Sökning: "Graf lärande"

Hittade 3 uppsatser innehållade orden Graf lärande.

1. 1. Anomaly Detection in the EtherCAT Network of a Power Station : Improving a Graph Convolutional Neural Network Framework

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Niklas Barth; [2023]
   Nyckelord :Unsupervised Learning; Multivariate Time Series; Graph Convolutional Neural Networks; Anomaly Detection; Industrial Control System; EtherCAT; Power Station; Electricity Grid;

   Sammanfattning : In this thesis, an anomaly detection framework is assessed and fine-tuned to detect and explain anomalies in a power station, where EtherCAT, an Industrial Control System, is employed for monitoring. The chosen framework is based on a previously published Graph Neural Network (GNN) model, utilizing attention mechanisms to capture complex relationships between diverse measurements within the EtherCAT system. LÄS MER

3. 2. Software Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Rémi Bourgerie; [2023]
   Nyckelord :5G 4G; Federated Learning; Graoh Learning; Graph-based Federated Learning; Temporal Graph Neural Networks; Time Series; Anomaly Detection; Fault Detection; 5G 4G; Federerat lärande; Graf lärande; Grafbaserat federerat lärande; Temporal Graph Neural Networks; Tidsserier; Upptäckt av anomalier; Upptäckt av fel;

   Sammanfattning : The increasing complexity of telecom networks, induced by the recent development of 5G, is a challenge for detecting faults in the telecom network. In addition to the structural complexity of telecommunication systems, data accessibility has become an issue both in terms of privacy and access cost. LÄS MER

4. 3. Dynamic Graph Embedding on Event Streams with Apache Flink

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Massimo Perini; [2019]
   Nyckelord :Dynamic Graph; Representation Learning; Stream; Real-Time Data Processing; Scalable Graph Processing; Graph Neural Network; Experience Replay; Grafi dinamici; Representation Learning; Flussi di dati; Elaborazione in tempo reale; Elaborazione di grafi scalabile; Reti neurali per grafi; Experience Replay; Dynamisk graf; Representationsinlärning; ström; databehandling i realtid; skalbar grafbehandling; grafiskt neuralt nätverk; erfarenhetsåterspelning;

   Sammanfattning : Graphs are often considered an excellent way of modeling complex real-world problems since they allow to capture relationships between items. Because of their ubiquity, graph embedding techniques have occupied research groups, seeking how vertices can be encoded into a low-dimensional latent space, useful to then perform machine learning. LÄS MER