Sökning: "Computation"

Visar resultat 1 - 5 av 912 uppsatser innehållade ordet Computation.

1. 1. Evaluating the accuracy of NEWA, ERA5 and NORA3 in predicting onshore wind conditions: a comparative study using ICOS meteorological mast data in Sweden

   Master-uppsats, Uppsala universitet/Institutionen för geovetenskaper

   Författare :Svetlana Kuru; [2024]
   Nyckelord :NEWA; ERA5; NORA3; wind speed; wind distribution; correlation coefficient;

   Sammanfattning : The ECMWF Reanalysis v5 (ERA5), the New European Wind Atlas (NEWA), and the 3 km Norwegian Reanalysis (NORA3) are reference datasets that are available for industry and research. The resolution of 3km in both the NORA3 and NEWA datasets sets them apart, while ERA5, with its 31km resolution, continues to serve as a reliable data source that is widely used in the industry. LÄS MER

3. 2. Measuring the Utility of Synthetic Data : An Empirical Evaluation of Population Fidelity Measures as Indicators of Synthetic Data Utility in Classification Tasks

   Master-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

   Författare :Alexander Florean; [2024]
   Nyckelord :Synthetic Data; Machine Learning; Population Fidelity Measures; Utility Metrics; Synthetic Data Quality Evaluation; Classification Algorithms; Utility Estimation; Data Privacy; Artificial Intelligence; Experiment Framework; Model Performance Assessment; Syntetisk Data; Maskininlärning; Population Fidelity Mätvärden; Användbarhetsmätvärden; Kvalitetsutvärdering av Syntetisk Data; Klassificeringsalgoritmer; Användbarhetsutvärdering; Dataintegritet; Artificiell Intelligens; AI; Experiment Ramverk; Utvärdering av Modellprestanda;

   Sammanfattning : In the era of data-driven decision-making and innovation, synthetic data serves as a promising tool that bridges the need for vast datasets in machine learning (ML) and the imperative necessity of data privacy. By simulating real-world data while preserving privacy, synthetic data generators have become more prevalent instruments in AI and ML development. LÄS MER

4. 3. 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

5. 4. Simuleringsdriven inferens av stokastiska dynamiska system

   Kandidat-uppsats, Göteborgs universitet/Institutionen för matematiska vetenskaper

   Författare :Alfred Andersson; Vilgot Jansson; Noah Trädgårdh; Jacob Welander; Victor Wellsmo; [2023-11-28]
   Nyckelord :;

   Sammanfattning : Stokastiska modeller, som ger tillförlitlig och användbar information om ett systems beteende, består ofta av stokastiska differentialekvationer (SDE) vars likelihoodfunktion inte är analytiskt tillgänglig. Mer traditionella Markov Chain Monte Carlo-metoder (MCMC) samt relativt nyligen utvecklade likelihood-fria Approximate Bayesian Computation-metoder (ABC) utgör populära angrepssätt för att utföra inferens på dessa typer av problem. LÄS MER

7. 5. Decoding the surface code using graph neural networks

   Master-uppsats, Göteborgs universitet / Institutionen för fysik

   Författare :Moritz Lange; [2023-10-17]
   Nyckelord :;

   Sammanfattning : Quantum error correction is essential to achieve fault-tolerant quantum computation in the presence of noisy qubits. Among the most promising approaches to quantum error correction is the surface code, thanks to a scalable two-dimensional architecture, only nearest-neighbor interactions, and a high error threshold. Decoding the surface code, i.e. LÄS MER