Suchergebnisse - sparse convolutional autoencoder (cae)*

Convolutional autoencoder-augmented sparse identification of noisy nonlinear fluid dynamics from unlabelled data.

Autoren: Zhang, Meng Zeng, Fanfan Yousif, Mustafa Z. et al.

Quelle: Physics of Fluids; Sep2025, Vol. 37 Issue 9, p1-15, 15p

Schlagwörter: FLUID dynamics, AUTOENCODERS, NONLINEAR mechanics, FLOW simulations, TURBULENT flow, METADATA, SPARSE approximations, NOISE control

Leveraging variant of CAE with sparse convolutional embedding and two-stage application-driven data augmentation for image clustering.

Autoren: Liu, Yanming Liu, Jinglei

Quelle: Soft Computing - A Fusion of Foundations, Methodologies & Applications; Feb2025, Vol. 29 Issue 3, p1419-1435, 17p

Schlagwörter: DATA augmentation, AUTOENCODERS

Sparse deep encoded features with enhanced sinogramic red deer optimization for fault inspection in wafer maps.

Autoren: Altantawy, Doaa A. Yakout, Mohamed A.

Quelle: Journal of Intelligent Manufacturing. Jun2025, Vol. 36 Issue 5, p3359-3397. 39p.

Schlagwörter: *MANUFACTURING processes, *INFORMATION processing, CONVOLUTIONAL neural networks, OPTIMIZATION algorithms, AUTOENCODERS

Estimation of Damaged Surface Images in Drilled Unidirectional Carbon Fiber Reinforced Polymer Sheets Using Convolutional Autoencoder and Multi-Layer Perceptron Decoder.

Autoren: Kurniawan, Eddy Hur, Yong Chan Kim, Ji Hoon

Quelle: Fibers & Polymers; Dec2025, Vol. 26 Issue 12, p5647-5660, 14p

Multi-resolution reconstruction of longitudinal streambed footprints using embedded sparse convolutional autoencoders

Autoren: Yang, Yifan Tang, Zihao 邵东 et al.

Weitere Verfasser: Yang, Yifan Tang, Zihao 邵东 et al.

Schlagwörter: Machine learning, Streambed footprint, Convolutional autoencoder, Multi-resolution reconstruction, RIVER, PROBABILITY, MODEL, Engineering, Geology, Water Resources, Civil, Geosciences, Multidisciplinary, 一类

Relation: JOURNAL OF HYDROLOGY; http://dspace.imech.ac.cn/handle/311007/100158; http://dspace.imech.ac.cn/handle/311007/100159

Verfügbarkeit: http://dspace.imech.ac.cn/handle/311007/100158
http://dspace.imech.ac.cn/handle/311007/100159
https://doi.org/10.1016/j.jhydrol.2025.132852

Sensor-Driven Surrogate Modeling and Control of Nonlinear Dynamical Systems Using FAE-CAE-LSTM and Deep Reinforcement Learning

Autoren: Kherad, M. Moayyedi, M. K. Fotouhi-Ghazvini, F. et al.

Quelle: Sensors. 25(16)

Schlagwörter: Autoencoders, Control Of Dynamical Systems, Cyber-physical Systems, Deep Reinforcement Learning, Long Short-term Memory, Sensor-driven Modeling, Circular Cylinders, Computer Control Systems, Convolution, Dynamical Systems, Dynamics, Embedded Systems, Flow Control, Fuel Additives, Nonlinear Dynamical Systems, Real Time Control, Robust Control, Auto Encoders, Control Of Dynamical System, Cybe-physical Systems, High-dimensional, Higher-dimensional, Reinforcement Learning Agent, Reinforcement Learnings, Short Term Memory

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-73172
https://doi.org/10.3390/s25165149

Domain-Adaptive Direction of Arrival (DOA) Estimation in Complex Indoor Environments Based on Convolutional Autoencoder and Transfer Learning.

Autoren: Shen, Lingyu Li, Jianfeng Pan, Jingjing et al.

Quelle: Sensors (14248220); May2025, Vol. 25 Issue 10, p2959, 24p

Schlagwörter: AUTOENCODERS, DEEP learning, FEATURE extraction, SMART homes, WIRELESS communications

Unsupervised sparse convolutional autoencoder for multi-class change detection in hyperspectral images

Autoren: Bergamasco, Luca Bovolo, Francesca Bruzzone, Lorenzo

Weitere Verfasser: Bergamasco, Luca Bovolo, Francesca Bruzzone, Lorenzo

Schlagwörter: Convolutional Autoencoder, Hyperspectral image, Multi-class Change Detection, Spectral Unmixing, Unsupervised Change Detection

Dateibeschreibung: ELETTRONICO

Relation: ispartofbook:Proceedings of SPIE - The International Society for Optical Engineering; Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX 2024; volume:13196; serie:PROCEEDINGS OF SPIE; alleditors:Bruzzone, Lorenzo; https://hdl.handle.net/11572/444099

Verfügbarkeit: https://hdl.handle.net/11572/444099
https://doi.org/10.1117/12.3030005

Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images.

Autoren: Nageswara Rao, B. Acharya, U. Rajendra Tan, Ru-San et al.

Quelle: Cognitive Neurodynamics; 5/19/2025, Vol. 19 Issue 1, p1-13, 13p

Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models

Autoren: Renato Melo Rafaelle Finotti António Guedes et al.

Quelle: Applied Sciences ; Volume 15 ; Issue 5 ; Pages: 2662

Schlagwörter: structural health monitoring, railways, damage detection, out of roundness, sparse autoencoder, convolutional autoencoder, variational autoencoder

Geographisches Schlagwort: agris

Dateibeschreibung: application/pdf

Relation: Civil Engineering; https://dx.doi.org/10.3390/app15052662

Verfügbarkeit: https://doi.org/10.3390/app15052662

Temperature scaling unmixing framework based on convolutional autoencoder

Autoren: Jin Xu Mingming Xu Shanwei Liu et al.

Quelle: International Journal of Applied Earth Observations and Geoinformation, Vol 129, Iss , Pp 103864- (2024)

Schlagwörter: Hyperspectral unmixing, Convolutional autoencoder, Sparsity constraint, Temperature scaling, Equal-frequency binning, Physical geography, GB3-5030, Environmental sciences, GE1-350

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S1569843224002188; https://doaj.org/toc/1569-8432

Zugangs-URL: https://doaj.org/article/aa7533b7fbf447e1a8b51942e4c8c5f4

Motor Fault Diagnosis and Detection with Convolutional Autoencoder (CAE) Based on Analysis of Electrical Energy Data.

Autoren: Choi, YuRim Joe, Inwhee

Quelle: Electronics (2079-9292); Oct2024, Vol. 13 Issue 19, p3946, 29p

Schlagwörter: ARTIFICIAL neural networks, REAL-time computing, ELECTRIC faults, FAULT diagnosis, SYSTEM analysis, ELECTRIC motors

Surrogate Modeling of Hydrogen-Enriched Combustion Using Autoencoder-Based Dimensionality Reduction.

Autoren: Zhang, Lanfei Chu, Xu Ding, Siyu et al.

Quelle: Processes; Apr2025, Vol. 13 Issue 4, p1093, 21p

Schlagwörter: AUTOENCODERS, JET nozzles, ENGINEERING design, FLUID flow, SPATIAL resolution, DEEP learning

Deep Learning Augmented Data Assimilation: Reconstructing Missing Information with Convolutional Autoencoders.

Autoren: Wang, Yueya Shi, Xiaoming Lei, Lili et al.

Quelle: Monthly Weather Review; Aug2022, Vol. 150 Issue 8, p1977-1991, 15p, 3 Diagrams, 2 Charts, 5 Graphs

Schlagwörter: DEEP learning, NUMERICAL weather forecasting, BAROTROPIC equation, REMOTE sensing, SQUARE root

Cascaded hybrid convolutional autoencoder network for spectral-spatial nonlinear hyperspectral unmixing.

Autoren: Wu, Jinbin Zhao, Jiankang Long, Haihui

Quelle: International Journal of Remote Sensing; Dec2024, Vol. 45 Issue 24, p9267-9286, 20p

Schlagwörter: AUTOENCODERS, DEEP learning, IMAGE reconstruction, SELF-perception, PHOTONS

Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models.

Autoren: Melo, Renato Finotti, Rafaelle Guedes, António et al.

Quelle: Applied Sciences (2076-3417); Mar2025, Vol. 15 Issue 5, p2662, 25p

Schlagwörter: STRUCTURAL health monitoring, AUTOENCODERS, RAILROAD safety measures, RAILROAD trains, COMPARATIVE studies

Out-of-roundness damage wheel idenficaon in railway vehicles using autoencoder models

Autoren: Melo, Renato da Silva Cury, Alexandre Abrahão http://lattes.cnpq.br/1092810690505352 et al.

Weitere Verfasser: Melo, Renato da Silva Cury, Alexandre Abrahão http://lattes.cnpq.br/1092810690505352 et al.

Schlagwörter: Monitoramento de integridade estrutural, Ferrovias, Detecção de danos, Ovalização, Autocodificador esparso, Autocodificador convolucional, Autocodificador variacional, Structural health monitoring, Railways, Damage detection, Out-of-roundness, Sparse autoencoder, Convolutional autoencoder, Variational autoencoder, CNPQ::ENGENHARIAS

Dateibeschreibung: application/pdf

Relation: https://repositorio.ufjf.br/jspui/handle/ufjf/18552

Verfügbarkeit: https://repositorio.ufjf.br/jspui/handle/ufjf/18552

An efficient model for diabetic detection using heuristic approach based serial cascaded convolutional ensemble network.

Autoren: Bejugam, Santosh Kumar Vankara, Jyothi

Quelle: Artificial Intelligence Review; Oct2025, Vol. 58 Issue 10, p1-43, 43p

TriMap thermography with convolutional autoencoder for enhanced defect detection of polymer composites.

Autoren: Liu, Yi Zheng, Mingkai Liu, Kaixin et al.

Quelle: Journal of Applied Physics; 4/14/2022, Vol. 131 Issue 14, p1-9, 9p

Schlagwörter: THERMOGRAPHY, CARBON fiber-reinforced plastics, IMAGE analysis, FEATURE extraction, POLYMERS

Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach.

Autoren: Ogundokun, Roseline Oluwaseun Owolawi, Pius Adewale Tu, Chunling et al.

Quelle: Tomography: A Journal for Imaging Research; Aug2025, Vol. 11 Issue 8, p91, 22p

Schlagwörter: AUTOENCODERS, ENSEMBLE learning, DEEP learning, FEATURE extraction, CANCER diagnosis, HISTOPATHOLOGY, MACHINE learning