Unsupervised anomaly detection in production lines
With an ongoing digital transformation towards industry 4.0 and the corresponding growth of
collected sensor data based on cyberphysical systems, the need for automatic data analysis …
collected sensor data based on cyberphysical systems, the need for automatic data analysis …
[HTML][HTML] AI lifecycle zero-touch orchestration within the edge-to-cloud continuum for Industry 5.0
The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0
is a new phase of industrialization that places the worker at the center of the production …
is a new phase of industrialization that places the worker at the center of the production …
Metric indexing for efficient data access in the internet of things
Data are a central phenomenon in our digital information age. They impact the way we live,
work, and play and provide unprecedented opportunities to simplify our daily life and …
work, and play and provide unprecedented opportunities to simplify our daily life and …
A knowledge graph for query-induced analyses of hierarchically structured time series information
This paper introduces the concept of a knowledge graph for time series data, which allows for
a structured management and propagation of characteristic time series information and the …
a structured management and propagation of characteristic time series information and the …
[PDF][PDF] Gaussian Processes for Anomaly Description in Production Environments.
…, KW Schmidt, F Berns, A Graß… - EDBT/ICDT …, 2019 - star.informatik.rwth-aachen.de
Concomitant with the rapid spread of cyber-physical systems and the advancement of
technologies from the Internet of Things, many modern production environments are characterized …
technologies from the Internet of Things, many modern production environments are characterized …
Interpreting black-box machine learning models for high dimensional datasets
MR Karim, M Shajalal, A Graß… - 2023 IEEE 10th …, 2023 - ieeexplore.ieee.org
Many datasets are of increasingly high dimension- ality, where a large number of features
could be irrelevant to the learning task. The inclusion of such features would not only …
could be irrelevant to the learning task. The inclusion of such features would not only …
[PDF][PDF] Multi-step threshold algorithm for efficient feature-based query processing in large-scale multimedia databases
C Beecks, A Graß - 2016 IEEE International Conference on Big …, 2016 - drive.google.com
Accessing very large multimedia databases in a content-based way has become one of the
major challenges in todays’ multimedia analysis and retrieval applications. Accompanied by …
major challenges in todays’ multimedia analysis and retrieval applications. Accompanied by …
A new approach for efficient structure discovery in IoT
F Berns, K Schmidt, A Grass… - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
Complex, multivariate data streams frequently comprise subjacent behavioral patterns,
which are subsumable by a process of statistical structure discovery. Revealing these hidden …
which are subsumable by a process of statistical structure discovery. Revealing these hidden …
Sample-based Kernel Structure Learning with Deep Neural Networks for Automated Structure Discovery
Time series are prominent in a broad variety of application domains. Given a time series,
how to automatically derive its inherent structure? While Gaussian process models can …
how to automatically derive its inherent structure? While Gaussian process models can …
Entwicklung einer KI für automatisierte Tierschutzkontrollen in der Schweineschlachtung
Künstliche Intelligenz (KI) ist eine der Schlüsseltechnologien in unserem digitalen
Informationszeitalter. Innovative KI-Ansätze finden sich in nahezu allen Lebensbereichen wieder und …
Informationszeitalter. Innovative KI-Ansätze finden sich in nahezu allen Lebensbereichen wieder und …