HFTor - HfT Open Repository (Hochschule für Technik Stuttgart)
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404 research outputs found
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Insertion loss of pipe clamps: Quantity for prediction or manufacturer's advertising?
No full textThe noise emitted by water conveying pipes as part of sanitary installations is, in most cases, dominated by structure-borne sound transmission. The water flowing through the pipe causes a vibration of the pipe system that is transmitted to the installation wall through pipe clamps. A common mitigation measure is an elastic inlay inside the pipe clamps. Some manufacturers of these clamps advertise the insertion losses determined by non-standardized tests. Recent studies show a high dependency of the insertion loss on the mounting conditions, e.g. how tight are pipes fastened in the clamp. This contribution aims to answer the question of whether and how the mounting condition affects the insertion loss of the pipe clamps and thus how representative the specified values can be for the building situation. The investigations are carried out in-situ on the reception plate and in a building like test stand with an excitation by flowing water as well as with impact hammer excitation. This study is part of a research project with the overall aim to provide source data for the prediction of the sound transmission in buildings using EN 12354-5
An introduction to and survey of biological network visualization
No full textBiological networks describe complex relationships in biological systems, which represent biological entities as vertices and their underlying connectivity as edges. Ideally, for a complete analysis of such systems, domain experts need to visually integrate multiple sources of heterogeneous data, and visually, as well as numerically, probe said data in order to explore or validate (mechanistic) hypotheses. Such visual analyses require the coming together of biological domain experts, bioinformaticians, as well as network scientists to create useful visualization tools. Owing to the underlying graph data becoming ever larger and more complex, the visual representation of such biological networks has become challenging in its own right. This introduction and survey aims to describe the current state of biological network visualization in order to identify scientific gaps for visualization experts, network scientists, bioinformaticians, and domain experts, such as biologists, or biochemists, alike. Specifically, we revisit the classic visualization pipeline, upon which we base this paper’s taxonomy and structure, which in turn forms the basis of our literature classification. This pipeline describes the process of visualizing data, starting with the raw data itself, through the construction of data tables, to the actual creation of visual structures and views, as a function of task-driven user interaction. Literature was systematically surveyed using API-driven querying where possible, and the collected papers were manually read and categorized based on the identified sub-components of this visualization pipeline’s individual steps. From this survey, we highlight a number of exemplary visualization tools from multiple biological sub-domains in order to explore how they adapt these discussed techniques and why. Additionally, this taxonomic classification of the collected set of papers allows us to identify existing gaps in biological network visualization practices. We finally conclude this report with a list of open challenges and potential research directions. Examples of such gaps include (i) the overabundance of visualization tools using schematic or straight-line node-link diagrams, despite the availability of powerful alternatives, or (ii) the lack of visualization tools that also integrate more advanced network analysis techniques beyond basic graph descriptive statistics
Architekturarbeit gegen den Klimawandel – Ein Lehrplan für Softwarearchitekt:innen
No full textGalt die IT lange Zeit als Lösung vieler Probleme, die in Zusammenhang mit dem Klimawandel stehen, ist sie mittlerweile selbst Gegenstand von Optimierungsbetrachtungen geworden. Ineffiziente Programmierung, oft hervorgerufen durch die Notwendigkeit eines schnellen Time-to-market, wurde vielfach durch immer schnellere Hardware oder mehr Ressourcen in der Cloud kompensiert. Diesen Weg gilt es zu verlassen. Die CO2-Emissionen, die durch Software entstehen, müssen konsequent reduziert werden. Das kann nur gelingen, wenn wir eine bessere Energieeffizienz als Teil unserer täglichen Arbeit als Softwarearchitekt:in sehen.
Dies war für den iSAQB e.V. der Anlass, das Advanced-Modul GREEN zu konzipieren. Dort lernen Softwarearchitekt:innen, das Thema Green Software ganzheitlich im Auge zu behalten. Dies beginnt mit der Betrachtung der Rolle der IT beim Aufhalten des Klimawandels, einer Einführung in die aktuelle Regulatorik, die Sicht auf die Anforderungen verschiedener Stakeholder und der Benennung von Handlungsfeldern in Unternehmen. Von dort geht es über das Messen und das Monitoring von CO2-Emissionen bzw. Energieverbrauch hin zum Kernthema der Softwareentwicklung. In dieses zentrale Themengebiet fällt die Energieeffizienz verschiedener Softwarearchitekturen sowie grundlegender verwendeter Konzepte, energieeffizientes Datenhandling, optimierte Algorithmen aber auch der Einfluss und das Management von Qualitätsanforderungen in Bezug auf Energieeffizienz. Ein weiterer wichtiger Bestandteil ist das Thema Cloud, sowohl bezüglich Auswahl des Providers als auch den Möglichkeiten zum CO2-armen Betrieb darin. Zuletzt finden Optionen zur Verbesserung der Energieeffizienz im Entwicklungsprozess Betrachtung
HR-Risiken im Employee Lifecycle - Teil I
No full textIn Anbetracht des Fachkräftemangels stellen personelle Risiken eine zentrale Risikokategorie dar. Die systematisierte Analyse personeller Risiken und deren Quantifizierung sind allerdings erst wenig erforscht. Während sich dieser Artikel primär mit der Identifikation von Risiken entlang des Employee Lifecycles befasst, werden in einem folgenden Beitrag Ansätze zu deren Quantifizierung und Steuerung vorgestellt
Smart EV Charging With Context-Awareness: Enhancing Resource Utilization via Deep Reinforcement Learning
No full textThe widespread adoption of electric vehicles (EVs) has introduced new challenges for stakeholders ranging from grid operators to EV owners. A critical challenge is to develop an effective and economical strategy for managing EV charging while considering the diverse objectives of all involved parties. In this study, we propose a context-aware EV smart charging system that leverages deep reinforcement learning (DRL) to accommodate the unique requirements and goals of participants. Our DRL-based approach dynamically adapts to changing contextual factors such as time of day, location, and weather to optimize charging decisions in real time. By striking a balance between charging cost, grid load reduction, fleet operator preferences, and charging station energy efficiency, the system offers EV owners a seamless and cost-efficient charging experience. Through simulations, we evaluate the efficiency of our proposed Deep Q-Network (DQN) system by comparing it with other distinct DRL methods: Proximal Policy Optimization (PPO), synchronous Advantage Actor-Critic (A3C), and Deep Deterministic Policy Gradient (DDPG). Notably, our proposed methodology, DQN, demonstrated superior computational performance compared to the others. Our results reveal that the proposed system achieves a remarkable, approximately 18% enhancement in energy efficiency compared to traditional methods. Moreover, it demonstrates about a 12% increase in cost-effectiveness for EV owners, effectively reducing grid strain by 20% and curbing CO2 emissions by 10% due to the utilization of natural energy sources. The system’s success lies in its ability to facilitate sequential decision-making, decipher intricate data patterns, and adapt to dynamic contexts. Consequently, the proposed system not only meets the efficiency and optimization requirements of fleet operators and charging station maintainers but also exemplifies a promising stride toward sustainable and balanced EV charging management
Automatisierte Geometrieaufbereitung digitaler 3D-Stadtmodelle für die Strömungssimulation
No full textFahrradinfrastruktur auf beengten Straßen : Das Konzept 2-minus-1-Straße zur Stärkung der Sicherheit von Radfahrenden
No full textOvertaking in Stuttgart — analysis of the lateral distances between motor vehicles and bicycle traffic with reference to traffic volume and cycling infrastructure
No full textIn the context of climate change, it is desirable to increase the share of cycling. One way of doing this can be to strengthen subjective safety of cyclists. At present, many people perceive cycling as unsafe. In particular, overtaking by motor vehicles is a cause of low subjective safety and stress. In built-up areas, German road traffic regulations stipulate a minimum lateral distance of 1.50 m for motor vehicles, while overtaking cyclists. Previous research has shown that this rule is often not followed by motor vehicles. The aim of this study is to find out which factors influence the lateral distance of overtaking manoeuvres. The lateral distances of 4 081 overtaking manoeuvres were recorded using an ultrasonic sensor on 14 selected routes in the city of Stuttgart, Germany. 42% of the recorded overtaking manoeuvres were carried out with a lateral distance of less than 1.50 m. The mean value of all overtaking manoeuvres was 1.59 m. On roads with mixed traffic, higher lateral distances occurred than on roads with cycle lanes. In Germany, the motor vehicle traffic volume on a road is a key criterion for planning cycling infrastructure. However, it is not possible to confirm an influence of the motor vehicle traffic volume on the occurring lateral distances. The time of day at which overtaking manoeuvres take place also seems to have no effect on lateral distances
Machine learning for predictions of road traffic accidents and spatial network analysis for safe routing on accident and congestion-prone road networks
No full textRoad traffic accidents (RTAs) and the resulting traffic congestion are global concerns mainly in metropolitan environments. The need for road safety is directly correlated with the rapidly increasing impact of urbanization on infrastructure and day-to-day living. In this study, we have introduced an innovative approach integrating a Random Forest (RF) model, crash rates, and spatial network analysis to provide safe route recommendations for drivers aiming to reduce RTAs and congestion. Based on historical accident data from 2014–2019, the analysis of the RF model and crash rate served as a prediction of the likelihood and occurrence of RTAs. In applying the spatial network analysis, lower predicted crash counts from spatial joining were taken into consideration, as well as areas with lower crash rates that have had fewer incidents in the past. An alternative safe route and an optimum route that covers 32.27 km in 50.78 mins of travel time and 28.6 km in 41.58 mins of travel time were successfully identified, respectively. Having demonstrated 78 % predictive capability on the target variable, the RF model has proved its worth. Analyzing historically lower accident counts on segments leading to minimal crash rates validates the accuracy of the identified safe route. This advanced method significantly aids in improving traffic safety by making drivers and travelers aware of potentially high rates of accidents and traffic congestion on road segments. It assists travelers with their trip planning to anticipate potential risks and suggest safer alternate routes, making it a valuable contribution to the field
KI und Hyperautomation: Der Boost für Effizienz und Innovation
No full textUm im heutigen Wettbewerb zu bestehen, reicht es längst nicht mehr aus, nur einzelne Prozesse zu optimieren – Unternehmen müssen ihre gesamte Wertschöpfungskette konsequent digitalisieren und automatisieren. Doch wo hört Prozessautomatisierung auf und wo beginnt „Hyperautomation“? Der Begriff ist vielschichtig und umfasst eine faszinierende Palette an Technologien und Tools, die alle darauf abzielen, Unternehmensprozesse intelligenter und produktiver zu gestalten. Dabei wird Künstliche Intelligenz (KI) zum entscheidenden Faktor, der es erlaubt, Routineaufgaben zu vereinfachen, Entscheidungen zu beschleunigen und Ressourcen optimal zu nutzen