Sökning: "kth elektroteknik"

Visar resultat 1 - 5 av 5178 uppsatser innehållade orden kth elektroteknik.

1. 1. Robustness Analysis of Perfusion Parameter Calculations

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

   Författare :Alicia Palmér; [2024]
   Nyckelord :Perfusion; Medical image analysis; Dynamic Contrast Enhanced Magnetic Resonance Imaging; Tofts model; Functional imaging; Optimization; T1 map; Perfusion; Medicinsk bildanalys; Dynamisk kontrastförstärkt magnetisk resonanstomografibildtagning; Tofts-modell; Funktionell bildbehandling; Optimering; T1 karta;

   Sammanfattning : Cancer is one of the most common causes of death worldwide. When given optimal treatment, however, the risk of severe illness may greatly be reduced. Determining optimal treatment in turn requires evaluation of disease progression and response to potential, previous treatment. LÄS MER

3. 2. Variational AutoEncoders and Differential Privacy : balancing data synthesis and privacy constraints

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

   Författare :Baptiste Bremond; [2024]
   Nyckelord :TVAE; Differential privacy; Tabular data; Synthetic data; DP-SGD; TVAE; differentiell integritet; tabelldata; syntetiska data; DP-SGD;

   Sammanfattning : This thesis investigates the effectiveness of Tabular Variational Auto Encoders (TVAEs) in generating high-quality synthetic tabular data and assesses their compliance with differential privacy principles. The study shows that while TVAEs are better than VAEs at generating synthetic data that faithfully reproduces the distribution of real data as measured by the Synthetic Data Vault (SDV) metrics, the latter does not guarantee that the synthetic data is up to the task in practical industrial applications. LÄS MER

4. 3. Exploring the Depth-Performance Trade-Off : Applying Torch Pruning to YOLOv8 Models for Semantic Segmentation Tasks

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

   Författare :Xinchen Wang; [2024]
   Nyckelord :Deep Learning; Semantic segmentation; Network optimization; Network pruning; Torch Pruning; YOLOv8; Network Depth; Djup lärning; Semantisk segmentering; Nätverksoptimering; Nätverksbeskärning; Fackelbeskärning; YOLOv8; Nätverksdjup;

   Sammanfattning : In order to comprehend the environments from different aspects, a large variety of computer vision methods are developed to detect objects, classify objects or even segment them semantically. Semantic segmentation is growing in significance due to its broad applications in fields such as robotics, environmental understanding for virtual or augmented reality, and autonomous driving. LÄS MER

5. 4. FPGA Based Control of Multiple Electric Machines for Marine Propulsion Systems

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

   Författare :Simon Weideskog; [2024]
   Nyckelord :FPGA; Field Oriented Control; BLDC; SVPWM; SMO; Electric Drives; Electric Machines; Sliding Mode Observer; Marine Propulsion; Multiple Machines; Boat Motor; Fältorienterad styrning; marin framdrivning; elektriska drivsystem; elmaskiner; flertal maskiner; båtmotor;

   Sammanfattning : This master thesis addresses the control of electric propulsion motors in a marine context. The focus lies mainly on the implementation of field oriented control (FOC) in a field programmable gate array (FPGA). The hypothesis is that FPGAs provide performance advantages over microcontroller-based control solutions by enabling parallel processing. LÄS MER

7. 5. Utilizing energy-saving techniques to reduce energy and memory consumption when training machine learning models : Sustainable Machine Learning

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

   Författare :Khalid El Yaacoub; [2024]
   Nyckelord :Sustainable AI; Machine learning; Quantization-Aware Training; Model Distillation; Quantized Distillation; Siamese Neural Networks; Continual Learning; Experience Replay; Data Efficient AI; Energy Consumption; Energy-Savings; Sustainable ML; Computation resources; Hållbar maskin inlärning; Hållbar AI; Maskininlärning; Quantization-Aware Training; Model Distillation; Quantized Distillation; siamesiska neurala nätverk; Continual Learning; Experience Replay; Dataeffektiv AI; Energiförbrukning; Energibesparingar; Beräkningsresurser;

   Sammanfattning : Emerging machine learning (ML) techniques are showing great potential in prediction performance. However, research and development is often conducted in an environment with extensive computational resources and blinded by prediction performance. LÄS MER