Sökning: "Djupinlärning"

Visar resultat 6 - 10 av 421 uppsatser innehållade ordet Djupinlärning.

1. 6. Deep Learning Model Deployment for Spaceborne Reconfigurable Hardware : A flexible acceleration approach

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

   Författare :Javier Ferre Martin; [2023]
   Nyckelord :Space Situational Awareness; Deep Learning; Convolutional Neural Networks; FieldProgrammable Gate Arrays; System-On-Chip; Computer Vision; Dynamic Partial Reconfiguration; High-Level Synthesis; Rymdsituationstänksamhet; Djupinlärning; Konvolutionsnätverk; Omkonfigurerbara Field-Programmable Gate Arrays FPGAs ; System-On-Chip SoC ; Datorseende; Dynamisk partiell omkonfigurering; Högnivåsyntes.;

   Sammanfattning : Space debris and space situational awareness (SSA) have become growing concerns for national security and the sustainability of space operations, where timely detection and tracking of space objects is critical in preventing collision events. Traditional computer-vision algorithms have been used extensively to solve detection and tracking problems in flight, but recently deep learning approaches have seen widespread adoption in non-space related applications for their high accuracy. LÄS MER

3. 7. A lightweight deep learning architecture for text embedding : Comparison between the usage of Transformers and Mixers for textual embedding

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

   Författare :Corentin Royer; [2023]
   Nyckelord :Deep Learning; Entity Retrieval; Mixer; Transformer;

   Sammanfattning : Text embedding is a widely used method for comparing pieces of text together by mapping them to a compact vector space. One such application is deduplication which consists in finding textual records that refer to the same underlying idea in order to merge them or delete one of them. LÄS MER

4. 8. Traffic State Estimation on Swedish Highways : Model Comparison using Multisource Data

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

   Författare :Jiaqi Xu; [2023]
   Nyckelord :Traffic State Estimation; Macroscopic Traffic Model; Extended Kalman Filter; Particle Filter; Data Fusion; Trafiklägesuppskattning; Makroskopisk trafikmodell; Utökad Kalman-filter; Partikelfilter; Datafusion;

   Sammanfattning : Due to the escalating demand for traffic information and management, the significance of traffic state estimation, which involves the assessment of traffic conditions on road segments with limited measurement data, is increasing. Two primary estimation methods are model-driven and data-driven. LÄS MER

5. 9. Adaptive Model-Based Temperature Monitoring for Electric Powertrains : Investigation and Comparative Analysis of Transfer Learning Approaches

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

   Författare :Chenzhou Huang; [2023]
   Nyckelord :Transfer Learning; Condition Monitoring; Domain Adaptation; Neural Network; Powerstrain.; Siirto-oppiminen; kunnonvalvonta; verkkotunnuksen mukauttaminen; neuroverkko; voimansiirto.; Överföring lärande; tillståndsövervakning; domänanpassning; neuralt nätverk; Powerstrain;

   Sammanfattning : In recent years, deep learning has been widely used in industry to solve many complex problems such as condition monitoring and fault diagnosis. Powertrain condition monitoring is one of the most vital and complicated problems in the automation industry since the condition of the drive affects its health, performance, and reliability. LÄS MER

7. 10. aiLangu - Real-time Transcription and Translation to Reduce Language Barriers : An Engineering Project to Develop an Application for Enhancing Human Verbal Communication

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

   Författare :Vincent Ringström1; Iley Alvarez Funcke; [2023]
   Nyckelord :Sound transcription; Sound translation; AI; Deep learning; Real-time; Language barrier; Concurrency; Ljud transkription; Ljud översättning; AI; Djupinlärning; Real-tid; Språkbarriär; Samtidighet;

   Sammanfattning : The research area this report relates to is real-time automatic transcription and translation. The purpose of the work done for the report is to reduce the perceived language barriers online and to make a user-friendly application to make use of the latest deep learning technology to transcribe and translate in real-time. LÄS MER