@book{HaeuslerRoeser2014, author = {H{\"a}usler, Alexander and Roeser, Rainer}, title = {Gegen Rassismus und rechte Hetze stimmen! Rechte Propaganda in NRW vor den Europa- und Kommunalwahlen 2014}, publisher = {Landesintegrationsrat Nordrhein-Westfalen}, address = {D{\"u}sseldorf}, organization = {Landesintegrationsrat Nordrhein-Westfalen}, year = {2014}, language = {de} } @article{WolfUlrichBraun2023, author = {Wolf, Dominik Werner and Ulrich, Markus and Braun, Alexander}, title = {Novel developments of refractive power measurement techniques in the automotive world}, series = {Metrologia}, volume = {60}, journal = {Metrologia}, number = {064001}, publisher = {IOP Publishing}, issn = {1681-7575}, doi = {10.1088/1681-7575/acf1a4}, year = {2023}, abstract = {Refractive power measurements serve as the primary quality standard in the automotive glazing industry. In the light of autonomous driving new optical metrics are becoming more and more popular for specifying optical quality requirements for the windshield. Nevertheless, the link between those quantities and the refractive power needs to be established in order to ensure a holistic requirement profile for the windshield. As a consequence, traceable high-resolution refractive power measurements are still required for the glass quality assessment. Standard measurement systems using Moir{\´e} patterns for refractive power monitoring in the automotive industry are highly resolution limited, wherefore they are insufficient for evaluating the camera window area. Consequently, there is a need for more sophisticated refractive power measurement systems that provide a higher spatial resolution. In addition, a calibration procedure has to be developed in order to guarantee for comparability of the measurement results. For increasing the resolution, a measurement setup based on an auto-correlation algorithm is tested in this paper. Furthermore, a calibration procedure is established by using a single reference lens with a nominal refractive power of 100 km-1. For the calibration of the entire measurement range of the system, the lens is tilted by an inclination angle orthogonal to the optical axis. The effective refractive power is then given by the Kerkhof model. By adopting the measurement and calibration procedure presented in this paper, glass suppliers in the automotive industry will be able to detect relevant manufacturing defects within the camera window area more accurately paving the way for a holistic quality assurance of the windshield for future advanced driver-assistance system (ADAS) functionalities. Concurrently, the traceability of the measurement results is ensured by establishing a calibration chain based on a single reference lens, which is traced back to international standards.}, language = {en} } @article{MuellerBraun2023, author = {M{\"u}ller, Patrick and Braun, Alexander}, title = {MTF as a performance indicator for AI algorithms?}, series = {Electronic Imaging: Society for Imaging Science and Technology}, volume = {35}, journal = {Electronic Imaging: Society for Imaging Science and Technology}, number = {16}, publisher = {Society for Imaging Science and Technology}, issn = {2470-1173}, doi = {10.2352/EI.2023.35.16.AVM-125}, pages = {1 -- 7}, year = {2023}, abstract = {Abstract The modulation-transfer function (MTF) is a fundamental optical metric to measure the optical quality of an imaging system. In the automotive industry it is used to qualify camera systems for ADAS/AD. Each modern ADAS/AD system includes evaluation algorithms for environment perception and decision making that are based on AI/ML methods and neural networks. The performance of these AI algorithms is measured by established metrics like Average Precision (AP) or precision-recall-curves. In this article we research the robustness of the link between the optical quality metric and the AI performance metric. A series of numerical experiments were performed with object detection and instance segmentation algorithms (cars, pedestrians) evaluated on image databases with varying optical quality. We demonstrate with these that for strong optical aberrations a distinct performance loss is apparent, but that for subtle optical quality differences - as might arise during production tolerances - this link does not exhibit a satisfactory correlation. This calls into question how reliable the current industry practice is where a produced camera is tested end-of-line (EOL) with the MTF, and fixed MTF thresholds are used to qualify the performance of the camera-under-test.}, language = {en} } @inproceedings{MuenchReichAdam2020, author = {M{\"u}nch, Justin and Reich, Marius and Adam, Mario}, title = {Entwicklung eines Tools zur automatisierten Optimierung von Energiesystemen durch Maschinelles Lernen}, series = {Tagungsband, RETCon - 3. Regenerative Energietechnik Konferenz, 13.-14.02.2020, Nordhausen}, booktitle = {Tagungsband, RETCon - 3. Regenerative Energietechnik Konferenz, 13.-14.02.2020, Nordhausen}, editor = {Wesselak, Viktor}, publisher = {Hochschule Nordhausen}, address = {Nordhausen}, organization = {Hochschule Nordhausen}, isbn = {978-3-940820-16-7}, doi = {10.22032/dbt.46243}, pages = {152 -- 156}, year = {2020}, language = {de} } @unpublished{PriesmannMuenchRidhaetal.2021, author = {Priesmann, Jan and M{\"u}nch, Justin and Ridha, Elias and Spiegel, Thomas and Reich, Marius and Adam, Mario and Nolting, Lars and Praktiknjo, Aaron}, title = {Artificial Intelligence and Design of Experiments for Assessing Security of Electricity Supply: A Review and Strategic Outlook}, doi = {https://doi.org/10.48550/arXiv.2112.04889}, year = {2021}, abstract = {Assessing the effects of the energy transition and liberalization of energy markets on resource adequacy is an increasingly important and demanding task. The rising complexity in energy systems requires adequate methods for energy system modeling leading to increased computational requirements. Furthermore, with complexity, uncertainty increases likewise calling for probabilistic assessments and scenario analyses. To adequately and efficiently address these various requirements, new methods from the field of data science are needed to accelerate current methods. With our systematic literature review, we want to close the gap between the three disciplines (1) assessment of security of electricity supply, (2) artificial intelligence, and (3) design of experiments. For this, we conduct a large-scale quantitative review on selected fields of application and methods and make a synthesis that relates the different disciplines to each other. Among other findings, we identify metamodeling of complex security of electricity supply models using AI methods and applications of AI-based methods for forecasts of storage dispatch and (non-)availabilities as promising fields of application that have not sufficiently been covered, yet. We end with deriving a new methodological pipeline for adequately and efficiently addressing the present and upcoming challenges in the assessment of security of electricity supply.}, language = {en} } @techreport{ScherpFranzSaathoffetal.2009, author = {Scherp, Ansgar and Franz, Thomas and Saathoff, Carsten and Staab, Steffen}, title = {A Model of Events based on a Foundational Ontology}, series = {Arbeitsberichte aus dem Bereich Informatik}, journal = {Arbeitsberichte aus dem Bereich Informatik}, publisher = {Universit{\"a}t Koblenz-Landau}, address = {Koblenz}, organization = {Universit{\"a}t Koblenz-Landau}, issn = {1864-0850}, year = {2009}, language = {en} } @article{HiesingerTophoven2019, author = {Hiesinger, Karolin and Tophoven, Silke}, title = {Job requirement level, work demands, and health: a prospective study among older workers}, series = {International Archives of Occupational and Environmental Health}, volume = {92}, journal = {International Archives of Occupational and Environmental Health}, publisher = {Springer}, doi = {10.1007/s00420-019-01451-2}, pages = {1139 -- 1149}, year = {2019}, language = {en} } @article{HiesingerTophovenMarch2018, author = {Hiesinger, Karolin and Tophoven, Silke and March, Stefanie}, title = {Geschlechtsspezifische Verzerrungen bei der Erfassung von Depressivit{\"a}t}, series = {Pr{\"a}vention und Gesundheitsf{\"o}rderung}, volume = {13}, journal = {Pr{\"a}vention und Gesundheitsf{\"o}rderung}, number = {3}, publisher = {Springer}, doi = {10.1007/s11553-017-0634-x}, pages = {211 -- 217}, year = {2018}, language = {de} } @misc{BuchClaasEilertetal.2024, author = {Buch, Susanne and Claas, Oliver and Eilert, Eva and Fliegner, Angelika and Raatz, Philip and Reiter, Martin and Sauther-Patrascu, Katharina Sofie and Schick, Elena and Slowig, Benjamin}, title = {Fragen? Antworten! FDM-FAQ - Die Wissensbasis der FDMScout:innen}, publisher = {Zenodo, GitHub}, doi = {10.5281/zenodo.12760388}, year = {2024}, abstract = {Know-how im Forschungsdatenmanagement (FDM) aufzubauen und zu erhalten, ist eine grundlegende, stetig an Bedeutung zunehmende Aufgabe an Hochschulen f{\"u}r angewandte Wissenschaften (HAW) (vgl. Buch et al. 2023). Dieses Know-how kommt Mitarbeitenden der FDM-Servicestellen einer Hochschule ebenso zugute wie Forschenden selbst: Es versetzt FDM-Personal in die Lage, Forschende zielf{\"u}hrend zu beraten, zu schulen und ihnen ad{\"a}quate Tools und Services zu vermitteln; Forschenden werden konkrete L{\"o}sungswege f{\"u}r (neuartige) praktische Herausforderungen in Forschungsvorhaben aufgezeigt. Beide Gruppen werden weiterhin bef{\"a}higt, hochschulinterne und -{\"u}bergreifende Diskurse und Initiativen zum FDM mitzugestalten. Auf diese Weise tr{\"a}gt FDM-Know-how dazu bei, anforderungsgerechte und gewinnbringende Rahmenbedingungen f{\"u}r Forschung an HAW zu schaffen. Im gemeinsamen Handlungsfeld "Information und Sensibilisierung" haben es sich die Projektverb{\"u}nde der F{\"o}rderlinie FDMScouts.nrw zur Aufgabe gemacht, das gewonnene Handlungswissen in Form einer FDM-Wissensbasis strukturiert und frei zug{\"a}nglich zu bewahren (vgl. Eilert/Slowig 2022), um die Weitergabe der gesammelten Erfahrungen auch {\"u}ber die F{\"o}rderlaufzeit hinaus zu erm{\"o}glichen und vor allem andere HAW beim Aufbau von Know-how in den FDM-Servicestellen zu bef{\"a}higen. Zweck der nun vorliegenden Wissensbasis ist es, den Zielgruppen der Besch{\"a}ftigten in FDM-Servicestellen und der Forschenden den Einstieg ins Forschungsdatenmanagement zu erleichtern - gerade in Anbetracht der Tatsache, dass sich FDM-Strukturen an HAW vielfach noch in der Aufbau- oder Konsolidierungsphase befinden. Sie tr{\"a}gt dementsprechend solche Aspekte zusammen, die sich erfahrungsgem{\"a}ß als besonders relevant erweisen: Was ist zwecks Orientierung im weitreichenden Feld FDM von besonderer Wichtigkeit (Begriffe, Einstiegsseiten, Umsetzungstipps etc.)? Welche Ressourcen und Anlaufstellen sind zentral? Welche Fragen stellen sich f{\"u}r Forschende und FDM-Personal immer wieder? FDM-Wissen aufzubauen und zu vermitteln bedeutet, diese und {\"a}hnliche Fragen zu beantworten. In diesem Sinn dient das Format der Frequently Asked Questions als grundlegende Organisationsmethode der vorliegenden Wissensbasis. Als Verkn{\"u}pfung h{\"a}ufig gestellter Fragen mit kompakten Antworten stellen die FAQs niedrigschwellig Wissen bereit und tragen im Idealfall durch ihre Interaktionsorientierung zur Gestaltung konkreter Kommunikationssituationen (Beratungsgespr{\"a}che, Schulungen) bei. Als Einstiegspunkte dienen die einzelnen Phasen des Forschungsdatenlebenszyklus und die Fragen, die sie regelm{\"a}ßig aufwerfen. Darunter finden Forschende und FDM-Servicepersonal Antworten, Informationen und Links, die Orientierung bieten und Umsetzungsschritte benennen. Als GitHub-Repository realisiert, ist die M{\"o}glichkeit zur dezentralen Aktualisierung und Erweiterung gegeben. Insgesamt leistet die Wissensbasis einen Beitrag zur Dokumentation der Arbeit der FDMScouts.nrw-Verb{\"u}nde und soll die nachhaltige Verankerung von FDM an HAW unterst{\"u}tzen.}, language = {de} }