Suchergebnisse - "Deep convolutional autoencoder"

Unsupervised Induction Motor Anomaly Detection Using a Deep Convolutional Autoencoder Based on Multi-Sensor Data Fusion

Autoren: Andrea Visentin Marco Dalla Benjamin Provan-Bessell et al.

Quelle: 2025 IEEE International Conference on Smart Computing (SMARTCOMP). :194-201

Superficial Maxillofacial Venous Imaging Enhancement Using a Deep Convolutional Autoencoder with Wavelet Downsampling

Autoren: Zengkai Li Jiuai Sun Chaoli Wang et al.

Quelle: 2025 2nd International Conference on Electronic Engineering and Information Systems (EEISS). :1-5

Automated Anomaly Detection and Classification in Crystal Lattice Defect Mapping via Deep Convolutional Autoencoder-Enhanced Feature Extraction and HyperScore-Based Prioritization

Autoren: Freederia AI Researcher

Latent Space Classification for Cardiovascular Disease Detection: A Deep Convolutional Autoencoder-Based Approach for Telemedicine Applications

Autoren: G Antoni Gracy Sheena Christabel Pravin

Quelle: IEEE Access, Vol 13, Pp 129195-129212 (2025)

Schlagwörter: MIT-BIH arrhythmia database, deep convolutional autoencoders, telemedicine, Electrical engineering. Electronics. Nuclear engineering, Cardio-vascular diseases, energy efficiency, latent space classification system, TK1-9971

Zugangs-URL: https://doaj.org/article/673f3d321fcf4c7db059c82fb5f3960a

Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition

Autoren: Nana Han Wending Miao Mingzhi Li et al.

Quelle: Frontiers in Earth Science, Vol 13 (2025)

Schlagwörter: health monitoring, Science, rainfall, slope displacement, multi-source data fusion, deep convolutional autoencoder

Zugangs-URL: https://doaj.org/article/4440996c733b4eaf8040edb8e0c3f0dd

A Deep Convolutional Autoencoder with Metaheuristic Optimization based Feature Reduction Framework for Genetic Disorder Detection Model

Quelle: Journal of Intelligent Systems and Internet of Things. 18

Broadband Communication Signal Detection Using Deep Convolutional Autoencoder and Spectrum Center Network

Autoren: Bin Wei

Quelle: Informatica. 49

Bleaching Detection in Coral Reef using Deep Convolutional Autoencoder based Model

Autoren: Neeraj Kumar Gautam Mayank Mishra Umesh C. Pati

Quelle: 2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP). :01-05

Efficient Bayesian full-waveform inversion using a deep convolutional autoencoder prior

Autoren: Shuhua Hu Mrinal K Sen Zeyu Zhao et al.

Quelle: Fourth International Meeting for Applied Geoscience & Energy. :993-997

A deep convolutional autoencoder to predict the position of the full-body centre of mass from lower limb kinematics

Autoren: Maxime Devanne Morgan Sangeux

Quelle: Gait & Posture. 121:61-62

Interference Suppression for an FM-Radio-Based Passive Radar via Deep Convolutional Autoencoder

Autoren: Do-Hyun Park Geun-Ho Park Ji-Hun Park et al.

Quelle: IEEE Transactions on Aerospace and Electronic Systems. 60:106-118

Schlagwörter: 0203 mechanical engineering, 0211 other engineering and technologies, 02 engineering and technology

DOA estimation method for sparse arrays based on deep convolutional autoencoder and deep convolutional neural network

Autoren: Shuhan Guo Qin Zhang Xiaolong Fu et al.

Quelle: Digital Signal Processing. 168:105627

Mine Microseismic Signal Denoising Based on a Deep Convolutional Autoencoder

Autoren: Ting Hu Bin Xu Yongfa Wang et al.

Quelle: Shock and Vibration, Vol 2023 (2023)

Schlagwörter: Physics, QC1-999, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, 0105 earth and related environmental sciences

Dateibeschreibung: text/xhtml

Zugangs-URL: https://doaj.org/article/d211f2fa16324c2f8f5ddc71c917d688

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

Autoren: Łukasz Górski Małgorzata Jakubowska

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

Schlagwörter: 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

Dateibeschreibung: electronic resource

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

Zugangs-URL: https://doaj.org/article/e5f44f18c48948af8ddb6d7b35698b37

Spatiotemporal Image-Based Flight Trajectory Clustering Model with Deep Convolutional Autoencoder Network

Autoren: Ye Liu Kam K. H. Ng Nana Chu et al.

Quelle: Journal of Aerospace Information Systems. 20:575-587

Schlagwörter: 0502 economics and business, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Deep convolutional autoencoder thermography for artwork defect detection

Autoren: Yi Liu Fumin Wang Kaixin Liu et al.

Quelle: Quantitative InfraRed Thermography Journal. 21:367-383

Schlagwörter: Non-destructive testing, infrared thermography, autoencoder, noise reduction, artwork

DEPTH IMAGE RECONSTRUCTION USING DEEP CONVOLUTIONAL AUTOENCODER

Autoren: Mau Uyen Nguyen Van Ha Tang Dai Duong Ha et al.

Quelle: FAIR KỶ YẾU HỘI NGHỊ KHOA HỌC CÔNG NGHỆ QUỐC GIA LẦN THỨ XVII NGHIÊN CỨU CƠ BẢN VÀ ỨNG DỤNG CÔNG NGHỆ THÔNG TIN - Proceedings of the 17th National Conference on Fundamental and Applied Information Technology Research (FAIR’2024).

Content-based image retrieval for the diagnosis of myocardial perfusion imaging using a deep convolutional autoencoder

Autoren: Akinori, Higaki Naoto, Kawaguchi Tsukasa, Kurokawa et al.

Quelle: Journal of Nuclear Cardiology. 30:540-549

Schlagwörter: Tomography, Emission-Computed, Single-Photon, 03 medical and health sciences, 0302 clinical medicine, Myocardial Perfusion Imaging, Humans, Heart, Neural Networks, Computer, Coronary Artery Disease, 3. Good health

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/35802346

Clustering of spatio-temporal precipitation patterns in the Legal Amazon using deep convolutional autoencoder ; Clusterização de padrões espaço-temporais de precipitação na Amazônia Legal via deep convolutional autoencoder

Autoren: Silva, Vander Augusto Oliveira da Texeira, Raphael Barros

Quelle: Ciência e Natura; Vol. 47 (2025): Publicação contínua; e85042 ; Ciência e Natura; v. 47 (2025): Publicação contínua; e85042 ; 2179-460X ; 0100-8307

Schlagwörter: Machine learning, Deep convolutional autoencoder, Clustering, Pattern recognition, Precipitation time series, Aprendizado de máquina, Autoencoder convolucional profundo, Agrupamentos, Reconhecimento de padrões, Séries temporais de precipitação

Dateibeschreibung: application/pdf; text/html

Relation: https://periodicos.ufsm.br/cienciaenatura/article/view/85042/65968; https://periodicos.ufsm.br/cienciaenatura/article/view/85042/66553; https://periodicos.ufsm.br/cienciaenatura/article/view/85042

Verfügbarkeit: https://periodicos.ufsm.br/cienciaenatura/article/view/85042
https://doi.org/10.5902/2179460X85042

Drone Ego-Noise Cancellation for Improved Speech Capture using Deep Convolutional Autoencoder Assisted Multistage Beamforming

Autoren: Song, Yanjue Kindt, Stijn Madhu, Nilesh

Quelle: 2022 25th International Conference on Information Fusion (FUSION)

Schlagwörter: Technology and Engineering, UNet, 02 engineering and technology, Drone, ego-noise reduction, REDUCTION, ENHANCEMENT, unmanned aerial vehicle (UAV), autoencoders, MVDR, MWF, 0202 electrical engineering, electronic engineering, information engineering, beamformer, speech enhancement

Dateibeschreibung: application/pdf

Zugangs-URL: https://biblio.ugent.be/publication/8767408/file/8767411
http://hdl.handle.net/1854/LU-8767408
http://doi.org/10.23919/fusion49751.2022.9841383
https://biblio.ugent.be/publication/8767408