Fraunhofer-Institut für Produktionstechnologie IPT
Fraunhofer-Institut für Produktionstechnologie IPT

Referenzen

Referenzen

  • [1] F. Biermann, J. Mathews, B. Nießing, N. König und R. H. Schmitt, »Automating Laboratory Processes by Connecting Biotech and Robotic Devices—An Overview of the Current Challenges, Existing Solutions and Ongoing Developments,« Processes, Jg. 9, Nr. 6, 2021, Art. Nr. 966, doi: 10.3390/pr9060966.
  • [2] Y. Han, E. Makarova, M. Ringel und V. Telpis, »Digitization, automation, and online testing: The future of pharma quality control: Emerging technologies can make quality control (QC) faster and more efficient. What do pharma companies need to do to become QC leaders?,« 2019. Zugriff am: 22. Januar 2025. [Online]. Verfügbar unter: Digitization, automation, and online testing: The future of pharma quality control
  • [3] A. Sinsel, Das Internet der Dinge in der Produktion: Smart Manufacturing für Anwender und Lösungsanbieter. Berlin, Heidelberg: Springer Vieweg, 2020.
  • [4] System und Software-Engineering – Begriffe, ISO/IEC/IEEE 24765:2017, ISO Internationale Organisation für Normung, IEC Internationale Elektrotechnische Kommission und IEEE The Institute of Electrical and Electronics Engineers, Inc., Sep. 2017.
  • [5] MQTT. »MQTT:: The Standard for IoT Messaging.« Zugriff am: 22. Januar 2025. [Online.] Verfügbar: https://mqtt.org/
  • [6] OPC Foundation. »OPC 10000-1: UA Part 1: Overview and Concepts.« Zugriff am: 22. Januar 2025. [Online.] Verfügbar: https://reference.opcfoundation.org/Core/Part1/v104/docs/
  • [7] Association Consortium Standardization in Lab Automation (SiLA). »SiLA 2 Part (A) – Overview, Concepts and Core Specification: Working Draft Version.« Zugriff am: 22. Januar 2025. [Online.] Verfügbar: https://docs.google.com/document/ /1nGGEwbx45ZpKeKYH18VnNysREbr1EXH6FqlCo03yASM/edit#heading=h.6hlm463x8ygx
  • [8] A. Hideg und F. Dorfmüller. »LADS: Laboratory and Analytical Device Standard: Bridging the Gap, Connecting the Lab!« Zugriff am: 12. März 2025.
  • [9] OPC Foundation. »OPC 30500-1: Laboratory and Analytical Devices.« Zugriff am: 22. Januar 2025. [Online.] Verfügbar: https://reference.opcfoundation.org/LADS/v100/docs/
  • [10] Faunhofer-Institut für Produktionstechnologie IPT. »Software für die Laborautomatisierung: cope.life – Zentrale Steuerung für die gesamte Zell- und Genproduktion.« Zugriff am: 22. Januar 2025. [Online.] Verfügbar: https://www.ipt.fraunhofer.de/de/angebot/sondermaschinen/laborautomatisierung/software.html
  • [11] J. Krauß, J. Dorißen, H. Mende, M. Frye und R. H. Schmitt, »Machine Learning and Artificial Intelligence in Production: Application Areas and Publicly Available Data Sets,« in Production at the leading edge of technology: Proceedings of the 9th Congress of the German Academic Association for Production Technology (WGP), September 30th - October 2nd, Hamburg 2019, J. P. Wulfsberg, W. Hintze und B.-A. Behrens, Hg., Berlin, Heidelberg: Springer Vieweg, 2019, S. 493–501.
  • [12] R. Jonak. »Labor-Informations- und Management-Systeme: Overwiew. Positionierung, LIMS-Einsatzbereiche und Trends.« Zugriff am: 22. Januar 2025. [Online.] Verfügbar: https://analyticalscience.wiley.com/content/article-do/labor-informations--und-management-systeme
  • [13] Software engineering–Software product Quality Requirements and Evaluation (SQuaRE)  Data quality model, ISO/IEC 25012:2008, ISO Internationale Organisation für Normung und IEC Internationale Elektrotechnische Kommission, Dez. 2008.
  • [14] J. Riley, Understanding metadata: What is metadata, and what is it for? (A Primer Publication of the National Information Standards Organization) (NISO Primer series). Baltimore: National Information Standards Organization, 2017. Zugriff am: 22. Januar 2025. [Online]. Verfügbar unter: https://groups.niso.org/higherlogic/ws/public/download/17446/Understanding%20Metadata.pdf
  • [15] M. Baca, Hg. Introduction to Metadata: Third Edition (The Getty Research Institute publications program). Los Angeles: Getty Research Institute, 2016. [Online]. Verfügbar unter: https://www.getty.edu/publications/intrometadata
  • [16] H. Herre, B. Heller, P. Burek, R. Hoehndorf, F. Loebe und H. Michalek, »General Formal Ontology (GFO). A Foundational Ontology Integrating Objects and Processes: Part I: Basic Principles. Version 1.0.1,« Research Group Ontologies in Medicine (Onto-Med), University of Leipzig, 2007. Zugriff am: 12. März 2025. [Online]. Verfügbar unter: https://www.onto-med.de/sites/www.onto-med.de/files/files/uploads/Publications/2007/gfo-part1-v1-0-1.pdf
  • [17] A. Chang et al., »BRENDA, the ELIXIR core data resource in 2021: new developments and updates,« Nucleic acids research, Jg. 49, D1, D498-D508, 2021, doi: 10.1093/nar/gkaa1025.
  • [18] N. Kühl, M. Goutier, R. Hirt und G. Satzger, »Machine Learning in Artificial Intelligence: Towards a Common Understanding,« in Proceedings of the 52nd Hawaii International Conference on System Sciences | 2019, T. X. Bui, Hg., University of Hawaii at Manoa, Hamilton Library, ScholarSpace: Honolulu, 2019, S. 5236–5245. Zugriff am: 22. Januar 2025. [Online]. Verfügbar unter: https://hdl.handle.net/10125/59960
  • [19] Fraunhofer Institut für Produktionstechnik IPT. »Vertrauenswürdigkeit schaffen für industrielle KI-Anwendungen.« Zugriff am: 22. Januar 2025. [Online.] Verfügbar: https://www.ipt.fraunhofer.de/de/angebot/digitalisierung/ki/vertrauen-in-ki.html
  • [20] N. Bäckel et al., »Elaborating the potential of Artificial Intelligence in automated CAR-T cell manufacturing,« Front. Mol. Med, Jg. 3, 2023, Art. Nr. 1250508, doi: 10.3389/fmmed.2023.1250508.
  • [21] D. Gurevitch, »Economic Justification of Laboratory Automation,« JALA: Journal of the Association for Laboratory Automation, Jg. 9, Nr. 1, S. 33–43, 2004, doi: 10.1016/S1535-5535-03-00086-8.
  • [22] B. Nießing, R. Kiesel, L. Herbst und R. H. Schmitt, »Techno-Economic Analysis of Automated iPSC Production,« Processes, Jg. 9, Nr. 2, 2021, Art. Nr. 240, doi: 10.3390/pr9020240.
  • [23] A. Elanzew et al., »The StemCellFactory: A Modular System Integration for Automated Generation and Expansion of Human Induced Pluripotent Stem Cells,« Frontiers in bioengineering and biotechnology, Jg. 8, 2020, Art. Nr. 580352, doi: 10.3389/fbioe.2020.580352.
  • [24] B. Nießing et al., »Automated CRISPR/Cas9-based genome editing of human pluripotent stem cells using the StemCellFactory,« Frontiers in bioengineering and biotechnology, Jg. 12, 2024, Art. Nr. 1459273, doi: 10.3389/fbioe.2024.1459273.
  • [25] J. Ochs, F. Barry, R. Schmitt und J. M. Murphy, »Advances in automation for the production of clinical-grade mesenchymal stromal cells: the AUTOSTEM robotic platform,« Cell Gene Therapy Insights, Jg. 3, Nr. 8, S. 739–748, 2017, doi: 10.18609/cgti.2017.073.
  • [26] F. Erkens, »AIDPATH - Modular Manufacturing Platform for AI-enabled hospital-based ATMP Production,« 2022, doi: 10.24406/PUBLICA-338.
  • [27] J. Krieger et al., »Implementation of an Automated Manufacturing Platform for Engineering of Functional Osteochondral Implants,« Procedia CIRP, Nr. 110, S. 32–35, 2022, doi: 10.1016/j.procir.2022.06.008.
  • [28] F. W. Schenk, N. Brill, U. Marx, D. Hardt, N. König und R. Schmitt, »High-speed microscopy of continuously moving cell culture vessels,« Scientific reports, Jg. 6, 2016, Art. Nr. 34038, doi: 10.1038/srep34038.
  • [29] L. Leyendecker et al., »A Modular Deep Learning Pipeline for Cell Culture Analysis: Investigating the Proliferation of Cardiomyocytes,« in International Conference on Medical Imaging with Deep Learning, MIDL 2022, E. Konukoglu, Hg., 2022, S. 760–773. [Online]. Verfügbar unter: https://openreview.net/pdf?id=hTil-xs1xNq
  • [30] F. Narrog et al., »LIFTOSCOPE: development of an automated AI-based module for time-effective and contactless analysis and isolation of cells in microtiter plates,« Journal of biological engineering, Jg. 17, 2023, Art. Nr. 10, doi: 10.1186/s13036-023-00329-9.
  • [31] F. Nienhaus, T. Piotrowski, B. Nießing, N. König und R. H. Schmitt, »Adaptive phase contrast microscopy to compensate for the meniscus effect,« Scientific reports, Jg. 13, 2023, Art. Nr. 5785, doi: 10.1038/s41598-023-32917-6.
  • [32] J. G. Fujimoto, C. Pitris, S. A. Boppart und M. E. Brezinski, »Optical coherence tomography: an emerging technology for biomedical imaging and optical biopsy,« Neoplasia (New York, N.Y.), Jg. 2, 1-2, S. 9–25, 2000, doi: 10.1038/sj.neo.7900071.
  • [33] R. Wessels, D. M. de Bruin, D. J. Faber, T. G. van Leeuwen, M. van Beurden und T. J. M. Ruers, »Optical biopsy of epithelial cancers by optical coherence tomography (OCT),« Lasers in medical science, Jg. 29, Nr. 3, S. 1297–1305, 2014, doi: 10.1007/s10103-013-1291-8.
  • [34] M. Brehove et al., »Cell monitoring with optical coherence tomography,« Cytotherapy, Jg. 25, Nr. 2, S. 120–124, 2023, doi: 10.1016/j.jcyt.2022.09.008.