Sökning: "Domänanpassning"
Visar resultat 1 - 5 av 22 uppsatser innehållade ordet Domänanpassning.
1. Adaptive Model-Based Temperature Monitoring for Electric Powertrains : Investigation and Comparative Analysis of Transfer Learning Approaches
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : In recent years, deep learning has been widely used in industry to solve many complex problems such as condition monitoring and fault diagnosis. Powertrain condition monitoring is one of the most vital and complicated problems in the automation industry since the condition of the drive affects its health, performance, and reliability. LÄS MER
2. Domain Adaptation for Multi-Contrast Image Segmentation in Cardiac Magnetic Resonance Imaging
Master-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)Sammanfattning : Accurate segmentation of the ventricles and myocardium on Cardiac Magnetic Resonance (CMR) images is crucial to assess the functioning of the heart or to diagnose patients suffering from myocardial infarction. However, the domain shift existing between the multiple sequences of CMR data prevents a deep learning model trained on a specific contrast to be used on a different sequence. LÄS MER
3. Domain Adaptation Of Front View Synthetic Point Clouds Using GANs For Autonomous Driving
Master-uppsats, KTH/Väg- och spårfordon samt konceptuell fordonsdesignSammanfattning : The perception of the environment is one of the main enablers of Autonomous Driving and is driven by Cameras, RADAR, and LiDAR sensors. Deep Learning algorithms used in perception need a vast amount of labeled, high-quality data which is costly to obtain for LiDAR sensors. LÄS MER
4. Real-time Unsupervised Domain Adaptation
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Machine learning systems have been demonstrated to be highly effective in various fields, such as in vision tasks for autonomous driving. However, the deployment of these systems poses a significant challenge in terms of ensuring their reliability and safety in diverse and dynamic environments. LÄS MER
5. Unsupervised Domain Adaptation for 3D Object Detection Using Adversarial Adaptation : Learning Transferable LiDAR Features for a Delivery Robot
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : 3D object detection is the task of detecting the full 3D pose of objects relative to an autonomous platform. It is an important perception system that can be used to plan actions according to the behavior of other dynamic objects in an environment. LÄS MER