30 citations · 89 across the 21 of their papers we have counts for
21 papers
Using Autoencoders and AutoDiff to Reconstruct Missing Variables in a Set of Time Series
Jan-Philipp Roche, Oliver Niggemann, Jens Friebe
Existing black box modeling approaches in machine learning suffer from a fixed input and output feature combination. In this paper, a new approach to reconstruct missing variables…
Graph Structural Residuals: A Learning Approach to Diagnosis
Jan Lukas Augustin, Oliver Niggemann
Traditional model-based diagnosis relies on constructing explicit system models, a process that can be laborious and expertise-demanding. In this paper, we propose a novel framewor…
Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study
Alexander Windmann, Henrik Steude, Oliver Niggemann
Deep learning (DL) models have seen increased attention for time series forecasting, yet the application on cyber-physical systems (CPS) is hindered by the lacking robustness of th…
A Diagnosis Algorithms for a Rotary Indexing Machine
Maria Krantz, Oliver Niggemann
Rotary Indexing Machines (RIMs) are widely used in manufacturing due to their ability to perform multiple production steps on a single product without manual repositioning, reducin…
A Cross-Frequency Protective Emblem: Protective Options for Medical Units and Wounded Soldiers in the Context of (fully) Autonomous Warfare
Daniel C. Hinck, Jonas J. Schöttler, Maria Krantz +2
The protection of non-combatants in times of (fully) autonomous warfare raises the question of the timeliness of the international protective emblem. Incidents in the recent past i…
High Accuracy Uncertainty-Aware Interatomic Force Modeling with Equivariant Bayesian Neural Networks
Tim Rensmeyer, Benjamin Craig, Denis Kramer +1
Even though Bayesian neural networks offer a promising framework for modeling uncertainty, active learning and incorporating prior physical knowledge, few applications of them can…
Plan-Based Derivation of General Functional Structures in Product Design
Philipp Rosenthal, Niels Demke, Frank Mantwill +1
In product design, a decomposition of the overall product function into a set of smaller, interacting functions is usually considered a crucial first step for any computer-supporte…
On a Uniform Causality Model for Industrial Automation
Maria Krantz, Alexander Windmann, Rene Heesch +2
The increasing complexity of Cyber-Physical Systems (CPS) makes industrial automation challenging. Large amounts of data recorded by sensors need to be processed to adequately perf…
Problem examination for AI methods in product design
Philipp Rosenthal, Oliver Niggemann
Artificial Intelligence (AI) has significant potential for product design: AI can check technical and non-technical constraints on products, it can support a quick design of new pr…
Learning Physical Concepts in Cyber-Physical Systems: A Case Study
Henrik S. Steude, Alexander Windmann, Oliver Niggemann
Machine Learning (ML) has achieved great successes in recent decades, both in research and in practice. In Cyber-Physical Systems (CPS), ML can for example be used to optimize syst…
A Research Agenda for AI Planning in the Field of Flexible Production Systems
Aljosha Köcher, Rene Heesch, Niklas Widulle +4
Manufacturing companies face challenges when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches that encapsul…
The Two Scaling Regimes of the Thermodynamic Uncertainty Relation for the KPZ-Equation
Oliver Niggemann, Udo Seifert
We investigate the thermodynamic uncertainty relation for the dimensional Kardar-Parisi-Zhang equation on a finite spatial interval. In particular, we extend the results fo…
Reconfiguring Hybrid Systems Using SAT
Kaja Balzereit, Oliver Niggemann
Reconfiguration aims at recovering a system from a fault by automatically adapting the system configuration, such that the system goal can be reached again. Classical approaches ty…
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:…
The DigitalTwin from an Artificial Intelligence Perspective
Oliver Niggemann, Alexander Diedrich, Christian Kuehnert +2
Services for Cyber-Physical Systems based on Artificial Intelligence and Machine Learning require a virtual representation of the physical. To reduce modeling efforts and to synchr…
Numerical Study of the Thermodynamic Uncertainty Relation for the KPZ-Equation
Oliver Niggemann, Udo Seifert
A general framework for the field-theoretic thermodynamic uncertainty relation was recently proposed and illustrated with the dimensional Kardar-Parisi-Zhang equation. In t…
Field-Theoretic Thermodynamic Uncertainty Relation -- General formulation exemplified with the Kardar-Parisi-Zhang equation
Oliver Niggemann, Udo Seifert
We introduce a field-theoretic thermodynamic uncertainty relation as an extension of the one derived so far for a Markovian dynamics on a discrete set of states and for overdamped…
Evaluation of Cognitive Architectures for Cyber-Physical Production Systems
Andreas Bunte, Andreas Fischbach, Jan Strohschein +3
Cyber-physical production systems (CPPS) integrate physical and computational resources due to increasingly available sensors and processing power. This enables the usage of data,…
"Sinc"-Noise for the KPZ Equation
Oliver Niggemann, Haye Hinrichsen
In this paper we study the one-dimensional Kardar-Parisi-Zhang equation (KPZ) with correlated noise by field-theoretic dynamic renormalization group techniques (DRG). We focus on s…