Sökning: "network optimization."

Visar resultat 1 - 5 av 364 uppsatser innehållade orden network optimization..

  1. 1. Machine Learning with Reconfigurable Privacy on Resource-Limited Edge Computing Devices

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

    Författare :Zannatun Nayem Tania; [2021]
    Nyckelord :Data Privacy; Resource Management; Machine Learning; Fitbit; Internet of Things IoT ; Optimization; Dataintegritet; Resurshantering; Machine Learning; Fitbit; Internet of Things IoT ; Optimering;

    Sammanfattning : Distributed computing allows effective data storage, processing and retrieval but it poses security and privacy issues. Sensors are the cornerstone of the IoT-based pipelines, since they constantly capture data until it can be analyzed at the central cloud resources. However, these sensor nodes are often constrained by limited resources. LÄS MER

  2. 2. Bridging Sim-to-Real Gap in Offline Reinforcement Learning for Antenna Tilt Control in Cellular Networks

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

    Författare :Mayank Gulati; [2021]
    Nyckelord :reinforcement learning; transfer learning; simulation-to-reality; simulator; realworld; real-world network data; remote electrical tilt optimization; cellular networks; antenna tilt; network optimization.;

    Sammanfattning : Antenna tilt is the angle subtended by the radiation beam and horizontal plane. This angle plays a vital role in determining the coverage and the interference of the network with neighbouring cells and adjacent base stations. LÄS MER

  3. 3. Unsupervised 3D Human Pose Estimation

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

    Författare :Sri Datta Budaraju; [2021]
    Nyckelord :Computer Vision; Projective Geometry; Deep Learning; Unsupervised Learning; 3D Human Pose Estimation; GAN; AutoEncoder; Hybrid Generative Model; Self Supervision;

    Sammanfattning : The thesis proposes an unsupervised representation learning method to predict 3D human pose from a 2D skeleton via a VAEGAN (Variational Autoencoder Generative Adversarial Network) hybrid network. The method learns to lift poses from 2D to 3D using selfsupervision and adversarial learning techniques. LÄS MER

  4. 4. Generative Neural Network for Portfolio Optimization

    Master-uppsats, Mälardalens högskola/Akademin för utbildning, kultur och kommunikation

    Författare :Mengxin Liu; [2021]
    Nyckelord :GAN; Portfolio Optimization; Neural Networks;

    Sammanfattning : This thesis aims to overcome the drawbacks of traditional portfolio optimization by employing Generative Deep Neural Networks on real stock data. The proposed framework is capable of generating return data that have similar statistical characteristics as the original stock data. LÄS MER

  5. 5. Deep reinforcement learning for real-time power grid topology optimization

    Kandidat-uppsats, Lunds universitet/Matematisk statistik

    Författare :Jacob Rothschild; [2021]
    Nyckelord :Deep reinforcement learning; Dueling Deep Q-Network; electricity transmission network; real-time topology optimization; sustainable energy; Mathematics and Statistics;

    Sammanfattning : In our pursuit of carbon neutrality, drastic changes to generation and consumption of electricity will cause new and complex demands on the power grid and its operators. A cheap, promising, and under-exploited mitigation is real-time power grid topology optimization (RTTO). LÄS MER