Sökning: "deep time"

Visar resultat 1 - 5 av 672 avhandlingar innehållade orden deep time.

1. 1. Visualizing the abyss of time : Students’ interpretation of visualized deep evolutionary time

   Författare :Jörgen Stenlund; Konrad Schönborn; Lena Tibell; Gunnar Höst; Camillia Matuk; Linköpings universitet; []
   Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; Deep evolutionary time; Visualization; Evolution; Science Education; Djup evolutionär tid; Visualiseringar; Naturvetenskapernas didaktik;

   Sammanfattning : The immense time scales involved in Deep evolutionary time (DET) is a threshold concept in biology and interpreting temporal aspects of DET is demanding. DET is communicated through various visualizations that include static two-dimensional representations, low interactivity animations, as well as high interactivity interfaces. LÄS MER

3. 2. Modeling time-series with deep networks

   Författare :Martin Längkvist; Amy Loutfi; Lars Karlsson; Tapani Raiko; Örebro universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; multivariate time-series; deep learning; representation learning; unsupervised; Information technology; Informationsteknologi;

   Sammanfattning : .... LÄS MER

4. 3. Time, space and control: deep-learning applications to turbulent flows

   Författare :Luca Guastoni; Ricardo Vinuesa; Hossein Azizpour; Philipp Schlatter; Andrea Beck; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; turbulence; deep learning; deep reinforcement learning; flow control; turbulens; djupinlärning; djupförstärkningsinlärning; flödeskontroll; Teknisk mekanik; Engineering Mechanics;

   Sammanfattning : In the present thesis, the application of deep learning and deep reinforcement learning to turbulent-flow simulations is investigated. Deep-learning models are trained to perform temporal and spatial predictions, while deep reinforcement learning is applied to a flow-control problem, namely the reduction of drag in an open channel flow. LÄS MER

5. 4. Supervised and Unsupervised Deep Learning Models for Flood Detection

   Författare :Ritu Yadav; Yifang Ban; Andrea Nascetti; Nicolas Audebert; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Floods; Remote Sensing; Sentinel-1 SAR; Segmentation; Change Detection; DEM; Data Fusion; Time Series; Deep Learning; Unsupervised Learning; Contrastive Learning; Self-Attention; Convolutional LSTM; Variational AutoEncoder VAE ; Geoinformatik; Geoinformatics;

   Sammanfattning : Human civilization has an increasingly powerful influence on the earthsystem. Affected by climate change and land-use change, floods are occurringacross the globe and are expected to increase in the coming years. Currentsituations urge more focus on efficient monitoring of floods and detecting impactedareas. LÄS MER

7. 5. Pith location and annual ring detection for modelling of knots and fibre orientation in structural timber : A Deep-Learning-Based Approach

   Författare :Tadios Habite; Anders Olsson; Osama Abdeljaber; Jan Oscarsson; Welf Löwe; Julie Cool; Linnéuniversitetet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Sawn timber; Pith location; Deep learning; Artificial neural networks; Convolutional neural network; Conditional generative adversarial network; Knot detection; Knot modelling; Knot reconstruction; Fibre orientation; Annual ring profile; Byggteknik; Civil engineering;

   Sammanfattning : Detection of pith, annual rings and knots in relation to timber board cross-sections is relevant for many purposes, such as for modelling of sawn timber and for real-time assessment of strength, stiffness and shape stability of wood materials. However, the methods that are available and implemented in optical scanners today do not always meet customer accuracy and/or speed requirements. LÄS MER