Dr. rer. nat. Christopher Bonenberger
| Öffnungs-/Sprechzeiten | nach Vereinbarung per E-Mail |
|---|---|
| Telefon | |
| christopher.bonenberger@rwu.de | |
| Weblinks | |
| Raum | E 104 |
| Besuchsadresse |
Gebäude E
Leibnizstr. 15
88250 Weingarten |
| Postadresse |
RWU Hochschule Ravensburg-Weingarten University of Applied Sciences Dr. rer. nat. Christopher Bonenberger Postfach 30 22
D 88216 Weingarten |
Publikationen
Bonenberger, C., 2025. Time series analysis via maximum variance frames (PhD Thesis). Universität Ulm.
https://doi.org/10.18725/OPARU-54913
Bonenberger, C., Scholz, S., & Schneider, M. (2024, December). From Data-Driven to Model-Driven Learning via Structured Dynamic Mode Decomposition. In 2024 IEEE 63rd Conference on Decision and Control (CDC) (pp. 2144-2149). IEEE. 10.1109/CDC56724.2024.10886450
Bonenberger, C., Schneider, M., Ertel, W., Schwenker, F. (2024). A Note on Linear Time Series Prediction. In: Hotho, A., Rudolph, S. (eds) KI 2024: Advances in Artificial Intelligence. KI 2024. Lecture Notes in Computer Science(), vol 14992 . Springer, Cham.
https://doi.org/10.1007/978-3-031-70893-0_3
Haasis, J., Bonenberger, C., Schneider, M. (2024). Instance Segmentation with a Novel Tree Log Detection Dataset. In: Hotho, A., Rudolph, S. (eds) KI 2024: Advances in Artificial Intelligence. KI 2024. Lecture Notes in Computer Science(), vol 14992 . Springer, Cham.
https://doi.org/10.1007/978-3-031-70893-0_23
Bonenberger, C., Scholz, S., & Scheiter, N. (2024). Data-adaptive Dynamic Simulation via Structured Dynamic Mode Decomposition. In Tagungsband Kurzbeiträge, 27. Symposium Simulationstechnik (pp. 41-44). ARGESIM Report 47 (ISBN 978-3-903347-65-6).
Scholz, S., Bonenberger, C., Scheiter, N. & Berger, L. (2024). Simulation and Control of 2-Dimensional Anisotropic Heat Conduction. In Tagungsband Kurzbeiträge, 27. Symposium Simulationstechnik (pp. 45-48). ARGESIM Report 47 (ISBN 978-3-903347-65-6).
Locherer, M., Bonenberger, C., Ertel, W. et al. (2024). Multi-label semantic segmentation of magnetic resonance images of the prostate gland. Discov Artif Intell 4, 66.
https://doi.org/10.1007/s44163-024-00162-z
Bonenberger, C., Schneider, M., Schwenker, F., and Ertel, W. (2023). A Novel Approach to Spectral Estimation and Moving Average Model Parameter Estimation. In IEEE Signal Processing Letters, vol. 30, pp. 1367-1371, doi: 10.1109/LSP.2023.3320564
Bonenberger, C., Ertel, W., Schneider, M., Schwenker, F. (2023). Structured Nonlinear Discriminant Analysis. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13713. Springer, Cham.
https://doi.org/10.1007/978-3-031-26387-3_3
Bonenberger, C., Ertel, W., Schwenker, F., & Schneider, M. (2022). Singular spectrum analysis and circulant maximum variance frames. Advances in Data Science and Adaptive Analysis, 14(03n04), 2250008.
https://doi.org/10.1142/S2424922X22500085
Bonenberger, C., Schwenker, F., Ertel, W., & Schneider, M., (2022). Cyclic Nonlinear Correlation Analysis for Time Series. In IEEE Access, vol. 10, pp. 114223-114231, doi: 10.1109/ACCESS.2022.3218163
Bonenberger, C., Ertel, W., Schneider, M. (2021). k-Circulant Maximum Variance Bases. In: Edelkamp, S., Möller, R., Rueckert, E. (eds) KI 2021: Advances in Artificial Intelligence. KI 2021. Lecture Notes in Computer Science(), vol 12873. Springer, Cham.
https://doi.org/10.1007/978-3-030-87626-5_2
Bonenberger, C., Kathan, B., Ertel, W., 2019. Feature-Based Gait Pattern Classification for a Robotic Walking Frame, in: Workshop on Advanced Analytics and Learning on Temporal Data (ECML/PKDD). Würzburg.
Bonenberger, C. M., & Kark, K. W. (2018). A broadband impedance- matching method for microstrip patch antennas based on the Bode-Fano theory. Frequenz, 72(7-8), 373-380.
https://doi.org/10.1515/freq-2018-0037