Avancerad sökning

Visar resultat 1 - 5 av 29 uppsatser som matchar ovanstående sökkriterier.

1. 1. 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

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. Machine Learning-Based Instruction Scheduling for a DSP Architecture Compiler : Instruction Scheduling using Deep Reinforcement Learning and Graph Convolutional Networks

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

   Författare :Lucas Alava Peña; [2023]
   Nyckelord :Instruction Scheduling; Deep reinforcement Learning; Compilers; Graph Convolutional Networks; Schemaläggning av instruktioner; Deep Reinforcement Learning; kompilatorer; grafkonvolutionella nätverk;

   Sammanfattning : Instruction Scheduling is a back-end compiler optimisation technique that can provide significant performance gains. It refers to ordering instructions in a particular order to reduce latency for processors with instruction-level parallelism. LÄS MER

7. 5. Estimation of Voltage Drop in Power Circuits using Machine Learning Algorithms : Investigating potential applications of machine learning methods in power circuits design

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

   Författare :Dimitrios Koutlis; [2023]
   Nyckelord :Voltage drop estimation; Application-specific Integrated Circuits ASICs ; Machine learning algorithms; XGBoost; Convolutional Neural Networks; Graph Neural Networks; Power circuit optimization; Uppskattning av spänningsfall; applikationsspecifika integrerade kretsar ASIC ; maskininlärningsalgoritmer; XGBoost; konvolutionella neurala nätverk; optimering av strömkretsar;

   Sammanfattning : Accurate estimation of voltage drop (IR drop), in Application-Specific Integrated Circuits (ASICs) is a critical challenge, which impacts their performance and power consumption. As technology advances and die sizes shrink, predicting IR drop fast and accurate becomes increasingly challenging. LÄS MER