07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/8

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Institute: search.filters.UBSInstitut.Fakultätsübergreifend / Sonstige EinrichtungSubject: search.filters.subject.004

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Now showing 1 - 6 of 6
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    Are you sure? : prediction revision in automated decision‐making
    (2020) Burkart, Nadia; Robert, Sebastian; Huber, Marco F.
    With the rapid improvements in machine learning and deep learning, decisions made by automated decision support systems (DSS) will increase. Besides the accuracy of predictions, their explainability becomes more important. The algorithms can construct complex mathematical prediction models. This causes insecurity to the predictions. The insecurity rises the need for equipping the algorithms with explanations. To examine how users trust automated DSS, an experiment was conducted. Our research aim is to examine how participants supported by an DSS revise their initial prediction by four varying approaches (treatments) in a between‐subject design study. The four treatments differ in the degree of explainability to understand the predictions of the system. First we used an interpretable regression model, second a Random Forest (considered to be a black box [BB]), third the BB with a local explanation and last the BB with a global explanation. We noticed that all participants improved their predictions after receiving an advice whether it was a complete BB or an BB with an explanation. The major finding was that interpretable models were not incorporated more in the decision process than BB models or BB models with explanations.
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    Prediction of in-flight particle properties and mechanical performances of HVOF-sprayed NiCr-Cr3C2 coatings based on a hierarchical neural network
    (2023) Gui, Longen; Wang, Botong; Cai, Renye; Yu, Zexin; Liu, Meimei; Zhu, Qixin; Xie, Yingchun; Liu, Shaowu; Killinger, Andreas
    High-velocity oxygen fuel (HVOF) spraying is a promising technique for depositing protective coatings. The performances of HVOF-sprayed coatings are affected by in-flight particle properties, such as temperature and velocity, that are controlled by the spraying parameters. However, obtaining the desired coatings through experimental methods alone is challenging, owing to the complex physical and chemical processes involved in the HVOF approach. Compared with traditional experimental methods, a novel method for optimizing and predicting coating performance is presented herein; this method involves combining machine learning techniques with thermal spray technology. Herein, we firstly introduce physics-informed neural networks (PINNs) and convolutional neural networks (CNNs) to address the overfitting problem in small-sample algorithms and then apply the algorithms to HVOF processes and HVOF-sprayed coatings. We proposed the PINN and CNN hierarchical neural network to establish prediction models for the in-flight particle properties and performances of NiCr-Cr3C2 coatings (e.g., porosity, microhardness, and wear rate). Additionally, a random forest model is used to evaluate the relative importance of the effect of the spraying parameters on the properties of in-flight particles and coating performance. We find that the particle temperature and velocity as well as the coating performances (porosity, wear resistance, and microhardness) can be predicted with up to 99% accuracy and that the spraying distance and velocity of in-flight particles exert the most substantial effects on the in-flight particle properties and coating performance, respectively. This study can serve as a theoretical reference for the development of intelligent HVOF systems in the future.
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    Energy efficiency in ROS communication : a comparison across programming languages and workloads
    (2025) Albonico, Michel; Cannizza, Manuela Bechara; Wortmann, Andreas
    Introduction: The Robot Operating System (ROS) is a widely used framework for robotic software development, providing robust client libraries for both C++ and Python. These languages, with their differing levels of abstraction, exhibit distinct resource usage patterns, including power and energy consumption–an increasingly critical quality metric in robotics.
    Methods: In this study, we evaluate the energy efficiency of ROS two nodes implemented in C++ and Python, focusing on the primary ROS communication paradigms: topics, services, and actions. Through a series of empirical experiments, with programming language, message interval, and number of clients as independent variables, we analyze the impact on energy efficiency across implementations of the three paradigms.
    Results: Our data analysis demonstrates that Python consistently demands more computational resources, leading to higher power consumption compared to C++. Furthermore, we find that message frequency is a highly influential factor, while the number of clients has a more variable and less significant effect on resource usage, despite revealing unexpected architectural behaviors of underlying programming and communication layers.
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    Cystoscopic depth estimation using gated adversarial domain adaptation
    (2023) Somers, Peter; Holdenried-Krafft, Simon; Zahn, Johannes; Schüle, Johannes; Veil, Carina; Harland, Niklas; Walz, Simon; Stenzl, Arnulf; Sawodny, Oliver; Tarín, Cristina; Lensch, Hendrik P. A.
    Monocular depth estimation from camera images is very important for surrounding scene evaluation in many technical fields from automotive to medicine. However, traditional triangulation methods using stereo cameras or multiple views with the assumption of a rigid environment are not applicable for endoscopic domains. Particularly in cystoscopies it is not possible to produce ground truth depth information to directly train machine learning algorithms for using a monocular image directly for depth prediction. This work considers first creating a synthetic cystoscopic environment for initial encoding of depth information from synthetically rendered images. Next, the task of predicting pixel-wise depth values for real images is constrained to a domain adaption between the synthetic and real image domains. This adaptation is done through added gated residual blocks in order to simplify the network task and maintain training stability during adversarial training. Training is done on an internally collected cystoscopy dataset from human patients. The results after training demonstrate the ability to predict reasonable depth estimations from actual cystoscopic videos and added stability from using gated residual blocks is shown to prevent mode collapse during adversarial training.
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    Künstliche Intelligenz in betrieblichen Prozessen : Ein Vorgehensmodell zur partizipativen Gestaltung von KI-Anwendungen
    (2024) Ruess, Patrick; Staffa, Anna; Kreutz, Anna; Busch, Christine; Saba Gayoso, Christian Oswaldo; Pollmann, Kathrin
    Schon heute gilt Künstliche Intelligenz (KI) als betrieblicher Wertschöpfungsfaktor, von dem sich Unternehmen neue Impulse für bestehende Prozesse und Geschäftsmodelle versprechen. Während der derzeitige Diskurs vor allem technische Möglichkeiten und Anwendungsfälle in den Blick nimmt, umfasst die erfolgreiche betriebliche Integration allerdings auch wesentliche soziale und organisatorische Aspekte. Im vorliegenden Artikel werden daher inner- und überbetriebliche Anforderungen identifiziert, die eine Mitarbeiter\*innen-gerechte und partizipative Gestaltung von KI-Anwendungen im betrieblichen Umfeld ermöglichen. Die empirische Grundlage hierfür bildet eine Interviewstudie, in der der KI-Einsatz in unterschiedlichen Branchen und Unternehmensbereichen untersucht wurde. Darauf aufbauend wird ein Vorgehensmodell eingeführt, dass gemäß den identifizierten Kriterien eine partizipative Teilhabe bei der Gestaltung von betrieblichen KI-Anwendungen erlaubt. Das Modell bezieht sich auf die Qualifizierung und Akzeptanzbildung in der Belegschaft, aber auch auf die organisatorische Umsetzung und Verstetigung. Die Herangehensweise verknüpft infrastrukturelle, interaktive als auch konzeptionelle Bausteine miteinander und zielt darauf ab, die Beteiligung der Mitarbeiter*innen in allen Phasen der KI-Entwicklung zu fördern und in der betrieblichen Umsetzung zu berücksichtigen. Die Ergebnisse dieser Forschung bieten praktische Anknüpfungspunkte für Unternehmen, die sich mit Fragen der KI-Implementierung befassen. Gleichzeitig ergänzen sie den aktuellen wissenschaftlichen Diskurs um die Perspektive, die eine konsequente betriebliche Gestaltung und Teilhabe vorsieht. Die zu diesem Zweck identifizierten Anforderungen komplementieren die empirische Grundlage in der Forschung.