Essays about: "Konvolutionellt Neuralt Nätverk"

Showing result 6 - 10 of 16 essays containing the words Konvolutionellt Neuralt Nätverk.

  1. 6. Classification of ultrasonic signals using machine learning to identify optimal frequency for elongation control : Threaded fastening tools

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

    Author : Mazen Bahy; [2022]
    Keywords : Machine Learning; Convolutions Neural Network CNN ; Barker signals; Ultrasonic sensors; Clamp-force; Threaded fastening assembly; Maskininlärning; konvolutionellt neuralt nätverk; Barker-signaler; Ultraljudssensorer; klämkraft; Åtdragningsmontering;

    Abstract : Studying the preload in a screw joint has been the focus of today’s industry. The manufacturer reflects that demand by investigating different opportunities and techniques to develop this area. There are four different ways of controlling the tightening of bolts and joints to achieve the required clamp force that can hold for a specific preload. READ MORE

  3. 7. Modeling rush hour vehicular traffic using a machine learning approach

    University essay from Lunds universitet/Matematik LTH

    Author : Erik Ackzell; [2022]
    Keywords : convolutional neural networks; traffic modeling; Mathematics and Statistics;

    Abstract : In this thesis, a convolutional neural network is used to model the behaviour of individual vehicles on a stretch of the U.S. 101 highway during rush hour. This model is then extended to model the collective behaviour of all vehicles on the stretch of road and a 15 minute simulation is carried out. READ MORE

  4. 8. Semantic segmentation of off-road scenery on embedded hardware using transfer learning

    University essay from KTH/Mekatronik

    Author : Filip Elander; [2021]
    Keywords : Semantic Segmentation; forestry navigation; Deep Neural Network; autonomous navigation; residual neural network; Convolutional neural network; Semantisk Segmentering; Autonom Terrängnavigering; Residuala Nätverk; Konvolutionellt Neuralt Nätverk; Autonom navigering;

    Abstract : Real-time semantic scene understanding is a challenging computer vision task for autonomous vehicles. A limited amount of research has been done regarding forestry and off-road scene understanding, as the industry focuses on urban and on-road applications. READ MORE

  5. 9. Development of a Software Reliability Prediction Method for Onboard European Train Control System

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

    Author : Guillaume Pierre Longrais; [2021]
    Keywords : Machine Learning; Software Reliability Growth Prediction; Linear Regression; Artificial Neural Network; Multi-Layer Perceptron; Imperialist Competitive Algorithm; Long Short-Term Memory Network; Convolutional Neural Network; Maskininlärning; förutsägelse av tillväxten av programvarans tillförlitlighet; linjär regression; artificiellt neuralt nätverk; flerskikts-perceptron; imperialistisk konkurrensalgoritm; nätverk med långt korttidsminne; konvolutionellt neuralt nätverk;

    Abstract : Software prediction is a complex area as there are no accurate models to represent reliability throughout the use of software, unlike hardware reliability. In the context of the software reliability of on-board train systems, ensuring good software reliability over time is all the more critical given the current density of rail traffic and the risk of accidents resulting from a software malfunction. READ MORE

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

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

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

    Abstract : 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. READ MORE