Sökning: "Detektering"

Visar resultat 11 - 15 av 373 uppsatser innehållade ordet Detektering.

1. 11. Analogue Circuit for Detection of Ageing Phenomena in Electric Drives

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

   Författare :Yudong Lin; [2023]
   Nyckelord :Electric Drives; Ageing Process; Pulse-Width Modulation; Ringing Current; Peak Holder; Analogue Circuits; Elektriska drivsystem; Åldringsprocess; Pulsbreddsmodulering; Ringningar i strömmen; Toppvärdesdetektor; Analog krets;

   Sammanfattning : This master thesis presents an analogue peak holder circuit design aimed at facilitating the non-invasive inspection of the ageing process in electric drives. The ageing process of electric drives is a crucial aspect that demands accurate monitoring to ensure their long-term performance and reliability. LÄS MER

3. 12. 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

4. 13. Finding the QRS Complex in a Sampled ECG Signal Using AI Methods

   Master-uppsats, KTH/Fysik

   Författare :Jeanette Marie Victoria Skeppland Hole; [2023]
   Nyckelord :ECG; ECG-analysis; QRS detector; Artificial Intelligence; Machine Learning; Deep neural networks; Long short-term memory; Convolutional neural network; Multilayer perceptron; EKG; EKG-analys; QRS detektor; Artificiell intelligens; Maskininlärning; Djupa neurala nätverk; Long short-term memory; Convolutional neural network; Multilayer perceptron;

   Sammanfattning : This study aimed to explore the application of artificial intelligence (AI) and machine learning (ML) techniques in implementing a QRS detector forambulatory electrocardiography (ECG) monitoring devices. Three ML models, namely long short-term memory (LSTM), convolutional neural network (CNN), and multilayer perceptron (MLP), were compared and evaluated using the MIT-BIH arrhythmia database (MITDB) and the MIT-BIH noise stress test database (NSTDB). LÄS MER

5. 14. Using Machine Learning to Optimize Near-Earth Object Sighting Data at the Golden Ears Observatory

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

   Författare :Laura Murphy; [2023]
   Nyckelord :Near-Earth Object Detection; Machine Learning; Deep Learning; Visual Transformers;

   Sammanfattning : This research project focuses on improving Near-Earth Object (NEO) detection using advanced machine learning techniques, particularly Vision Transformers (ViTs). The study addresses challenges such as noise, limited data, and class imbalance. LÄS MER

7. 15. Football Trajectory Modeling Using Masked Autoencoders : Using Masked Autoencoder for Anomaly Detection and Correction for Football Trajectories

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

   Författare :Sandra Tor; [2023]
   Nyckelord :Machine Learning; Autoencoders; Masked autoencoders; Time series; Trajectory modeling; Time series modeling; Anomaly detection; Anomaly correction; Football; Maskininlärning; Autoencoders; Maskerade autoencoders; Tidsserie; Banmodellering; Tidsseriemodellering; Avvikelsedetektering; Avvikelsekorrigering; Fotboll;

   Sammanfattning : Football trajectory modeling is a powerful tool for predicting and evaluating the movement of a football and its dynamics. Masked autoencoders are scalable self-supervised learners used for representation learning of partially observable data. LÄS MER