are first discussed and the approach is presented using multiple examples. This results in the detection of data points with potentially harmful properties for the use in safety-critical systems.
Beschrieben wird eine optoelektronische Sensoreinrichtung für ein Kraftfahrzeug zur unterscheidenden Detektion von Feuchtigkeit und Salzlösungen auf einer Fahrzeugscheibe, mit einer Beleuchtungseinheit zur Einstrahlung von Licht in eine Fahrzeugscheibe und mit einem Lichtempfänger, welcher von der Fahrzeugscheibe reflektiertes Licht erfasst, sowie mit einem Koppelelement zur Ein- und Auskopplung von Licht in bzw. aus der Fahrzeugscheibe, wobei die Beleuchtungseinrichtung Licht über einen Einfallswinkelbereich in die Fahrzeugscheibe einstrahlt, der sich über mehrere Winkelgrade erstreckt, wobei der Lichtempfänger eine Vielzahl von Empfangselementen aufweist, wobei ein optisches System das von der Fahrzeugscheibe in einen Ausfallswinkelbereich reflektierte Licht auf Empfangselemente des Lichtempfängers abbildet und wobei der Lichtempfänger ein Signal generiert, aus dem eine Auswertevorrichtung den Grenzwinkel der Totalreflexion an der Fahrzeugscheibe ermittelt.
Spatial precision and recall indices to assess the performance of instance segmentation algorithms
(2022)
The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well known MNIST data set based an handwritten digits.
