170 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Institute of Electronics, Computer and Telecommunication EngineeringIT4 papers
- National Research CouncilLK4 papers
- Fraunhofer-GesellschaftDE2 papers
- Fraunhofer Institute of Optronics, System Technologies and Image ExploitationDE2 papers
- Politecnico di TorinoIT2 papers
- TH Köln - University of Applied SciencesDE2 papers
- Aachen Institute for Nuclear TrainingDE1 paper
- Joint Institute for Nuclear ResearchRU1 paper
- Kreiskrankenhaus GummersbachDE1 paper
- Marmara UniversityTR1 paper
- Peoples' Friendship University of RussiaRU1 paper
- Phoenix Contact (Germany)DE1 paper
21 papers
Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning
Gabriele Formis, Gianluca Cena, Lukasz Wisniewski +1
Wireless communications are characterized by their unpredictability, posing challenges for maintaining consistent communication quality. This paper presents a comprehensive analysi…
Compression of executable QR codes or sQRy for Industry: an example for Wi-Fi access points
Stefano Scanzio, Gabriele Formis, Pietro Chiavassa +2
Executable QR codes, or sQRy, is a technology dated 2022 that permits to include a runnable program inside a QR code, enabling interaction with the user even in the absence of an I…
Executable QR codes with Machine Learning for Industrial Applications
Stefano Scanzio, Francesco Velluto, Matteo Rosani +2
Executable QR codes, also known as eQR codes or just sQRy, are a special kind of QR codes that embed programs conceived to run on mobile devices like smartphones. Since the program…
Mixing Neural Networks and Exponential Moving Averages for Predicting Wireless Links Behavior
Gabriele Formis, Stefano Scanzio, Lukasz Wisniewski +1
Predicting the behavior of a wireless link in terms of, e.g., the frame delivery ratio, is a critical task for optimizing the performance of wireless industrial communication syste…
Investigation of the Impact of Synthetic Training Data in the Industrial Application of Terminal Strip Object Detection
Nico Baumgart, Markus Lange-Hegermann, Mike Mücke
In industrial manufacturing, deploying deep learning models for visual inspection is mostly hindered by the high and often intractable cost of collecting and annotating large-scale…
PGNAA Spectral Classification of Metal with Density Estimations
Helmand Shayan, Kai Krycki, Marco Doemeland +1
For environmental, sustainable economic and political reasons, recycling processes are becoming increasingly important, aiming at a much higher use of secondary raw materials. Curr…
Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design
Tanja Hernández Rodríguez, Anton Sekulic, Markus Lange-Hegermann +1
Development and optimization of biopharmaceutical production processes with cell cultures is cost- and time-consuming and often performed rather empirically. Efficient optimization…
Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control
Arthur Müller, Vishal Rangras, Georg Schnittker +7
Sub-optimal control policies in intersection traffic signal controllers (TSC) contribute to congestion and lead to negative effects on human health and the environment. Reinforceme…
Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems
Jan Strohschein, Andreas Fischbach, Andreas Bunte +3
This paper presents the cognitive module of the cognitive architecture for artificial intelligence (CAAI) in cyber-physical production systems (CPPS). The goal of this architecture…
LSTM for Model-Based Anomaly Detection in Cyber-Physical Systems
Benedikt Eiteneuer, Oliver Niggemann
Anomaly detection is the task of detecting data which differs from the normal behaviour of a system in a given context. In order to approach this problem, data-driven models can be…
A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata
Nemanja Hranisavljevic, Oliver Niggemann, Alexander Maier
Performing anomaly detection in hybrid systems is a challenging task since it requires analysis of timing behavior and mutual dependencies of both discrete and continuous signals.…
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder
Benedikt Eiteneuer, Nemanja Hranisavljevic, Oliver Niggemann
Unsupervised anomaly detection (AD) is a major topic in the field of Cyber-Physical Production Systems (CPPSs). A closely related concern is dimensionality reduction (DR) which is:…
CAAI -- A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems
Andreas Fischbach, Jan Strohschein, Andreas Bunte +4
This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implement…
Linearly Constrained Gaussian Processes with Boundary Conditions
Markus Lange-Hegermann
One goal in Bayesian machine learning is to encode prior knowledge into prior distributions, to model data efficiently. We consider prior knowledge from systems of linear partial d…
An algorithmic approach to Chevalley's Theorem on images of rational morphisms between affine varieties
Mohamed Barakat, Markus Lange-Hegermann
The goal of this paper is to introduce a new constructive geometric proof of the affine version of Chevalley's Theorem. This proof is algorithmic and a verbatim implementation resu…
Resource Allocation for a Wireless Coexistence Management System Based on Reinforcement Learning
Philip Soeffker, Dimitri Block, Nico Wiebusch +1
In industrial environments, an increasing amount of wireless devices are used, which utilize license-free bands. As a consequence of these mutual interferences of wireless systems…
A Software-Defined Channel Sounder for Industrial Environments with Fast Time Variance
Niels Hendrik Fliedner, Dimitri Block, Uwe Meier
Novel industrial wireless applications require wideband, real-time channel characterization due to complex multipath propagation. Rapid machine motion leads to fast time variance o…
The MAPLE package TDDS for computing Thomas decompositions of systems of nonlinear PDEs
Vladimir P. Gerdt, Markus Lange-Hegermann, Daniel Robertz
We present the Maple package TDDS (Thomas Decomposition of Differential Systems). Given a polynomially nonlinear differential system, which in addition to equations may contain ine…
Algorithmic Linearly Constrained Gaussian Processes
Markus Lange-Hegermann
We algorithmically construct multi-output Gaussian process priors which satisfy linear differential equations. Our approach attempts to parametrize all solutions of the equations u…
Marmara Turkish Coreference Corpus and Coreference Resolution Baseline
Peter Schüller, Kübra Cıngıllı, Ferit Tunçer +4
We describe the Marmara Turkish Coreference Corpus, which is an annotation of the whole METU-Sabanci Turkish Treebank with mentions and coreference chains. Collecting eight or more…
Wireless Interference Identification with Convolutional Neural Networks
Malte Schmidt, Dimitri Block, Uwe Meier
The steadily growing use of license-free frequency bands requires reliable coexistence management for deterministic medium utilization. For interference mitigation, proper wireless…