Essays about: "Dependable Neural Networks"

Found 3 essays containing the words Dependable Neural Networks.

  1. 1. ERROR DETECTION IN PRODUCTION LINES VIA DEPENDABLE ARCHITECTURES IN CONVOLUTIONAL NEURAL NETWORKS

    University essay from Mälardalens universitet/Akademin för innovation, design och teknik

    Author : Erik Olsson; [2023]
    Keywords : Neural Networks; Production lines; Faster R-CNN; YOLO V8;

    Abstract : The need for products has increased during the last few years, this high demand needs to bemet with higher means of production. The use of neural networks can be the key to increasedproduction without having to compromise product quality or human workers well being. READ MORE

  3. 2. Monitored Neural Networks for Autonomous Articulated Machines

    University essay from Mälardalens högskola/Akademin för innovation, design och teknik

    Author : Erik Beckman; Linus Harenius; [2020]
    Keywords : Slip; ANN; Neural Networks; Hauler; Monitored Neural Networks; Verification Neural Networks; Curvature Steering; Autonomous machines; Articulated Machines; Volvo CE; side-slip; slip error; Reliable Neural Networks; Dependable Neural Networks;

    Abstract : Being able to safely control autonomous heavy machinery is of uttermost importance for the conversion of traditional machines to autonomous machines. With the continuous growth of autonomous vehicles around the globe, an increasing effort has been put into certifying autonomous vehicles in terms of reliability and safety. READ MORE

  4. 3. Terrain Classification to find Drivable Surfaces using Deep Neural Networks : Semantic segmentation for unstructured roads combined with the use of Gabor filters to determine drivable regions trained on a small dataset

    University essay from KTH/Robotik, perception och lärande, RPL

    Author : Agneev Guin; [2018]
    Keywords : Semantic segmentation; Deep learning; Gabor filters; Drivable surfaces;

    Abstract : Autonomous vehicles face various challenges under difficult terrain conditions such as marginally rural or back-country roads, due to the lack of lane information, road signs or traffic signals. In this thesis, we investigate a novel approach of using Deep Neural Networks (DNNs) to classify off-road surfaces into the types of terrains with the aim of supporting autonomous navigation in unstructured environments. READ MORE