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Die Erfindung betrifft einen elektromagnetischen Aktuator zur Betätigung eines Stellgliedes (7) mit wenigstens einem gesteuert bestrombaren Elektromagneten (1, 2) und einem mit dem Stellglied (7) in Wirkverbindung stehenden Anker (5), der bei Bestromung des Elektromagneten (1, 2) gegen die Kraft wenigstens einer an einem Gehäuse (12) abgestützten Rückstellfeder (10) an der Polfläche (3, 4) des Elektromagneten (1, 2) zur Anlage kommt, und daß zumindest der Anker (5) über eine sphärische Gelenkanordnung (11) auf der Rückstellfeder (10) abgestützt ist.
Up in the clouds and above fuels and construction materials must be very carefully selected to ensure a smooth flight and touchdown. Out of around 38,000 single and dual-engined propeller aeroplanes, roughly a third are affected by a new trend in the fuel sector that may lead to operating troubles or even emergency landings: The admixture of bio-ethanol to conventional gasoline. Experiences with these fuels may be projected to alternative mixtures containing new components.
The eVTOL industry is a rapidly growing mass market expected to start in 2024. eVTOL compete, caused by their predicted missions, with ground-based transportation modes, including mainly passenger cars. Therefore, the automotive and classical aircraft design process is reviewed and compared to highlight advantages for eVTOL development. A special focus is on ergonomic comfort and safety. The need for further investigation of eVTOL’s crashworthiness is outlined by, first, specifying the relevance of passive safety via accident statistics and customer perception analysis; second, comparing the current state of regulation and certification; and third, discussing the advantages of integral safety and applying the automotive safety approach for eVTOL development. Integral safety links active and passive safety, while the automotive safety approach means implementing standardized mandatory full-vehicle crash tests for future eVTOL. Subsequently, possible crash impact conditions are analyzed, and three full-vehicle crash load cases are presented.
Die IMechE Railway Challenge wird jährlich in Stapleford, Großbritannien ausgetragen. Im Rahmen der Challenge entwickeln und bauen Studierende eine Lokomotive und vergleichen sich in verschiedenen Disziplinen, darunter eine automatisierte Zielbremsung, optimale Energierückgewinnung beim Bremsen und minimale Geräuschemissionen. Neben diesen und weiteren technischen Wettbewerbsdisziplinen treten die Fahrzeuge und die Teams auch in nicht-technischen Disziplinen wie einer Business Case Challenge an.
In general aviation, too, it is desirable to be able to operate existing internal combustion engines with fuels that produce less CO₂ than Avgas 100LL being widely used today It can be assumed that, in comparison, the fuels CNG, LPG or LNG, which are gaseous under normal conditions, produce significantly lower emissions. Necessary propulsion system adaptations were investigated as part of a research project at Aachen University of Applied Sciences.
Auch in der allgemeinen Luftfahrt wäre es wünschenswert, die bereits vorhandenen Verbrennungsmotoren mit weniger CO₂-trächtigen Kraftstoffen als dem heute weit verbreiteten Avgas 100LL betreiben zu können. Es ist anzunehmen, dass im Vergleich die unter Normalbedingungen gasförmigen Kraftstoffe CNG, LPG oder LNG deutlich weniger Emissionen produzieren. Erforderliche Antriebssystemanpassungen wurden im Rahmen eines Forschungsprojekts an der FH Aachen untersucht.
This paper addresses the pixel based recognition of 3D objects with bidirectional associative memories. Computational power and memory requirements for this approach are identified and compared to the performance of current computer architectures by benchmarking different processors. It is shown, that the performance of special purpose hardware, like neurocomputers, is between one and two orders of magnitude higher than the performance of mainstream hardware. On the other hand, the calculation of small neural networks is performed more efficiently on mainstream processors. Based on these results a novel concept is developed, which is tailored for the efficient calculation of bidirectional associative memories. The computational efficiency is further enhanced by the application of algorithms and storage techniques which are matched to characteristics of the application at hand.
This paper addresses the pixel based classification of three dimensional objects from arbitrary views. To perform this task a coding strategy, inspired by the biological model of human vision, for pixel data is described. The coding strategy ensures that the input data is invariant against shift, scale and rotation of the object in the input domain. The image data is used as input to a class of self organizing neural networks, the Kohonen-maps or self-organizing feature maps (SOFM). To verify this approach two test sets have been generated: the first set, consisting of artificially generated images, is used to examine the classification properties of the SOFMs; the second test set examines the clustering capabilities of the SOFM when real world image data is applied to the network after it has been preprocessed to be invariant against shift, scale and rotation. It is shown that the clustering capability of the SOFM is strongly dependant on the invariance coding of the images.
This paper describes the realization of a novel neurocomputer which is based on the concepts of a coprocessor. In contrast to existing neurocomputers the main interest was the realization of a scalable, flexible system, which is capable of computing neural networks of arbitrary topology and scale, with full independence of special hardware from the software's point of view. On the other hand, computational power should be added, whenever needed and flexibly adapted to the requirements of the application. Hardware independence is achieved by a run time system which is capable of using all available computing power, including multiple host CPUs and an arbitrary number of neural coprocessors autonomously. The realization of arbitrary neural topologies is provided through the implementation of the elementary operations which can be found in most neural topologies.
Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.
Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.
In der Vergangenheit basierten große Systemintegrationsprojekte in der Regel auf Individualentwicklungen für einzelne Kunden. Getrieben durch Kostendruck steigt aber der Bedarf nach standardisierten Lösungen, die gleichzeitig die individuellen Anforderungen des jeweiligen Umfelds berücksichtigen. T-Systems GEI GmbH wird beiden Anforderungen mit Produktkerneln gerecht. Neben den technischen Aspekten der Kernelentwicklung spielen besonders organisatorische Aspekte eine Rolle, um Kernel effizient und qualitativ hochwertig zu entwickeln, ohne deren Funktionalitäten ins Uferlose wachsen zu lassen. Umgesetzt hat T-Systems dieses Konzept für Flughafeninformationssysteme. Damit kann dem wachsenden Bedarf der Flughafenbetreiber nach einer effizienten und kostengünstigen Softwarelösung zur Unterstützung Ihrer Geschäftsprozesse entsprochen werden.
Der Erfolg eines Softwarenentwicklungsprojektes insbesondere eines Systemintegrationsprojektes wird mit der Erfüllung des „Teufelsdreiecks“, „In-Time“, „In-Budget“, „In-Quality“ gemessen. Hierzu ist die Kenntnis der Software- und Prozessqualität essenziell, um die Einhaltung der Qualitätskriterien festzustellen, aber auch, um eine Vorhersage hinsichtlich Termin- und Budgettreue zu treffen. Zu diesem Zweck wurde in der T-Systems Systems Integration ein System aus verschiedenen Key Performance Indikatoren entworfen und in der Organisation implementiert, das genau das leistet und die Kriterien für CMMI Level 3 erfüllt.
In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway.
Existing residential buildings have an average lifetime of 100 years. Many of these buildings will exist for at least another 50 years. To increase the efficiency of these buildings while keeping costs at reasonable rates, they can be retrofitted with sensors that deliver information to central control units for heating, ventilation and electricity. This retrofitting process should happen with minimal intervention into existing infrastructure and requires new approaches for sensor design and data transmission. At FH Aachen University of Applied Sciences, students of different disciplines work together to learn how to design, build, deploy and operate such sensors. The presented teaching project already created a low power design for a combined CO2, temperature and humidity measurement device that can be easily integrated into most home automation systems
This work proposes a hybrid algorithm combining an Artificial Neural Network (ANN) with a conventional local path planner to navigate UAVs efficiently in various unknown urban environments. The proposed method of a Hybrid Artificial Neural Network Avoidance System is called HANNAS. The ANN analyses a video stream and classifies the current environment. This information about the current Environment is used to set several control parameters of a conventional local path planner, the 3DVFH*. The local path planner then plans the path toward a specific goal point based on distance data from a depth camera. We trained and tested a state-of-the-art image segmentation algorithm, PP-LiteSeg. The proposed HANNAS method reaches a failure probability of 17%, which is less than half the failure probability of the baseline and around half the failure probability of an improved, bio-inspired version of the 3DVFH*. The proposed HANNAS method does not show any disadvantages regarding flight time or flight distance.
Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers’ cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines.