Sökning: "Long Short Time Memory"

Visar resultat 16 - 20 av 218 uppsatser innehållade orden Long Short Time Memory.

1. 16. Data Driven Modeling for Aerodynamic Coefficients

   Master-uppsats, KTH/Matematisk statistik

   Författare :Erik Jonsäll; Emma Mattsson; [2023]
   Nyckelord :Master s thesis; System identification; Parameter estimation; Ordinary least squares; Machine learning; Aerodynamic coefficients; F18--HARV; Flight simulations.; Masteruppsats; Systemidentifiering; Parameteruppskattning; Minstakvadratmetoden; Maskininlärning; Aerodynamiska koefficienter; F18-HARV; Flygsimuleringar.;

   Sammanfattning : Accurately modeling aerodynamic forces and moments are crucial for understanding thebehavior of an aircraft when performing various maneuvers at different flight conditions.However, this task is challenging due to complex nonlinear dependencies on manydifferent parameters. LÄS MER

3. 17. Detecting flight patterns using deep learning

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för systemteknik

   Författare :Victor Carlsson; [2023]
   Nyckelord :machine learning; AI; pattern detection; CNN; LSTM; transfer learning; ADS-B; flight pattern; trajectory classification;

   Sammanfattning : With more aircraft in the air than ever before, there is a need for automating the surveillance of the airspace. It is widely known that aircraft with different intentions fly in different flight patterns. Support systems for finding different flight patterns are therefore needed. LÄS MER

4. 18. Temporal Localization of Representations in Recurrent Neural Networks

   Master-uppsats, Högskolan Dalarna/Institutionen för information och teknik

   Författare :Asadullah Najam; [2023]
   Nyckelord :Recurrent Neural Networks RNNs ; Deep Learning; Time Series Prediction; Exploding Values; Gradient Decay; Long Short-Term Memory LSTMs ; Gated Recurrent Units GRUs ; Attention Mechanism; Moving Representations; Localizing Representations;

   Sammanfattning : Recurrent Neural Networks (RNNs) are pivotal in deep learning for time series prediction, but they suffer from 'exploding values' and 'gradient decay,' particularly when learning temporally distant interactions. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) have addressed these issues to an extent, but the precise mitigating mechanisms remain unclear. LÄS MER

5. 19. Dynamic Warning Signals and Time Lag Analysis for Seepage Prediction in Hydropower Dams : A Case Study of a Swedish Hydropower Plant

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Datalogi

   Författare :Lovisa Olsson; Julia Hellström; [2023]
   Nyckelord :hydropower; dam safety; dam monitoring; seepage; prediction models; data-driven methods; artificial intelligence; AI;  linear regression; Support Vector Regression; SVR; Long Short-Term Memory; LSTM;

   Sammanfattning : Hydropower is an important energy source since it is fossil-free, renewable, and controllable. Characteristics that become especially important as the reliance on intermittent energy sources increases. However, the dams for the hydropower plants are also associated with large risks as a dam failure could have fatal consequences. LÄS MER

7. 20. Passenger flow prediction : Finding and developing a sustainable machine learning model for airport passenger flow prediction

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Matematiska institutionen

   Författare :Tomas Haglund; Oskar Jonsson; [2023]
   Nyckelord :Maskininlärning flyplats flyg flygindustrin passanger flow AI;

   Sammanfattning : There are many outdated routines and processes in today's aviation industry that major airlines lack the motivation to update. While this may not hold any direct security concerns, it creates bottlenecks at checks and high salary costs for otiose airport personnel. LÄS MER