Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 12 of 12 for “"convolutional autoencoder"”.
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Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model
… specifically, Artificial Neural Networks(ANN), Convolutional Autoencoder, and Convolution Long Short-Term Memory(convLSTM), in predicting wildfire spread. The three ML models developed were based on domain knowledge of fire behaviour and utilised Google Earth Engine(GEE) weather data and …
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Learning low-dimensional feature dynamics using convolutional recurrent autoencoders
… constructs a modular model consisting of a deep convolutional autoencoder and a modified LSTM network. The deep convolutional autoencoder returns a low-dimensional representation in terms of coordinates on some expressive nonlinear data-supporting manifold. The dynamics on this manifold are then …
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Representation Learning Associates Patients’ Risks for Metabolic Diseases with Features of Their Lipocytes
… multi-channel lipocyte microscopy images using a convolutional autoencoder, we perform unsupervised clustering on the learnt representations to identify different cell states. We analyze the distribution of these cell states in different individuals and associate their PRS to the observed cell …
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Audio compression via nonlinear transform coding and stochastic binary activation
… concepts, then systematically presents a convolutional autoencoder network with a stochastic binary activation for a sparse representation of the code space to achieve compression. A similar network is employed for encoding the residual of the main network. Our network achieves average …
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Neural networks for the prediction of chaos and turbulence
… combine the optimised echo state networks with a convolutional autoencoder (CAE) into the convolutional autoencoder echo state network (CAE-ESN) to predict two-dimensional flows. The architecture computes the latent space, which is the manifold onto which the turbulent dynamics live, through a …
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Neural Network Methods for Improving Signal Processing in High-Purity Germanium Detectors for Rare Event Searches
This thesis introduces a hybrid convolutional Transformer-autoencoder for self-supervised denoising of high-purity germanium p-type point contact detector signals. Faithful extraction of information from noisy signals is critical to the sensitivity of neutrinoless double-beta decay experiments, and …
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Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data
… unused. Accordingly, a deep SEmi-Supervised Convolutional Autoencoder (SECA) architecture is proposed to not only automatically extract relevant features from GPS segments but also exploit useful information in unlabeled data. The SECA integrates a convolutional-deconvolutional autoencoder …
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Synthetic Electronic Medical Record Generation using Generative Adversarial Networks
… to create EHR data. This work, leverages Deep Convolutional Neural Networks to extract and understand spatial dependencies in EHR data. To improve our model's performance, we focus on our Deep Convolutional AutoEncoder to better map our real EHR data to our latent space where we train the …
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On the Use of Deep Learning Models for Interference Detection and Mitigation
… learning architectures such as using a hybrid Convolutional Neural Network(CNN) models knows as InceptionTimePlus and MiniRocketPlus, with results compared to classical methods including matched filtering, energy detection, and FFT-based thresholding. Interference suppression is achieved …
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Efficient Edge Intelligence in the Era of Big Data
… leverage Deep Learning (DL), more specifically, Convolutional Autoencoder (CAE), to learn a sparse representation of the vital big data. The minimized energy need, even taking into consideration the CAE-induced overhead, is tremendously lower than the original energy need. Further, compared with …
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Characterization of electroluminescence signals from nuclear recoil events in the dual-phase argon Time Projection Chamber of the Red experiment with Convolutional Autoencoders
… analysis method. Specifically, I implemented a convolutional autoencoder (CAE) to classify electroluminescence signals recorded by silicon photomultipliers at cryogenic temperatures. By leveraging machine learning, the CAE efficiently identified patterns in experimental data, offering a novel, …
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Applications of Data-Driven Learning Models in Fluid Mechanics: Solid-Fluid Multiphase Systems and Bat Flight
… gap, we develop deep learning models—including convolutional neural networks (CNNs) and graph neural networks (GNNs)—that incorporate both local particle neighborhood information and global suspension parameters. We demonstrate the effectiveness of these models in predicting particle-scale drag …