Pre-PhD
2019
Knauer, U., Styp von Rekowski, C., Stecklina, M., Krokotsch, T., Pham Minh, T., Hauffe, V., … & Seiffert, U. (2019). Tree species classification based on hybrid ensembles of a convolutional neural network (CNN) and random forest classifiers. Remote Sensing, 11(23), 2788.
Krokotsch, T., & Böck, R. (2019). Generative Adversarial Networks and Simulated + Unsupervised Learning in Affect Recognition from Speech. In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE.
PhD
2020
Krokotsch, T., Knaak, M., & Gühmann, C. (2020). A Novel Evaluation Framework for Unsupervised Domain Adaption on Remaining Useful Lifetime Estimation. In 2020 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE.
2022
Krokotsch, T., Knaak, M., & Gühmann, C. (2022). Improving Semi-Supervised Learning for Remaining Useful Lifetime Estimation Through Self-Supervision. International Journal of Prognostics and Health Management, 13(1).
2025
Krokotsch, T., Ragab, M., Wu, M., Li, X., Chen, Z., & Gühmann, C. (2025). From Inconsistency to Unity: Benchmarking Deep Learning-Based Unsupervised Domain Adaptation for RUL. IEEE Transactions on Automation Science and Engineering
Krokotsch, T. (2025). Methods for proper evaluation of unsupervised domain adaptation for remaining useful life estimation. PhD Thesis