Sökning: "utvärdering av algoritmen."

Visar resultat 1 - 5 av 80 uppsatser innehållade orden utvärdering av algoritmen..

1. 1. Shape depiction using Local Light Alignment : Evaluating the shape enhancement capabilities of the Local Light Alignment technique at multiple scales

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

   Författare :Edvard Ahlsén; [2023]
   Nyckelord :Shape depiction; Non-photorealistic rendering; Shading; Multiple scales; Formskildring; ickerealistisk rendering; skuggning; skalrymdsmetoder;

   Sammanfattning : Local Light Alignment is a new shading based technique in the field of shape depiction, a field which concerns itself with techniques to represent and enhance the three-dimensional (3D) shape of objects in two-dimensional (2D) visual media. The main idea of Local Light Alignment is to locally adjust the incoming direction of light to create contrast in a way that enhances the perception of shape and surface detail. LÄS MER

3. 2. Development of a new SBRT dose planning strategy for thoracic tumours in RayStation

   Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Radiofysik

   Författare :Olof Westman; [2023]
   Nyckelord :radiotherapy sbrt lung robust; radioterapi;

   Sammanfattning : Umeå University Hospital has acquired a new treatment planning system, RayStation, for radiotherapy. It has a different set of dose calculation algorithms that require new planning strategies for stereotactic lung cancer treatments. LÄS MER

4. 3. Domain Knowledge and Representation Learning for Centroid Initialization in Text Clustering with k-Means : An exploratory study

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

   Författare :David Yu; [2023]
   Nyckelord :Natural language processing; Sentiment analysis; Clustering; Language model; Transformer; Heuristic; Språkteknologi; Sentimentanalys; Klustering; Språkmodell; Transformer; Heuristik;

   Sammanfattning : Text clustering is a problem where texts are partitioned into homogeneous clusters, such as partitioning them based on their sentiment value. Two techniques to address the problem are representation learning, in particular language representation models, and clustering algorithms. 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. Optimizing Realistic 3D Facial Models for VR Avatars through Mesh Simplification

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

   Författare :Beiqian Liu; [2023]
   Nyckelord :Mesh Simplification; Virtual Reality; Realistic Avatars; 3D Face Reconstruction;

   Sammanfattning : The use of realistic 3D avatars in Virtual Reality (VR) has gained significant traction in applications such as telecommunication and gaming, offering immersive experiences and face-to-face interactions. However, standalone VR devices often face limitations in computational resources and real-time rendering requirements, necessitating the optimization of 3D models through mesh simplification to enhance performance and ensure a smooth user experience. LÄS MER