Sökning: "edge learning"

Visar resultat 1 - 5 av 182 uppsatser innehållade orden edge learning.

1. 1. Exploring the Depth-Performance Trade-Off : Applying Torch Pruning to YOLOv8 Models for Semantic Segmentation Tasks

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

   Författare :Xinchen Wang; [2024]
   Nyckelord :Deep Learning; Semantic segmentation; Network optimization; Network pruning; Torch Pruning; YOLOv8; Network Depth; Djup lärning; Semantisk segmentering; Nätverksoptimering; Nätverksbeskärning; Fackelbeskärning; YOLOv8; Nätverksdjup;

   Sammanfattning : In order to comprehend the environments from different aspects, a large variety of computer vision methods are developed to detect objects, classify objects or even segment them semantically. Semantic segmentation is growing in significance due to its broad applications in fields such as robotics, environmental understanding for virtual or augmented reality, and autonomous driving. LÄS MER

3. 2. AI for innovators - An exploratory study on the application of Artificial Intelligence as a supportive tool in the innovation process

   Master-uppsats, Göteborgs universitet/Graduate School

   Författare :Mauro Campus; [2023-07-19]
   Nyckelord :Innovation; Innovation Drivers; Stages of Innovation; Innovation Models; Innovation Process; Artificial Intelligence; Industry 4.0 and Artificial Intelligence; Machine Learning; Deep Learning; Generative AI; Artificial Intelligence in Business; Artificial Intelligence and Innovation;

   Sammanfattning : The technological evolution we are experiencing nowadays has impacted many businesses and industries. In this sense, one of the most influential technologies is certainly Artificial Intelligence, which especially in recent months has been at the centre of numerous debates. LÄS MER

4. 3. Knowledge distillation for anomaly detection

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Nils Gustav Erik Pettersson; [2023]
   Nyckelord :;

   Sammanfattning : The implementation of systems and methodologies for time series anomaly detection holds the potential of providing timely detection of faults and issues in a wide variety of technical systems. Ideally, these systems are able to identify deviations from the normal behavior of systems even before any problems manifest, thus enabling proactive maintenance. LÄS MER

5. 4. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition

   Master-uppsats, KTH/Mekatronik och inbyggda styrsystem

   Författare :Helgi Hrafn Björnsson; Jón Kaldal; [2023]
   Nyckelord :Recurrent Neural Networks; Long Short-Term Memory Networks; Embedded Systems; Human Activity Recognition; Edge AI; TensorFlow Lite Micro; Recurrent Neural Networks; Long Short-Term Memory Networks; Innbyggda systyem; Mänsklig aktivitetsigenkänning; Edge AI; TensorFlow Lite Micro;

   Sammanfattning : Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. LÄS MER

7. 5. Deep Reinforcement Learning in Games Based on Extracted Features

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

   Författare :Emilia Sjögren; Erika Weidenhaijn; [2023]
   Nyckelord :;

   Sammanfattning : FlappyBird is a popular mobile game that captured many people's attention because itwas easy to understand but difficult to perform --- players were often right on the edge ofsucceeding, which led to a strong desire to play again. The purpose of this project is to investigatethe possibility of using a neural network trained with reinforcement learning to play the game usingextracted features rather than raw images. LÄS MER