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Found 6 papers, 0 papers with code
Towards eXplainable AI for Mobility Data Science
This paper presents our ongoing work towards XAI for Mobility Data Science applications, focusing on explainable models that can learn from dense trajectory data, such as GPS tracks of vehicles and vessels using temporal graph neural networks (GNNs) and counterfactuals.
Explainable Artificial Intelligence (XAI)
Explainable Models
Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications
Data-driven machine learning is playing a crucial role in the advancements of Industry 4. 0, specifically in enhancing predictive maintenance and quality inspection.
Smart Active Sampling to enhance Quality Assurance Efficiency
We propose a new sampling strategy, called smart active sapling, for quality inspections outside the production line.
Machine Learning Methods for Health-Index Prediction in Coating Chambers
Our overall goal is to predict the future condition of the coating chamber to allow cost and quality optimized maintenance of the equipment.
Minimal-Configuration Anomaly Detection for IIoT Sensors
The increasing deployment of low-cost IoT sensor platforms in industry boosts the demand for anomaly detection solutions that fulfill two key requirements: minimal configuration effort and easy transferability across equipment.
Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning
Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry.
