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OPEN FAU

Online publication system of Friedrich-Alexander-Universität Erlangen-Nürnberg

The online publication system OPEN FAU is the central publication platform for Open Access publishing for all members of Friedrich-Alexander-Universität. Qualified works from research and teaching may be published here free of charge, either as a primary or secondary publication. The full texts are permanently available worldwide and are findable and citable via catalogues and search engines.


To search for documents in OPEN FAU, please select "Search" (via the magnifying glass at the top right); this will provide you with various search options. If you want to publish a document, go to "Login" and "My Publications". Then drag you document into the field provided and enter the metadata. In just a few steps, you can submit your document. Please note our guidelines, the publication contract and FAQs.

 

Recent Submissions

Doctoral thesis
Open Access
Smart Manufacturing System for Process Optimization Regarding Deviations among Material Batches
FAU Studien aus dem Maschinenbau : 432, (FAU University Press, 2024) Lutz, Benjamin; Franke, Jörg; Franke, Jörg; Hanenkamp, Nico; Hausotte, Tino; Merklein, Marion; Müller, Sebastian; Schmidt, Michael; Wartzack, Sandro
With the recent advances in digital technologies, the subtractive manufacturing industry is striving for smart machine tools, capable of data-driven self-optimization. As a building block, this work proposes an approach for incorporating awareness regarding the material and its batch-specific characteristics for process optimization. The proposed smart manufacturing system utilizes cutting tool images for an initial condition assessment. Methods are proposed for the semantic segmentation of the defect classes encountered in tool condition monitoring, enabling a detailed analysis regarding their presence, location, and size. Furthermore, novel methods are proposed that support the image annotation process and the adaptation of existing training data to new scenes. During machining, internal control data is used for material batch identification. The high-frequency control data is preprocessed, error-compensated, and aggregated into features. Using a novelty detection algorithm, unknown batches are identified. Subsequently, a classification algorithm is used to classify known batches, whereas a clustering approach is used to analyze unknown batches. In a final step, historic process knowledge is used to compute optimized cutting parameters, thus enabling batch-adaptive machining. Furthermore, operational routines are proposed for the automated incorporation of material batches with novel behavior, continuous model improvement, and efficient adaptation to new machining scenarios.
Article
Open Access
Rolf Krafft Ligniez: Bild der Nacht
(Verlag der Starnberger Hefte, 2024-04-01) Schöntag, Roger
Rolf Krafft Ligniez (1916-1944), gebürtig aus Frankfurt a.M., gefallen an der Westfront (in Floverich bei Aachen), studierte Germanistik, Theater-Wissenschaft, Kunstgeschichte und Philosophie in Frankfurt sowie für ein Gastsemester in München. Ligniez selbst gehörte in seinen Studienjahren in Frankfurt zum Dichterkreis um Max Kommerell (1902-1944), der in seiner Jugend Sekretär (1924-1928) Stefan Georges (1868-1933) und Teil dessen Dichterkreises war, mit dem er aber 1930 brach. Die Gedichte Ligniez' lassen sich in dieser schwierigen Zeit am ehesten als eskapistisch einordnen, mit einer teilweisen Anknüpfung an eine späte Neo-Romantik.
Book
Open Access
Eine Stadttour durch Hamburg im Jahr 1686. Die App Hidden Hamburg als erlebbare Geschichte und Digital-Public-History-Experiment (2. Auflage)
(edition lumière, 2024-05-02) Bellingradt, Daniel; Heise, Claudia
Mit der App Hidden Hamburg liegt ein kostenloses und zweisprachiges Angebot zu einer Stadttour im Hamburg des Jahres 1686 vor. Die App ist ein digitales Experiment, das historisches story telling als „erlebbare Geschichte“ mal anders akzentuiert: bequem am Computer-Bildschirm daheim oder als echte Stadttour durch Hamburg mit GPS-Tracking und einem Smartphone. In diesem Begleitbuch, das mit didaktischen Hinweisen, vertiefenden Themeninseln und stadthistorischen Einordnungen für den Einsatz in Klassenzimmern und Seminarräumen ebenso geeignet ist wie für den Museumsbesuch, werden die Ideen und Kontexte der vorbereiteten Stadttour beleuchtet. Rund um den virtuellen Stadtführer Johann, der in der Nachrichtenhochburg Hamburg im Jahr 1686 lebt, bietet dieses reich bebilderte Begleitbuch neue Einblicke in das frühneuzeitliche Hamburg als eine „Stadt der Neuigkeiten“, bekannt für ihre vielen Druckereien, stete Publizistik-Herstellung und Fülle an medialen Echos. Dieses Buch führt zu Buchhandlungen in Kirchen, zu Zeitungsständen auf Marktplätzen, in ein Kaffeehaus, zum Opernhaus, und bietet in Kombination mit App und Webseite einen alternativen Einstieg in Mediengeschichte, Stadtgeschichte und Pressegeschichte. Viele integrierte Museumsobjekte aus Hamburg, u.a. aus dem Museum für Hamburgische Geschichte, vermitteln einen lebendigen Eindruck von Hamburg vor rund 350 Jahren.
Doctoral thesis
Open Access
Ultraschallbasiertes Sensorprinzip für die eingriffsfreie Messung des hydrostatischen Drucks
FAU Studien aus der Elektrotechnik : 24, (FAU University Press, 2024) Ponschab, Michael; Rupitsch, Stefan J.
In dieser Arbeit wird ein Sensorprinzip zur Messung der statischen mechanischen Spannung in der Rohrwand und damit des hydrostatischen Drucks basierend auf geführten elastischen Wellen entwickelt. Dabei wird auf die Verwendbarkeit von parasitär in der Rohrwand laufender Signalanteile in neuen Durchflusssensoren abgezielt, die auf der gezielten Anregung geführter elastischer Wellen in der Rohrwand basieren. Idealerweise ist so der Einbau weiterer Sensorik verzichtbar. Zur Modellierung des Messprinzips wurde eine effiziente Methode zur Lösung des linearen Randwertproblems der geführten Wellenausbreitung implementiert und der Einfluss der statischen mechanischen Spannung basierend auf dem akustoelastischen Effekt integriert. Ein weiterer Schwerpunkt der Arbeit ist die messtechnische Verifikation der implementierten Modelle und der empirische Nachweis der Verwendbarkeit des Effekts zur Messung des hydrostatischen Drucks. Sowohl Sende-Empfänger-Messungen, wie sie das entwickelte Sensorprinzip vorsieht, als auch Mehrkanalmessungen an verteilten Empfangsorten mittels eines Laser-Doppler-Vibrometers wurden durchgeführt. Die Genauigkeit der Modelle konnte durch eine eigens entwickelte inverse Materialcharakterisierungsmethode verbessert werden. Es wurde außerdem ein neuer Ansatz zur inversen Charakterisierung der Elastizitätskonstanten dritter Ordnung entwickelt. Das neue Sensorprinzip konnte anhand eines Versuchsstands demonstriert werden.
Doctoral thesis
Open Access
Design Space Exploration for Analog Spiking Neural Networks
(2024) Elmasry, Moamen; Weigel, Robert
The human brain will always remain one of the greatest creations of all time. The development of brain-inspired neuromorphic computing has become mainstream nowadays due to its high parallelism and energy efficiency. Neuromorphic computing is an emerging field that aims to mimic the functionality of the human brain using electronic circuits. One of the most important aspects of this field is the implementation of neural models and the evaluation of their performance. Developing and deploying neuromorphic computing systems is an exceptionally challenging undertaking, requiring the collaboration of researchers from a variety of disciplines. Collaboration is necessary to bring together the development of data processing techniques, computational structures and the fundamental technologies on which these systems are based. The development of neuromorphic computing systems requires the simultaneous processing of large amounts of data, adaptation to dynamic environmental changes and replication of the complex functionality of the human brain. Artificial intelligence plays a role both in large data centers and in end devices such as smartphones, drones and networked household appliances. Data processing directly on these devices is called edge computing. Neuromorphic computing systems for edge computing offer promising prospects. Using these systems, data can be processed and analyzed in real time at the source, rather than in centralized data centers, thereby increasing responsiveness and reducing latencies. Furthermore, local computation satisfies the security requirements of potentially sensitive data by eliminating the need to communicate data over wireless networks to central computing systems. Additionally, neuromorphic systems are well-suited for a range of cognitive applications due to their intrinsic learning capabilities and ability to process complex data. Their versatility and efficiency can significantly advance areas such as pattern recognition, natural language processing and robotics. Moreover, neuromorphic systems are an attractive choice for energy-efficient computing applications, which are increasingly important in today’s world of sustainable technologies, due to their ability to achieve significant energy savings compared to traditional processors (Christensen et al., 2022). The efficient and effective execution of AI on conventional hardware is hindered by the so-called von Neumann architecture. This is characterized by the fact that a processor is responsible for calculating the data and a memory for storing the results. Both are connected by a data bus, which leads to the so-called "memory wall" during the execution of artificial neural networks. In this case, data transfer to and from external memory devices becomes a major obstacle to energy efficiency. In addition, current hardware accelerators struggle with performance and power consumption issues. To address these challenges, non-volatile memory devices such as ReRAM, CBRAM, PCM, MRAM and FeFET offer promising possibilities, as they can partially handle the computation directly in memory, thus helping to reduce extensive data movement. In addition, optimization of device parameters such as memory capacity, lifetime, cycle stability, variation, and speed is crucial for an effective use in neuromorphic applications. Material innovations to enhance analog switching capabilities and three-dimensional component architectures are also being investigated to advance the design of neuromorphic hardware. In this work, a comprehensive design for a spike-based neural network platform has been presented to integrate a full ReRAM (Resistive Random Access Memory) IP implementation into a dynamic Spiking Neural Network architecture. A fully integrated System-on-Module (SoM) has been implemented, enabling a comprehensive exploration of the design space for neurosynaptic behavior in Spiking Neural Networks. This platform includes several state-of the-art components, including a ReRAM array, buffers, word line/bit line (WL/BL) drive and a digital interface, all working in synergy with replaceable non-volatile memory (NVM) modules. These components are designed to accommodate different types of spike-based modules. The interconnectivity of the platform has been a key driver for the introduction of the latest generation NVM to keep pace with ReRAM technology available in the market. This platform provides a comprehensive evaluation of ReRAM technology compared to other non-volatile memory (NVM) technologies. The integration not only increases versatility, but also expands the platform’s capabilities and applications. One of the key innovations in our platform is the emergence of a hybrid Memristor CMOS multi IP architecture. Within this framework, a ReRAM IP module coexists harmoniously with a neural network composed of spiking components. A novel weighted synaptic structure that combines weight detection and synaptic current provisioning is introduced as an interface structure between the NVM macro and the evolved SNN components. This innovative architecture provides practical solutions to real-world problems while providing a research platform for computational neuroscience. Our fully integrated system-on-module, realized in 28 nm CMOS technology, enables enhanced evaluation of the spatio-temporal properties of Spiking Neural Networks (SNNs). This platform provides a reconfigurable and interchangeable environment for modular testing of spike-based components. Integrated non-volatile memory in the form of a 128x128 ReRAM cell array enables storage of synaptic weights, while fully traceable signals ensure reliable data acquisition and analysis. The integration of a considerable number of I/O pins improves the testability of the system. Furthermore, the integrated neurons show competitive performance in terms of parameter sets, reconfigurability and energy efficiency, with activation requiring only about 0.2 pJ per spike.