Sökning: "Convolutional Variational Autoencoders"

Visar resultat 1 - 5 av 8 uppsatser innehållade orden Convolutional Variational Autoencoders.

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. Deep convolution neural network for attention decoding in multi-channel EEG with conditional variational autoencoder for data augmentation

   Uppsats för yrkesexamina på avancerad nivå, Lunds universitet/Institutionen för reglerteknik

   Författare :M Asjid Tanveer; [2023]
   Nyckelord :Technology and Engineering;

   Sammanfattning : Objectives: This project aims to develop a deep learning-based attention decoding system that can distinguish between noise and speech in noise and also identify the direction of attended speech from the brain data recorded with electroencephalography (EEG) instruments. Two deep convolutional neural network (DCNN) models will be designed: (1) one DCNN model capable of classifying incoming segments of sound as speech or speech in background noise, and (2) one DCNN model identifying the direction (left vs. LÄS MER

4. 3. Sign of the Times : Unmasking Deep Learning for Time Series Anomaly Detection

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

   Författare :Daniel Richards Ravi Arputharaj; [2023]
   Nyckelord :Anomaly detection; multivariate time series data; deep learning models; model complexity; resource-constrained systems; Variational Autoencoders VAEs ; Convolutional Variational Autoencoders; evaluation metrics in time series; Anomalidetektering; Multivariata tidsseriedata; Djupinlärningsmodeller; Modellkomplexitet; Resursbegränsade system; Variational Autoencoders VAEs ; Konvolutionella Variational Autoencoders; Utvärderingsmått inom tidsserier;

   Sammanfattning : Time series anomaly detection has been a longstanding area of research with applications across various domains. In recent years, there has been a surge of interest in applying deep learning models to this problem domain. LÄS MER

5. 4. Narrow Pretraining of Deep Neural Networks : Exploring Autoencoder Pretraining for Anomaly Detection on Limited Datasets in Non-Natural Image Domains

   Master-uppsats, Linköpings universitet/Medie- och Informationsteknik; Linköpings universitet/Tekniska fakulteten

   Författare :Matilda Eriksson; Astrid Johansson; [2022]
   Nyckelord :CNN; convolutional neural network; autoencoder; variational autoencoder; anomaly detection; SPADE; limited dataset; non-natural image domain; depth domain; scatter domain; intensity domain; machine learning;

   Sammanfattning : Anomaly detection is the process of detecting samples in a dataset that are atypical or abnormal. Anomaly detection can for example be of great use in an industrial setting, where faults in the manufactured products need to be detected at an early stage. LÄS MER

7. 5. Optimisation of autoencoders for prediction of SNPs determining phenotypes in wheat

   Master-uppsats, Uppsala universitet/Institutionen för biologisk grundutbildning; Uppsala universitet/Institutionen för informationsteknologi

   Författare :Karthik Nair; [2021]
   Nyckelord :Deep Learning; Machine Learning; Convolutional Neural Networks; Convolutional Variational Autoencoders; Autoencoders; SNP-Phenotype relationships; Genetics; Agricultural Bioinformatics; Neural Networks; Unsupervised Learning;

   Sammanfattning : The increase in demand for food has resulted in increased demand for tools that help streamline plant breeding process in order to create new varieties of crops. Identifying the underlying genetic mechanism of favourable characteristics is essential in order to make the best breeding decisions. LÄS MER