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6825 Publikationen
2019 | Kurzbeitrag Konferenz | ELSA-ID: 5473
Sürmeli, B. G., Weishaupt, I., Schwarzer, K., & Schneider, J. (2019). Beverage Classification Using Linear Discriminant Analysis with Covariance Matrix Shrinkage. Euroanalysis 2019 – Europe’s Analytical Chemistry Meeting.
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2019 | Kurzbeitrag Konferenz | ELSA-ID: 5474
Schneider, J., Regtmeier, J., Conradi, F., & Wefing, P. (2019). Das Labor in der Leitung – Smarte Qualitätskontrolle von Lebensmitteln durch innovative Sensortechnik. Focustag Food and Farming Technologietrends, Bielefeld.
ELSA
2019 | Kurzbeitrag Konferenz | ELSA-ID: 5475
Schneider, J., Conradi, F., Wefing, P., Weishaupt, I., Zimmer, M., & Schwarzer, K. (2019). Muss man die Aufheizzonen einer KZE in die PE-Berechnung einbeziehen? Montagsreihe des IWD, Cafe Vielfalt, Lemgo.
ELSA
2019 | Kurzbeitrag Konferenz | ELSA-ID: 5476
Schneider, J., Zimmer, M., Weishaupt, I., Schattenberg, B., Conradi, F., & Schwarzer, K. (2019). Lebensmittelverschwendung – Welchen Beitrag kann die Digitalisierung leisten? . Focustag Food and Farming Technologietrends, Bielefeld.
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2019 | Konferenz - Beitrag | ELSA-ID: 5506
Brandenburg, S., Schröder, F., Maren, H., Mörike, F., & Minge, M. (2019). Eigentlich ein wichtiger Punkt... Ethische Aspekte in der Mensch-Technik-Interaktion. In Workshop auf der Konferenz Mensch und Computer 2019. Hamburg.
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2019 | Konferenz - Beitrag | ELSA-ID: 5507 | 
Selent, J., & Minge, M. (2019). IM-UX – Fragebogen zu intrinsischer Motivation in der User Experience. In Ralph H. Stelzer, Jens Krzywinski (Eds.): Entwerfen Entwicklen Erleben in Produktentwicklung und Design 2019 (Vol. 2, pp. 195–204). Dresden: Thelem Universitätsverlag & Buchhandlung GmbH & Co. KG.
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2019 | Konferenz - Beitrag | ELSA-ID: 5514
Mücke, S., & Iseke, A. (2019). How Does Job Autonomy Influence Job Performance? A Meta-Analytic Test of Theoretical Mechanisms. In Academy of Management Annual Meeting. Boston, USA.
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2019 | Konferenz - Beitrag | ELSA-ID: 4756
Ehrlich, M., Trsek, H., Wisniewski, L., & Jasperneite, J. (2019). Survey of Security Standards for an automated Industrie 4.0 compatible Manufacturing. In IEEE Industrial Electronics Society. Annual Conference , IEEE Industrial Electronics Society , Universidade Nova de Lisboa , & Institute of Electrical and Electronics Engineers (Eds.), IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society (IES) (pp. 2849–2854). IEEE.
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2019 | Konferenz - Beitrag | ELSA-ID: 4757
Frimpong, A., Majumder, M., de Meer, H., & Jasperneite, J. (2019). Network Slicing : An Industry Perspective. In 24th International Conference on Emerging Technologies and Factory Automation (ETFA). Saragossa (Spanien).
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2019 | Konferenz - Beitrag | ELSA-ID: 4758
Qu, S., Wang, J., & Jasperneite, J. (2019). Dynamic scheduling in modern processing networks using expert-guided distributed reinforcement learning. In 24rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2019). Zaragoza, Spain.
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2019 | Sammelwerk - Beitrag | ELSA-ID: 4759
Neumann, A., Wisniewski, L., & Jasperneite, J. (2019). 5G into Profinet integration as a use case for network slicing. In 24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019. Zaragoza, Spain.
ELSA
2019 | Konferenz - Beitrag | ELSA-ID: 4760
Ehrlich, M., Trsek, H., Paffrath, J., Gergeleit, M., Simkin, K., & Jasperneite, J. (2019). Secure and Flexible Deployment of Industrial Applications inside Cloud-Based Environments. In 24th International Conference on Emerging Technologies and Factory Automation (ETFA). Saragossa (Spanien).
ELSA
2019 | Konferenz - Beitrag | ELSA-ID: 4761
Lang, D., Grunau, S., Wisniewski, L., & Jasperneite, J. (2019). Utilization of the Asset Administration Shell to Support Humans During the Maintenance Process. In Aalto-Yliopisto, Tampereen yliopisto , Suomen Automaatioseura , & Institute of Electrical and Electronics Engineers (Eds.), 17th International Conference on Industrial Informatics (IEEE-INDIN 2019) (pp. 768–773). IEEE. https://doi.org/10.1109/indin41052.2019.8972236
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| DOI
2019 | Konferenz - Beitrag | ELSA-ID: 4762
Panda, S. K., Majumder, M., Ehrlich, M., Neumann, A., Wisniewski, L., & Jasperneite, J. (2019). Topology Detection as a Base for Efficient Management of Heterogeneous Industrial Network Systems Using Software-Defined Networking. In IEEE Workshop on Factory Communication Systems 2019 (p. 8). Sundsvall, Sweden: IEEE.
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2019 | Konferenz - Beitrag | ELSA-ID: 4763
Jasperneite, J. (2019). Auf ewig verbunden – Was ist der digitale Zwilling? . In Vortrag. Museumsrunde IHK Lippe und TH OWL.
ELSA
2019 | Konferenz - Beitrag | ELSA-ID: 4764
Ehrlich, M., Neumann, A., Biendarra, A., & Jasperneite, J. (2019). Quality-of-Service Monitoring of Hybrid Industrial Communication Networks. In at - Automatisierungstechnik 67 (1), Special Issue: Methods for a reliable wireless communication in the industry (pp. 69–78). De Gruyter.
ELSA
2019 | Konferenz - Beitrag | ELSA-ID: 4780
Bunte, A., Wunderlich, P., Moriz, N., Li, P., Mankowski, A., Rogalla, A., & Niggemann, O. (2019). Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS. In 24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). Zaragoza, Spain.
ELSA
2019 | Konferenz - Beitrag | ELSA-ID: 4781
Bunte, A., Fischbach, A., Strohschein, J., Bartz-Beielstein, T., Faeskorn-Woyke, H., & Niggemann, O. (2019). Evaluation of Cognitive Architectures for Cyber-Physical Production Systems. In Institute of Electrical and Electronics Engineers & Universidad de Zaragoza (Eds.), 24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 729–736). IEEE. https://doi.org/10.1109/etfa.2019.8869038
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| DOI
2019 | Zeitschriftenaufsatz | ELSA-ID: 4784
Bunte, A., Fischbach, A., Strohschein, J., Bartz-Beielstein, T., Faeskorn-Woyke, H., & Niggemann, O. (2019). Evaluation of Cognitive Architectures for Cyber-Physical ProductionSystems. ArXiv E-Prints.
ELSA
2019 | Konferenz - Beitrag | ELSA-ID: 4785
Bunte, A., Stein, B., & Niggemann, O. (2019). Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models. Hawaii, USA: Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19).
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