Sökning: "Lång Korttidsminne"
Hittade 5 uppsatser innehållade orden Lång Korttidsminne.
1. Supervised Algorithm for Predictive Maintenance
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Predictive maintenance plays a crucial role in preventing unexpected equipment failures and maintaining assets in good operating conditions in various systems. One such scenario where predictive maintenance has been widely used is in battery management systems for electronic vehicles based on lithium batteries, where the risk of failure can be reduced by predicting the remaining useful life of the lithium battery. LÄS MER
2. Comparing decentralized learning to Federated Learning when training Deep Neural Networks under churn
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Decentralized Machine Learning could address some problematic facets with Federated Learning. There is no central server acting as an arbiter of whom or what may benefit from Machine Learning models created by the vast amount of data becoming available in recent years. LÄS MER
3. Digital Signal Characterization for Seizure Detection Using Frequency Domain Analysis
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Nowadays, a significant proportion of the population in the world is affected by cerebral diseases like epilepsy. In this study, frequency domain features of electroencephalography (EEG) signals were studied and analyzed, with a view being able to detect epileptic seizures more easily. LÄS MER
4. Point Process Based Phoneme Recognition Acceleration
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Stochastic gradient descent (SGD) is the core technology to train a deep learning model. It is well known that SGD suffers from the variance of gradients in each iteration. Deep learning has already been widely used in many applications because of its great performance in tasks such as image recognition. LÄS MER
5. The impact of parsing methods on recurrent neural networks applied to event-based vehicular signal data
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : This thesis examines two different approaches to parsing event-based vehicular signal data to produce input to a neural network prediction model: event parsing, where the data is kept unevenly spaced over the temporal domain, and slice parsing, where the data is made to be evenly spaced over the temporal domain instead. The dataset used as a basis for these experiments consists of a number of vehicular signal logs taken at Scania AB. LÄS MER