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Institute
- Fakultät Elektrotechnik und Informationstechnik (E+I) (bis 03/2019) (453)
- Fakultät Maschinenbau und Verfahrenstechnik (M+V) (286)
- Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) (213)
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- Fakultät Medien (M) (ab 22.04.2021) (33)
Open Access
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The collection of selected papers of the TRIZ Future Conference 2017 is in open access and is included to the Innovator, the journal of the European TRIZ Assocation.
Deafblindness, a form of dual sensory impairment, signifcantly impacts communication, access to information and mobility. Inde- pendent navigation and wayfnding are main challenges faced by individuals living with combined hearing and visual impairments. We developed a haptic wearable that provides sensory substitution and navigational cues for users with deafblindness by conveying vibrotactile signals onto the body. Vibrotactile signals on the waist area convey directional and proximity information collected via a fisheye camera attached to the garment, while semantic informa- tion is provided with a tapping system on the shoulders. A playful scenario called “Keep Your Distance” was designed to test the navigation system: individuals with deafblindness were “secret agents” that needed to follow a “suspect”, but they should keep an opti- mal distance of 1.5 meters from the other person to win the game. Preliminary fndings suggest that individuals with deafblindness enjoyed the experience and were generally able to follow the directional cues.
This paper presents the development of an energy harvesting solution for a driven tool holder. The tool holder environment was analysed, a test stand built and the designed electromagnetic rotation harvester was evaluated. The reported harvester is based on low cost off-the-shelf components and 3D printed parts. The utilisation of SMD coils allows easy adaptation to changing parameters of the integration area. Energy harvesting in tool holders enables predictive maintenance or condition monitoring in the industrial production. These capabilities are mandatory nowadays in regards of IIoT. A reliable energy source is key for continuous monitoring. Changing batteries becomes obsolete. The results provide useful insight for future harvesters.
The purpose of this study was to 1) compare knee joint kinematics and kinetics of fake-and-cut tasks of varying complexity in 51 female handball players and 2) present a case study of one athlete who ruptured her ACL three weeks post data collection. External knee joint moments and knee joint angles in all planes at the instance of the peak external knee abduction moment (KAM) as well as moment and angle time curves were analyzed. Peak KAMs and knee internal rotation moments were substantially higher than published values obtained during simple change-of-direction tasks and, along with flexion angles, differed significantly between the tasks. Introducing a ball reception and a static defender increased joint loads while they partially decreased again when anticipation was lacking. Our results suggest to use game-specific assessments of injury risk while complexity levels do not directly increase knee loading. Extreme values of several risk factors for a post-test injured athlete highlight the need and usefulness of appropriate screenings.
Duplikaterkennung, -suche und -konsolidierung für Kunden- und Geschäftspartnerdaten, sog. „Identity Resolution“, ist die Voraussetzung für erfolgreiches Customer Relationship Management und Customer Experience Management, aber auch für das Risikomanagement zur Minimierung von Betrugsrisiken und Einhaltung regulatorischer Vorschriften und viele weitere Anwendungsfälle. Diese Systeme sind jedoch hochkomplex und müssen individuell an die kundenspezifischen Anforderungen angepasst werden. Der Einsatz lernbasierter Verfahren bietet großes Potenzial zur automatisierten Anpassung. In diesem Beitrag präsentieren wir für ein KMU praxisfähige, lernbasierte Verfahren zur automatischen Konfiguration von Business-Regeln in Duplikaterkennungssystemen. Dabei wurden für Fachanwender Möglichkeiten entwickelt, um beispielgetrieben das Match-System an individuelle Business-Regeln (u.a. Umzugserkennung, Sperrlistenabgleich) anzupassen und zu konfigurieren. Die entwickelten Verfahren wurden evaluiert und in einer prototypischen Lösung integriert. Wir konnten zeigen, dass unser Machine-Learning-Verfahren, die von einem Domainexperten erstellten Business-Regeln für das Duplikaterkennungssystem „identity“ verbessern konnte. Zudem konnte der hierzu erforderliche Zeitaufwand verkürzt werden.
Durch den verstärkten Einsatz von EDV-Geräten an den Arbeitsplätzen in Büroräumen stiegen die flächenbezogenen inneren Kühllasten deutlich an. Für die Arbeitsplätze in Fensternähe müssen EDV-gerechte Lichtverhältnisse vorhanden sein. Es werden Versuchsergebnisse eines neu entwickelten Kühlkonvektors im Fensterbereich im Zusammenhang mit Innenjalousien vorgestellt. Dieser Kühlkonvektor eignet sich auch für den kombinierten Einsatz mit Kühldecken und raumlufttechnischen (RLT)-Anlagen, wie weitere Versuche bestätigen. Der Kühlkonvektor kann auch beim Ersatz von Induktionsgeräten angewandt werden, die z.B. nach dem 4-Leiter-Prinzip angeschlossen sind. Die Untersuchungen haben gezeigt, daß mit der Kombination von Kühldecke, Kühlkonvektor und RLT-Anlage große flächenbezogene Kühllasten abgeführt werden können, ohne daß dadurch die thermische Behaglichkeit negativ beeinflußt wird. Der Luftstrom läßt sich auf das hygienisch bzw. auf das zur Entfeuchtung notwendige Maß reduzieren, wodurch sich die Betriebskosten senken. Der Anteil der drei Komponenten an der gesamten Kühllast beträgt im Mittel: 15 % RLT-Anlage, 15 % Kühlkonvektor und 70 % Kühldecke.
Fallstudien sollen theoretische Lerninhalte zu Konzepten von Business Intelligence und Data Warehousing veranschaulichen und in einen praxisnahen Kontext bringen. Außerdem sollen Studierende umsetzungsorientierte Kompetenzen mit praxisrelevanten Systemen erwerben. Um diese Kompetenzen abzuprüfen und um die Auseinandersetzung mit Software und Konzepten zu vertiefen, haben sich Projekte als Ergänzung zu Fallstudien und Klausuren vielfach bewährt. Der Vortrag stellt dar, welche Möglichkeiten Dozierende im Rahmen der vom UCC zur Verfügung gestellten Plattform SAP Data Warehouse Cloud (SAP DWC) haben, um studentische Projekte zu Data Warehousing und Analytics durchzuführen. Der Autor berichtet über seine Erfahrung aus der Betreuung von über 30 Projekten mit SAP DWC aus verschiedenen Studiengängen seit 2020. Neben einer Übersicht über die von Studierenden gewählten Themen werden ausgewählte Projektergebnisse vorgestellt. Außerdem wird auf den Modus der Durchführung sowie existierende systemseitige Limitationen eingegangen. Für Dozierende, die mit ihren Studierenden eigene Projekte erfolgreich durchführen möchten, werden konkrete Hinweise und Maßnahmen dargestellt.
Kundendaten im E-Commerce – Optimierungspotenzial im Checkout-Prozess des deutschen Online-Handels
(2023)
Die Gestaltung eines benutzungsfreundlichen Checkout-Prozesses ist für den Erfolg des E-Commerce von großer Bedeutung. Die Abfrage der Kundendaten bildet einen wichtigen Teil der Customer Journey. Auf der einen Seite wollen die Handelsunternehmen so viel wie möglich über ihre Kundschaft erfahren, um möglichst zielgenaue Angebote und Marketingmaßnahmen ausspielen und das perfekte Einkaufserlebnis generieren zu können. Auf der anderen Seite möchten sich die Kundinnen und Kunden beim Online-Shopping auf den Kauf konzentrieren und erwarten einen reibungslosen Ablauf. Der Checkout-Prozess ist in diesem Zusammenhang ein kritischer Punkt. Dies spiegelt sich auch in den hohen Warenkorbabbruchraten wider. Um Online-Shoppende nachhaltig zu begeistern, gibt es noch viel Raum für Verbesserungen. Mit dem Ziel, den Status quo im deutschen Online-Handel besser zu verstehen und Usability und User Experience für eine höhere Konvertierungsrate zu optimieren, untersuchte die hier vorgestellte Forschungsarbeit den Anmelde- und Checkout-Prozess der 100 umsatzstärksten Online-Shops in Deutschland. Es werden die Ergebnisse der Studie präsentiert und aufgezeigt, an welchen Stellen Optimierungspotenzial besteht – bspw. bei zu komplizierten Formularen, unnötigen Datenabfragen oder erzwungenen Registrierungen – sowie Vorschläge für die Praxis des Online-Handels diskutiert.
Sowohl Angebot als auch Nachfrage nach Videos mit Lehrinhalt verzeichnen aktuell ein nahezu explosionsartiges Wachstum; MOOCs machen dabei jedoch nur einen geringen Anteil aus. Während Studierende einen positiven Effekt von Lehrvideos auf den eigenen Lernerfolg wahrnehmen, ist dieser bei objektiver Prüfung jedoch oft nicht nachweisbar. Für Dozenten eröffnet dies ein Spannungsfeld, für dessen Lösung dieser Beitrag die im Selbstversuch erprobte Möglichkeit der Produktion von Kurzvideos und deren Veröffentlichung auf YouTube vorstellt.
Die Fachhochschule Offenburg bietet seit dem Wintersemester 1990/91 den Studenten des Fachbereichs Nachrichtentechnik das Wahlpflichtfach ASIC-Design an. Schon kurz nach der Errichtung des ASIC-Design-Centers im Frühjahr 1990 ermöglicht sie damit künftigen Ingenieuren eine Ausbildung in einem Bereich, der in der modernen Schaltungsentwicklung nicht mehr wegzudenken ist.
The excessive control signaling in Long Term Evolution networks required for dynamic scheduling impedes the deployment of ultra-reliable low latency applications. Semi-persistent scheduling was originally designed for constant bit-rate voice applications, however, very low control overhead makes it a potential latency reduction technique in Long Term Evolution. In this paper, we investigate resource scheduling in narrowband fourth generation Long Term Evolution networks through Network Simulator (NS3) simulations. The current release of NS3 does not include a semi-persistent scheduler for Long Term Evolution module. Therefore, we developed the semi-persistent scheduling feature in NS3 to evaluate and compare the performance in terms of uplink latency. We evaluate dynamic scheduling and semi-persistent scheduling in order to analyze the impact of resource scheduling methods on up-link latency.
The next generation cellular networks are expected to improve reliability, energy efficiency, data rate, capacity and latency. Originally, Machine Type Communication (MTC) was designed for low-bandwidth high-latency applications such as, environmental sensing, smart dustbin, etc., but there is additional demand around applications with low latency requirements, like industrial automation, driver-less cars, and so on. Improvements are required in 4G Long Term Evolution (LTE) networks towards the development of next generation cellular networks for providing very low latency and high reliability. To this end, we present an in-depth analysis of parameters that contribute to the latency in 4G networks along with a description of latency reduction techniques. We implement and validate these latency reduction techniques in the open-source network simulator (NS3) for narrowband user equipment category Cat-Ml (LTE-M) to analyze the improvements. The results presented are a step towards enabling narrowband Ultra Reliable Low Latency Communication (URLLC) networks.
Enabling ultra-low latency is one of the major drivers for the development of future cellular networks to support delay sensitive applications including factory automation, autonomous vehicles and tactile internet. Narrowband Internet of Things (NB-IoT) is a 3 rd Generation Partnership Project (3GPP) Release 13 standardized cellular network currently optimized for massive Machine Type Communication (mMTC). To reduce the latency in cellular networks, 3GPP has proposed some latency reduction techniques that include Semi Persistent Scheduling (SPS) and short Transmission Time Interval (sTTI). In this paper, we investigate the potential of adopting both techniques in NB-IoT networks and provide a comprehensive performance evaluation. We firstly analyze these techniques and then implement them in an open-source network simulator (NS3). Simulations are performed with a focus on Cat-NB1 User Equipment (UE) category to evaluate the uplink user-plane latency. Our results show that SPS and sTTI have the potential to greatly reduce the latency in NB-IoT systems. We believe that both techniques can be integrated into NB-IoT systems to position NB-IoT as a preferred technology for low data rate Ultra-Reliable Low-Latency Communication (URLLC) applications before 5G has been fully rolled out.
Generative adversarial networks are the state of the art approach towards learned synthetic image generation. Although early successes were mostly unsupervised, bit by bit, this trend has been superseded by approaches based on labelled data. These supervised methods allow a much finer-grained control of the output image, offering more flexibility and stability. Nevertheless, the main drawback of such models is the necessity of annotated data. In this work, we introduce an novel framework that benefits from two popular learning techniques, adversarial training and representation learning, and takes a step towards unsupervised conditional GANs. In particular, our approach exploits the structure of a latent space (learned by the representation learning) and employs it to condition the generative model. In this way, we break the traditional dependency between condition and label, substituting the latter by unsupervised features coming from the latent space. Finally, we show that this new technique is able to produce samples on demand keeping the quality of its supervised counterpart.