Výsledky vyhledávání - complex convolutional autoencoder

Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system

Autoři: Teutsch, Philipp Pfeffer, Philipp Ghazijahani, Mohammad Sharifi a další

Témata: Physics - Fluid Dynamics, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2501.03070

Convolutional autoencoder based condition monitoring system for unique complex technical systems.

Autoři: Tahtali E Adamscheck M Overmeyer L a další

Zdroj: Scientific reports [Sci Rep] 2025 Nov 12; Vol. 15 (1), pp. 39582. Date of Electronic Publication: 2025 Nov 12.

Způsob vydávání: Journal Article

Informace o časopise: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE

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

Autoři: Shen L Li J Pan J a další

Zdroj: Sensors (Basel, Switzerland) [Sensors (Basel)] 2025 May 08; Vol. 25 (10). Date of Electronic Publication: 2025 May 08.

Způsob vydávání: Journal Article

Informace o časopise: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: PubMed not MEDLINE; MEDLINE

Research on UAV Jamming Signal Generation Based on Intelligent Jamming

Autoři: Haonan Xue Zhihai Zhuo Weihao Yan a další

Zdroj: IEEE Access, Vol 13, Pp 14686-14701 (2025)

Témata: communication jamming, jamming signal generation, Intelligent jamming, Electrical engineering. Electronics. Nuclear engineering, complex convolutional autoencoder, TK1-9971

Přístupová URL adresa: https://doaj.org/article/94fd74577da94b4ba512de9bffc4f45e

CAECC-Subtyper: A Novel Convolutional Autoencoder Framework for Integrating Multi-omics Data in Cancer Subtyping.

Autoři: Uyar H Gumus O

Zdroj: Biochemical genetics [Biochem Genet] 2025 Dec 04. Date of Electronic Publication: 2025 Dec 04.

Způsob vydávání: Journal Article

Informace o časopise: Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 0126611 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-4927 (Electronic) Linking ISSN: 00062928 NLM ISO Abbreviation: Biochem Genet Subsets: MEDLINE

High performance computing and artificial intelligence algorithms for dimensionality reduction of turbulent flows

Autoři: Eiximeno Franch, Benet

Přispěvatelé: University/Department: Universitat Politècnica de Catalunya. Departament de Física

Thesis Advisors: Rodríguez Pérez, Ivette María, Lehmkuhl Barba, Oriol

Zdroj: TDX (Tesis Doctorals en Xarxa)

Témata: reduced order models, high performance computing, deep learning, singular value decomposition, variational autoencoders, Àrees temàtiques de la UPC::Física, Àrees temàtiques de la UPC::Informàtica, 531/534 - Mecànica. Vibracions. Acústica, 004 - Informàtica

Popis souboru: application/pdf

Přístupová URL adresa: http://hdl.handle.net/10803/695826
https://dx.doi.org/10.5821/dissertation-2117-446576

Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system

Autoři: Philipp Teutsch Philipp Pfeffer Mohammad Sharifi Ghazijahani a další

Zdroj: APL Machine Learning, Vol 3, Iss 1, Pp 016112-016112-15 (2025)

Témata: Physics, QC1-999, Electronic computers. Computer science, QA75.5-76.95

Popis souboru: electronic resource

Relation: https://doaj.org/toc/2770-9019

Přístupová URL adresa: https://doaj.org/article/236f8e1f2be64001a1d9c1d7b38ae1f2

Compact representation and identification of important regions of metal microstructures using complex-step convolutional autoencoders

Autoři: Dharanidharan Arumugam Ravi Kiran

Zdroj: Materials & Design, Vol 223, Iss , Pp 111236- (2022)

Témata: Interpretable AI, ASTM A992, Saliency maps, Pixel relevance, Convolutional autoencoder, Materials of engineering and construction. Mechanics of materials, TA401-492

Popis souboru: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S0264127522008589; https://doaj.org/toc/0264-1275

Přístupová URL adresa: https://doaj.org/article/f86f63c05f2043d7b5571943ad9899a3

Reconstructing Complex Cardiac Excitation Waves From Incomplete Data Using Echo State Networks and Convolutional Autoencoders

Autoři: Sebastian Herzog Roland S. Zimmermann Johannes Abele a další

Zdroj: Frontiers in Applied Mathematics and Statistics, Vol 6 (2021)

Témata: reservoir computing, convolutional autoencoder, image enhancement, cross-prediction, cardiac arrhythmias, excitable media, Applied mathematics. Quantitative methods, T57-57.97, Probabilities. Mathematical statistics, QA273-280

Popis souboru: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fams.2020.616584/full; https://doaj.org/toc/2297-4687

Přístupová URL adresa: https://doaj.org/article/5dc0a11dc2524c988b826b0ad85109d7

Complex-Valued Convolutional Autoencoder and Spatial Pixel-Squares Refinement for Polarimetric SAR Image Classification

Autoři: Ronghua Shang Guangguang Wang Michael A. Okoth a další

Zdroj: Remote Sensing, Vol 11, Iss 5, p 522 (2019)

Témata: complex-valued convolutional autoencoder, PolSAR image classification, spatial pixel-squares refinement, deep learning, Science

Popis souboru: electronic resource

Relation: http://www.mdpi.com/2072-4292/11/5/522; https://doaj.org/toc/2072-4292

Přístupová URL adresa: https://doaj.org/article/07bf32964c3942d499337bf71c9e5a9a

Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder

Autoři: Shubham Chaudhry Azzedine Abdedou Azzeddine Soulaïmani

Zdroj: Advanced Modeling and Simulation in Engineering Sciences, Vol 12, Iss 1, Pp 1-23 (2025)

Témata: Reduced-order model, Convolutional autoencoder, Additive manufacturing, Deep learning, Proper orthogonal decomposition, Mechanics of engineering. Applied mechanics, TA349-359, Systems engineering, TA168

Popis souboru: electronic resource

Relation: https://doaj.org/toc/2213-7467

Přístupová URL adresa: https://doaj.org/article/1acb9d02b46540cbb5ab687871ded7c0

EXPLORING TRANSFER LEARNING AND CONVOLUTIONAL AUTOENCODER FOR EFFECTIVE KITCHEN UTENSILS CLASSIFICATION

Autoři: Hashim Rosli Rozniza Ali Muhamad Suzuri Hitam a další

Zdroj: Malaysian Journal of Computing, Vol 10, Iss 1, Pp 2012-2025 (2025)

Témata: classification, convolutional autoencoder, deep learning, images, kitchen utensils, transfer learning, Electronic computers. Computer science, QA75.5-76.95

Popis souboru: electronic resource

Relation: https://doaj.org/toc/2600-8238

Přístupová URL adresa: https://doaj.org/article/85f951980f0846ed95037aa76ab46a7c

An Elliptic Kernel Unsupervised Autoencoder—Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing

Autoři: Estefania Alfaro-Mejia Carlos J. Delgado Vidya Manian

Zdroj: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 14594-14614 (2025)

Témata: Abundance maps, convolutional autoencoder, endmembers, ensemble, graph convolutional network (GCN), hyperspectral images (HSIs), Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809

Popis souboru: electronic resource

Relation: https://ieeexplore.ieee.org/document/11021649/; https://doaj.org/toc/1939-1404; https://doaj.org/toc/2151-1535

Přístupová URL adresa: https://doaj.org/article/30bdd4d0412b4964adf8b67b0384ef64

Optimal satellite selection using quantum convolutional autoencoder for low-cost GNSS receiver applications

Autoři: Nalineekumari Arasavali Mogadala Vinod Kumar Sasibhushana Rao Gottapu a další

Zdroj: Scientific Reports, Vol 15, Iss 1, Pp 1-17 (2025)

Témata: Satellite selection, Quantum convolutional autoencoder, GNSS, Combined constellation, Sustainable Development Goal (SDG) 9:Industry, Innovation, and Infrastructure, SDG 11: Sustainable Cities and Communities, Medicine, Science

Popis souboru: electronic resource

Relation: https://doaj.org/toc/2045-2322

Přístupová URL adresa: https://doaj.org/article/baf00827405b4ae7bdeb1dbfabf77448

River Channel Microgeomorphic Feature Extraction and Potential Sandstorm Source Identification Method Based on a Convolutional Autoencoder Model

Autoři: Kecong Wu Lirong Chen Yalige Bai a další

Zdroj: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 2602-2617 (2025)

Témata: Anomaly values, convolutional autoencoder (CAE), microgeomorphic feature, sandstorm sources, Yarlung Zangbo River, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809

Popis souboru: electronic resource

Relation: https://ieeexplore.ieee.org/document/10812012/; https://doaj.org/toc/1939-1404; https://doaj.org/toc/2151-1535

Přístupová URL adresa: https://doaj.org/article/94424c5cfbc34885963f0ffe4a93a78c

Detection, quantification, malignancy prediction and growth forecasting of pulmonary nodules using deep learning in follow-up CT scans

Autoři: Rafael-Palou, Xavier

Přispěvatelé: University/Department: Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions

Thesis Advisors: González Ballester, Miguel Ángel, Piella Fenoy, Gemma, Ribas Ripoll, Vicent Jordi

Zdroj: TDX (Tesis Doctorals en Xarxa)

Témata: Lung cancer prediction, Nodule detection, Nodule segmentation, Nodule characterization, Nodule malignancy estimation, Nodule follow-up, Nodule re-identification, Nodule growth forecasting, Uncertainty modelling and quantification, Convolutional neural networks, Variational autoencoders, Siamese neural networks, Generative networks, Transfer learning, Computer vision, Deep learning, Machine learning, Predicció del càncer de pulmó, Detecció de nòduls, Segmentació de nòduls, Caracterització de nòduls, Predicció de malignitat de nòduls, Seguiment de nòduls, Re-identificació de nòduls, Creixement de nòduls, Modelatge i quantificació de la malignitat, Xarxes neuronals convolucionals, Autoencoders variacional, Xarxes neuronals siameses, Xarxes generatives, Aprenentatge per transferència, Pipelines, Visió per computador, Aprenentatge profund, Aprenentatge automàtic

Popis souboru: application/pdf

Přístupová URL adresa: http://hdl.handle.net/10803/672964

Damage mode classification in CFRP laminates using convolutional autoencoder and convolutional neural network on acoustic emission waveforms

Autoři: R, Gangadharan Desarkar, Maunendra Sankar

Témata: Article

URL: https://raiith.krc.iith.ac.in/handle/123456789/61166

Automatic Baseline Correction of 1D Signals Using a Parameter-Free Deep Convolutional Autoencoder Algorithm

Autoři: Łukasz Górski Małgorzata Jakubowska

Zdroj: Applied Sciences, Vol 15, Iss 22, p 12069 (2025)

Témata: baseline correction, deep neural networks, convolutional autoencoder, voltammetry, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Popis souboru: electronic resource

Relation: https://www.mdpi.com/2076-3417/15/22/12069; https://doaj.org/toc/2076-3417

Přístupová URL adresa: https://doaj.org/article/e5f44f18c48948af8ddb6d7b35698b37

Efficient Coupling of Urban Wind Fields and Drone Flight Dynamics Using Convolutional Autoencoders

Autoři: Zack Krawczyk Ryan Paul Kursat Kara

Zdroj: Drones, Vol 9, Iss 11, p 802 (2025)

Témata: urban wind fields, urban boundary layer, convolutional autoencoders, Reduced-Order Modeling (ROM), Non-Intrusive ROM, Large-Eddy Simulation (LES), Motor vehicles. Aeronautics. Astronautics, TL1-4050

Popis souboru: electronic resource

Relation: https://www.mdpi.com/2504-446X/9/11/802; https://doaj.org/toc/2504-446X

Přístupová URL adresa: https://doaj.org/article/1d4b41821c8e43b9bcc79eda0400cdc8

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Autoři: Pichi, Federico Moya, Beatriz Hesthaven, Jan S.

Témata: info:eu-repo/semantics/article, info:eu-repo/semantics/publishedVersion

URL: http://zaguan.unizar.es/record/132810
info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-113463RB-C31/AEI/10.13039/501100011033
info:eu-repo/grantAgreement/ES/UZ-IBERCAJA-CAI/IT1-21