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Eine 3D-Interpolation des Pulsfrequenz, Prozentuale Laserleistungseinstellung, und der Leistung in Watt Phasenraums. Zusätzlich kann man hiermit die Prozentuale Laserleistungseinstellung finden die dem realen Leistungsmaxmium entpricht.

Test how vocoreg behaves against FhG repos.

The primary objective of methods in continual learning is to learn tasks in a sequential manner over time from a stream of data, while mitigating the detrimental phenomenon of catastrophic forgetting. In this paper, we focus on learning an optimal representation between previous class prototypes and newly encountered ones. We propose a prototypical network with a Bayesian learning-driven contrastive loss (BLCL) tailored specifically for class-incremental learning scenarios. Therefore, we introduce a contrastive loss that incorporates new classes into the latent representation by reducing the intra-class distance and increasing the inter-class distance. Our approach dynamically adapts the balance between the cross-entropy and contrastive loss functions with a Bayesian learning technique. Empirical evaluations conducted on both the CIFAR-10 and CIFAR-100 dataset for image classification and images of a GNSS-based dataset for interference classification validate the efficacy of our method, showcasing its superiority over existing state-of-the-art approaches.

Project implemented as part of Master Thesis: Generative AI Driven Systems Engineering Competency Assessment. Developed By: Derik Roby (derik.roby@outlook.com) Supervisor: Ulf Könemann Professor: Prof. Dr.-Ing. Roman Dumitrescu

Terraform module for VMWare VCenter instanace