Suchergebnisse - Convolutional neural network (CNN) Overcomplete Autoencoder application*

Enhancing Border Learning for Better Image Denoising.

Autoren: Ge, Xin Zhu, Yu Qi, Liping et al.

Quelle: Mathematics (2227-7390); Apr2025, Vol. 13 Issue 7, p1119, 30p

Schlagwörter: ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, AUTOENCODERS, IMAGE denoising, LEARNING ability

Fast Anomaly Detection for Vision-Based Industrial Inspection Using Cascades of Null Subspace PCA Detectors.

Autoren: Bilal, Muhammad Hanif, Muhammad Shehzad

Quelle: Sensors (14248220); Aug2025, Vol. 25 Issue 15, p4853, 27p

Schlagwörter: OUTLIER detection, PRINCIPAL components analysis, COMPUTER vision, CONVOLUTIONAL neural networks, MANUFACTURING process automation, MATHEMATICAL optimization, QUALITY assurance

Moving forward in water distribution network leak identification through an innovative features engineering step.

Autoren: Damonti, Elvio Bernasconi, Giancarlo

Quelle: Digital Signal Processing. Jan2026:Part C, Vol. 168, pN.PAG-N.PAG. 1p.

Schlagwörter: *WATER distribution, *LEAK detection, *FEATURE selection, *PATTERN perception, *AUTOENCODERS, *CONVOLUTIONAL neural networks, *OUTLIER detection

Application of 1D ResNet for Multivariate Fault Detection on Semiconductor Manufacturing Equipment †.

Autoren: Tchatchoua, Philip Graton, Guillaume Ouladsine, Mustapha et al.

Quelle: Sensors (14248220); Nov2023, Vol. 23 Issue 22, p9099, 19p

Schlagwörter: SEMICONDUCTOR manufacturing, CONVOLUTIONAL neural networks, MANUFACTURING processes, SEMICONDUCTOR wafers, SEMICONDUCTOR industry

Face recognition algorithm using collaborative sparse representation based on CNN features.

Alternate Title: 基于 CNN 特征的协同稀疏表示人脸识别算法. (Chinese)

Autoren: ZHAO, Shilin XU, Chengjun LIU, Changrong

Quelle: Journal of Measurement Science & Instrumentation; Mar2025, Vol. 16 Issue 1, p85-95, 11p

Schlagwörter: CONVOLUTIONAL neural networks, DEEP learning, DATABASES, ENCYCLOPEDIAS & dictionaries, ALGORITHMS

A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification.

Autoren: Kadhim, Yezi Ali Guzel, Mehmet Serdar Mishra, Alok

Quelle: Diagnostics (2075-4418); Jul2024, Vol. 14 Issue 14, p1469, 25p

Schlagwörter: COMPUTER-aided diagnosis, METAHEURISTIC algorithms, CONVOLUTIONAL neural networks, DEEP learning, COMPUTER science

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

Self-supervised structural similarity-based convolutional neural network for cardiac diffusion tensor image denoising.

Autoren: Yuan N Wang L Ye C et al.

Quelle: Medical physics [Med Phys] 2023 Oct; Vol. 50 (10), pp. 6137-6150. Date of Electronic Publication: 2023 Apr 17.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: John Wiley and Sons, Inc Country of Publication: United States NLM ID: 0425746 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2473-4209 (Electronic) Linking ISSN: 00942405 NLM ISO Abbreviation: Med Phys Subsets: MEDLINE

MeSH-Schlagworte: Diffusion Tensor Imaging* , Neural Networks, Computer*, Humans ; Algorithms ; Signal-To-Noise Ratio ; Heart/diagnostic imaging ; Image Processing, Computer-Assisted/methods

Development of a Super-Resolution Scheme for Pediatric Magnetic Resonance Brain Imaging Through Convolutional Neural Networks.

Autoren: Molina-Maza, Juan Manuel Galiana-Bordera, Adrian Jimenez, Mar et al.

Quelle: Frontiers in Neuroscience; 10/25/2022, Vol. 16, p1-16, 16p

Schlagwörter: CONVOLUTIONAL neural networks, MAGNETIC resonance imaging, CHILD patients, DIAGNOSTIC imaging, ARTIFICIAL intelligence

Spatial and Spectral Joint Super-Resolution Using Convolutional Neural Network.

Autoren: Mei, Shaohui Jiang, Ruituo Li, Xu et al.

Quelle: IEEE Transactions on Geoscience & Remote Sensing; Jul2020, Vol. 58 Issue 7, p4590-4603, 14p

Schlagwörter: CONVOLUTIONAL neural networks, SPECTRAL imaging

High-Resolution Remote Sensing Image Scene Classification via Key Filter Bank Based on Convolutional Neural Network.

Autoren: Li, Fengpeng Feng, Ruyi Han, Wei et al.

Quelle: IEEE Transactions on Geoscience & Remote Sensing; Nov2020, Vol. 58 Issue 11, p8077-8092, 16p

Schlagwörter: CONVOLUTIONAL neural networks, REMOTE-sensing images, FILTER banks, DEEP learning

Designing CNNs for Multimodal Image Restoration and Fusion via Unfolding the Method of Multipliers.

Autoren: Marivani, Iman Tsiligianni, Evaggelia Cornelis, Bruno et al.

Quelle: IEEE Transactions on Circuits & Systems for Video Technology; Sep2022, Vol. 32 Issue 9, p5830-5845, 16p

Schlagwörter: IMAGE reconstruction, IMAGE fusion, CONVOLUTIONAL neural networks, DEEP learning

Tire defect classification using a deep convolutional sparse-coding network.

Autoren: Zheng, Zhouzhou Shen, Jinyue Shao, Yuyi et al.

Quelle: Measurement Science & Technology; May2021, Vol. 32 Issue 5, p1-11, 11p

Schlagwörter: CONVOLUTIONAL neural networks, TRAFFIC safety, CONSTRUCTION defects (Buildings), CLASSIFICATION algorithms, FEATURE extraction

Using convolutional neural networks incorporating hierarchical active learning for target-searching in large-scale remote sensing images.

Autoren: Xu, Guang Zhu, Xuan Tapper, Nigel

Quelle: International Journal of Remote Sensing; Jun2020, Vol. 41 Issue 11, p4057-4079, 23p, 7 Diagrams, 3 Charts, 4 Graphs

Schlagwörter: ARTIFICIAL neural networks, DEEP learning, REMOTE sensing, HIGH resolution imaging, REMOTE-sensing images, CONVOLUTIONAL neural networks

Unsupervised Deep Embedded Clustering for High-Dimensional Visual Features of Fashion Images.

Autoren: Malhi, Umar Subhan Zhou, Junfeng Yan, Cairong et al.

Quelle: Applied Sciences (2076-3417); Mar2023, Vol. 13 Issue 5, p2828, 18p

Schlagwörter: CONVOLUTIONAL neural networks

Hyperspectral Image Denoising Using a 3-D Attention Denoising Network.

Autoren: Shi, Qian Tang, Xiaopei Yang, Taoru et al.

Quelle: IEEE Transactions on Geoscience & Remote Sensing; Dec2021, Vol. 59 Issue 12, p10348-10363, 16p

Schlagwörter: CONVOLUTIONAL neural networks, IMAGE denoising, DEEP learning, HYPERSPECTRAL imaging systems, BRANCHING processes, MARKOV random fields, PARALLEL processing

Unsteady reduced-order model of flow over cylinders based on convolutional and deconvolutional neural network structure.

Autoren: Peng, Jiang-Zhou Chen, Siheng Aubry, Nadine et al.

Quelle: Physics of Fluids; Dec2020, Vol. 32 Issue 12, p1-17, 17p

Schlagwörter: CONVOLUTIONAL neural networks, REDUCED-order models, ARTIFICIAL neural networks, NAVIER-Stokes equations, FLUID flow, HYDRAULIC cylinders

Sparse MR Image Reconstruction Considering Rician Noise Models: A CNN Approach.

Autoren: Manimala, M. V. R. Dhanunjaya Naidu, C. Giri Prasad, M. N.

Quelle: Wireless Personal Communications; 2021, Vol. 116 Issue 1, p491-511, 21p

Schlagwörter: MAGNETIC resonance imaging, IMAGE reconstruction, SIGNAL-to-noise ratio, IMAGE denoising, CONVOLUTIONAL neural networks, RANDOM noise theory

A novel state of health estimation model for lithium-ion batteries incorporating signal processing and optimized machine learning methods.

Autoren: Zhang, Xing Feng, Juqiang Cai, Feng et al.

Quelle: Frontiers in Energy; Jun2025, Vol. 19 Issue 3, p348-364, 17p

Mapping the BCPNN Learning Rule to a Memristor Model.

Autoren: Wang, Deyu Xu, Jiawei Stathis, Dimitrios et al.

Quelle: Frontiers in Neuroscience; 12/9/2021, Vol. 15, p1-16, 16p

Schlagwörter: CONVOLUTIONAL neural networks, ANALOG circuits, MACHINE learning