Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
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Das Ziel des Projekts PRYSTINE war es, eine fehlertolerante 360°-Rundumwahrnehmung für das hochautomatisierte Fahren in städtischen und ländlichen Umgebungen, auf Basis einer robusten Radar- und Lidar-Sensorfusion sowie Kontrollfunktionen, zu realisieren.
Im Teilvorhaben "Entwurf der Systemarchitektur von Radarsensoren auf Grundlage identifizierter Szenarien" stand die Entwicklung eines zukunftsfähigen RF-CMOS basierten Radarsystems im Fokus, das sich durch eine hohe Robustheit und Fehlertoleranz bei gleichzeitiger Reduktion der Kosten, Chipfläche und Leistungsaufnahme auszeichnet.
Darin war die Hochschule Offenburg sowohl an der Spezifizierung und am Entwurf einer Systemarchitektur für einen neuartigen RF-CMOS basierten Radarchip als auch an der anschließenden Untersuchung und Validierung des im Projekt realisierten hochauflösenden Radarsensors beteiligt.
Mit dem Klimaschutzgesetz 2021 wurden von der Bundesregierung die Klimaschutzvorgaben verschärft und die Treibhausgasneutralität bis 2045 als Ziel verankert. Zur Erreichung dieses ambitionierten Ziels ist es notwendig, im Bereich der Mobilität weitgehend von Verbrennungsmotoren mit fossilen Kraftstoffen auf Elektromobilität mit regenerativ erzeugtem Strom umzusteigen. Dabei ist die zügige Bereitstellung einer ausreichenden Ladeinfrastruktur für die Elektrofahrzeuge eine große Herausforderung. Neben der Installation einer ausreichend großen Zahl von Ladepunkten selbst besteht die Herausforderung darin, diese in das bestehende Verteilungsnetz zu integrieren bzw. das Verteilungsnetz so auszubauen, dass weiter ein sicherer Netzbetrieb gewährleistet werden kann. Dabei sind insbesondere Lösungen gefragt, bei denen der Ausbau der Ladeinfrastruktur und der Netzbetriebsmittel durch intelligentes Management des Ladens so gering wie möglich gehalten wird, indem vorhandene oder neu zu installierender Hardware möglichst effizient genutzt wird.
Hier setzte das Projekt „Intelligente Ladeinfrastruktur für Elektrofahrzeuge auf dem Parkplatz der Hochschule Offenburg (INTLOG)“ (Projektlaufzeit 15.11.2020 – 30.09.2022) an. Inhalt des Projekts war es, einen Ladepark für den Parkplatz der Hochschule Offenburg mit 20 Ladepunkten à 11 kW und somit einer Gesamtladeleistung von 220 kW an einen vorhandenen Ortsnetztransformator mit 200 kW Nennleistung anzuschließen, der aber bereits von anderen Verbrauchern genutzt wurde. Das übergeordnete Ziel war es also, eine Ladeinfrastruktur von maßgeblichem Umfang in die bestehende Netzinfrastruktur ohne zusätzlichen Ausbau zu integrieren.
Dabei wurden zukunftsweisende Technologien genutzt und weiterentwickelt sowie teilweise in Praxis, im Labor und in der Computersimulation demonstriert.
Organizations striving to achieve success in the long term must have a positive brand image which will have direct implications on the business. In the face of the rising cyber threats and intense competition, maintaining a threat-free domain is an important aspect of preserving that image in today's internet world. Domain names are often near-synonyms for brand names for numerous companies. There are likely thousands of domains that try to impersonate the big companies in a bid to trap unsuspecting users, usually falling prey to attacks such as phishing or watering hole. Because domain names are important for organizations for running their business online, they are also particularly vulnerable to misuse by malicious actors. So, how can you ensure that your domain name is protected while still protecting your brand identity? Brand Monitoring, for example, may assist. The term "Brand Monitoring" applies only to keep tabs on an organization's brand performance, reception, and overall online presence through various online channels and platforms [1]. There has been a rise in the need of maintaining one's domain clear of any linkages to malicious activities as the threat environment has expanded. Since attackers are targeting domain names of organizations and luring unsuspecting users to visit malicious websites, domain monitoring becomes an important aspect. Another important aspect of brand abuse is how attackers leverage brand logos in creating fake and phishing web pages. In this Master Thesis, we try to solve the problem of classification of impersonated domains using rule-based and machine learning algorithms and automation of domain monitoring. We first use a rule-based classifier and Machine Learning algorithms to classify the domains gathered into two buckets – "Parked" and "Non-Parked". In the project's second phase, we will deploy object detection models (Scale Invariant Feature Transform - SIFT and Multi-Template Matching – MTM) to detect brand logos from the domains of interest.
Server Side Rendering (SSR), Single Page Application (SPA), and Static Site Generation (SSG) are the three most popular ways of making modern Web applications today. If we go deep into these processes, this can be helpful for the developers and clients. Developers benefit since they do not need to learn other programming languages and can instead utilize their own experience to build different kinds of Web applications; for example, a developer can use only JavaScript in the three approaches. On the other hand, clients can give their users a better experience.
This Master Thesis’s purpose was to compare these processes with a demo application for each and give users a solid understanding of which process they should follow. We discussed the step-by-step process of making three applications in the above mentioned categories. Then we compared those based on criteria such as performance, security, Search Engine Optimization, developer preference, learning curve, content and purpose of the Web, user interface, and user experience. It also talked about the technologies such as JavaScript, React, Node.js, and Next.js, and why and where to use them. The goals we specified before the program creation were fulfilled and can be validated by comparing the solutions we gave for user problems, which was the application’s primary purpose.
Sweaty has already participated several times in RoboCup soccer competitions (Adult Size). Now the work is focused on stabilizing the gait. Moreover, we would like to overcome the constraints of a ZMP-algorithm that has a horizontal footplate as precondition for the simplification of the equations. In addition we would like to switch between impedance and position control with a fuzzy-like algorithm that might help to minimize jerks when Sweaty’s feet touch the ground.
To deal with frequent power outages in developing countries, people turn to solutions like uninterruptible power supply (UPS), which stores electric energy during normal operating hours and use it to meet energy needs during rolling blackout intervals. Locally produced UPSs of poorer power quality are widely accessible in the marketplaces, and they have a negative impact on power quality. The charging and discharging of the batteries in these UPSs generate significant amount of power losses in weak grid environments. The Smart-UPS is our proposed smart energy metering (SEM) solution for low voltage consumers that is provided by the distribution company. It does not require batteries, therefore there is no power loss or harmonic distortion due to corresponding charging and discharging. Through load flow and harmonic analysis of both traditional UPS and Smart-UPS systems on ETAP, this paper examines their impact on the harmonics and stability of the distribution grid. The simulation results demonstrate that Smart-UPS can assist fixing power quality issues in a developing country like Pakistan by providing cleaner energy than the battery-operated traditional UPSs.
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