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- Technical University of MunichDE24 papers
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8 papers · 1 filter
Does Your Model Think Like an Engineer? Explainable AI for Bearing Fault Detection with Deep Learning
Thomas Decker, Michael Lebacher, Volker Tresp
Deep Learning has already been successfully applied to analyze industrial sensor data in a variety of relevant use cases. However, the opaque nature of many well-performing methods…
Explaining Deep Neural Networks for Bearing Fault Detection with Vibration Concepts
Thomas Decker, Michael Lebacher, Volker Tresp
Concept-based explanation methods, such as Concept Activation Vectors, are potent means to quantify how abstract or high-level characteristics of input data influence the predictio…
Towards Scenario-based Safety Validation for Autonomous Trains with Deep Generative Models
Thomas Decker, Ananta R. Bhattarai, Michael Lebacher
Modern AI techniques open up ever-increasing possibilities for autonomous vehicles, but how to appropriately verify the reliability of such systems remains unclear. A common approa…
The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research
Thomas Decker, Ralf Gross, Alexander Koebler +3
In this paper, we investigate the practical relevance of explainable artificial intelligence (XAI) with a special focus on the producing industries and relate them to the current s…
Qualitative and quantitative evaluation of a methodology for the Digital Twin creation of brownfield production systems
Dominik Braun, Nasser Jazdi, Wolfgang Schloegl +1
The Digital Twin is a well-known concept of industry 4.0 and is the cyber part of a cyber-physical production system providing several benefits such as virtual commissioning or pre…
Influence of HW-SW-Co-Design on Quantum Computing Scalability
Hila Safi, Karen Wintersperger, Wolfgang Mauerer
The use of quantum processing units (QPUs) promises speed-ups for solving computational problems. Yet, current devices are limited by the number of qubits and suffer from significa…