Sökning: "avhandling- skolan"

Visar resultat 1 - 5 av 1073 uppsatser innehållade orden avhandling- skolan.

1. 1. 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

3. 2. Exploring potential E-fuel production pathways for maritime and aviation sectors in France : a techno-economic and environmental assessment

   Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)

   Författare :Mathieu Minaud; [2024]
   Nyckelord :;

   Sammanfattning : The aviation and maritime industries pose significant challenges for decarbonization, having increased activity in the past decade. One promising solution to limit emissions in these sectors is Electrofuels (E-fuel), derived from water electrolysis hydrogen and captured CO2 or nitrogen. 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. Business Model Innovation for Energy Communities : A Cross-Comparative Analysis with the Business Model Canvas in the Swedish energy market

   Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)

   Författare :Sohel Abdu; [2024]
   Nyckelord :Energy Communities; Renewable Energy Systems; Business Model Canvas Innovation; Swedish Energy Policy; Prosumer engagement; Circular Energy Economy; Smart Energy Technology; Sustainable Energy Transition.; Energigemenskaper; Förnybara Energisystem; Affärsmodells Canvas Innovation; Svensk Energipolitik; Prosumer engagemang; Cirkulär Energi Ekonomi; Smart Energiteknik; Hållbar Energi Övergång;

   Sammanfattning : This thesis undertakes a thorough exploration of business models for energy communities, specifically tailored to the unique requirements of the Swedish energy sector. Its objective is to identify and evaluate global business models for energy communities, focusing on their applicability within Sweden's regulatory, market, and socio-cultural contexts. 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