Essays about: "graph neural networks GNN"

Showing result 6 - 10 of 21 essays containing the words graph neural networks GNN.

  1. 6. Time synchronization error detection in a radio access network

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Moulika Madana; [2023]
    Keywords : GNSS - Global Navigation Satellite System; OAS - Over-the air-synchronization; PRTC - primary reference time clock; PTP - precision time protocol; Gauss Jordan elimination; GNN- Graph Neural Network; GNSS -Globalt navigationssatellitsystem; OAS - Över-the-air tidssynkronisering; PRTC - Primär referenstidklocka; PTP - Precisionstidprotokoll; Gauss Jordan eliminering; GNN- Graf neurala nätverk;

    Abstract : Time synchronization is a process of ensuring all the time difference between the clocks of network components(like base stations, boundary clocks, grandmasters, etc.) in the mobile network is zero or negligible. It is one of the important factors responsible for ensuring effective communication between two user-equipments in a mobile network. READ MORE

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

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Dimitrios Koutlis; [2023]
    Keywords : 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;

    Abstract : 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. READ MORE

  4. 8. The Applicability and Scalability of Graph Neural Networks on Combinatorial Optimization

    University essay from KTH/Matematik (Avd.)

    Author : Peder Hårderup; [2023]
    Keywords : applied mathematics; combinatorial optimization; machine learning; graph neural networks; scalability; tillämpad matematik; kombinatorisk optimering; maskininlärning; grafiska neurala nätverk; skalbarhet;

    Abstract : This master's thesis investigates the application of Graph Neural Networks (GNNs) to address scalability challenges in combinatorial optimization, with a primary focus on the minimum Total Dominating set Problem (TDP) and additionally the related Carrier Scheduling Problem (CSP) in networks of Internet of Things. The research identifies the NP-hard nature of these problems as a fundamental challenge and addresses how to improve predictions on input graphs of sizes much larger than seen during training phase. READ MORE

  5. 9. Graph Attention Networks for Link Prediction in Semantic Word Grouping

    University essay from Uppsala universitet/Avdelningen för beräkningsvetenskap

    Author : Anton Gollbo; [2023]
    Keywords : ;

    Abstract : Manually extracting relevant information from extensive amounts of data can betime-consuming and labour-intensive. Automating this process can allow for a shift of focus toward analysis and utilization of the extracted information, rather than allocating time and resources to data collection and preparation. READ MORE

  6. 10. Trigger-Level Multiple Electron Event Classification with LDMX using Artificial Neural Networks

    University essay from Lunds universitet/Partikel- och kärnfysik; Lunds universitet/Fysiska institutionen

    Author : Jacob Lindahl; [2023]
    Keywords : LDMX; ANN; CNN; GNN; RNN; DM; Physics and Astronomy;

    Abstract : Artificial neural networks is a powerful tool for classifying and identifying patterns in large amounts of data. One of the possible tasks of these networks is classification of data into categories. READ MORE