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Research Knowledge Graphs in NFDI4DS

2023 Designing Futures: Zukunfte gestalten, INFORMATIK 2023 - 2023 Designing Futures: Shaping the Future, INFORMATIK 2023

作者： Karmakar, Saurav Zloch, Matthäus Limani, Fidan Zapilko, Benjamin Upadhyaya, Sharmila D Souza, Jennifer Castro, Leyla Jael Rehm, Georg Ackermann, Marcel R. Sack, Harald Boukhers, Zeyd Schimmler, Sonja Dessí, Danilo Mutschke, Peter Dietze, Stefan GESIS Leibniz Institute for the Social Sciences Cologne Germany ZBW Leibniz Information Centre for Economics Kiel Germany TIB Leibniz Information Centre for Science and Technology Hannover Germany ZB MED Information Centre for Life Sciences Cologne Germany Berlin Germany DBLP Computer Science Bibliography Schloss Dagstuhl-Leibniz Center for Informatics Trier Germany FIZ Karlsruhe Leibniz Institute for Information Infrastructure Karlsruhe Germany Fraunhofer Institute for Applied Information Technology-FIT Sankt Augustin Germany Fraunhofer Institute for Open Communication Systems-FOKUS Berlin Germany Heinrich Heine University Dösseldorf Germany

ISBN: (纸本)9783885797319

The ever-increasing amount of research output through scientific articles requires means to enable transparency and a better understanding of key entities of the research lifecycle, referred to as research artifacts, such as methods, software, datasets, etc. Research Knowledge Graphs (RKG) make research artifacts findable, accessible, interoperable, and reusable (FAIR) and facilitate their interpretability. In this article, we describe the role of RKGs, from their construction to the expected benefits, including an overview and a vision of their use within the German National Research Data Infrastructure (NFDI) consortium NFDI4DataScience (NFDI4DS). This paper includes insights about the existing RKGs, how to formally represent research artifacts, and how this supports better transparency and reproducibility in data science and artificial intelligence. We also discuss key challenges, such as RKG construction, and integration, and give an outlook on future work. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Knowledge graph

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Modular Fault Diagnosis Framework for Complex Autonomous Driving Systems

arXiv

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arXiv 2024年

作者： Orf, Stefan Ochs, Sven Doll, Jens Schotschneider, Albert Heinrich, Marc Zofka, Marc René Zöllner, J. Marius Department of Technical Cognitive Systems FZI Research Center for Information Technology Haid-und-Neu-Str. 10-14 Karlsruhe76131 Germany Institute of Applied Informatics and Formal Description Methods Karlsruhe76131 Germany

Fault diagnosis is crucial for complex autonomous mobile systems, especially for modern-day autonomous driving (AD). Different actors, numerous use cases, and complex heterogeneous components motivate a fault diagnosis of the system and overall system integrity. AD systems are composed of many heterogeneous components, each with different functionality and possibly using a different algorithm (e.g., rule-based vs. AI components). In addition, these components are subject to the vehicle’s driving state and are highly dependent. This paper, therefore, faces this problem by presenting the concept of a modular fault diagnosis framework for AD systems. The concept suggests modular state monitoring and diagnosis elements, together with a state- and dependency-aware aggregation method. Our proposed classification scheme allows for the categorization of the fault diagnosis modules. The concept is implemented on AD shuttle buses and evaluated to demonstrate its capabilities. © 2024, CC BY.

关键词： Fault detection

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Technical sound event classification applying recurrent and convolutional neural networks 1

Technical sound event classification applying recurrent and ...

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1st International Conference on Deep Learning Theory and Applications, DeLTA 2020

作者： Rieder, Constantin Germann, Markus Mezger, Samuel Scherer, Klaus Peter Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen Germany

ISBN: (纸本)9789897584411

In many intelligent technical assistance systems (especially diagnostics), the sound classification is a significant and useful input for intelligent diagnostics. A high performance classification of the heterogeneous sounds of any mechanical components can support the diagnostic experts with a lot of information. Classical pattern recognition methods fail because of the complex features and the heterogeneous state noise. Because of no explicit human knowledge about the characteristic representation of the classes, classical feature generation is impossible. A new approach by generation of a concept for neural networks and realization by especially convolutional networks shows the power of technical sound classification methods. After the concept finding a parametrized network model is devised and realized. First results show the power of the RNNs and CNNs. Dependent on the parametrized configuration of the net architecture and the training sets an enhancement of the sound event classification is possible. © 2020 by SCITEPRESS - Science and technology Publications, Lda. All rights reserved

关键词： Recurrent neural networks

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Capacitive Proximity Sensor for Non-Contact Endoscope Localization

Capacitive Proximity Sensor for Non-Contact Endoscope Locali...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Christian Marzi Hosam Alagi Olivia Rau Jochen Hampe Jan Gerrit Korvink Bjö rn Hein Franziska Mathis-Ullrich Health Robotics and Automation Laboratory Institute for Anthropo-matics and Robotics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Intelligent Process Automation and Robotics Laboratory Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Institute of Microstructure Technology Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Department of Medicine I University Hospital Dresden Technical University (TU) Dresden Dresden Germany Karlsruhe University of Applied Sciences Karlsruhe Germany

The promising automation of flexible surgical instruments and robots is impeded by the lack of sensory means, which allow for sensing of an instrument's position to the surrounding tissue. This work presents a novel sensory method utilizing capacitive proximity sensing to derive a relative localization of a flexible instrument inside a hollow organ. The method is evaluated by exemplary integration of a sensor in a commercial gastroendoscope and accuracy analysis using a high precision robot. The results show an accuracy of distance sensing from a medical phantom's center of 2%. The method is also evaluated for the irregularly shaped surrounding of ex-vivo tissue in a dynamic scenario. This promising approach holds potential for transfer to clinical scenarios and for further development towards pose estimation of flexible surgical robots and shape sensing of a minimally invasive environment.

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Improving 3D deep learning segmentation with biophysically motivated cell synthesis

arXiv

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arXiv 2024年

作者： Bruch, Roman Vitacolonna, Mario Nürnberg, Elina Sauer, Simeon Rudolf, Rüdiger Reischl, Markus Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Institute of Molecular and Cell Biology Mannheim University of Applied Sciences Paul-Wittsack-Straße 10 Mannheim68163 Germany CeMOS Mannheim University of Applied Sciences Paul-Wittsack-Straße 10 Mannheim68163 Germany CHARISMA Mannheim University of Applied Sciences Paul-Wittsack-Straße 10 Mannheim68163 Germany

Biomedical research increasingly relies on 3D cell culture models and AI-based analysis can potentially facilitate a detailed and accurate feature extraction on a single-cell level. However, this requires for a precise segmentation of 3D cell datasets, which in turn demands high-quality ground truth for training. Manual annotation, the gold standard for ground truth data, is too time-consuming and thus not feasible for the generation of large 3D training datasets. To address this, we present a novel framework for generating 3D training data, which integrates biophysical modeling for realistic cell shape and alignment. Our approach allows the in silico generation of coherent membrane and nuclei signals, that enable the training of segmentation models utilizing both channels for improved performance. Furthermore, we present a new GAN training scheme that generates not only image data but also matching labels. Quantitative evaluation shows superior performance of biophysical motivated synthetic training data, even outperforming manual annotation and pretrained models. This underscores the potential of incorporating biophysical modeling for enhancing synthetic training data quality. Copyright © 2024, The Authors. All rights reserved.

关键词： Cell culture

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Modification of the sliding joint in equipment tribotestor m`89

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Materials Science Forum 2020年 994卷 296-303页

作者： Jarmila, Oravcová Eva, Labašová Slovak University of Technology Faculty of Materials Science and Technology Institute of Applied Informatics Automation and Mechatronics Bottova 25 Slovakia

This work deals with the modification of the equipment Tribotestor. The equipment was originally designed to measure the friction coefficient of sliding bearings. The structural modification of the attachment of the sliding node has expanded its use. The equipment now also allows to measure the parameters of sliding nodes, which consist of a plane surface and a cylindrical surface. Correctness of this modification was verifieded by the experimental tests performed on several samples. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Friction

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Design of aspheric rotation optics

Design of aspheric rotation optics

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Frontiers in Optics, FIO 2019 - Part of Frontiers in Optics + Laser Science APS/DLS

作者： Sieber, Ingo Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

Design of a novel varifocal freeform optics with aspheric surface profiles to reduce aberration effects. Its focal length will be tuned by rotating one of two helically formed lens bodies around the optical axis. ... 详细信息

ISBN: (纸本)9781943580675

关键词： Optical design

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Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree Search

Learning Reward Models for Cooperative Trajectory Planning w...

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IEEE Symposium on Intelligent Vehicle

作者： Karl Kurzer Matthias Bitzer J. Marius Zö llner Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany Bosch Center for Artificial Intelligence Robert Bosch GmbH Renningen Germany

Cooperative trajectory planning methods for automated vehicles can solve traffic scenarios that require a high degree of cooperation between traffic participants. However, for cooperative systems to integrate into human-centered traffic, the automated systems must behave human-like so that humans can anticipate the system’s decisions. While Reinforcement Learning has made remarkable progress in solving the decision-making part, it is non-trivial to parameterize a reward model that yields predictable actions. This work employs feature-based Maximum Entropy Inverse Reinforcement Learning combined with Monte Carlo Tree Search to learn reward models that maximize the likelihood of recorded multi-agent cooperative expert trajectories. The evaluation demonstrates that the approach can recover a reasonable reward model that mimics the expert and performs similarly to a manually tuned baseline reward model.

关键词： Monte Carlo methods Trajectory planning Intelligent vehicles MIMICs Decision making Reinforcement learning Predictive models

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*** — A Julia Package for Polynomial Chaos in Systems and Control

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IFAC-PapersOnLine 2020年第2期53卷 7210-7216页

作者： Tillmann Mühlpfordt Frederik Zahn Veit Hagenmeyer Timm Faulwasser Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

Polynomial chaos expansion (pce) is an increasingly popular technique for uncertainty propagation and quantification in systems and control. Based on the theory of Hilbert spaces and orthogonal polynomials, PCE allows for a unifying mathematical framework to study systems under arbitrary uncertainties of finite variance; we introduce this problem as a so-called mapping under uncertainty. For practical PCE-based applications we require orthogonal polynomials relative to given probability densities, and their quadrature rules. With *** we provide a Julia software package that delivers the desired functionality: given a probability density function, *** offers several numerical routines to construct the respective orthogonal polynomials, and the quadrature rules together with tensorized scalar products. *** is the first PCE-related software written in Julia, a scientific programming language that combines the readability of scripted languages with the speed of compiled languages. We provide illustrating numerical examples that show both PCE and *** in action.

关键词： polynomial chaos expansion uncertainties stochastic optimal control Julia

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Design-for-manufacture of high-resolution 3D printed rotation optics

Design-for-manufacture of high-resolution 3D printed rotatio...

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Freeform Optics, Freeform 2019 - Part of Optical Design and Fabrication 2019

作者： Sieber, Ingo Moser, Daniel Gengenbach, Ulrich Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

Design and manufacture of a novel varifocal freeform optics by means of high-resolution 3D printing are presented. Tuning of its focal length is achieved by mutually rotating two lens bodies around the optical axis. &... 详细信息

ISBN: (纸本)9781943580606

关键词： Lenses

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