Sökning: "deep models"

Visar resultat 21 - 25 av 398 avhandlingar innehållade orden deep models.

1. 21. Effects of deep excavations in soft clay on the immediate sourroundings-Analysis of the possibility to predict deformations and reactions against the retaining system

   Författare :Anders Kullingsjö; Chalmers tekniska högskola; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; MIT-S1; Sheet pile wall; Soil-structure interaction; Strain; Stress; Ground surface settlement; Finite element method; Case history; Shear test; Shear strength; Pore pressure; Analysis; Constitutive models; Plane strain; Elastoplasticity; Laboratory tests; Factor of safety; Stiffness; e-ADP; Earth pressure; Anisotropy; Soft Clay; Deep excavation; Retaining wall; Shear stress; Deformation;

   Sammanfattning : When excavating in an urban environment, the evaluation of the magnitude and distribution of ground movements is an important part of the design process, since excessive movements can damage adjacent buildings and utilities. In order to minimize movement of the surrounding soil, a retaining wall support system is used for deep excavations to provide lateral support. LÄS MER

3. 22. Deep learning solutions to protein quaternary structure

   Författare :Gabriele Pozzati; Arne Elofsson; Emmanuel Levy; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; protein interactions; interface prediction; structure prediction; docking; deep learning; biokemi med inriktning mot bioinformatik; Biochemistry towards Bioinformatics;

   Sammanfattning : Interactions between proteins are directly involved in most biological processes and are essential for the correct functioning of every form of life. The nature of protein-protein interactions allows functional assemblies of hundreds of protein chains. LÄS MER

4. 23. Evolving intelligence : Overcoming challenges for Evolutionary Deep Learning

   Författare :Mohammed Ghaith Altarabichi; Sławomir Nowaczyk; Sepideh Pashami; Peyman Sheikholharam Mashhadi; Niklas Lavesson; Högskolan i Halmstad; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; neural networks; evolutionary deep learning; evolutionary machine learning; feature selection; hyperparameter optimization; evolutionary computation; particle swarm optimization; genetic algorithm;

   Sammanfattning : Deep Learning (DL) has achieved remarkable results in both academic and industrial fields over the last few years. However, DL models are often hard to design and require proper selection of features and tuning of hyper-parameters to achieve high performance. These selections are tedious for human experts and require substantial time and resources. LÄS MER

5. 24. Learning Spatiotemporal Features in Low-Data and Fine-Grained Action Recognition with an Application to Equine Pain Behavior

   Författare :Sofia Broomé; Hedvig Kjellström; Efstratios Gavves; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Equine pain; computer vision for animals; deep learning; deep video models; spatiotemporal features; video understanding; action recognition; frame dependency; video data; end-to-end learning; temporal modeling; Datalogi; Computer Science;

   Sammanfattning : Recognition of pain in animals is important because pain compromises animal welfare and can be a manifestation of disease. This is a difficult task for veterinarians and caretakers, partly because horses, being prey animals, display subtle pain behavior, and because they cannot verbalize their pain. LÄS MER

7. 25. Contributions to deep learning for imaging in radiotherapy

   Författare :Attila Simkó; Joakim Jonsson; Tommy Löfstedt; Anders Garpebring; Tufve Nyholm; Veronika Cheplygina; Umeå universitet; []
   Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; deep learning; medical imaging; radiotherapy; artefact correction; bias field correction; contrast transfer; synthetic CT; reproducibility;

   Sammanfattning : Purpose: The increasing importance of medical imaging in cancer treatment, combined with the growing popularity of deep learning gave relevance to the presented contributions to deep learning solutions with applications in medical imaging.Relevance: The projects aim to improve the efficiency of MRI for automated tasks related to radiotherapy, building on recent advancements in the field of deep learning. LÄS MER