Open Access

In dem Projekt BioMeth wurde der Ansatz der Membranbegasung zur Erhöhung der Verfügbarkeit von gelöstem Wasserstoff für die biologische Methanisierung im Sinn der Etablierung eines Power-to-Gas-Konzeptes zur Energiespeicherung verfolgt. Übergeordnetes Ziel war die Entwicklung eines skalierbaren Verfahrenskonzeptes, dass sich zur Nutzung CO2-haltiger Gasvolumenström eignet. Geplant war es, das Verfahren am Beispiel der Biogasanlage der Biokäserei Monte-Ziego in Teningen zu demonstrieren und dort das bestehende Konzept der parallelen Abwasseraufbereitung und Energieerzeugung zu erweitern. Die ursprüngliche Struktur des Arbeitspaketplanes ist in nachfolgender Abbildung gezeigt.

Dort, wo Modelle der operativen Energiesystemanalyse untereinander Überschneidungen aufweisen, stellt sich zunächst die Frage, ob sie bei gleichgearteten Fragestellungen auch die gleichen Antworten liefern. Dies zu beantworten war erstes Ziel des hier beschriebenen Vorhabens. Das zweite Ziel war, im Falle von Differenzen zu ermitteln, worin diese begründet liegen. Es waren nicht nur die Modelle selbst, sondern auch das methodische Vorgehen zur Modellerstellung und Simulation in Betracht zu ziehen. Die darauf aufbauende Identifikation von individuellen Optimierungspotenzialen war das dritte Ziel. Da die operative Energiesystemanalyse noch ein recht junger Forschungsbereich ist, existiert darüber hinaus Klärungsbedarf, welches Modell sich für welche Untersuchungen besonders eignet und welches methodische Vorgehen sich empfiehlt. Die Beantwortung dieser Fragen stellte das vierte Ziel des Vorhabens dar.

Convolutional neural networks (CNN) define the state-of-the-art solution on many perceptual tasks. However, current CNN approaches largely remain vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to the human eye. In recent years, various approaches have been proposed to defend CNNs against such attacks, for example by model hardening or by adding explicit defence mechanisms. Thereby, a small “detector” is included in the network and trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations. In this work, we propose a simple and light-weight detector, which leverages recent findings on the relation between networks’ local intrinsic dimensionality (LID) and adversarial attacks. Based on a re-interpretation of the LID measure and several simple adaptations, we surpass the state-of-the-art on adversarial detection by a significant margin and reach almost perfect results in terms of F1-score for several networks and datasets. Sources available at: https://github.com/adverML/multiLID

Within the framework of a developed blended learning concept, a lot of experience has already been gained with a mixture of theoretical lectures and hands-on activities, combined with the advantages of modern digital media. Here, visualizations using videos, animations and augmented reality have proven to be effective tools to convey learning content in a sustainable way. In the next step, ideas and concepts were developed to implement hands-on laboratory experiments in a virtual environment. The main focus is on the realization of virtual experiments and environments that give the students a deep insight into selected subfields of optics and photonics.

The artistic research cycle DE\GLOBALIZE is a media ecological search movement for the terrestrial. After examining matters of fact in India (2014-18), matters of concern in Egypt (2016-2019) and matters of care in the Upper Rhine (2018-22), the focus turns toward matters of violence in the Congo (2022). From matter to mater, mother-earth, the garden to exploitation. From science, water and climate to migration, oppression and extermination. The long-term research is accessible through interactive web documentation. The platform serves as a continuous media-archaeological archive for a speculative ethnography. The relational structure of the videographic essay is enabling the forensic processing of single documents in the sense of the actor-network theory. The subject of the presentation at IFM is a field trip to the Congo planned for March 2022, which will focus on the ambivalence of violence and care in collaboration with local artists. The field trip is based on the postcolonial reflection luderitzcargo by the author from 1996, in which a freight container was transformed into a translocal cinema in Namibia. Through the journey to Congo, a group of media artists, a psychotherapist, a theater dramaturg, a filmmaker and a philosopher intend to explore the political, technological and psycho-geographic borders. By artistic interventions with locals, we want to interfere with relational string figures as part of the new Earth Politics. They are focusing on the displaced consumption of resources which are hard-fought and guarantee prosperity in the global north. The so-called ghost acreages are repressed and justified as part of a civilizational mission. With this trip, we want to confront our self-lies with the ones of our hosts. We want to confront ourselves with the foreign, the dark and the displaced ghosts within ourselves. In the presentation at the #IFM2022 Conference, the platform DE\GLOBALIZE will be problematized itself as an example of epistemic violence for the ethnographic memory of (Western) knowledge. We are not the missionaries but the perplexed travellers. In our search movement, we are dealing with psychoanalysis, video, performance and trance. As disoriented white men we try the reversal of Black Skin and White Mask by Franz Fanon without blackfacing. We will not only care about the sensitivity of our skin but that of our g/hosts and the one of mother earth.

A circuit arrangement of a motor vehicle includes a high-voltage battery for storing electrical energy, an electric machine for driving the motor vehicle, a converter via which high-voltage direct current voltage provided by the high-voltage battery is convertible into high-voltage alternating current voltage for operating the electric machine, and a charging connection for providing electrical energy for charging the high-voltage battery. The converter is a three-stage converter having a first switch unit which is assigned to a first phase of the electric machine. The first switch unit has two switch groups connected in series which each have two insulated-gate bipolar transistors (IGBTs) connected in series, where a connection is disposed between the IGBTs of one of the two switch groups, which connection is electrically connected directly to a line of the charging connection.

Featherweight Generic Go (FGG) is a minimal core calculus modeling the essential features of the programming language Go. It includes support for overloaded methods, interface types, structural subtyping and generics. The most straightforward semantic description of the dynamic behavior of FGG programs is to resolve method calls based on runtime type information of the receiver. This article shows a different approach by defining a type-directed translation from FGG to an untyped lambda-calculus. The translation of an FGG program provides evidence for the availability of methods as additional dictionary parameters, similar to the dictionary-passing approach known from Haskell type classes. Then, method calls can be resolved by a simple lookup of the method definition in the dictionary. Every program in the image of the translation has the same dynamic semantics as its source FGG program. The proof of this result is based on a syntactic, step-indexed logical relation. The step-index ensures a well-founded definition of the relation in the presence of recursive interface types and recursive methods.

Narrowband Internet-of-Things (NB-IoT) is a 3rd generation partnership project (3GPP) standardized cellular technology, adopted for 5G and optimized for massive Machine Type Communication (mMTC). Applications are anticipated around infrastructure monitoring, asset management, smart city and smart energy applications. In this paper, we evaluate the suitability of NB-IoT for private (campus) networks in industrial environments, including complex cloud-based applications around process automation. An end-to-end system has been developed, comprising of a sensor unit connected to a NB-IoT modem, a base station (gNodeB) equipped with a beamforming array and a local (private) network architecture comprising a sensor management system in the edge cloud. The experimental study includes field tests in realistic industrial environments with latency, reliability and coverage measurements. The results show a good suitability of NB-IoT for process automation with high scalability, low-power requirements and moderate latency requirements.

The identification of vulnerabilities is an important element in the software development life cycle to ensure the security of software. While vulnerability identification based on the source code is a well studied field, the identification of vulnerabilities on basis of a binary executable without the corresponding source code is more challenging. Recent research has shown, how such detection can be achieved by deep learning methods. However, that particular approach is limited to the identification of only 4 types of vulnerabilities. Subsequently, we analyze to what extent we could cover the identification of a larger variety of vulnerabilities. Therefore, a supervised deep learning approach using recurrent neural networks for the application of vulnerability detection based on binary executables is used. The underlying basis is a dataset with 50,651 samples of vulnerable code in the form of a standardized LLVM Intermediate Representation. The vectorised features of a Word2Vec model are used to train different variations of three basic architectures of recurrent neural networks (GRU, LSTM, SRNN). A binary classification was established for detecting the presence of an arbitrary vulnerability, and a multi-class model was trained for the identification of the exact vulnerability, which achieved an out-of-sample accuracy of 88% and 77%, respectively. Differences in the detection of different vulnerabilities were also observed, with non-vulnerable samples being detected with a particularly high precision of over 98%. Thus, the methodology presented allows an accurate detection of 23 (compared to 4) vulnerabilities.

In diesem Kapitel geht es um die Frage, welche systematischen Vorgehensmodelle dazu beitragen können, dass einzelne Bereiche in Industrieunternehmen auf ihrem Weg im Rahmen der digitalen Transformation unterstützt werden können. Die Darstellung erfolgt anhand eines Anwendungsbeispiels im Service-Sektor des Maschinenbaus.

Das duale Krankenversicherungssystem, mit der Gesetzlichen und Privaten Krankenversicherung (GKV und PKV), ist sehr heterogen, und beide agieren in einem unterschiedlichen komplexen rechtlichen und strukturellen Rahmen. In der GKV sind rund 90 % der Bevölkerung versichert. Der folgende Beitrag zum Kundenmanagement in Krankenversicherungen fokussiert daher die GKV. Viele Maßnahmen des Kundenmanagements, insbesondere bezüglich der Services, haben jedoch trotz der unterschiedlichen Rahmenbedingungen sowohl in der GKV als auch in der PKV Relevanz und werden von Marktteilnehmern beider Systeme ergriffen.

This review provides an overview on the production and analysis techniques of antioxidative peptides from food proteins. Regarding the production of antioxidative peptides, interlinked factors must be considered. Depending on the protein substrate, different peptidases or peptidase systems containing multiple enzymes as well as a specific production process must be chosen. The antioxidative peptides might be produced in a batch process including multiple pre- and post-treatments, besides the hydrolyses with peptidases itself. As an alternative, the potential of continuous production systems is discussed in this review. Furthermore, robust analyses tools are needed to gain control of the process and final product properties. With no standardized methodology available for antioxidative peptide evaluation, pros and cons of various strategies for peptide separation and antioxidative measurement are discussed in this review. Therefore, this review provides a roadmap for antioxidative peptide generation from various sources for research and development as well as for potential industrial use.

Synthesizing voice with the help of machine learning techniques has made rapid progress over the last years. Given the current increase in using conferencing tools for online teaching, we question just how easy (i.e. needed data, hardware, skill set) it would be to create a convincing voice fake. We analyse how much training data a participant (e.g. a student) would actually need to fake another participants voice (e.g. a professor). We provide an analysis of the existing state of the art in creating voice deep fakes and align the identified as well as our own optimization techniques in the context of two different voice data sets. A user study with more than 100 participants shows how difficult it is to identify real and fake voice (on avg. only 37% can recognize a professor’s fake voice). From a longer-term societal perspective such voice deep fakes may lead to a disbelief by default.

Dieses Lehrbuch ermöglicht dem Anfänger in der 3D-Modellierung einen schnellen Einstieg in die Arbeit mit dem cloudbasierten CAD-System Autodesk® Fusion 360TM. Der Schwerpunkt liegt dabei auf den grundlegenden Funktionen zur Modellierung von Einzelteilen und dem Zusammenbau von Produkten, sowie in der Erstellung von einfachen technischen Zeichnungen. Dabei werden bei jedem Schritt die besonderen Anforderungen an eine 3D-Druck-gerechte Gestaltung erläutert und umgesetzt. Somit ist das Ergebnis dieser „Schritt für Schritt“-Anleitung die vollständige Modellierung eines Miniatur-Automobils, das am 3D-Drucker in ein reales Modell umgesetzt werden kann. Das didaktische Konzept ist so ausgelegt, dass alle Schritte für ein Selbststudium geeignet sind. In der vorliegenden Auflage wurde die Konstruktionsmethodik überarbeitet und einige Kapitel wurden ergänzt, beispielweise um das Erstellen von Teams.

Um die Geräuschanregung von Schneckengetrieben im Automobil möglichst gering zu halten, gilt es die dafür ausschlaggebenden Parameter der Schnecke zu untersuchen. Die primären Einflussfaktoren bilden die Schiefstellung sowie die Exzentrizität der Achse des Mittenzylinders der Schnecke. Diese beiden Werte werden aktuell im Rundlauf Fr zusammengefasst, welcher auf einer 3D-Koordinatenmessmaschine, durch vermessen der Schnecke bestimmt wird. Aufgrund des hohen Zeitaufwands von 20 Minuten pro Schnecke befindet sich zudem ein optischer Rundlaufprüfstand in der Entwicklung. Mit jenem ist es möglich die Schiefstellung und Exzentrizität in einem Bruchteil der Zeit zu bestimmen. In dieser Arbeit wird in einer Problemanalyse zunächst untersucht, ob die alleinige Ausgabe des Rundlaufs den Anforderungen an die Vorhersage der Geräuschanregung genügt. Aus den daraus gewonnen Erkenntnissen wird mittels der Software MATLAB® ein Algorithmus entwickelt, welcher aus den Daten der 3D-Koordinatenmessmaschine die Schiefstellung und Exzentrizität auf unterschiedliche Weise berechnet. In mehreren Versuchsreihen werden FPS-Schnecken aus der Fertigung auf beiden Versuchsständen vermessen und die daraus gewonnenen Messwerte ausgewertet. Bei dieser Auswertung werden beide Versuchsstände miteinander, aber auch die Werte der Auswerteverfahren innerhalb der Algorithmen verglichen. Bei diesem Vergleich stellt sich heraus, dass die Ergebnisse ähnliche Tendenzen aufzeigen. Die Abweichung der beiden Vergleichsprozesse ist vor allem beim Vergleich mit den Ergebnissen am Zahnkopf des optischen Rundlaufprüfstands meist gering. Große Abweichungen zwischen den Vergleichsprozessen lassen sich auch innerhalb der Auswertung des optischen Rundlaufprüfstandes erkennen, da hier die Differenz zwischen Zahnflanke und Zahnkopf ebenfalls bis zu 100 % betragen kann.