@inproceedings{58233,
  abstract     = {{Extreme weather situations increasingly lead to hazardous situations with high demands on de-cision-making and communication. Weather and impact forecasts serve to prepare for such an emergency situation, and extreme data must be processed during operations. Therefore, extreme data is categorised with reference to global weather data and data from local situation reconnais-sance using sensors carried by mobile robots. A concept is presented in which information quality is explicitly considered in visualization for situational awareness. This extends existing principles of information visualisation with regard to the uncertainty resulting from extreme data. The re-sults are intended to help decision-makers at different management levels to make informed decisions.}},
  author       = {{Gräßler, Iris and Pottebaum, Jens and Hieb, Michael and Pratzler-Wanczura, Sylvia and Krüger, Oliver and Kruijff, Ivana and Rupp, Nicola}},
  booktitle    = {{Proceedings of the Days of Security Research 2023}},
  editor       = {{Bernsdorf, Bodo}},
  location     = {{Dortmund}},
  publisher    = {{Technische Hochschule Georg Agricola}},
  title        = {{{Weather-induced emergency situations: Extreme data in situational awareness and visualisation}}},
  year         = {{2024}},
}

@inproceedings{34395,
  author       = {{Gräßler, Iris and Hieb, Michael and Roesmann, Daniel and Unverzagt, Marc}},
  editor       = {{Lohweg, Volker}},
  pages        = {{95--106}},
  publisher    = {{Springer Vieweg}},
  title        = {{{Creating Synthetic Training Data for Machine Vision Quality Gates}}},
  doi          = {{10.1007/978-3-662-66769-9_7 }},
  year         = {{2023}},
}

@inproceedings{46974,
  author       = {{Gräßler, Iris and Hieb, Michael}},
  publisher    = {{CIRP }},
  title        = {{{Creating Synthetic Datasets for Deep Learning used in Machine Vision}}},
  year         = {{2023}},
}

@inproceedings{52839,
  author       = {{Gräßler, Iris and Hieb, Michael and Roesmann, Daniel and Unverzagt, Marc and Pottebaum, Jens}},
  booktitle    = {{SSRN Electronic Journal}},
  issn         = {{1556-5068}},
  keywords     = {{General Earth and Planetary Sciences, General Environmental Science}},
  publisher    = {{Elsevier BV}},
  title        = {{{Virtual learning environment for teaching the handling of collaborative robots}}},
  doi          = {{10.2139/ssrn.4471596}},
  year         = {{2023}},
}

@inproceedings{46973,
  author       = {{Gräßler, Iris and Hieb, Michael}},
  booktitle    = {{Automation 2023}},
  pages        = {{765--776}},
  publisher    = {{VDI Verlag }},
  title        = {{{Cloud-Computing für die Verwendung synthetischer Trainingsdaten für Machine Vision Quality Gates}}},
  doi          = {{10.51202/9783181024195-765}},
  volume       = {{2419}},
  year         = {{2023}},
}

@inproceedings{52816,
  abstract     = {{Manufacturing companies face the challenge of reaching required quality standards. Using
optical sensors and deep learning might help. However, training deep learning algorithms
require large amounts of visual training data. Using domain randomization to generate synthetic
image data can alleviate this bottleneck. This paper presents the application of synthetic
image training data for optical quality inspections using visual sensor technology. The results
show synthetically generated training data are appropriate for visual quality inspections.}},
  author       = {{Gräßler, Iris and Hieb, Michael}},
  booktitle    = {{Lectures}},
  keywords     = {{synthetic training data, machine vision quality gates, deep learning, automated inspection and quality control, production control}},
  location     = {{Nuremberg}},
  pages        = {{253--524}},
  publisher    = {{AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany}},
  title        = {{{Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing}}},
  doi          = {{10.5162/smsi2023/d7.4}},
  year         = {{2023}},
}

@inproceedings{46988,
  abstract     = {{Extremwettersituationen sind durch die Kombination von globalen und lokalen Wirkzusammenhän-gen gekennzeichnet. In der Gefahrenanalyse und -reaktion ist deshalb der Umgang mit extremen Daten erforderlich, die von heterogenen Datenquellen bezogen und mittels unterschiedlicher Ver-fahren bis hin zum maschinellen Lernen ausgewertet werden. Die Visualisierung dieser zwangsläufig unsicherheitsbehafteten Daten stellt eine Herausforderung dar. Diese wirkt umso bedeutsamer, je weniger Fachexpertise in Bereichen wie Meteorologie, Geologie oder Sensortechnik in einer Füh-rungs- oder Leitstelle eingebunden werden kann. Das Management kritischer Situationen in Echtzeit bei extremen und komplexen Daten muss daher auf einer Bewertung der Informationsqualität von extremen Daten beruhen. Diese Bewertung ist abhängig vom Anwendungskontext in unterschiedli-chen Führungs- und Assistenzstellen sowie der verfügbaren Infrastruktur mit Geräten zur Visualisie-rung, Schnittstellen von Wetterdiensten, Sensorsysteme und Rettungsrobotik. Im Beitrag wird der Ansatz des EU-Projekts CREXDATA in Bezug auf mögliche pluviale Hochwassersituationen in Inns-bruck vorgestellt. Grundlage bildet die Kategorisierung von extremen Daten, die Schnittstellen zu Datenquellen mit globalem und lokalem Bezug sowie Anwendungsfälle für die Visualisierung von Informationen. Es werden somit Grundlagen präsentiert, die in allen Formen von geobasierten Lage- und Führungsinformationssystemen zum Einsatz kommen können.}},
  author       = {{Pottebaum, Jens and Rechberger, Christina and Hieb, Michael and Gräßler, Iris and Resch, Christian}},
  booktitle    = {{Tagungsband der Fachtagung Katastrophenforschung 2023}},
  isbn         = {{978-3-900397-11-1}},
  location     = {{Leoben}},
  pages        = {{26--29}},
  title        = {{{Extremwettersituationen in alpinen Gebieten: Management kritischer Situationen in Echtzeit bei extremen und komplexen Daten}}},
  year         = {{2023}},
}

@article{32174,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Increasing system complexity can be controlled by using systems engineering processes. INCOSE defines processes with inputs and outputs (artifacts) for this purpose. Specific SE roles are used to organize the tasks of the processes within the company. In this work, the responsibilities for artifacts are evaluated by means of the RACI scheme and examined by a cluster analysis and discussed for a SE transformation project with a German automotive OEM. As a result of the study, the optimal composition for systems engineering teams is identified and the systems engineering roles are prioritized.</jats:p>}},
  author       = {{Gräßler, Iris and Thiele, Henrik and Grewe, Benedikt and Hieb, Michael}},
  issn         = {{2732-527X}},
  journal      = {{Proceedings of the Design Society}},
  keywords     = {{systems engineering (SE), project management, model-based systems engineering (MBSE)}},
  location     = {{Dubrovnik}},
  pages        = {{1875--1884}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Responsibility Assignment in Systems Engineering}}},
  doi          = {{10.1017/pds.2022.190}},
  volume       = {{2}},
  year         = {{2022}},
}

@article{31791,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Requirements changes are a leading cause for project failures. Due to propagation effects, change management requires dependency analysis. Existing approaches have shortcomings regarding ability to process large requirement sets, availability of required data, differentiation of propagation behavior and consideration of higher order dependencies. This paper introduces a new method for advanced requirement dependency analysis based on machine learning. Evaluation proves applicability and high performance by means of a case example, 4 development projects and 3 workshops with industry experts.</jats:p>}},
  author       = {{Gräßler, Iris and Oleff, Christian and Hieb, Michael and Preuß, Daniel}},
  issn         = {{2732-527X}},
  journal      = {{Proceedings of the Design Society}},
  pages        = {{1865--1874}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Automated Requirement Dependency Analysis for Complex Technical Systems}}},
  doi          = {{10.1017/pds.2022.189}},
  volume       = {{2}},
  year         = {{2022}},
}

@inproceedings{33892,
  author       = {{Gräßler, Iris and Tusek, Alena Marie and Thiele, Henrik and Preuß, Daniel and Grewe, Benedikt and Hieb, Michael}},
  booktitle    = {{XXXIII ISPIM Innovation Conference Proceedings}},
  isbn         = {{978-952-335-694-8}},
  location     = {{Copenhagen, Denmark}},
  publisher    = {{ LUT Scientific and Expertise Publications}},
  title        = {{{Literature study on the potential of Artificial Intelligence in Scenario-Technique}}},
  year         = {{2022}},
}

@inproceedings{34262,
  author       = {{Gräßler, Iris and Hieb, Michael and Roesmann, Daniel}},
  booktitle    = {{Automation 2022}},
  publisher    = {{VDI Verlag}},
  title        = {{{Gestaltung einer Forschungsinfrastruktur für die Anwendung digitaler Werkzeuge in Cyber-Physischen Produktionssystemen}}},
  doi          = {{10.51202/9783181023990-667}},
  year         = {{2022}},
}