Sökning: "Grafiskt Neuralt Nätverk"
Hittade 4 uppsatser innehållade orden Grafiskt Neuralt Nätverk.
1. Automatic Detection of Structural Deformations in Batteries from Imaging data using Machine Learning : Exploring the potential of different approaches for efficient structural deformation detection
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The increasing occurrence of structural deformations in the electrodes of the jelly roll has raised quality concerns during battery manufacturing, emphasizing the need to detect them automatically with the advanced techniques. This thesis aims to explore and provide two models based on traditional computer vision (CV) and deep neural network (DNN) techniques using computed tomography (CT) scan images of jelly rolls to ensure that the product is of high quality. LÄS MER
2. Real-time Anomaly Detection on Financial Data
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : This work presents an investigation of tailoring Network Representation Learning (NRL) for an application in the Financial Industry. NRL approaches are data-driven models that learn how to encode graph structures into low-dimensional vector spaces, which can be further exploited by downstream Machine Learning applications. LÄS MER
3. Prediction of compound solubility in Dimethyl sulfoxide using machinelearning methods including graph neural networks
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : In drug discovery, compounds that are insoluble in Dimethyl sulfoxide (DMSO) are not wanted and can be disregarded. To avoid wasting time and resources pharmaceutical companies are trying to predict compound solubility before selecting compounds for further research. LÄS MER
4. Dynamic Graph Embedding on Event Streams with Apache Flink
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)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