{"id":160,"date":"2024-06-26T09:27:42","date_gmt":"2024-06-26T07:27:42","guid":{"rendered":"https:\/\/www.hsu-hh.de\/iai\/?page_id=160"},"modified":"2026-02-17T12:56:54","modified_gmt":"2026-02-17T11:56:54","slug":"veroeffentlichungen","status":"publish","type":"page","link":"https:\/\/www.hsu-hh.de\/iai\/veroeffentlichungen\/","title":{"rendered":"Ver\u00f6ffentlichungen"},"content":{"rendered":"\n
2025<\/summary>\n

Zhao, H.; Yang, K.; Madhu, N.: \u201cTowards complex-valued VAE-based distillation for representation learning in speech enhancement,\u201d in 16th ITG Speech Communications conference 2025, Berlin, Germany, 2025, pp. 101\u2013105. http:\/\/hdl.handle.net\/1854\/LU-01KBCYTXTKA0FW1Y2SJDV35GH6<\/a><\/p>\n\n\n\n

Upretee, P.; Botermans, W.; Martens, K.; Marion, S.; Fostier, J.; Madhu, N.: \u201cMethods for single-biomolecule translocation event detection from nanopore current signal\u202f: a review,\u201d IEEE SENSORS JOURNAL, vol. 25, no. 9, pp. 14505\u201314521, 2025. http:\/\/doi.org\/10.1109\/JSEN.2025.3551262<\/a><\/p>\n\n\n\n

Neves, A.; Gro\u00dfmann, W.; Puskiel, J.; Warfsmann, J.; Hosseini, V.; Passing, M.; Carraro, T.; Klassen, T.; Niggemann, O.; Jepsen, J.: Kinetic-model identification in metal-hydride reactions using neural network autoencoder surrogate models, 2025 https:\/\/doi.org\/10.1016\/j.egyai.2025.100659<\/a><\/p>\n\n\n\n

Furat, O.; Gr\u00e4fensteiner, P.; Saxena, R.; Osenberg, M.; Neumann, M.; Manke, I.; Carraro, T.; Schmidt, V.: Super-resolving 3D nanostructures using artificially generated image data and spatial transport simulations, 2025
https:\/\/doi.org\/10.1088\/2632-2153\/ae0c55<\/a><\/p>\n\n\n\n

Heuschmid, D.; Wacker, O.; Zimmermann, Y.; Penava, P.; Buettner, R.: Advancements in Landmine Detection: Deep Learning-Based Analysis with Thermal Drones, 2025
https:\/\/doi.org\/10.1109\/ACCESS.2025.3572196<\/a><\/p>\n\n\n\n

Hoffmann, J.; Mai, C.; Hu, B.; Buettner, R.: A Systematic Literature Review on System Dynamics, 2025
https:\/\/doi.org\/10.1109\/ACCESS.2025.3571620<\/a><\/p>\n\n\n\n

Auweiler, S.; Mueller, M.; Puhla, D.; Penava, P.; Buettner, R.: A Novel High Performance Object Identification Approach in Care Homes Using Gaussian Preprocessing, 2025
https:\/\/doi.org\/10.1109\/ACCESS.2025.3566223<\/a><\/p>\n<\/details>\n\n\n\n

2024<\/summary>\n

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<\/a><\/p>\n\n\n\n

Schreiber, G.; Ohly, L.: KI:Text. Diskurse \u00fcber KI-Textgeneratoren, Berlin\/Boston: De Gruyter (xi + 581 S.), 2024
https:\/\/doi.org\/10.1515\/9783111351490<\/a><\/p>\n\n\n\n

Schreiber, G.: Reconsidering Agency in the Age of AI, in: Filozofia, Bd.<\/abbr> 79(5), S. 529-537, 2024
https:\/\/doi.org\/10.31577\/filozofia.2024.79.5.5<\/a><\/p>\n\n\n\n

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<\/a><\/p>\n\n\n\n

Godbole, S.; Vo\u00df, 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.; Schl\u00fcter, H.; Sch\u00fcller, 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<\/p>\n\n\n\n

Deppe, C.; Fotescu, A.; Schaal, G. S.: The understanding of Cognitive Warfare in comparative perspective: Taking stock and bridging the gap to extant literatures. \u2013 Full conference paper. NATO STO Human Factors and Medicine (HFM) Panel HFM-361, Symposium on\u201c Mitigating and Responding to Cognitive Warfare\u201c, 13-14 November 2023, Madrid, Spain. NATO Science and Technology Organization (STO), 2024<\/p>\n\n\n\n

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<\/a><\/p>\n\n\n\n

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<\/p>\n\n\n\n

Liebert, A.; Dethof, F.; Ke\u00dfler, S; Niggemann, O.: Automated Impact Echo Spectrum Anomaly Detection using U-Net Autoencoder, PAIS24, Santiago de Compostela, Spain, October 2024<\/p>\n\n\n\n

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<\/p>\n\n\n\n

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<\/a><\/p>\n\n\n\n

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<\/p>\n\n\n\n

Fink, A.: Sovereign skill-constrained project scheduling under uncertainty and semi-autonomous workforces. In Fries, I.; Grabatin, M.; Hofmeier, M.: Sovereign by Design \u2013 The LIONS Approach to Digital Sovereignty, 125\u2013142, 2024 (in print)<\/p>\n\n\n\n

K\u00f6cher, 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<\/a><\/p>\n\n\n\n

Megahed, F.; Chen, Y.; Zwetsloot, I.; Knoth, S.; Montgomery, D.; Jones-Farmer L.: Introducing ChatSQC: Enhancing statistical quality control with augmented AI, 2024<\/p>\n<\/details>\n\n\n\n

2023<\/summary>\n

Song, Y.; Madhu, N. (ICASSP 2023): „Aiding speech harmonic recovery in DNN-based single-channel noise reduction using cepstral excitation manipulation (CEM).“ http:\/\/doi.org\/10.1109\/icassp49357.2023.10096868<\/a><\/p>\n\n\n\n

Bohlender, A.; Spriet, A.; Tirry, W.; Madhu, N. (IEEE TASLP 2023): „Spatially selective speaker separation using a DNN with location dependent feature extraction (LDE).“ http:\/\/doi.org\/10.1109\/TASLP.2023.3343605<\/a><\/p>\n\n\n\n

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<\/a><\/p>\n\n\n\n

Sander, I.: Critical datafication literacy \u2013 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<\/a><\/p>\n\n\n\n

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<\/a><\/p>\n\n\n\n

Benninger, M.; Liebschner, M.; Kreischer, C.: Fault Detection of Induction Motors with Combined Modeling- and Machine-Learning-Based Framework, In: Energies \u2013 Special Issue Reprint: Early Detection of Faults in Induction Motors, November 2023
https:\/\/doi.org\/10.3390\/books978-3-0365-9334-0<\/a><\/p>\n\n\n\n

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<\/a><\/p>\n\n\n\n

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<\/a><\/p>\n\n\n\n

Clemett, N.; Rapps, C.; G\u00fcndel, 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<\/a><\/p>\n\n\n\n

Thomas, D.; G\u00fcndel, 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<\/a><\/p>\n\n\n\n

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<\/a><\/p>\n\n\n\n

Berg, S.: Im Maschinenraum politischer Repr\u00e4sentation: \u00dcber 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<\/a><\/p>\n\n\n\n

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)<\/em>, pp. 1-5, Grenoble, France, 2023
https:\/\/doi.org\/10.1109\/ISGTEUROPE56780.2023.10407357<\/a><\/p>\n\n\n\n

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<\/a><\/p>\n\n\n\n

Sch\u00e4fer, P.J.; Karpouchtsis, C.B.; Schaal, G.S.: Bericht zur Konferenz Politische Kommunikation und KI \u2013 Chancen und Herausforderungen f\u00fcr die Regierungskommunikation. Zeitschrift f\u00fcr Au\u00dfen Sicherheitspolit, 2023
https:\/\/doi.org\/10.1007\/s12399-023-00945-9<\/a><\/p>\n\n\n\n

K\u00f6cher, 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 \u2013 Automatisierungstechnik, Vol. 71 (Issue 2), pp. 94-104, 2023
https:\/\/doi.org\/10.1515\/auto-2022-0117<\/a><\/p>\n\n\n\n

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
https:\/\/doi.org\/10.1137\/1.9781611977653.ch60<\/a><\/p>\n\n\n\n

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 \u2013 International Joint Conference on Artificial Intelligence, Macao, China<\/p>\n\n\n\n

Niggemann, O.; Zimmering, B.; Steude, H.; Augustin, J.L.; Windmann, A.; Multaheb, S.: Machine Learning for Cyber-Physical Systems. In: Vogel-Heuser, B.; Wimmer, M. (eds) Digital Transformation. Springer Vieweg, Berlin, Heidelberg, 2023
https:\/\/doi.org\/10.1007\/978-3-662-65004-2_17<\/a><\/p>\n\n\n\n

Megahed, F.; Chen, Y.; Ferris, J.; Knoth, S.; Jones-Farmer L.: How generative AI models such as ChatGPT can be (mis)used in SPC practice, education, and research? An exploratory study, 2023
https:\/\/doi.org\/10.1080\/08982112.2023.2206479<\/a><\/p>\n<\/details>\n\n\n\n

2022<\/summary>\n

Bronder, S.; Herter, F.; B\u00e4hre, 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<\/a><\/p>\n\n\n\n

Gertheiss, J.; Selk, L.: Nonparametric regression and classification with functional, categorical, and mixed covariates, 2022
https:\/\/rdcu.be\/dPYJg<\/a><\/p>\n\n\n\n

Gro\u00dfmann, W.; Horn, H.; Niggemann, O.: Improving remote material classification ability with thermal imagery. Sci Rep 12, 17288, 2022,
https:\/\/doi.org\/10.1038\/s41598-022-21588-4<\/a><\/p>\n\n\n\n

Sprengel, M.; Tomat, M.; Wendt, M.; Knoth, S.; Jacobsen, T.: Dissociating selectivity adjustments from temporal learning\u2013introducing the context-dependent proportion congruency effect. 2022
https:\/\/doi.org\/10.1371\/journal.pone.0276611<\/a><\/p>\n\n\n\n

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\u2013934, 2022
https:\/\/doi.org\/10.1145\/3511808.3557267<\/a><\/p>\n\n\n\n

Diedrich, Alexander; Niggemann, Oliver: On Residual-based Diagnosis of Physical Systems. Elsevier Engineering Applications of Artificial Intelligence, Volume 109, March 2022, 104636.<\/p>\n\n\n\n

Balzereit, Kaja; Niggemann, Oliver: AutoConf: A New Algorithm for Reconfiguration of Cyber-Physical Production Systems,  IEEE Transactions on Industrial Informatics, January 2022<\/p>\n\n\n\n

Steuer, D.; Garczarek, U.: Datenethik im Journalismus, 2022
https:\/\/doi.org\/10.1007\/978-3-662-64693-9_14<\/a><\/p>\n<\/details>\n\n\n\n

2021<\/summary>\n

Borchert, H.; Sch\u00fctz, T.; Verbovszky, J.: Beware the Hype: What Military Conflicts in Ukraine, Syria, Libya, and N\u00adagorno-Karabakh (Don\u2019t) Tell Us About the Future of War\u201c, Hamburg: Defense AI Observatory, 2021
https:\/\/doi.org\/10.13140\/RG.2.2.10456.62723<\/a><\/p>\n\n\n\n

Borchert, H.; Sch\u00fctz, T.; Verbovszky, J.: Beware the Hype: What Military Conflicts in Ukraine, Syria, Libya, and N\u00adagorno-Karabakh (Don\u2019t) Tell Us About the Future of War, Hamburg: Defense AI Observatory, 2021
https:\/\/doi.org\/10.13140\/RG.2.2.10456.62723<\/a><\/p>\n\n\n\n

Fink, A.; Gerhards, P.: Negotiation mechanisms for the multi-agent multi-mode resource investment problem. European Journal of Operational Research 295, 261\u2013274, 2021,
https:\/\/doi.org\/10.1016\/j.ejor.2021.02.023<\/a><\/p>\n<\/details>\n\n\n\n

2020<\/summary>\n

Steuer, D.: Time for Data Science to Professionalise, 2020
https:\/\/doi.org\/10.1111\/1740-9713.01430<\/a><\/p>\n<\/details>\n\n\n\n

Projekte<\/h1>\n\n\n\n

HiBRAIN \u2013 Holistic method of a combined data- and
model-based Electrode design supported by artificial intelligence (2022\u20132026)<\/a><\/p>\n\n\n\n

DTEC<\/strong> Projekt SMASCH (Smarte Schulen) (Laufzeit 2020-2026)<\/a><\/p>\n\n\n\n

Erasmus + Projekt ETH-TECH: Anchoring Ethical Technology (AI and data) Usage in the Educational Practice (Laufzeit 2024-2026)<\/p>\n\n\n\n

Predictive Governance: the example of Early Warning Systems in Education (Laufzeit 2020-2025)<\/p>\n\n\n\n

DTEC<\/strong>: Optimierung von Entscheidungsprozessen in Kompaktlagern mittels KI (zusammen mit Alice Kirchheim, TU<\/abbr> Dortmund \/ Fraunhofer IML)<\/p>\n\n\n\n

Algorithmenauswahl im Operations Research durch neuronale Netze (zusammen mit Dominik Kre\u00df, HSU<\/abbr>)<\/p>\n\n\n\n

The Trustworthy AI Lab at Helmut Schmidt University (HSU<\/abbr>\/UniBwH<\/abbr>)<\/a><\/p>\n\n\n\n

Responsible Algorithmic Decision-Making (RADM)<\/a><\/p>\n\n\n\n

KI-basierte, anziehbare K\u00f6rper-Unterst\u00fctzungssysteme (KIKU)<\/a><\/p>\n\n\n\n

Entwicklung eines intelligenten Wellenschwingungs-Torsionssensors (AI-Torque)<\/p>\n\n\n\n

AI-Planning f\u00fcr Cyber-physische Systemfunktionen und Montageplanung<\/p>\n\n\n\n

LLM-basierte Erstellung von Funktionsmodellen f\u00fcr Cyber-physische Systeme<\/p>\n\n\n\n

Nutzung von LLMs und Chatbots f\u00fcr die intuitive Kommunikation zwischen Anwendern und Ontologien in Assistenzsystemen<\/p>\n\n\n\n

Digitalisierung von Infrastrukturbauwerken zur Bauwerks\u00fcberwachung: Structural Health Monitoring<\/a><\/p>\n\n\n\n

Zustandserfassung von Infrastrukturbauwerken aus Stahl mittels multivariater Inspektionssysteme und Drohnen<\/a><\/p>\n\n\n\n

Convolutional Recurrent Autoencoder for Molecular – Continuum Coupling<\/p>\n\n\n\n

Morphology – Based Molecular Classification of Spinal Cord Ependymomas Using Deep Neural Networks<\/p>\n\n\n\n

BMVg, DTEC:<\/strong> SmartShip \u2013 Digitale Zwillinge f\u00fcr Intelligente Schiffe und f\u00fcr Schiffsflotten<\/a><\/p>\n\n\n\n

GhostPlay is a simulation environment for AI-based decision-making at machine speed<\/a><\/p>\n\n\n\n

Smarte Systeme analyses the transformation of local administration in Germany to E-Government, including the use of AI to increase the performance of local administrations<\/a><\/p>\n\n\n\n

Data Analytics im dtec.bw Projekt SHM \u2013 Digitalisierung und \u00dcberwachung von Infrastrukturbauwerken<\/a><\/p>\n\n\n\n

HPC f\u00fcr semiparametrische statistische Modellierung auf massiven Datens\u00e4tzen<\/a><\/p>\n\n\n\n

BMWK<\/strong>: progressivKI \u2013 Unterst\u00fctzung der Entwicklung von effizienten und sicheren Elektroniksystemen f\u00fcr zuk\u00fcnftige KFZ-Anwendungen mit automatisierten Fahrfunktionen mittels einer modular strukturierten KI-Plattform<\/a><\/p>\n\n\n\n

DTEC<\/strong>: ESAS Elektromagnetische St\u00f6rfestigkeit autonomer Systeme<\/a><\/p>\n\n\n\n

DTEC: <\/strong>emob Digitalisierung und Elektromobilit\u00e4t \u2013 Netz- und Ladeinfrastruktur: Durchgehend digital gesteuerte Netz- und Ladeinfrastruktur f\u00fcr Land-, Luft- und Wassermobilit\u00e4t<\/a><\/p>\n\n\n\n

Optimisation of mechanical metamaterials using machine learning – establishing a new workflow<\/p>\n\n\n\n

BMBF<\/abbr>:<\/strong> EvalSpek-ML \u2013 Zerlegung von Linearkombinationen mit Maschinellem Lernen<\/a><\/p>\n\n\n\n

DFG (SPP 2422):<\/strong> Datenbasierte Werkzeugeinarbeitung in der Blechumformung<\/a><\/p>\n\n\n\n

(K)ISS \u2013 K\u00fcnstliche Intelligenz f\u00fcr die Diagnose der Internationalen Raumstation ISS<\/a><\/p>\n\n\n\n

DiaKids \u2013 KI-basierte Vorhersage von Hypoglyk\u00e4mie bei Diabetes Patienten<\/a><\/p>\n\n\n\n

Angsterkennung und Therapiepersonalisierung bei Spinnenphobie<\/a><\/p>\n\n\n\n

Vitaldatenbasierte Stress- und Emotionserkennung<\/a><\/p>\n\n\n\n

DTEC:<\/strong> Planungsverfahren f\u00fcr das Energie-Managementim Rahmen des Projekts „CoupleIT! -Digitalisierte Kopplung des Strom-und Gasnetztes“<\/p>\n\n\n\n

DTEC:<\/strong> Optimierungsmethoden auf HPC-Maschinen im Rahmen des Projekts „hpc.bw: Kompetenzplatform f\u00fcr Softwareeffizienz und H\u00f6chstleistungsrechnen“<\/p>\n\n\n\n

DTEC:<\/strong> Decision Analytics f\u00fcr IT<\/abbr>-Supply-Chains im Rahmen des Projekts \u201eLIONS – Ledger Innovation and Operation Network for Sovereignty\u201c<\/p>\n\n\n\n

<\/p>\n\n\n\n

<\/p>\n\n\n\n

<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Projekte HiBRAIN \u2013 Holistic method of a combined data- andmodel-based Electrode design supported by artificial intelligence (2022\u20132026) DTEC Projekt SMASCH (Smarte Schulen) (Laufzeit 2020-2026) Erasmus + Projekt ETH-TECH: Anchoring Ethical […]<\/p>\n","protected":false},"author":2355,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[60],"tags":[],"class_list":["post-160","page","type-page","status-publish","hentry","category-veroeffentlichungen"],"_links":{"self":[{"href":"https:\/\/www.hsu-hh.de\/iai\/wp-json\/wp\/v2\/pages\/160","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.hsu-hh.de\/iai\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.hsu-hh.de\/iai\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.hsu-hh.de\/iai\/wp-json\/wp\/v2\/users\/2355"}],"replies":[{"embeddable":true,"href":"https:\/\/www.hsu-hh.de\/iai\/wp-json\/wp\/v2\/comments?post=160"}],"version-history":[{"count":88,"href":"https:\/\/www.hsu-hh.de\/iai\/wp-json\/wp\/v2\/pages\/160\/revisions"}],"predecessor-version":[{"id":717,"href":"https:\/\/www.hsu-hh.de\/iai\/wp-json\/wp\/v2\/pages\/160\/revisions\/717"}],"wp:attachment":[{"href":"https:\/\/www.hsu-hh.de\/iai\/wp-json\/wp\/v2\/media?parent=160"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/iai\/wp-json\/wp\/v2\/categories?post=160"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/iai\/wp-json\/wp\/v2\/tags?post=160"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}