Publikationen des TMDT
- 2024
- Jäkel, J., Zoghian, P. M., & Klemt-Albert, K. (2024). "Anwendungsfelder und Implementierungsmodelle von Robotik im Bauwesen" , 395—412.
ISBN: 978-3-658-42795-5
- Jantunen, M., Meyes, R., Kurchyna, V., Meisen, T., Abrahamsson, P., & Mohanani, R. (2024). "Researchers’ Concerns on Artificial Intelligence Ethics: Results from a Scenario-Based Survey" in Proceedings of the 7th ACM/IEEE International Workshop on Software-intensive Business , New York, NY, USA : ACM 24—31.
ISBN: 9798400705717
- Saini, L., Su, Y., Tercan, H., & Meisen, T. (2024). "CenterPoint Transformer for BEV Object Detection with Automotive Radar" in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops . 4451—4460.
- Hoseini, S., Burgdorf, A., Paulus, A., Meisen, T., Quix, C., & Pomp, A. (2024). "Towards LLM-augmented Creation of Semantic Models for Dataspaces" in The Second International Workshop on Semantics in Dataspaces, co-located with the Extended Semantic Web Conference .
- Brune, M., Meisen, T., & Pomp, A. (2024). "Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization" , Applied Sciences , 14 (6), 2267.
- Weiss, M., Brierley, N., Schmid, M., & Meisen, T. (2024). "Simulation Study: Data-Driven Material Decomposition in Industrial X-ray Computed Tomography" , NDT , 2 (1), 1—15.
- Weiss, M., & Meisen, T. (2024). "Reviewing Material-Sensitive Computed Tomography: From Handcrafted Algorithms to Modern Deep Learning" , NDT , 2 (3), 286—310.
- Waubert-de-Puiseau, C., Dörpelkus, C., Peters, J., Tercan, H., & Meisen, T. Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling.
- 2024
- Zoghian, P. M., Oberhoff, T., Gölzhäuser, P., Großner, M., Jäkel, J., & Klemt-Albert, K. (2024). "Künstliche Intelligenz zur semantischen Extraktion von Bestandsdokumenten der Bauwirtschaft" , 361—374.
ISBN: 978-3-658-42795-5
- Alves-Gomes, M., Meyes, R., Meisen, P., & Meisen, T. (2024). "It's Not Always about Wide and Deep Models: Click-Through Rate Prediction with a Customer Behavior-Embedding Representation" , Journal of Theoretical and Applied Electronic Commerce Research , 19 (1), 135--151.
- Langer, T., Meyes, R., & Meisen, T. (2024). "Guided Exploration of Industrial Sensor Data" , Computer Graphics Forum , 43 (1),
- Weiss, M., Brierley, N., Schmid, M., & Meisen, T. (2024). "End-To-End Deep Learning Material Discrimination Using Dual-Energy LINAC-CT" , e-Journal of Nondestructive Testing , 29 (3),
- Hütten, N., Alves-Gomes, M., Hölken, F., Andricevic, K., Meyes, R., & Meisen, T. (2024). "Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers" , Applied System Innovation , 7 (1), 11.
- Alves-Gomes, M., Meyes, R., Meisen, P., & Meisen, T. (2024). "It’s Not Always about Wide and Deep Models: Click-Through Rate Prediction with a Customer Behavior-Embedding Representation" , Journal of Theoretical and Applied Electronic Commerce Research , 19 (1), 135—151.
- Tousside, B., Frochte, J., & Meisen, T. (2024). "CNNs Sparsification and Expansion for Continual Learning" in Proceedings of the 16th International Conference on Agents and Artificial Intelligence , SCITEPRESS - Science and Technology Publications 110—120.
ISBN: 978-989-758-680-4