Suchergebnisse - sparse convolutional autoencoder (((same OR sage) OR cae))

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

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

Enhanced heart sound anomaly detection via WCOS: a semi-supervised framework integrating wavelet, autoencoder and SVM.

Autoren: Zeng, Peipei Kang, Shuimiao Fan, Fan et al.

Quelle: Frontiers in Neuroinformatics; 2025, p1-15, 15p

Schlagwörter: ANOMALY detection (Computer security), HEART murmurs, SUPPORT vector machines, CONGENITAL heart disease, AUTOENCODERS

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

Autoren: Bergamasco, Luca Bovolo, Francesca

Quelle: Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX. :22

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

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

Sparse convolutional autoencoder‐based fault location for drive circuits in nuclear reactors.

Autoren: Yang, Cheng Yuan, Yannan Wang, Fu et al.

Quelle: Quality & Reliability Engineering International; Mar2024, Vol. 40 Issue 2, p819-837, 19p

Schlagwörter: FAULT location (Engineering), NUCLEAR reactors, NUCLEAR reactor control, FAULT diagnosis, ELECTRONIC equipment, LINE drivers (Integrated circuits)

Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia.

Autoren: Akila, Serag Mohamed Imanov, Elbrus Almezhghwi, Khaled

Quelle: Diagnostics (2075-4418); Jul2023, Vol. 13 Issue 13, p2199, 14p

Schlagwörter: MACHINE learning, PNEUMONIA, ANOMALY detection (Computer security), LUNG infections, DIAGNOSIS, KLEBSIELLA infections

Deep spectral unmixing framework via 3D denoising convolutional autoencoder.

Autoren: Jia, Peiyuan Zhang, Miao Shen, Yi

Quelle: IET Image Processing (Wiley-Blackwell); May2021, Vol. 15 Issue 7, p1399-1409, 11p

Schlagwörter: HYPERSPECTRAL imaging systems, IMAGE denoising, IMAGE processing, PIXELS, GEOMETRIC analysis, DATA

Human Activity Recognition Using Convolutional Autoencoder and Advanced Preprocessing.

Autoren: Zaoui, Chaimae Benabbou, Faouzia Ettaoufik, Abdelaziz et al.

Quelle: International Journal of Online & Biomedical Engineering; 2024, Vol. 20 Issue 4, p144-159, 16p

Feature extraction of fields of fluid dynamics data using sparse convolutional autoencoder

Autoren: Wataru Obayashi Hikaru Aono Tomoaki Tatsukawa et al.

Quelle: AIP Advances, Vol 11, Iss 10, Pp 105211-105211-6 (2021)

Schlagwörter: Physics, QC1-999

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2158-3226

Zugangs-URL: https://doaj.org/article/7d4dc31e6e08442fba36ddd862379ce8

Feature extraction of fields of fluid dynamics data using sparse convolutional autoencoder.

Autoren: Obayashi, Wataru Aono, Hikaru Tatsukawa, Tomoaki et al.

Quelle: AIP Advances; Oct2021, Vol. 11 Issue 10, p1-6, 6p

Schlagwörter: FLUID dynamics, FEATURE extraction, PROPER orthogonal decomposition, NAVIER-Stokes equations, CONVOLUTIONAL neural networks, UNSTEADY flow, COMPRESSIBLE flow

Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder.

Autoren: An, Yanling Hu, Shaohai Duan, Xiaoying et al.

Quelle: Frontiers in Computational Neuroscience; 10/18/2021, Vol. 15, p1-13, 13p

Schlagwörter: EMOTION recognition, BRAIN-computer interfaces, SIGNAL convolution, DIFFERENTIAL entropy, CONVOLUTIONAL neural networks, ELECTROENCEPHALOGRAPHY, EMOTIONS, FEATURE extraction

A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification.

Autoren: Sun, Yanan Xue, Bing Zhang, Mengjie et al.

Quelle: IEEE Transactions on Neural Networks & Learning Systems; Aug2019, Vol. 30 Issue 8, p2295-2309, 15p

Schlagwörter: PARTICLE swarm optimization, CLASSIFICATION, COMPUTER architecture

Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review.

Autoren: Kalu, Kingsley Ataguba, Grace Onifade, Oyepeju et al.

Quelle: Nutrients. Oct2025, Vol. 17 Issue 20, p3223. 33p.

A 3D Sparse Autoencoder for Fully Automated Quality Control of Affine Registrations in Big Data Brain MRI Studies.

Autoren: Thadikemalla, Venkata Sainath Gupta Focke, Niels K. Tummala, Sudhakar

Quelle: Journal of Digital Imaging; Feb2024, Vol. 37 Issue 1, p412-427, 16p

Schlagwörter: RECEIVER operating characteristic curves, MAGNETIC resonance imaging, QUALITY control, DATA analytics, DESCRIPTIVE statistics, DIAGNOSIS of brain abnormalities

Multi-scale asynchronous correlation and 2D convolutional autoencoder for adolescent health risk prediction with limited fMRI data.

Autoren: Di Gao Guanghao Yang Jiarun Shen et al.

Quelle: Frontiers in Computational Neuroscience; 2024, p01-12, 12p

Schlagwörter: HEALTH risk assessment, FUNCTIONAL magnetic resonance imaging, VALUE engineering, EXTRACTION techniques, DATA mining

Lameness detection in dairy cows from overhead view: high-precision keypoint localization and multi-feature fusion classification.

Autoren: Duan, Weijun Wang, Fang Li, Honghui et al.

Quelle: Frontiers in Veterinary Science. 2025, p1-19. 19p.

A Novel Convolutional Autoencoder-Based Clutter Removal Method for Buried Threat Detection in Ground-Penetrating Radar.

Autoren: Temlioglu, Eyyup Erer, Isin

Quelle: IEEE Transactions on Geoscience & Remote Sensing. Jan2022, Vol. 60 Issue 1, p1-13. 13p.

Schlagwörter: GROUND penetrating radar, MATRIX decomposition, NONNEGATIVE matrices, SPARSE matrices, IMAGE reconstruction, NOISE control

Image reconstruction under visual disruption caused by rain

Autoren: Tang, Lai Meng

Schlagwörter: QA75 Electronic computers. Computer science

Zugangs-URL: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.838754

Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images.

Autoren: Hou, Le Nguyen, Vu Kanevsky, Ariel B. et al.

Quelle: Pattern Recognition. Feb2019, Vol. 86, p188-200. 13p.

Schlagwörter: *FEATURE extraction, *IMAGE processing, *HISTOPATHOLOGY, *TISSUE engineering, *MACHINE learning