2024″ class=“out
Jaehn, F.: Scheduling with jobs at fixed positions. European Journal of Operational Research, 318(2), 388-397, 2024
https://doi.org/10.1016/j.ejor.2024.05.029
Schreiber, G.; Ohly, L.: KI:Text. Diskurse über KI-Textgeneratoren, Berlin/Boston: De Gruyter (xi + 581 S.), 2024
https://doi.org/10.1515/9783111351490
Schreiber, G.: Reconsidering Agency in the Age of AI, in: Filozofia, Bd. 79(5), S. 529-537, 2024
https://doi.org/10.31577/filozofia.2024.79.5.5
Reinpold, L. M.; Wagner, L. P.; Gehlhoff, F.; Ramonat, M.; Kilthau, M.; Gill, M. S.; Reif, J. T.; Henkel, V. ; Scholz, L.; Fay, A.: Systematic comparison of software agents and Digital Twins: differences, similarities, and synergies in industrial production. In: Journal of Intelligent Manufacturing, Januar 2024
https://doi.org/10.1007/s10845-023-02278-y
Godbole, S.; Voß, H.; Gocke, A.; Schlumbohm, S.; Schumann, Y.; Peng, B.; Mynarek, M.; Rutkowski, S.; Dottermusch, M.; Dorostkar, M. M.; Korshunov, A.; Mair, T.; Pfister, S. M.; Kwiatkowski, M.; Hotze, M.; Neumann, P.; Hartmann, C.; Weis, J.; Liesche-Starnecker, F.; Guan, Y.; Moritz, M.; Siebels, B.; Struve, N.; Schlüter, H.; Schüller, U.; Krisp, C.; Neumann, J. E.: Multiomic profiling of medulloblastoma reveals subtype-specific targetable alterations at the proteome and N-glycan level. Nature Communications, 15, 6237, 2024
Deppe, C.; Fotescu, A.; Schaal, G. S.: The understanding of Cognitive Warfare in comparative perspective: Taking stock and bridging the gap to extant literatures. – Full conference paper. NATO STO Human Factors and Medicine (HFM) Panel HFM-361, Symposium on“ Mitigating and Responding to Cognitive Warfare“, 13-14 November 2023, Madrid, Spain. NATO Science and Technology Organization (STO), 2024
Bronder, S.; Jung, A.: Pentamode Structures Optimized by Machine Learning with Adaptive Sampling, Advanced Engineering Materials, 2302073, April 2024
https://doi.org/10.1002/adem.202302073
Nezhi, Z.; Stiemer, M.; Schierholz, M.; Schuster, C.: Dimensional Reduction by Auto-Encoders in Machine Learning Based Power Integrity Analysis, 2024 IEEE 28th Workshop on Signal and Power Integrity (SPI), pp. 1-4, Lisbon, Portugal, 2024
https://doi.org/10.1109/SPI60975.2024.10539211
Liebert, A.; Dethof, F.; Keßler, S; Niggemann, O.: Automated Impact Echo Spectrum Anomaly Detection using U-Net Autoencoder, PAIS24, Santiago de Compostela, Spain, October 2024
Heesch, R.; Cimatti, A; Ehrhardt, J.; Diedrich, A.; Niggemann, O.: A Lazy Approach to Neural Numerical Planning with Control Parameters, 27TH European Conference on Artificial Intelligence (ECAI), Santiago de Compostela, Spain, 2024
Cinar, B.; Grensing, F.; van den Boom, L.; Maleshkova, M.: Transfer Learning in Hypoglycemia Classification. In International Conference on AI in Healthcare (pp. 98-109). Cham: Springer Nature Switzerland, 2024
https://doi.org/10.1007/978-3-031-67278-1_8
Jennifer I.; Onwuchekwa,D.; Cinar, B.; van den Boom, L.; Maleshkova, M: Time to Hypoglycemia predition for personalized diabetes care and management, 46th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Orlando, FL, USA, 2024
Fink, A.: Sovereign skill-constrained project scheduling under uncertainty and semi-autonomous workforces. In Fries, I.; Grabatin, M.; Hofmeier, M.: Sovereign by Design – The LIONS Approach to Digital Sovereignty, 125–142, 2024 (in print)
Köcher, A.; da Silva, L.M.V.;Fay, A. : „Automated Process Planning Based on a Semantic Capability Model and SMT“. In: Proceedings of the Workshop on AI Planning for Cyber-Physical Systems at the 38th Annual AAAI Conference on Artificial Intelligence, Vancouver, Canada, 2024
https://doi.org/10.48550/arXiv.2312.08801
2023″ class=“out
Hartong, S.; Decuypere, M.: (2023) Platform(ed) professional(itie)s: Digitization and the ongoing transformation of education, Sonderheft der Tertium Comparationis, 29(1), 2023
https://doi.org/10.31244/tc.2023.01.01
Sander, I.: Critical datafication literacy – a framework for educating about datafication. Information and Learning Sciences, 125(3/4), S.270-292, 2023
https://www.hsu-hh.de/sozgov/wp-content/uploads/sites/841/2023/12/Critical-Datafication-Literacy-Framework-Paper-Preprint-AAM.pdf
Bachmat, E.; Erland, S.; Jaehn, F.; Neumann, S.: Air passenger preferences: An international comparison affects boarding theory. Operations Research, 71(3), 798-820, 2023
https://doi.org/10.1287/opre.2021.2148
Benninger, M.; Liebschner, M.; Kreischer, C.: Fault Detection of Induction Motors with Combined Modeling- and Machine-Learning-Based Framework, In: Energies – Special Issue Reprint: Early Detection of Faults in Induction Motors, November 2023
https://doi.org/10.3390/books978-3-0365-9334-0
Benninger, M.; Liebschner, M.; Kreischer, C.: Comparison of population-based algorithms for parameter identification for induction machine modeling, COMPEL, ISSN: 0332-1649, Januar 2023
https://doi.org/10.1108/COMPEL-09-2022-0327
M. Kilthau, M. Asman, A. Karmann, G. Suriyamoorthy, J.-P. Beck, V. Regener, C. Derksen, N. Loose, M. Volkmann, S. Tripathi, F. Gehlhoff, K. Korotkiewicz, P. Steinbusch, V. Skwarek, M. Zdrallek, A. Fay: Integrating Peer-to-Peer Energy Trading and Flexibility Market With Self-Sovereign Identity for Decentralized Energy Dispatch and Congestion Management. In: IEEE Access, Volume, pp. 145395-145420, November 2023
https://doi.org/10.1109/ACCESS.2023.3344855
Clemett, N.; Rapps, C.; Gündel, M.: Evaluation of typology-specific fragility curves used for risk-targeted seismic demand maps in regions of low seismicity: A German case-study. Earthquake Engng StructDyn. 1-22, 2023
https://doi.org/10.1002/eqe.3911
Thomas, D.; Gündel, M.; Wickers, A.; Alpen, M.; Horn, J.: Multivariate inspection of German steel civil infrastructure using autonomous UAS, in: Biondini, Fabio; Fragopol, Dan M. (Eds): Life-Cycle of Structures and Infrastructure Systems, Proceedings of the Eighth International Symposium on Life-Cycle Civil Engineering (IALCCE 2023), Mailand, Juli 2023
https://doi.org/10.1201/9781003323020
Jarmatz, P.; Lerdo, S.; Neumann, P.: Convolutional Recurrent Autoencoder for Molecular-Continuum Coupling. ICCS 2023 proceedings, LNCS 10476, pp. 535-549, 2023
https://doi.org/10.1007/978-3-031-36027-5_42
Berg, S.: Im Maschinenraum politischer Repräsentation: Über den Umgang mit politischen Grundbegriffen in der digitalen Konstellation, in: Tobias Adler-Bartels et al.: Politische Grundbegriffe im 21. Jahrhundert. Nomos, 365-388, 2023
https://doi.org/10.5771/9783748915591-365
Fu, Y.; Versen, D. S.; Plenz, M.; Stiemer, M.; Schulz, D.: Electric Vehicle Charging Management for Avoiding Transformer Congestion Using Policy-based Reinforcement Learning, 2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE), pp. 1-5, Grenoble, France, 2023
https://doi.org/10.1109/ISGTEUROPE56780.2023.10407357
Deppe, C.: Disinformation In Cognitive Warfare, Foreign Information Manipulation And Interference, And Hybrid Threats. The Defence Horizon Journal, Oktober 2023
https://doi.org/10.5281/zenodo.10005172
Schäfer, P.J.; Karpouchtsis, C.B.; Schaal, G.S.: Bericht zur Konferenz Politische Kommunikation und KI – Chancen und Herausforderungen für die Regierungskommunikation. Zeitschrift für Außen Sicherheitspolit, 2023
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Köcher, A.; Belyaev, A.; Hermann, J.; Bock, J.; Meixner, K.; Volkmann, M.; Winter, M.; Zimmermann, P.; Grimm, S.; Diedrich, C.: A reference model for common understanding of capabilities and skills in manufacturing. In: at – Automatisierungstechnik, Vol. 71 (Issue 2), pp. 94-104, 2023
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Gertheiss, J.; Shinohara, R.: Proceedings of the 2023 SIAM International Conference on Data Mining (SDM) Penalized Non-Linear Canonical Correlation Analysis for Ordinal Data with Application to the International Classification of Functioning, Disability and Health, 2023
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Windmann, A.; Steude, H.; Niggemann, O.: Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study. Workshop of Artificial Intelligence for Time Series Analysis (AI4TS), IJCAI 2023 – International Joint Conference on Artificial Intelligence, Macao, China
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Bronder, S.; Herter, F.; Bähre, D.; Jung, A.: Optimized design for modified auxetic structures based on a neural network approach, Materials Today Communications 32, 103931, 2022
https://doi.org/10.1016/j.mtcomm.2022.103931
Gertheiss, J.; Selk, L.: Nonparametric regression and classification with functional, categorical, and mixed covariates, 2022
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Großmann, W.; Horn, H.; Niggemann, O.: Improving remote material classification ability with thermal imagery. Sci Rep 12, 17288, 2022,
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Sprengel, M.; Tomat, M.; Wendt, M.; Knoth, S.; Jacobsen, T.: Dissociating selectivity adjustments from temporal learning–introducing the context-dependent proportion congruency effect. 2022
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Kacupaj, E.; Singh, K.; Maleshkova, M.; Lehmann, J.:Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphs. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM ’22). Association for Computing Machinery, New York, NY, USA, 925–934, 2022
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Diedrich, Alexander; Niggemann, Oliver: On Residual-based Diagnosis of Physical Systems. Elsevier Engineering Applications of Artificial Intelligence, Volume 109, March 2022, 104636.
Balzereit, Kaja; Niggemann, Oliver: AutoConf: A New Algorithm for Reconfiguration of Cyber-Physical Production Systems, IEEE Transactions on Industrial Informatics, January 2022
2021″ class=“out
Borchert, H.; Schütz, T.; Verbovszky, J.: Beware the Hype: What Military Conflicts in Ukraine, Syria, Libya, and Nagorno-Karabakh (Don’t) Tell Us About the Future of War“, Hamburg: Defense AI Observatory, 2021
https://doi.org/10.13140/RG.2.2.10456.62723
Borchert, H.; Schütz, T.; Verbovszky, J.: Beware the Hype: What Military Conflicts in Ukraine, Syria, Libya, and Nagorno-Karabakh (Don’t) Tell Us About the Future of War, Hamburg: Defense AI Observatory, 2021
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Fink, A.; Gerhards, P.: Negotiation mechanisms for the multi-agent multi-mode resource investment problem. European Journal of Operational Research 295, 261–274, 2021,
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Projekte
DTEC Projekt SMASCH (Smarte Schulen) (Laufzeit 2020-2026)
Erasmus + Projekt ETH-TECH: Anchoring Ethical Technology (AI and data) Usage in the Educational Practice (Laufzeit 2024-2026)
Predictive Governance: the example of Early Warning Systems in Education (Laufzeit 2020-2025)
DTEC: Optimierung von Entscheidungsprozessen in Kompaktlagern mittels KI (zusammen mit Alice Kirchheim, TU Dortmund / Fraunhofer IML)
Algorithmenauswahl im Operations Research durch neuronale Netze (zusammen mit Dominik Kreß, HSU)
The Trustworthy AI Lab at Helmut Schmidt University (HSU/UniBwH)
Responsible Algorithmic Decision-Making (RADM)
KI-basierte, anziehbare Körper-Unterstützungssysteme (KIKU)
Entwicklung eines intelligenten Wellenschwingungs-Torsionssensors (AI-Torque)
AI-Planning für Cyber-physische Systemfunktionen und Montageplanung
LLM-basierte Erstellung von Funktionsmodellen für Cyber-physische Systeme
Nutzung von LLMs und Chatbots für die intuitive Kommunikation zwischen Anwendern und Ontologien in Assistenzsystemen
Digitalisierung von Infrastrukturbauwerken zur Bauwerksüberwachung: Structural Health Monitoring
Convolutional Recurrent Autoencoder for Molecular – Continuum Coupling
Morphology – Based Molecular Classification of Spinal Cord Ependymomas Using Deep Neural Networks
BMVg, DTEC: SmartShip – Digitale Zwillinge für Intelligente Schiffe und für Schiffsflotten
GhostPlay is a simulation environment for AI-based decision-making at machine speed
Data Analytics im dtec.bw Projekt SHM – Digitalisierung und Überwachung von Infrastrukturbauwerken
HPC für semiparametrische statistische Modellierung auf massiven Datensätzen
DTEC: ESAS Elektromagnetische Störfestigkeit autonomer Systeme
Optimisation of mechanical metamaterials using machine learning – establishing a new workflow
BMBF: EvalSpek-ML – Zerlegung von Linearkombinationen mit Maschinellem Lernen
DFG (SPP 2422): Datenbasierte Werkzeugeinarbeitung in der Blechumformung
(K)ISS – Künstliche Intelligenz für die Diagnose der Internationalen Raumstation ISS
DiaKids – KI-basierte Vorhersage von Hypoglykämie bei Diabetes Patienten
Angsterkennung und Therapiepersonalisierung bei Spinnenphobie
Vitaldatenbasierte Stress- und Emotionserkennung
DTEC: Planungsverfahren für das Energie-Managementim Rahmen des Projekts „CoupleIT! -Digitalisierte Kopplung des Strom-und Gasnetztes“
DTEC: Optimierungsmethoden auf HPC-Maschinen im Rahmen des Projekts „hpc.bw: Kompetenzplatform für Softwareeffizienz und Höchstleistungsrechnen“
DTEC: Decision Analytics für IT-Supply-Chains im Rahmen des Projekts „LIONS – Ledger Innovation and Operation Network for Sovereignty“
Letzte Änderung: 27. November 2024