Výsledky vyhľadávania - "Convolutional autoencoders"

Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations Autor Rautela, Mahindra, Senthilnath, J., Monaco, Ernesto, Gopalakrishnan, S.

“…With the introduction of damage tolerance-based design philosophies, the demand for reliable and robust structural health monitoring (SHM) procedures for…”
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Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation Autor Mack, Julian, Arcucci, Rossella, Molina-Solana, Miguel, Guo, Yi-Ke

“…We propose a new ‘Bi-Reduced Space’ approach to solving 3D Variational Data Assimilation using Convolutional Autoencoders. We prove that our approach has the…”
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Enhanced Intelligent Video Monitoring using Hybrid Integration of Spatiotemporal Autoencoders and Convolutional LSTMs Autor Umale-Nagmote, Ankita, Goel, Charu, Lal, Nidhi

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Graph convolutional autoencoders with co-learning of graph structure and node attributes Autor Wang, Jie, Liang, Jiye, Yao, Kaixuan, Liang, Jianqing, Wang, Dianhui

“…•We propose a novel end-to-end graph autoencoders model for the attributed graph.•The proposed model can reconstruct both the graph structure and node…”
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A deep learning framework for predicting and optimizing flow fields in reactive flows Autor Gharib, Mohsen, Maleki, Farideh Hoseinian, Rößger, Philip, Gräbner, Martin, Richter, Andreas

“…Computational Fluid Dynamics (CFD) is widely used for solving and optimizing the flow fields of different systems and applications. However, running CFD…”
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A Convolutional Autoencoder Topology for Classification in High-Dimensional Noisy Image Datasets Autor Pintelas, Emmanuel, Livieris, Ioannis E., Pintelas, Panagiotis E.

“…Deep convolutional neural networks have shown remarkable performance in the image classification domain. However, Deep Learning models are vulnerable to noise…”
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Convolutional autoencoders, clustering, and POD for low-dimensional parametrization of flow equations Autor Heiland, Jan, Kim, Yongho

“…Simulations of large-scale dynamical systems require expensive computations and large amounts of storage. Low-dimensional representations of high-dimensional…”
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Non-intrusive surrogate modeling for parametrized time-dependent partial differential equations using convolutional autoencoders Autor Nikolopoulos, Stefanos, Kalogeris, Ioannis, Papadopoulos, Vissarion

“…This paper presents a novel non-intrusive surrogate modeling scheme based on deep learning for predictive modeling of complex systems, described by…”
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High-Resolution SAR Image Classification via Deep Convolutional Autoencoders Autor Geng, Jie, Fan, Jianchao, Wang, Hongyu, Ma, Xiaorui, Li, Baoming, Chen, Fuliang

“…Synthetic aperture radar (SAR) image classification is a hot topic in the interpretation of SAR images. However, the absence of effective feature…”
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A Novel Convolutional Autoencoder-Based Clutter Removal Method for Buried Threat Detection in Ground-Penetrating Radar Autor Temlioglu, Eyyup, Erer, Isin

“…The clutter encountered in ground-penetrating radar (GPR) systems seriously affects the performance of the subsurface target detection methods. A new clutter…”
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Hyperspecral Unmixing Based on Multilinear Mixing Model Using Convolutional Autoencoders Autor Fang, Tingting, Zhu, Fei, Chen, Jie

“…Unsupervised spectral unmixing (SU) consists of representing each observed pixel as a combination of several pure materials known as endmembers, along with…”
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Hyperspectral Unmixing Based on Multilinear Mixing Model Using Convolutional Autoencoders Autor Fang, Tingting, Zhu, Fei, Chen, Jie

“…Unsupervised spectral unmixing (SU) consists of representing each observed pixel as a combination of several pure materials known as endmembers, along with…”
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Deep Neural Network Initialization Methods for Micro-Doppler Classification With Low Training Sample Support Autor Seyfioglu, Mehmet Saygin, Gurbuz, Sevgi Zubeyde

“…Deep neural networks (DNNs) require large-scale labeled data sets to prevent overfitting while having good generalization. In radar applications, however,…”
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Enhancing multi-step-ahead prediction of wave propagation with the CAE-LSTM model: a novel deep learning-based approach to flood dynamics Autor Zheng Han, Guanping Long, Changli Li, Yange Li, Bin Su, Linrong Xu, Weidong Wang, Guangqi Chen

“…A deep understanding of the wave propagation process during flood dynamics is fundamental for hazard prediction and mitigation, wherein up-to-date…”
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Polytopic autoencoders with smooth clustering for reduced-order modeling of flows Autor Heiland, Jan, Kim, Yongho

“…With the advancement of neural networks, there has been a notable increase, both in terms of quantity and variety, in research publications concerning the…”
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Contrast sensitivity functions in autoencoders Autor Li, Qiang, Gomez-Villa, Alex, Bertalmío, Marcelo, Malo, Jesús

“…Three decades ago, Atick et al. suggested that human frequency sensitivity may emerge from the enhancement required for a more efficient analysis of retinal…”
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A novel multichannel sparse convolutional autoencoder for electrocardiogram signal compression Autor Bekiryazıcı, Tahir, Damkacı, Mehmet, Aydemir, Gürkan, Gürkan, Hakan

“…Electrocardiogram (ECG) signal compression is paramount in continuously monitoring cardiac patients, as it reduces data storage and transmission costs. Deep…”
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Accurate microwave filter design based on particle swarm optimization and one‐dimensional convolution autoencoders Autor Wang, Yanxing, Zhang, Zhuowei, Yi, Yaxin, Zhang, Yongliang

“…This paper proposes a one‐dimensional convolutional autoencoders (1D‐CAE) surrogate‐based electromagnetic (EM) optimization technique exploiting particle swarm…”
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A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection Autor Scarpiniti, Michele, Sarv Ahrabi, Sima, Baccarelli, Enzo, Piazzo, Lorenzo, Momenzadeh, Alireza

“…Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed…”
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Dynamic feature capturing in a fluid flow reduced-order model using attention-augmented autoencoders Autor Beiki, Alireza, Kamali, Reza

“…This study looks into how adding adaptive attention to convolutional autoencoders can help reconstruct flow fields in fluid dynamics applications. The study…”
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