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Development of low-level controller for vision-based contact-rich tasks

Build optimization and MPC problems in python

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

A web page for publishing process-specific hydrogen potentials, using GitLab pages.

This is the code developed by Dr.-Ing. Dominique Koster and contributed to by Dr. Mariam R. Rizkallah for the workshop "Data Reconciliation: Integration of Electrochemical Data and Optical Sensor Data for Health Diagnostics of Lithium-Ion Batteries" https://www.bremen-research.de/data-train/courses/course-details?event_id=88

Conversion of MDBW data from GraphDesigner-Excel to RDF

Federated learning (FL) enables multiple devices to collaboratively train a global model while maintaining data on local servers. Each device trains the model on its local server and shares only the model updates (i.e., gradient weights) during the aggregation step. A significant challenge in FL is managing the feature distribution of novel, unbalanced data across devices. In this paper, we propose an FL approach using few-shot learning and aggregation of the model weights on a global server. We introduce a dynamic early stopping method to balance out-of-distribution classes based on representation learning, specifically utilizing the maximum mean discrepancy of feature embeddings between local and global models. An exemplary application of FL is orchestrating machine learning models along highways for interference classification based on snapshots from global navigation satellite system (GNSS) receivers. Extensive experiments on four GNSS datasets from two real-world highways and controlled environments demonstrate that our FL method surpasses state-of-the-art techniques in adapting to both novel interference classes and multipath scenarios.