Sökning: "spatio-temporal maskininlärning"

Visar resultat 1 - 5 av 8 uppsatser innehållade orden spatio-temporal maskininlärning.

  1. 1. Graph Neural Networks for Events Detection in Football

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Giovanni Castellano; [2023]
    Nyckelord :Machine Learning; Graph Neural Network; Recurrent Neural Network; Trajectory Data; Football; Events Detection; Maskininlärning; Graph Neural Network; Recurrent Neural Network; Bandata; fotboll; händelsedetektering;

    Sammanfattning : Tracab’s optical tracking system allows to track the 2-dimensional trajectories of players and ball during a football game. Using this data it is possible to train machine learning models to identify events that happen during the match. LÄS MER

  3. 2. Generating Geospatial Trip DataUsing Deep Neural Networks

    Master-uppsats, Linköpings universitet/Statistik och maskininlärning

    Författare :Ahmed Alhasan; [2022]
    Nyckelord :Deep Learning; Machine Learning; Statistics; Generative Adversarial Networks; Computer Science; Generative Models;

    Sammanfattning : Synthetic data provides a good alternative to real data when the latter is not sufficientor limited by privacy requirements. In spatio-temporal applications, generating syntheticdata is generally more complex due to the existence of both spatial and temporal dependencies. LÄS MER

  4. 3. Multimodal Machine Learning in Human Motion Analysis

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Jia Fu; [2022]
    Nyckelord :Multimodal machine learning; Modal fusion; Human motion classification; Multimodal maskininlärning; Modal fusion; Mänsklig rörelseklassificering;

    Sammanfattning : Currently, most long-term human motion classification and prediction tasks are driven by spatio-temporal data of the human trunk. In addition, data with multiple modalities can change idiosyncratically with human motion, such as electromyography (EMG) of specific muscles and respiratory rhythm. LÄS MER

  5. 4. Deep Learning for Earth Observation: improvement of classification methods for land cover mapping : Semantic segmentation of satellite image time series

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Benjamin Carpentier; [2021]
    Nyckelord :Satellite Image Time Series; Remote sensing; Land Cover Classification; Deep Learning; Convolutional Neural Network; Tidsserier av satellitbilder; Fjärranalys; Classificering; Djupinlärning; KonvolutionelltNeuralt Nätverk;

    Sammanfattning : Satellite Image Time Series (SITS) are becoming available at high spatial, spectral and temporal resolutions across the globe by the latest remote sensing sensors. These series of images can be highly valuable when exploited by classification systems to produce frequently updated and accurate land cover maps. LÄS MER

  7. 5. Machine Learning for Air Flow Characterization : An application of Theory-Guided Data Science for Air Fow characterization in an Industrial Foundry

    Master-uppsats, Karlstads universitet

    Författare :Robin Lundström; [2019]
    Nyckelord :Machine learning; ML; Echo State Map; ESM; Echo State Network; ESN; Gaussian Process; GP; Computational Fluid Dynamics; CFD; Theory-Guided Data Science; TGDS; Physics-Guided Data Science; Data science; Cross-discipline; Hybrid model; MatLab; Maskininlärning; ML; Echo State Map; ESM; Echo State Network; ESN; Gaussiska Processer; GP; Beräkningsströmningsdynamik; CFD; MatLab;

    Sammanfattning : In industrial environments, operators are exposed to polluted air which after constant exposure can cause irreversible lethal diseases such as lung cancer. The current air monitoring techniques are carried out sparely in either a single day annually or at few measurement positions for a few days. LÄS MER