Essays about: "Djupinlärningsmodeller"

Showing result 1 - 5 of 49 essays containing the word Djupinlärningsmodeller.

  1. 1. Deep Learning Model Deployment for Spaceborne Reconfigurable Hardware : A flexible acceleration approach

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Javier Ferre Martin; [2023]
    Keywords : Space Situational Awareness; Deep Learning; Convolutional Neural Networks; FieldProgrammable Gate Arrays; System-On-Chip; Computer Vision; Dynamic Partial Reconfiguration; High-Level Synthesis; Rymdsituationstänksamhet; Djupinlärning; Konvolutionsnätverk; Omkonfigurerbara Field-Programmable Gate Arrays FPGAs ; System-On-Chip SoC ; Datorseende; Dynamisk partiell omkonfigurering; Högnivåsyntes.;

    Abstract : Space debris and space situational awareness (SSA) have become growing concerns for national security and the sustainability of space operations, where timely detection and tracking of space objects is critical in preventing collision events. Traditional computer-vision algorithms have been used extensively to solve detection and tracking problems in flight, but recently deep learning approaches have seen widespread adoption in non-space related applications for their high accuracy. READ MORE

  3. 2. Mitigating Unintended Bias in Toxic Comment Detection using Entropy-based Attention Regularization

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Fabio Camerota; [2023]
    Keywords : XLNet; BERT; Toxic Comment Classification; Entropy-based Attention Regularization; XLNet; BERT; Toxisk Kommentar Klassificering; Entropibaserad uppmärksamhetsreglering;

    Abstract : The proliferation of hate speech is a growing challenge for social media platforms, as toxic online comments can have dangerous consequences also in real life. There is a need for tools that can automatically and reliably detect hateful comments, and deep learning models have proven effective in solving this issue. READ MORE

  4. 3. Dataset characteristics effect on time series forecasting : comparison of statistical and deep learning models

    University essay from Högskolan i Halmstad/Akademin för informationsteknologi

    Author : Adam Ahlman; Adam Taylor; [2023]
    Keywords : Time Series; Forecasting;

    Abstract : Time series are points of data measured throughout time in equally spaced periods. They present characteristics such as level, noise, trend, seasonality, and outliers. Time series forecasting is the attempt to predict single or multiple future values. READ MORE

  5. 4. Deep Learning-Driven EEG Classification in Human-Robot Collaboration

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Yuan Wo; [2023]
    Keywords : Human-robot collaboration; Electroencephalogram signal; Signal Processing Feature Extraction; Deep Learning method; Dilated Convolutional Neural Network; Människa-robot-samarbete; Elektroencefalogram-signal; Signalförädlingsfunktionsutvinning; Djupinlärningsmetod; Dilaterat konvolutionellt neuronnätverk.;

    Abstract : Human-robot collaboration (HRC) occurs when people and robots work together in a shared environment. Current robots often use rigid programs unsuitable for HRC. Multimodal robot programming offers an easier way to control robots using inputs like voice and gestures. READ MORE

  6. 5. Automatic Detection of Structural Deformations in Batteries from Imaging data using Machine Learning : Exploring the potential of different approaches for efficient structural deformation detection

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Maira Khan; [2023]
    Keywords : CT scan; electrode peaks; jelly roll; keypoints; structural deformation; traditional computer vision; deep neural network; CT-skanning; elektrodtoppar; gelérulle; nyckelpunkter; strukturell deformation; Traditionellt datorseende; djupt neuralt nätverk;

    Abstract : The increasing occurrence of structural deformations in the electrodes of the jelly roll has raised quality concerns during battery manufacturing, emphasizing the need to detect them automatically with the advanced techniques. This thesis aims to explore and provide two models based on traditional computer vision (CV) and deep neural network (DNN) techniques using computed tomography (CT) scan images of jelly rolls to ensure that the product is of high quality. READ MORE