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383 Publikationen
2023 | Kurzbeitrag Konferenz | FH-PUB-ID: 4561 | 
Schwan, L., Feige, M., Hütten, A., & Schöning, S. (2023). Equivalent Circuit for the Consideration of Frequency-Dependent Effects in Electronics Simulations of Induction Hobs. Presented at the DPG Frühjahrstagung Sektion Kondensierte Materie, Dresden. https://doi.org/10.57720/4561
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2023 | Konferenzbeitrag | FH-PUB-ID: 4378
Freiter, A., & Schwede, C. (2023). Integrating Scheduling of Logistic Support Processes in Agent-Based Industry 4.0 Assembly Simulation. In 2023 Winter Simulation Conference (WSC) (pp. 2112–2123). San Antonio, TX, USA: IEEE. https://doi.org/10.1109/WSC60868.2023.10408413
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2023 | Diskussionspapier | FH-PUB-ID: 4331
Bonnin, G., Hirschfeld, G., Von Brachel, R., & Margraf, J. (2023). Code for: How happy is happy enough? A cross-cultural comparison of optimal cut points for the Positive Mental Health Scale. PsychArchives. https://doi.org/10.23668/PSYCHARCHIVES.12979
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2023 | Artikel | FH-PUB-ID: 4285 |
Schnatmann, A. K., Schoden, F., Ehrmann, A., & Schwenzfeier-Hellkamp, E. (2023). R principles for circular economy in the textile industry – a mini-review. Communications in Development and Assembling of Textile Products, 4(2), 294–305. https://doi.org/10.25367/cdatp.2023.4.p295-305
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2023 | Lehrbuch | FH-PUB-ID: 4552
Horst, J., Gomeringer, R., Kilgus, R., Menges, V., Oesterle, S., Rapp, T., … Ziebart, J. R. (2023). Tabellenbuch Maschinenbau Hochschule. Haan-Gruiten: VERLAG EUROPA-LEHRMITTEL.
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2023 | Buchbeitrag | FH-PUB-ID: 3222
Sanaullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Streamlined Training of GCN for Node Classification with Automatic Loss Function and Optimizer Selection. In L. Iliadis, I. Maglogiannis, S. Alonso, C. Jayne, & E. Pimenidis (Eds.), Engineering Applications of Neural Networks. 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings (pp. 191–202). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-34204-2_17
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2023 | Artikel | FH-PUB-ID: 3482 |
Sanaullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Evaluation of Spiking Neural Nets-Based Image Classification Using the Runtime Simulator RAVSim. International Journal of Neural Systems. https://doi.org/10.1142/S0129065723500442
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2023 | Artikel | FH-PUB-ID: 3572 |
Sanaullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications. Frontiers in Computational Neuroscience, 17. https://doi.org/10.3389/fncom.2023.1215824
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2023 | Artikel | FH-PUB-ID: 3666
Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Evaluating Spiking Neural Network Models: A Comparative Performance Analysis. Preprint. https://doi.org/10.13140/RG.2.2.21295.71847
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2023 | Artikel | FH-PUB-ID: 3665 |
Sanaullah, S., Koravuna, S., Ruckert, U., & Jungeblut, T. (2023). Design-Space Exploration of SNN Models using Application-Specific Multi-Core Architectures. Preprint. https://doi.org/10.13140/RG.2.2.26328.88324
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2023 | Konferenzbeitrag | FH-PUB-ID: 3644 |
Sanaullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Transforming Event-Based into Spike-Rate Datasets for Enhancing Neuronal Behavior Simulation to Bridging the Gap for SNNs. Presented at the International Conference on Computer Vision (ICCV) 2023, Paris France . https://doi.org/10.13140/RG.2.2.14469.32485
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2023 | Konferenzbeitrag | FH-PUB-ID: 4207 |
Sanaullah, S., & Jungeblut, T. (2023). Analysis of MR Images for Early and Accurate Detection of Brain Tumor using Resource Efficient Simulator Brain Analysis. Presented at the 19th International Conference on Machine Learning and Data Mining MLDM, New York USA. https://doi.org/10.5281/zenodo.10457930
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2023 | Kurzbeitrag Konferenz | FH-PUB-ID: 4206 |
Sanaullah, S., Amanullah, A., Roy, K., Lee, J.-A., Chul-Jun, S., & Jungeblut, T. (2023). A Hybrid Spiking-Convolutional Neural Network Approach for Advancing High-Quality Image Inpainting. Presented at the International Conference on Computer Vision (ICCV) 2023, Paris France. https://doi.org/10.5281/zenodo.10458019
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2023 | Konferenzbeitrag | FH-PUB-ID: 4205 |
Sanaullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). A Novel Spike Vision Approach for Robust Multi-Object Detection using SNNs. Presented at the Conference: Novel Trends in Data Science 2023, Congressi Stefano Franscini at Monte Verità in Ticino, Switzerland. https://doi.org/10.5281/zenodo.10262228
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2023 | Konferenzbeitrag | FH-PUB-ID: 4208
Koravuna, S., Sanaullah, S., Jungeblut, T., & Rückert, U. (2023). Digit Recognition Using Spiking Neural Networks on FPGA. In I. Rojas, G. Joya, & A. Catala (Eds.), Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I (pp. 406–417). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43085-5_32
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2023 | Konferenzbeitrag | FH-PUB-ID: 3297 |
Schultenkämper, S., & Bäumer, F. (2023). Protecting Your Online Privacy: Insights on Digital Twins and Threat Detection. In IARIA (Ed.), The Fifteenth International Conference on Pervasive Patterns and Applications (PATTERNS 2023). Special Track DaMIA: Data Mining in Industrial Applications of Digital Twins. (pp. 2–5). IARIA.
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2023 | Konferenzbeitrag | FH-PUB-ID: 3296 |
Schultenkämper, S., & Bäumer, F. (2023). Looking for a Needle in a Haystack: How Can Vision-Language Understanding Help to Identify Privacy-Threatening Images on the Web. In IARIA (Ed.), ICIW 2023 : The Eighteenth International Conference on Internet and Web Applications and Services (pp. 2–6). Nizza.
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2023 | Konferenzbeitrag | FH-PUB-ID: 3612
Schultenkämper, S., Bäumer, F., Bellgrau, B., Lee, Y. S., & Geierhos, M. (2023). From Digital Tracks to Digital Twins: On the Path to Cross-Platform Profile Linking. In 27th International Conference on Enterprise Design, Operations Computing (EDOC 2023): First Workshop on the Modelling and Implementation of Digital Twins for Complex Systems). Groningen, Niederlande. https://doi.org/10.1007/978-3-031-54712-6_10
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2023 | Konferenzbeitrag | FH-PUB-ID: 3606
Schultenkämper, S., & Bäumer, F. (2023). Privacy Risks in German Patient Forums: A NER-based Approach to Enrich Digital Twins. In Information and Software Technologies. 29th International Conference, ICIST 2023, Kaunas, Lithuania, October 12–14, 2023, Proceedings. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-48981-5_9
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2023 | Konferenzbeitrag | FH-PUB-ID: 3614 |
Schultenkämper, S., Bäumer, F., Geierhos, M., & Lee, Y. S. (2023). From Unstructured Data to Digital Twins: From Tweets to Structured Knowledge. In Proceedings of the Thirteenth International Conference on Social Media Technologies, Communication, and Informatics (SOTICS 2023). Valencia, Spanien.
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