Sökning: "Mobile learning effectiveness"

Visar resultat 1 - 5 av 11 uppsatser innehållade orden Mobile learning effectiveness.

  1. 1. Explainable AI for Multi-Agent Control Problem

    Master-uppsats, Mälardalens universitet/Akademin för innovation, design och teknik

    Författare :Hanna Prokopova; [2023]
    Nyckelord :;

    Sammanfattning : This report presents research on the application of policy explanation techniques in the context of coordinated reinforcement learning (CRL) for mobile network optimization. The goal was to improve the interpretability and comprehensibility of decision-making processes in multi-agent environments, with a particular focus on the Remote Antenna Tilt (RET) problem. LÄS MER

  3. 2. Integration of Continual Learning and Semantic Segmentation in a vision system for mobile robotics

    Master-uppsats, Luleå tekniska universitet/Rymdteknik

    Författare :Cristian David Echeverry Valencia; [2023]
    Nyckelord :Continual Learning; Progressive Neural Networks; mobile robotics; Computer Vision; Machine Learning; Semantic Segmentation;

    Sammanfattning : Over the last decade, the integration of robots into various applications has seen significant advancements fueled by Machine Learning (ML) algorithms, particularly in autonomous and independent operations. While robots have become increasingly proficient in various tasks, object instance recognition, a fundamental component of real-world robotic interactions, has witnessed remarkable improvements in accuracy and robustness. LÄS MER

  4. 3. Road Damage Segmentation for Mobile Hardware

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

    Författare :Martti Yap; [2021]
    Nyckelord :Road Damage; Deep Learning; Image Segmentation.; Vägskador; Djupinlärning; Bildsegmentering.;

    Sammanfattning : The detection and early repair of road damage are paramount for the quality and safety of roads. Current detection efforts typically rely on Deep Learning methods for object detection with bounding boxes, with calculations performed on high-performance hardware. LÄS MER

  5. 4. Time-series Generative Adversarial Networks for Telecommunications Data Augmentation

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

    Författare :Hamid Dimyati; [2021]
    Nyckelord :Telecommunication; Time- series Forecasting; Data Augmentation; Generative Adversarial Networks; Telekommunikation; Prognoser för Tidsserier; Dataförstoring; Generative Adversarial Networks;

    Sammanfattning : Time- series Generative Adversarial Networks (TimeGAN) is proposed to overcome the GAN model’s insufficiency in producing synthetic samples that inherit the predictive ability of the original timeseries data. TimeGAN combines the unsupervised adversarial loss in the GAN framework with a supervised loss adopted from an autoregressive model. LÄS MER

  7. 5. Sensor numerical prediction based on long-term and short-term memory neural network

    Kandidat-uppsats, Mittuniversitetet/Institutionen för informationssystem och –teknologi

    Författare :Wen Yangyang; [2020]
    Nyckelord :Deep learning; long-and-short-term memory neural networks; sensor networks; Internet of Things; value prediction;

    Sammanfattning : Many sensor nodes are scattered in the sensor network,which are used in all aspects of life due to their small size, low power consumption, and multiple functions. With the advent of the Internet of Things, more small sensor devices will appear in our lives. LÄS MER