Sökning: "Oövervakad inlärning"

Visar resultat 1 - 5 av 31 uppsatser innehållade orden Oövervakad inlärning.

1. 1. Fault Detection and Diagnosis for Automotive Camera using Unsupervised Learning

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

   Författare :Ziyou Li; [2023]
   Nyckelord :Unsupervised Learning; Autoencoders; Image Clustering; Fault Detection and Diagnosis; Morphological Operations; Hardware-in-Loop; Advanced DriverAssistance System; Oövervakad inlärning; Autoencoders; Bildklustering; Felfindning och Diagnostik; Morfologiska Operationer; Hardware-in-Loop; Avancerade Förarassistanssystem;

   Sammanfattning : This thesis aims to investigate a fault detection and diagnosis system for automotive cameras using unsupervised learning. 1) Can a front-looking wide-angle camera image dataset be created using Hardware-in-Loop (HIL) simulations? 2) Can an Adversarial Autoencoder (AAE) based unsupervised camera fault detection and diagnosis method be crafted for SPA2 Vehicle Control Unit (VCU) using an image dataset created using Hardware-inLoop? 3) Does using AAE surpass the performance of using Variational Autoencoder (VAE) for the unsupervised automotive camera fault diagnosis model? In the field of camera fault studies, automotive cameras stand out for its complex operational context, particularly in Advanced Driver-Assistance Systems (ADAS) applications. LÄS MER

3. 2. Unsupervised Domain Adaptation for Regressive Annotation : Using Domain-Adversarial Training on Eye Image Data for Pupil Detection

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

   Författare :Erik Zetterström; [2023]
   Nyckelord :Neural networks; Deep learning; Convolutional neural networks; Transfer learning; Domain adaptation; Unsupervised training; Adversarial training; Keypoint detection; Regression; Neurala nätverk; Djupinlärning; Faltningsnätverk; Överförningsinlärning; Domänadaptering; Oövervakad inlärning; Motstående träning; Nyckelpunktsdetektion; Regression;

   Sammanfattning : Machine learning has seen a rapid progress the last couple of decades, with more and more powerful neural network models continuously being presented. These neural networks require large amounts of data to train them. LÄS MER

4. 3. 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)

   Författare :Mattias Hansson; [2023]
   Nyckelord :Unsupervised Domain Adaptation; 3D Object Detection; Mobile Robotics; Adversarial Adaptation; Computer Vision; Oövervakad Domänanpassning; 3D Objektigenkänning; Mobila Robotar; Motspelaranpassning; Datorseende;

   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

5. 4. Advancing Keyword Clustering Techniques: A Comparative Exploration of Supervised and Unsupervised Methods : Investigating the Effectiveness and Performance of Supervised and Unsupervised Methods with Sentence Embeddings

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

   Författare :Filippo Caliò; [2023]
   Nyckelord :Keyword Clustering; Supervised Learning; Unsupervised Learning; Cluster Labels; Natural Language Processing; Sentence Embeddings; Nyckelord Klustring; övervakad inlärning; oövervakad inlärning; klustermärkning; naturlig språkbehandling; Inbäddning av meningar;

   Sammanfattning : Clustering keywords is an important Natural Language Processing task that can be adopted by several businesses since it helps to organize and group related keywords together. By clustering keywords, businesses can better understand the topics their customers are interested in. LÄS MER

7. 5. Unsupervised Machine Learning Based Anomaly Detection in Stockholm Road Traffic

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

   Författare :Vilma Hellström; [2023]
   Nyckelord :Anomaly detection; DBSCAN; LSTM; Machine learning; Synthetic anomalies; Unsupervised learning; Anomalidetektering; DBSCAN; LSTM; maskininlärning; syntetiska anomalier; oövervakad inlärning;

   Sammanfattning : This thesis is a study of anomaly detection in vehicle traffic data in central Stockholm. Anomaly detection is an important tool in the analysis of traffic data for improved urban planing. Two unsupervised machine learning models are used, the DBSCAN clustering model and the LSTM deep learning neural network. LÄS MER