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Found 7 papers, 1 papers with code
Real-Time Event Detection with Random Forests and Temporal Convolutional Networks for More Sustainable Petroleum Industry
The petroleum industry is crucial for modern society, but the production process is complex and risky.
Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case
Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML).
Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case
Industry 4. 0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety.
ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics
Many machine learning (ML) libraries are accessible online for ML practitioners.
Query-based Industrial Analytics over Knowledge Graphs with Ontology Reshaping
Industrial analytics that includes among others equipment diagnosis and anomaly detection heavily relies on integration of heterogeneous production data.
Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding
One important approach of KG generation is to map the raw data to a given KG schema, namely a domain ontology, and construct the entities and properties according to the ontology.
SegTime: Precise Time Series Segmentation without Sliding Window
We propose a neural networks approach SegTime that finds precise breakpoints, obviates sliding windows, handles long-term dependencies, and it is insensitive to the label changing frequency.
