[{"has_accepted_license":"1","title":"Splatman : Automatisierte Erstellung von optimierten Datensets aus digitalen Szenen für Gaussian Splatting","ddc":["004"],"status":"public","publication_status":"submitted","publisher":"Technische Hochschule Ostwestfalen-Lippe","author":[{"id":"73022","last_name":"Behrens","full_name":"Behrens, Colin","first_name":"Colin"}],"type":"master_thesis","citation":{"din1505-2-1":"<span style=\"font-variant:small-caps;\">Behrens, Colin</span>: <i>Splatman : Automatisierte Erstellung von optimierten Datensets aus digitalen Szenen für Gaussian Splatting</i>. Lemgo : Technische Hochschule Ostwestfalen-Lippe","apa":"Behrens, C. (n.d.). <i>Splatman : Automatisierte Erstellung von optimierten Datensets aus digitalen Szenen für Gaussian Splatting</i>. Technische Hochschule Ostwestfalen-Lippe.","van":"Behrens C. Splatman : Automatisierte Erstellung von optimierten Datensets aus digitalen Szenen für Gaussian Splatting. Lemgo: Technische Hochschule Ostwestfalen-Lippe; 69 p.","ieee":"C. Behrens, <i>Splatman : Automatisierte Erstellung von optimierten Datensets aus digitalen Szenen für Gaussian Splatting</i>. Lemgo: Technische Hochschule Ostwestfalen-Lippe.","chicago":"Behrens, Colin. <i>Splatman : Automatisierte Erstellung von optimierten Datensets aus digitalen Szenen für Gaussian Splatting</i>. Lemgo: Technische Hochschule Ostwestfalen-Lippe, n.d.","mla":"Behrens, Colin. <i>Splatman : Automatisierte Erstellung von optimierten Datensets aus digitalen Szenen für Gaussian Splatting</i>. Technische Hochschule Ostwestfalen-Lippe.","havard":"C. Behrens, Splatman : Automatisierte Erstellung von optimierten Datensets aus digitalen Szenen für Gaussian Splatting, Technische Hochschule Ostwestfalen-Lippe, Lemgo, n.d.","chicago-de":"Behrens, Colin. <i>Splatman : Automatisierte Erstellung von optimierten Datensets aus digitalen Szenen für Gaussian Splatting</i>. Lemgo: Technische Hochschule Ostwestfalen-Lippe.","bjps":"<b>Behrens C</b> (n.d.) <i>Splatman : Automatisierte Erstellung von optimierten Datensets aus digitalen Szenen für Gaussian Splatting</i>. Lemgo: Technische Hochschule Ostwestfalen-Lippe.","ufg":"<b>Behrens, Colin</b>: Splatman : Automatisierte Erstellung von optimierten Datensets aus digitalen Szenen für Gaussian Splatting, Lemgo o. J.","ama":"Behrens C. <i>Splatman : Automatisierte Erstellung von optimierten Datensets aus digitalen Szenen für Gaussian Splatting</i>. Technische Hochschule Ostwestfalen-Lippe","short":"C. Behrens, Splatman : Automatisierte Erstellung von optimierten Datensets aus digitalen Szenen für Gaussian Splatting, Technische Hochschule Ostwestfalen-Lippe, Lemgo, n.d."},"supervisor":[{"id":"83513","full_name":"Kutter, Alexander","first_name":"Alexander","last_name":"Kutter"}],"file_date_updated":"2025-02-24T13:00:01Z","year":"2025","date_created":"2025-02-17T10:13:31Z","department":[{"_id":"DEP2001"}],"date_updated":"2025-02-24T13:00:02Z","user_id":"83781","file":[{"file_name":"Masterarbeit_Colin_Behrens.pdf","relation":"main_file","file_id":"12492","file_size":10558536,"date_updated":"2025-02-24T13:00:01Z","date_created":"2025-02-24T12:08:03Z","content_type":"application/pdf","access_level":"local","creator":"mhd-u1u"}],"page":"69","keyword":["Gaussian","Splatting","Computergraphics","Scanning","3D","NeRF","Datensätze"],"language":[{"iso":"ger"}],"_id":"12445","place":"Lemgo"},{"keyword":["3D-Rekonstruktion","Gaussian Splatting","Photogrammetrie","LiDAR","autonome Robotik","digitale Zwillinge","Industrie 4.0"],"file":[{"creator":"sa3-8ag","access_level":"open_access","content_type":"application/pdf","success":1,"date_created":"2025-03-04T09:48:08Z","date_updated":"2025-03-04T09:48:08Z","file_size":40444229,"file_id":"12647","relation":"main_file","file_name":"Bachelorarbeit_Sam-Wiemann_15457078_ELSA.pdf"}],"place":"Detmold","_id":"12646","language":[{"iso":"ger"}],"year":"2025","supervisor":[{"last_name":"Kutter","full_name":"Kutter, Alexander","first_name":"Alexander","id":"83513"},{"last_name":"Behrens","first_name":"Colin","full_name":"Behrens, Colin","id":"77083"}],"file_date_updated":"2025-03-04T09:48:08Z","citation":{"ama":"Wiemann S. <i>Entwicklung und Evaluierung eines multisensorischen Kamera-Setups zur 3D-Raumrekonstruktion in einer dynamischen Industrieumgebung mittels Gaussian Splatting auf einem autonomen Robotersystem</i>. Technische Hochschule Ostwestfalen-Lippe; 2025.","mla":"Wiemann, Sam. <i>Entwicklung und Evaluierung eines multisensorischen Kamera-Setups zur 3D-Raumrekonstruktion in einer dynamischen Industrieumgebung mittels Gaussian Splatting auf einem autonomen Robotersystem</i>. Technische Hochschule Ostwestfalen-Lippe, 2025.","chicago":"Wiemann, Sam. <i>Entwicklung und Evaluierung eines multisensorischen Kamera-Setups zur 3D-Raumrekonstruktion in einer dynamischen Industrieumgebung mittels Gaussian Splatting auf einem autonomen Robotersystem</i>. Detmold: Technische Hochschule Ostwestfalen-Lippe, 2025.","ieee":"S. Wiemann, <i>Entwicklung und Evaluierung eines multisensorischen Kamera-Setups zur 3D-Raumrekonstruktion in einer dynamischen Industrieumgebung mittels Gaussian Splatting auf einem autonomen Robotersystem</i>. Detmold: Technische Hochschule Ostwestfalen-Lippe, 2025.","van":"Wiemann S. Entwicklung und Evaluierung eines multisensorischen Kamera-Setups zur 3D-Raumrekonstruktion in einer dynamischen Industrieumgebung mittels Gaussian Splatting auf einem autonomen Robotersystem. Detmold: Technische Hochschule Ostwestfalen-Lippe; 2025.","short":"S. Wiemann, Entwicklung und Evaluierung eines multisensorischen Kamera-Setups zur 3D-Raumrekonstruktion in einer dynamischen Industrieumgebung mittels Gaussian Splatting auf einem autonomen Robotersystem, Technische Hochschule Ostwestfalen-Lippe, Detmold, 2025.","havard":"S. Wiemann, Entwicklung und Evaluierung eines multisensorischen Kamera-Setups zur 3D-Raumrekonstruktion in einer dynamischen Industrieumgebung mittels Gaussian Splatting auf einem autonomen Robotersystem, Technische Hochschule Ostwestfalen-Lippe, Detmold, 2025.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Wiemann, Sam</span>: <i>Entwicklung und Evaluierung eines multisensorischen Kamera-Setups zur 3D-Raumrekonstruktion in einer dynamischen Industrieumgebung mittels Gaussian Splatting auf einem autonomen Robotersystem</i>. Detmold : Technische Hochschule Ostwestfalen-Lippe, 2025","ufg":"<b>Wiemann, Sam</b>: Entwicklung und Evaluierung eines multisensorischen Kamera-Setups zur 3D-Raumrekonstruktion in einer dynamischen Industrieumgebung mittels Gaussian Splatting auf einem autonomen Robotersystem, Detmold 2025.","bjps":"<b>Wiemann S</b> (2025) <i>Entwicklung und Evaluierung eines multisensorischen Kamera-Setups zur 3D-Raumrekonstruktion in einer dynamischen Industrieumgebung mittels Gaussian Splatting auf einem autonomen Robotersystem</i>. Detmold: Technische Hochschule Ostwestfalen-Lippe.","chicago-de":"Wiemann, Sam. 2025. <i>Entwicklung und Evaluierung eines multisensorischen Kamera-Setups zur 3D-Raumrekonstruktion in einer dynamischen Industrieumgebung mittels Gaussian Splatting auf einem autonomen Robotersystem</i>. Detmold: Technische Hochschule Ostwestfalen-Lippe.","apa":"Wiemann, S. (2025). <i>Entwicklung und Evaluierung eines multisensorischen Kamera-Setups zur 3D-Raumrekonstruktion in einer dynamischen Industrieumgebung mittels Gaussian Splatting auf einem autonomen Robotersystem</i>. Technische Hochschule Ostwestfalen-Lippe."},"type":"bachelor_thesis","oa":"1","user_id":"83781","date_created":"2025-03-04T09:49:19Z","department":[{"_id":"DEP2001"}],"date_updated":"2025-03-04T15:29:49Z","status":"public","publisher":"Technische Hochschule Ostwestfalen-Lippe","author":[{"id":"78421","first_name":"Sam","full_name":"Wiemann, Sam","last_name":"Wiemann"}],"publication_status":"published","abstract":[{"text":"Die präzise und kosteneffiziente 3D-Rekonstruktion industrieller Umgebungen gewinnt zu-nehmend an Bedeutung, insbesondere für digitale Zwillinge und automatisierte Inspektions-prozesse. Diese Arbeit untersucht die Entwicklung und Evaluierung eines multisensorischen Kamera-Setups, das mit Gaussian Splatting eine speichereffiziente und schnelle Modellierung von Produktionsumgebungen ermöglicht. Dafür wurde ein mobiles, autonomes System auf Basis eines MiR-Roboters entwickelt, das Bilddaten von verschiedenen USB-Kameras (Lo-gitech Brio, Anker C200), einer DSLR und iPhone Kamera sowie Raspberry Pi Kameras er-fasst und verarbeitet. \r\nDie zentrale Forschungsfrage adressiert die Effizienz, Bildqualität und Wirtschaftlichkeit von Gaussian Splatting im Vergleich zu etablierten Verfahren wie Photogrammetrie und LiDAR. Erste Testläufe mit DSLR-Aufnahmen und einer manuell rekonstruierten Pipeline zeigen, dass Gaussian Splatting hochdetaillierte Punktwolken generieren kann, jedoch mit Herausfor-derungen hinsichtlich Kameraausrichtung, Überlappung der Bilder und automatisierter Missi-onsplanung des Roboters konfrontiert ist. \r\nDie Ergebnisse zeigen, dass das System eine schnelle und speichereffiziente 3D-Rekonstruk-tion ermöglicht, jedoch in seiner aktuellen Form noch Optimierungspotenzial aufweist. Be-sonders die genaue Kalibrierung der Kameras, eine verbesserte Synchronisation der Bilder so-wie eine präzisere Steuerung des Roboters sind entscheidend für die Qualität der Punktwol-ken. Während Gaussian Splatting eine überzeugende Alternative zu klassischen Verfahren darstellt, ist die Systemstabilität derzeit noch abhängig von manuellen Eingriffen und Justie-rungen. \r\nZukünftige Arbeiten sollten sich darauf konzentrieren, die Automatisierung des gesamten Workflows weiter voranzutreiben, insbesondere durch den Einsatz verbesserter Kamera-A-lignment-Algorithmen, Hardware-Trigger für synchrone Bildaufnahme und Machine-Learn-ing-gestützte Bildoptimierung. Zudem könnte eine Erweiterung des Systems um zusätzliche Sensortechnologien wie Time-of-Flight-Sensoren oder LiDAR-Module helfen, die Detailge-nauigkeit weiter zu verbessern und eine robuste Echtzeit-Rekonstruktion zu ermöglichen. ","lang":"ger"}],"has_accepted_license":"1","ddc":["000"],"title":"Entwicklung und Evaluierung eines multisensorischen Kamera-Setups zur 3D-Raumrekonstruktion in einer dynamischen Industrieumgebung mittels Gaussian Splatting auf einem autonomen Robotersystem"},{"author":[{"last_name":"Hernández Rodriguez","first_name":"Tanja","full_name":"Hernández Rodriguez, Tanja","id":"52466"},{"last_name":"Sekulic","first_name":"Anton","full_name":"Sekulic, Anton"},{"id":"71761","last_name":"Lange-Hegermann","full_name":"Lange-Hegermann, Markus","first_name":"Markus"},{"id":"45666","first_name":"Björn","full_name":"Frahm, Björn","last_name":"Frahm"}],"publisher":"MDPI AG","publication_status":"published","abstract":[{"lang":"eng","text":"<jats:p>consuming and often performed rather empirically. Efficient optimization of multiple objectives such as process time, viable cell density, number of operating steps &amp; cultivation scales, required medium, amount of product as well as product quality depicts a promising approach. This contribution presents a workflow which couples uncertainty-based upstream simulation and Bayes optimization using Gaussian processes. Its application is demonstrated in a simulation case study for a relevant industrial task in process development, the design of a robust cell culture expansion process (seed train), meaning that despite uncertainties and variabilities concerning cell growth, low variations of viable cell density during the seed train are obtained. Compared to a non-optimized reference seed train, the optimized process showed much lower deviation rates regarding viable cell densities (&lt;10% instead of 41.7%) using five or four shake flask scales and seed train duration could be reduced by 56 h from 576 h to 520 h. Overall, it is shown that applying Bayes optimization allows for optimization of a multi-objective optimization function with several optimizable input variables and under a considerable amount of constraints with a low computational effort. This approach provides the potential to be used in the form of a decision tool, e.g., for the choice of an optimal and robust seed train design or for further optimization tasks within process development."}],"status":"public","publication":"Processes","issue":"5","title":"Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design","publication_identifier":{"eissn":["2227-9717"]},"intvolume":"        10","volume":10,"place":"Basel","language":[{"iso":"eng"}],"_id":"11377","article_number":"883","keyword":["Gaussian processes","Bayes optimization","Pareto optimization","multi-objective","cell culture","seed train"],"user_id":"83781","date_created":"2024-04-25T13:35:04Z","department":[{"_id":"DEP4000"}],"date_updated":"2024-05-21T09:30:15Z","doi":"10.3390/pr10050883","year":"2022","type":"scientific_journal_article","citation":{"apa":"Hernández Rodriguez, T., Sekulic, A., Lange-Hegermann, M., &#38; Frahm, B. (2022). Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. <i>Processes</i>, <i>10</i>(5), Article 883. <a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>","din1505-2-1":"<span style=\"font-variant:small-caps;\">Hernández Rodriguez, Tanja</span> ; <span style=\"font-variant:small-caps;\">Sekulic, Anton</span> ; <span style=\"font-variant:small-caps;\">Lange-Hegermann, Markus</span> ; <span style=\"font-variant:small-caps;\">Frahm, Björn</span>: Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. In: <i>Processes</i> Bd. 10. Basel, MDPI AG (2022), Nr. 5","van":"Hernández Rodriguez T, Sekulic A, Lange-Hegermann M, Frahm B. Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. Processes. 2022;10(5).","ieee":"T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, and B. Frahm, “Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design,” <i>Processes</i>, vol. 10, no. 5, Art. no. 883, 2022, doi: <a href=\"https://doi.org/10.3390/pr10050883\">10.3390/pr10050883</a>.","chicago":"Hernández Rodriguez, Tanja, Anton Sekulic, Markus Lange-Hegermann, and Björn Frahm. “Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design.” <i>Processes</i> 10, no. 5 (2022). <a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>.","mla":"Hernández Rodriguez, Tanja, et al. “Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design.” <i>Processes</i>, vol. 10, no. 5, 883, 2022, <a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>.","bjps":"<b>Hernández Rodriguez T <i>et al.</i></b> (2022) Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. <i>Processes</i> <b>10</b>.","chicago-de":"Hernández Rodriguez, Tanja, Anton Sekulic, Markus Lange-Hegermann und Björn Frahm. 2022. Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. <i>Processes</i> 10, Nr. 5. doi:<a href=\"https://doi.org/10.3390/pr10050883\">10.3390/pr10050883</a>, .","ufg":"<b>Hernández Rodriguez, Tanja u. a.</b>: Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design, in: <i>Processes</i> 10 (2022), H. 5.","havard":"T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, B. Frahm, Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design, Processes. 10 (2022).","short":"T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, B. Frahm, Processes 10 (2022).","ama":"Hernández Rodriguez T, Sekulic A, Lange-Hegermann M, Frahm B. Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. <i>Processes</i>. 2022;10(5). doi:<a href=\"https://doi.org/10.3390/pr10050883\">10.3390/pr10050883</a>"}},{"_id":"10193","language":[{"iso":"eng"}],"quality_controlled":"1","place":"Basel","page":"21-48","keyword":["Gaussian processes","Bayes optimization","Pareto optimization","multi-objective","cell culture","seed train"],"doi":"https://doi.org/10.3390/pr10050883","series_title":"Processes : open access journal","date_created":"2023-08-08T12:58:36Z","department":[{"_id":"DEP4000"}],"date_updated":"2023-08-16T09:24:16Z","user_id":"83781","citation":{"bjps":"<b>Hernández Rodriguez T <i>et al.</i></b> (2022) Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. In Pörtner R and Möller J (eds), <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>, vol. special issue. Basel: MDPI, pp. 21–48.","chicago-de":"Hernández Rodriguez, Tanja, Anton Sekulic, Markus Lange-Hegermann und Björn Frahm. 2022. Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design. In: <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>, hg. von Ralf Pörtner und Johannes Möller, special issue:21–48. Processes : open access journal. Basel: MDPI. doi:<a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>, .","ufg":"<b>Hernández Rodriguez, Tanja u. a.</b>: Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design, in: <i>Pörtner, Ralf/Möller, Johannes (Hgg.)</i>: Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing, Band <i>special issue</i>, Basel 2022 (Processes : open access journal),  S. 21–48.","apa":"Hernández Rodriguez, T., Sekulic, A., Lange-Hegermann, M., &#38; Frahm, B. (2022). Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design. In R. Pörtner &#38; J. Möller (Eds.), <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing: Vol. special issue</i> (pp. 21–48). MDPI. <a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>","havard":"T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, B. Frahm, Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design, in: R. Pörtner, J. Möller (Eds.), Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing, MDPI, Basel, 2022: pp. 21–48.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Hernández Rodriguez, Tanja</span> ; <span style=\"font-variant:small-caps;\">Sekulic, Anton</span> ; <span style=\"font-variant:small-caps;\">Lange-Hegermann, Markus</span> ; <span style=\"font-variant:small-caps;\">Frahm, Björn</span>: Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design. In: <span style=\"font-variant:small-caps;\">Pörtner, R.</span> ; <span style=\"font-variant:small-caps;\">Möller, J.</span> (Hrsg.): <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>, <i>Processes : open access journal</i>. Bd. special issue. Basel : MDPI, 2022, S. 21–48","short":"T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, B. Frahm, in: R. Pörtner, J. Möller (Eds.), Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing, MDPI, Basel, 2022, pp. 21–48.","ama":"Hernández Rodriguez T, Sekulic A, Lange-Hegermann M, Frahm B. Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design. In: Pörtner R, Möller J, eds. <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>. Vol special issue. Processes : open access journal. MDPI; 2022:21-48. doi:<a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>","mla":"Hernández Rodriguez, Tanja, et al. “Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design.” <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>, edited by Ralf Pörtner and Johannes Möller, vol. special issue, MDPI, 2022, pp. 21–48, <a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>.","chicago":"Hernández Rodriguez, Tanja, Anton Sekulic, Markus Lange-Hegermann, and Björn Frahm. “Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design.” In <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>, edited by Ralf Pörtner and Johannes Möller, special issue:21–48. Processes : Open Access Journal. Basel: MDPI, 2022. <a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>.","ieee":"T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, and B. Frahm, “Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design,” in <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>, vol. special issue, R. Pörtner and J. Möller, Eds. Basel: MDPI, 2022, pp. 21–48. doi: <a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>.","van":"Hernández Rodriguez T, Sekulic A, Lange-Hegermann M, Frahm B. Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design. In: Pörtner R, Möller J, editors. Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing. Basel: MDPI; 2022. p. 21–48. (Processes : open access journal; vol. special issue)."},"type":"book_chapter","editor":[{"full_name":"Pörtner, Ralf","first_name":"Ralf","last_name":"Pörtner"},{"last_name":"Möller","full_name":"Möller, Johannes","first_name":"Johannes"}],"year":"2022","abstract":[{"lang":"eng","text":"Development and optimization of biopharmaceutical production processes with cell cultures is cost- and time-consuming and often performed rather empirically. Efficient optimization of multiple objectives such as process time, viable cell density, number of operating steps & cultivation scales, required medium, amount of product as well as product quality depicts a promising approach. This contribution presents a workflow which couples uncertainty-based upstream simulation and Bayes optimization using Gaussian processes. Its application is demonstrated in a simulation case study for a relevant industrial task in process development, the design of a robust cell culture expansion process (seed train), meaning that despite uncertainties and variabilities concerning cell growth, low variations of viable cell density during the seed train are obtained. Compared to a non-optimized reference seed train, the optimized process showed much lower deviation rates regarding viable cell densities (<10% instead of 41.7%) using five or four shake flask scales and seed train duration could be reduced by 56 h from 576 h to 520 h. Overall, it is shown that applying Bayes optimization allows for optimization of a multi-objective optimization function with several optimizable input variables and under a considerable amount of constraints with a low computational effort. This approach provides the potential to be used in the form of a decision tool, e.g., for the choice of an optimal and robust seed train design or for further optimization tasks within process development."}],"publication_status":"published","author":[{"last_name":"Hernández Rodriguez","first_name":"Tanja","full_name":"Hernández Rodriguez, Tanja","id":"52466"},{"last_name":"Sekulic","full_name":"Sekulic, Anton","first_name":"Anton"},{"id":"71761","first_name":"Markus","full_name":"Lange-Hegermann, Markus","last_name":"Lange-Hegermann"},{"id":"45666","first_name":"Björn","full_name":"Frahm, Björn","last_name":"Frahm"}],"publisher":"MDPI","publication":"Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing","status":"public","title":"Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design","volume":"special issue","publication_identifier":{"isbn":["978-3-0365-5210-1"],"eissn":["2227-9717"],"eisbn":["978-3-0365-5209-5"]}},{"publication":" Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1","status":"public","abstract":[{"lang":"eng","text":"Discerning unexpected from expected data patterns is the key challenge of anomaly detection. Although a multitude of solutions has been applied to this modern Industry 4.0 problem, it remains an open research issue to identify the key characteristics subjacent to an anomaly, sc. generate hypothesis as to why they appear. In recent years, machine learning models have been regarded as universal solution for a wide range of problems. While most of them suffer from non-self-explanatory representations, Gaussian Processes (GPs) deliver interpretable and robust statistical data models, which are able to cope with unreliable, noisy, or partially missing data. Thus, we regard them as a suitable solution for detecting and appropriately representing anomalies and their respective characteristics. In this position paper, we discuss the problem of automatic and interpretable anomaly detection by means of GPs. That is, we elaborate on why GPs are well suited for anomaly detection and what the current challenges are when applying these probabilistic models to large-scale production data."}],"publication_status":"published","publisher":"SCITEPRESS - Science and Technology Publications","author":[{"last_name":"Berns","full_name":"Berns, Fabian","first_name":"Fabian"},{"last_name":"Lange-Hegermann","first_name":"Markus","full_name":"Lange-Hegermann, Markus","id":"71761"},{"last_name":"Beecks","first_name":"Christian","full_name":"Beecks, Christian"}],"publication_identifier":{"isbn":["978-989-758-476-3"]},"title":"Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0","page":"87-92","keyword":["Anomaly Detection","Gaussian Processes","Explainable Machine Learning","Industry 4.0"],"_id":"12812","language":[{"iso":"eng"}],"type":"conference_editor_article","citation":{"short":"F. Berns, M. Lange-Hegermann, C. Beecks, Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, SCITEPRESS - Science and Technology Publications, 2020.","ieee":"F. Berns, M. Lange-Hegermann, and C. Beecks, <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>. SCITEPRESS - Science and Technology Publications, 2020, pp. 87–92. doi: <a href=\"https://doi.org/10.5220/0010130300870092\">10.5220/0010130300870092</a>.","van":"Berns F, Lange-Hegermann M, Beecks C. Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0. Panetto H, Madani K, Smirnov A, editors.  Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1. SCITEPRESS - Science and Technology Publications; 2020.","ama":"Berns F, Lange-Hegermann M, Beecks C. <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>. (Panetto H, Madani K, Smirnov A, eds.). SCITEPRESS - Science and Technology Publications; 2020:87-92. doi:<a href=\"https://doi.org/10.5220/0010130300870092\">10.5220/0010130300870092</a>","mla":"Berns, Fabian, et al. “Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0.” <i> Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1</i>, edited by H. Panetto et al., SCITEPRESS - Science and Technology Publications, 2020, pp. 87–92, <a href=\"https://doi.org/10.5220/0010130300870092\">https://doi.org/10.5220/0010130300870092</a>.","chicago":"Berns, Fabian, Markus Lange-Hegermann, and Christian Beecks. <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>. Edited by H. Panetto, K. Madani, and A. Smirnov. <i> Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1</i>. SCITEPRESS - Science and Technology Publications, 2020. <a href=\"https://doi.org/10.5220/0010130300870092\">https://doi.org/10.5220/0010130300870092</a>.","apa":"Berns, F., Lange-Hegermann, M., &#38; Beecks, C. (2020). Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0. In H. Panetto, K. Madani, &#38; A. Smirnov (Eds.), <i> Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1</i> (pp. 87–92). SCITEPRESS - Science and Technology Publications. <a href=\"https://doi.org/10.5220/0010130300870092\">https://doi.org/10.5220/0010130300870092</a>","ufg":"<b>Berns, Fabian/Lange-Hegermann, Markus/Beecks, Christian</b>: Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, hg. von Panetto, H./Madani, K./Smirnov, A., o. O. 2020.","bjps":"<b>Berns F, Lange-Hegermann M and Beecks C</b> (2020) <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>, Panetto H, Madani K and Smirnov A (eds). SCITEPRESS - Science and Technology Publications.","chicago-de":"Berns, Fabian, Markus Lange-Hegermann und Christian Beecks. 2020. <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>. Hg. von H. Panetto, K. Madani, und A. Smirnov. <i> Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1</i>. SCITEPRESS - Science and Technology Publications. doi:<a href=\"https://doi.org/10.5220/0010130300870092\">10.5220/0010130300870092</a>, .","din1505-2-1":"<span style=\"font-variant:small-caps;\">Berns, Fabian</span> ; <span style=\"font-variant:small-caps;\">Lange-Hegermann, Markus</span> ; <span style=\"font-variant:small-caps;\">Beecks, Christian</span> ; <span style=\"font-variant:small-caps;\">Panetto, H.</span> ; <span style=\"font-variant:small-caps;\">Madani, K.</span> ; <span style=\"font-variant:small-caps;\">Smirnov, A.</span> (Hrsg.): <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i> : SCITEPRESS - Science and Technology Publications, 2020","havard":"F. Berns, M. Lange-Hegermann, C. Beecks, Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, SCITEPRESS - Science and Technology Publications, 2020."},"conference":{"location":"Budapest, HUNGARY","start_date":"2020-11-02","end_date":"2020-11-04","name":"International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL)"},"year":"2020","editor":[{"last_name":"Panetto","full_name":"Panetto, H.","first_name":"H."},{"first_name":"K.","full_name":"Madani, K.","last_name":"Madani"},{"last_name":"Smirnov","first_name":"A.","full_name":"Smirnov, A."}],"doi":"10.5220/0010130300870092","date_updated":"2025-06-26T13:31:38Z","date_created":"2025-04-17T06:20:07Z","department":[{"_id":"DEP5000"}],"user_id":"83781"},{"keyword":["multivariate feature selection","Gaussian distribution","linear discriminant analysis"],"status":"public","publication":"European Conference on Data Analysis (ECDA2018)","place":"Paderborn, Germany","author":[{"full_name":"Dörksen, Helene","first_name":"Helene","last_name":"Dörksen","id":"46416"},{"id":"1804","orcid":"0000-0002-3325-7887","last_name":"Lohweg","full_name":"Lohweg, Volker","first_name":"Volker"}],"_id":"2008","language":[{"iso":"eng"}],"abstract":[{"text":"We concentrate our research activities on the multivariate feature selection, which is one important part of many machine learning tasks. In partucular, Linear Discriminant Analysis [1] belongs to the state-of-the-art methods for the multivariate analysis. From the theoretical point of view, it is the well-known fact that LDA is best suitable in the case the features are Gaussian distributed.\r\nIn the theoretical part of the presented paper, we analyse the properties of the multivariate discriminant analysis with respect to the feature selection. In this context, we consider a binary supervised learning task and assume that the features are Gaussian distributed. The discriminant analysis solves the mentioned supervised learning task by maximising of the discriminant value, calculated for the linear combination of the features.\r\nThe initial LDA solution a 2 Rd is considered for all given features from the feature space X \u001a Rd. The corresponding discriminant is calculated by the formula:\r\nd(a; x1, . . . , xd) := (μ+ − μ−)2\r\n\u001b2+\r\n+ \u001b2−\r\n,\r\nwhere μ+/− are projected class means and \u001b2 +/− are projected class variances (with respect to a). We proof several propositions with the aim to find subsets of the features having higher discriminant value as original d(a; x1, . . . , xd). For the suitability in the real world settings, here we are interested in fast searching for such subsets.\r\nThe performance of the mentioned propositions is examined experimentally on datasets from UCI repository [2]. Several application scenarien will be discussed and tested on the datasets. In addition, tests show that the performance can be achieved also in the case the features are not Gaussian distributed.","lang":"eng"}],"year":2018,"conference":{"end_date":"2018-07-06","name":"European Conference on Data Analysis","location":"Paderborn","start_date":"2018-07-04"},"citation":{"din1505-2-1":"<span style=\"font-variant:small-caps;\">Dörksen, Helene</span> ; <span style=\"font-variant:small-caps;\">Lohweg, Volker</span>: Multivariate Gaussian Feature Selection. . In: <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany, 2018","havard":"H. Dörksen, V. Lohweg, Multivariate Gaussian Feature Selection. , in: European Conference on Data Analysis (ECDA2018), Paderborn, Germany, 2018.","apa":"Dörksen, H., &#38; Lohweg, V. (2018). Multivariate Gaussian Feature Selection. . In <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany.","bjps":"<b>Dörksen H and Lohweg V</b> (2018) Multivariate Gaussian Feature Selection. . <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany.","ufg":"<b>Dörksen, Helene/Lohweg, Volker (2018)</b>: Multivariate Gaussian Feature Selection. , in: <i>European Conference on Data Analysis (ECDA2018)</i>, Paderborn, Germany.","chicago-de":"Dörksen, Helene und Volker Lohweg. 2018. Multivariate Gaussian Feature Selection. . In: <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany.","van":"Dörksen H, Lohweg V. Multivariate Gaussian Feature Selection. . In: European Conference on Data Analysis (ECDA2018). Paderborn, Germany; 2018.","ieee":"H. Dörksen and V. Lohweg, “Multivariate Gaussian Feature Selection. ,” in <i>European Conference on Data Analysis (ECDA2018)</i>, Paderborn, 2018.","chicago":"Dörksen, Helene, and Volker Lohweg. “Multivariate Gaussian Feature Selection. .” In <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany, 2018.","mla":"Dörksen, Helene, and Volker Lohweg. “Multivariate Gaussian Feature Selection. .” <i>European Conference on Data Analysis (ECDA2018)</i>, 2018.","ama":"Dörksen H, Lohweg V. Multivariate Gaussian Feature Selection. . In: <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany; 2018.","short":"H. Dörksen, V. Lohweg, in: European Conference on Data Analysis (ECDA2018), Paderborn, Germany, 2018."},"type":"conference","user_id":"15514","date_created":"2019-11-25T08:35:48Z","department":[{"_id":"DEP5023"}],"date_updated":"2023-03-15T13:49:38Z","title":"Multivariate Gaussian Feature Selection. "}]