Avancerad sökning

Visar resultat 1 - 5 av 83 uppsatser som matchar ovanstående sökkriterier.

1. 1. Machine learning for molecular property prediction and drug safety

   Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

   Författare :Kinga Jenei; [2023-10-23]
   Nyckelord :Molecular property prediction; Acid dissociation constant; pKa; Machine learning; Graph Neural Networks; Molecular descriptors; Drug Discovery;

   Sammanfattning : Utilizing machine learning methods for the prediction of acid dissociation (pKa ) values of compounds holds great significance, as pKa is an important parameter, optimized frequently in drug discovery. Accurate prediction of pKa values could potentially provide valuable insights on other molecular properties and thereby support compound design. LÄS MER

3. 2. 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

4. 3. Reconstruction of Radio Detector Data using Graph Neural Networks

   Master-uppsats, Uppsala universitet/Högenergifysik

   Författare :Arnau Serra Garet; [2023]
   Nyckelord :;

   Sammanfattning : The current neutrino detectors have been able to detect neutrinos in the range of TeV to 100 PeV, however, ultra high energy (UHE) neutrinos above 100 PeV still remain to be detected. A new neutrino detector, the RNO-G, is currently being constructed in Greenland with the purpose of detecting the first UHE neutrinos using radio antennas capable of measuring the Askaryan pulse generated after a neutrino interaction with the ice molecules. LÄS MER

5. 4. 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

7. 5. 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