DFG SFB Projekt "Offshore Megastrukturen"

AG Künstliche Intelligenz und Maschinelles Lernen

Emmanouil Panagiotou, Philip Naumann, Anton Kriese

23.08.2021 — 31.12.2024

Der Forschungsansatz des Projekts besteht darin, zunächst den gesamten Lebensprozess von Offshore-Trag-strukturen in einzelne Lebensphasen zu unterteilen und mit Entwurfsparametern zu beschreiben. Als Grundlage für die ML-Methoden soll eine Datenbank aus realen Tragstrukturen generiert und ...

KISWIND

AG Künstliche Intelligenz und Maschinelles Lernen

N.N.

01.01.2022 — 31.12.2024

The demand for automated damage detection in the field of civil infrastructures is high, for economic and safety reasons. This collaborative research project aims to contribute to a further development of automated damage detection in wind energy turbines based on acoustic emission testing (AET) and machine learning.

NoBIAS - AI without BIAS

AG Künstliche Intelligenz und Maschinelles Lernen

01.01.2020 — 31.12.2024

The core objective of NoBIAS is to research and develop novel methods for AI-based decision making without bias. NoBIAS will deliver a cohort of 15 researchers trained to identify biased and discriminating AI-decision making and able to provide solutions that reconcile and fully exploit AI while ensuring compliance with legal and social norms.

BIAS - Bias and Discrimination in Big Data and Algorithmic Processing. Philosophical Assessments, Legal Dimensions, and Technical Solutions

Artificial Intelligence and Machine Learning Group

01.12.2018 — 30.11.2022

AI techniques based on big data and algorithmic processing are increasingly used to guide decisions in important societal spheres, including hiring decisions, university admissions, loan granting, and crime prediction. However, there are strong evidence that algorithms may sometimes amplify rather than eliminate existing bias and discrimination, and thereby have negative effects on social cohesion and on democratic institutions.

Hephaestus– Machine learning methods for adaptive process planning of 5-axes milling

AG Künstliche Intelligenz und Maschinelles Lernen

Philip Naumann

15.04.2021 — 31.12.2023

Rising customer demands and a trend towards smaller batch sizes have led to an increased complexity in product design and subsequent process planning, e.g. of 5-axes milling operations. Thus, process simulations are commonly used in industry to support process planning. Especially, the dimensional accuracy is crucial for most machined parts, since it affects directly the functionality. Consequently, finishing operations have especially high requirements with respect to the surface tolerances. Despite the aforementioned simulation tools, manual adjustments, which rely on the expert knowledge of workers and process planners, are still necessary in order to cope with process- or machine-specific deviations such as tool stiffness or machine dynamics.

LernMINT: Dealing with bias and discrimination in learning analytics models

Künstliche Intelligenz und Maschinelles Lernen

Tai Le Quy, doctoral student

01.08.2020 — 31.07.2023

The goal of the PhD training network LernMINT is to provide interdisciplinary training for the doctoral candidates to qualify them as experts in a sub-area of digital education namely data-assisted learning and teaching in STEM (in German: MINT) subjects. In LernMINT, there are five subject areas: (1) Learning analytics methods for STEM teaching, (2) Data-based support of learning potential, (3) Informal learning in schools and universities, (4) Transition from school to university, (5) Data protection and fairness of learning analytics methods.