Sökning: "graph neural networks GNN"

Visar resultat 1 - 5 av 22 uppsatser innehållade orden graph neural networks GNN.

1. 1. Decoding the surface code using graph neural networks

   Master-uppsats, Göteborgs universitet / Institutionen för fysik

   Författare :Moritz Lange; [2023-10-17]
   Nyckelord :;

   Sammanfattning : Quantum error correction is essential to achieve fault-tolerant quantum computation in the presence of noisy qubits. Among the most promising approaches to quantum error correction is the surface code, thanks to a scalable two-dimensional architecture, only nearest-neighbor interactions, and a high error threshold. Decoding the surface code, i.e. LÄS MER

3. 2. Decreasing Training Time of Reinforcement Learning Agents for Remote Tilt Optimization using a Surrogate Neural Network Approximator

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

   Författare :Jiaming Huang; [2023]
   Nyckelord :;

   Sammanfattning : One possible application of reinforcement learning in the telecommunication field is antenna tilt optimization. However, one of key challenges we face is that the use of handcrafted simulators as environments to provide information for agents is often time-consuming regarding training reinforcement learning agents. LÄS MER

4. 3. 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

5. 4. Analysis of Flow Prolongation Using Graph Neural Network in FIFO Multiplexing System

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

   Författare :Weiran Wang; [2023]
   Nyckelord :Network Calculus; Flow Prolongation; Graph Neural Network; Fast Gradient Sign Method; Delay Bound; Nätverkskalkyl; Flödesförlängning; Graph Neural Network; Fast Gradient Sign Method; Fördröjningsgräns;

   Sammanfattning : Network Calculus views a network system as a queuing framework and provides a series of mathematical functions for finding an upper bound of an end-to-end delay. It is crucial for the design of networks and applications with a hard delay guarantee, such as the emerging Time Sensitive Network. LÄS MER

7. 5. RNN-based Graph Neural Network for Credit Load Application leveraging Rejected Customer Cases

   Uppsats för yrkesexamina på avancerad nivå, Högskolan i Halmstad/Akademin för informationsteknologi

   Författare :Oskar Nilsson; Benjamin Lilje; [2023]
   Nyckelord :Machine Learning; Deep Learning; Reject Inference; GNN; GCN; Graph Neural Networks; RNN; Recursive Neural Network; LSTM; Semi-Supervised Learning; Encoding; Decoding; Feature Elimination;

   Sammanfattning : Machine learning plays a vital role in preventing financial losses within the banking industry, and still, a lot of state of the art and industry-standard approaches within the field neglect rejected customer information and the potential information that they hold to detect similar risk behavior.This thesis explores the possibility of including this information during training and utilizing transactional history through an LSTM to improve the detection of defaults. LÄS MER