Sökning: "CGAN"

Hittade 3 avhandlingar innehållade ordet CGAN.

  1. 1. Machine learning for the free-form inverse design of metamaterials

    Författare :Timo Gahlmann; Chalmers tekniska högskola; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; CGAN; machine learning; free-form inverse design; Metasurface;

    Sammanfattning : We present our work on using deep neural networks for the prediction of the optical properties of nanophotonic structures and for the inverse design of such nanostructures. First we show that neural networks can indeed be used to predict the optical properties of nanostructured materials such as metasurfaces. LÄS MER

  3. 2. 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

  4. 3. 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