Sökning: "Edge Machine Learning"
Visar resultat 1 - 5 av 108 uppsatser innehållade orden Edge Machine Learning.
1. 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 SchoolSammanfattning : 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
2. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition
Master-uppsats, KTH/Mekatronik och inbyggda styrsystemSammanfattning : 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
3. Federated Machine Learning for Resource Allocation in Multi-domain Fog Ecosystems
Master-uppsats, Uppsala universitet/Institutionen för informationsteknologiSammanfattning : The proliferation of the Internet of Things (IoT) has increasingly demanded intimacy between cloud services and end-users. This has incentivised extending cloud resources to the edge in what is deemed fog computing. The latter is manifesting as an ecosystem of connected clouds, geo-dispersed and of diverse capacities. LÄS MER
4. Finding license-plates in varying lighting conditions using two machine learning methods
Kandidat-uppsats, Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)Sammanfattning : Object detection and machine learning are important fields in Computer science. This report presents two methods to find the bounding box of a license plate and tries to evaluate the best approach to deal with various lighting conditions. LÄS MER
5. A Mixed Time-Series & Machine Learning Approach for Price Forecasting in the Swedish Ancillary Market
Magister-uppsats, Lunds universitet/Nationalekonomiska institutionen; Lunds universitet/Statistiska institutionenSammanfattning : This study aims to forecast the Swedish FCR-D Down A2 market prices through a hybrid model combining a volatility model and a machine learning approach, and compares its performance with a standalone machine learning model. We further examine the impact of different lag orders (1-Hr vs. 24-Hr) on volatility estimates and forecast performance. LÄS MER