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The development of protype applications with sensors and actuators in the automation industry requires tools that are independent of manufacturer, and are flexible enough to be modified or extended for any specific requirements. Currently, developing prototypes with industrial sensors and actuators is not straightforward. First of all, the exchange of information depends on the industrial protocol that these devices have. Second, a specific configuration and installation is done based on the hardware that is used, such as automation controllers or industrial gateways. This means that the development for a specific industrial protocol, highly depends on the hardware and the software that vendors provide. In this work we propose a rapid-prototyping framework based on Arduino to solve this problem. For this project we have focused to work with the IO-Link protocol. The framework consists of an Arduino shield that acts as the physical layer, and a software that implements the IO-Link Master protocol. The main advantage of such framework is that an application with industrial devices can be rapid-prototyped with ease as its vendor independent, open-source and can be ported easily to other Arduino compatible boards. In comparison, a typical approach requires proprietary hardware, is not easy to port to another system and is closed-source.
One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.
A multi-sensor system is a chemical sensor system which quantitatively and qualitatively records gases with a combination of cross-sensitive gas sensor arrays and pattern recognition software. This paper addresses the issue of data analysis for identification of gases in a gas sensor array. We introduce a software tool for gas sensor array configuration and simulation. It concerns thereby about a modular software package for the acquisition of data of different sensors. A signal evaluation algorithm referred to as matrix method was used specifically for the software tool. This matrix method computes the gas concentrations from the signals of a sensor array. The software tool was used for the simulation of an array of five sensors to determine gas concentration of CH4, NH3, H2, CO and C2H5OH. The results of the present simulated sensor array indicate that the software tool is capable of the following: (a) identify a gas independently of its concentration; (b) estimate the concentration of the gas, even if the system was not previously exposed to this concentration; (c) tell when a gas concentration exceeds a certain value. A gas sensor data base was build for the configuration of the software. With the data base one can create, generate and manage scenarios and source files for the simulation. With the gas sensor data base and the simulation software an on-line Web-based version was developed, with which the user can configure and simulate sensor arrays on-line.
Visionsbild eines BMW AG internen Tools : Optimierung einer datenzentrierten Plattform der BMW AG
(2022)
Die BMW AG verfolgt mehrere Ansätze eine optimierte Effizienzsteigerung durch eigens entwickelte Softwaretools zu erreichen. Der Fokus hierbei liegt in der Ermöglichung einer datenzentrierten, nutzerorientierten und hochautomatisierten Fahrzeugentwicklung. Das eigen von der BMW AG entwickelte Software-Tool „Parts List“ dient als digitale Bauteilliste für Entwicklungsteams. Das Tool bündelt mehrere Datenbanken und stellt diese in Echtzeit zur Verfügung. Somit kann der manuelle Pflegeaufwand erheblich reduziert und eine Durchgängigkeit einer Vielzahl von Daten ermöglicht werden. Der Fokus der Abschlussarbeit liegt in der konzeptionellen und gestalterischen Optimierung des Tools. Dabei wird insbesondere der User Centered Design Prozess betrachtet, bei dem die Nutzer bzw. die Entwicklungsteams im Zentrum stehen.
Ein hundert Seiten starkes Protestdokument, mit humorvollen, kritischen und zornigen gestalterischen Beiträgen Studierender des SS 05 (Julia Balkwitz, Kathrin Hartmann, Sandra Janiec, Thilo Klüppel, Judith Koch, Julia Kuhrmeyer, Kristina Mickartz, Anna Neumann, Melanie Neumann, Melina Oberscheven, Veronika Ripplinger, Silke Stendenbach und Marcus Wacker) / Seminar Prof. Casse-Schlüter zum Thema Studienreform – Bachelor / Master – und zur momentanen Besetzungssperre von Professorenstellen im FB Design.