Suchergebnisse - Multi-scale convolutional autoencoder

An optimized multi-scale convolutional autoencoder for efficient abnormal event detection using rgb, depth and optical flow data

Autoren: Abdullah Alqahtani

Quelle: Multimedia Tools and Applications. 84:34401-34435

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: Front Comput Neurosci
Frontiers in Computational Neuroscience, Vol 18 (2024)

Schlagwörter: health risk assessment, deep learning, adolescence, Neurosciences. Biological psychiatry. Neuropsychiatry, behavioral health risks, functional magnetic resonance imaging, convolutional autoencoder, RC321-571, Neuroscience

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39473785
https://doaj.org/article/7e665edbb7b24faf972c7fe95a652ce0

A multi-scale low rank convolutional autoencoder for process monitoring of nonlinear uncertain systems

Autoren: Jiawei Yin Xuefeng Yan

Quelle: Process Safety and Environmental Protection. 188:53-63

Physics-assisted multi-scale convolutional autoencoder for turbulence reduced-order modeling

Autoren: Mengxue Lu Zi Ye Longye Qiao et al.

Quelle: Physics of Fluids. 37

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

Autoren: Teutsch, Philipp Pfeffer, Philipp Ghazijahani, Mohammad Sharifi et al.

Index Begriffe: Physics - Fluid Dynamics, Computer Science - Machine Learning, text

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

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

Autoren: Philipp Teutsch Philipp Pfeffer Mohammad Sharifi Ghazijahani et al.

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

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Physics, QC1-999, Electronic computers. Computer science, Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, QA75.5-76.95, Physics - Fluid Dynamics, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2501.03070
https://doaj.org/article/236f8e1f2be64001a1d9c1d7b38ae1f2

Micro-motion Signal Enhancement via Convolutional Autoencoder Equipped with Multi-scale Feature Pyramid

Autoren: Honglei Zhang Minglei Tang Wenpeng Zhang et al.

Quelle: 2023 8th International Conference on Signal and Image Processing (ICSIP). :758-762

IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks

Autoren: Suma Christal Mary Sundararajan Yamini Bhavani Shankar Sinthia Panneer Selvam et al.

Quelle: Sci Rep
Scientific Reports, Vol 15, Iss 1, Pp 1-21 (2025)

Schlagwörter: Internet of things, Aquaponic fish ponds, Science, Internet of Things, Fishes, Aquaculture, Article, Revamped fitness-based mother optimization algorithm, Water Quality, Medicine, Animals, Neural Networks, Computer, Water quality prediction, Ponds, Multi-scale feature fusion-based convolutional autoencoder with gated recurrent unit, Algorithms, Environmental Monitoring

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39809886
https://doaj.org/article/481247aba02e483987cf278ae390a214

An improved YOLOv8 model and mask convolutional autoencoder for multi-scale defect detection of ceramic tiles

Autoren: Yuesheng Liu Weibin Qiu Kailong Fu et al.

Quelle: Measurement. 248:116847

A Multi-scale Convolutional Autoencoder with Attention Mechanism for Fault Diagnosis of Rotating Machinery

Autoren: Zihao Lei Hongguang Yun Feiyu Tian et al.

Quelle: Applications of Generative AI ISBN: 9783031462375

Colour‐patterned fabric defect detection based on an unsupervised multi‐scale U‐shaped denoising convolutional autoencoder model

Autoren: Hongwei Zhang Shuting Liu Quanlu Tan et al.

Quelle: Coloration Technology. 138:522-537

Schlagwörter: 01 natural sciences, 0104 chemical sciences

Dynamic Texture Feature Extraction Based on multi-scale Convolutional Autoencoder

Autoren: Ziqi Zhu Yangwei Gu Huimin Yi

Quelle: 2021 4th International Conference on Intelligent Autonomous Systems (ICoIAS). :108-112

Zugangs-URL: https://www.computer.org/csdl/proceedings-article/icoias/2021/419500a108/1wG6cmFXGP6

Pansharpening based on convolutional autoencoder and multi-scale guided filter

Autoren: Ahmad AL Smadi Shuyuan Yang Zhang Kai et al.

Quelle: EURASIP Journal on Image and Video Processing, Vol 2021, Iss 1, Pp 1-20 (2021)

Schlagwörter: Pansharpening, Convolutional autoencoder, Guided image filtering, Adaptive intensity-hue-saturation AIHS, Electronics, TK7800-8360

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1687-5281

Zugangs-URL: https://doaj.org/article/98e86503a56348dda991a171974388e5

3D Face Reprentation and Reconstruction with Multi-scale Graph Convolutional Autoencoders

Autoren: Cunkuan Yuan Yu-Kun Lai Kun Li et al.

Quelle: 2019 IEEE International Conference on Multimedia and Expo (ICME). :1558-1563

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://dblp.uni-trier.de/db/conf/icmcs/icme2019.html#YuanLLLY19
https://orca.cardiff.ac.uk/121528/
https://ieeexplore.ieee.org/document/8784723/

Vessel Trajectory Anomaly Detection Based on Multi-scale Convolutional Autoencoder.

Autoren: Qi, Yuhao Yang, Jiaxuan Xu, Dongsheng et al.

Quelle: Ocean Engineering. Jan2026:Part 5, Vol. 343, pN.PAG-N.PAG. 1p.

Multi-scale convolutional autoencoder network-based Implicit Information mining of cultural relics

Autoren: Kai Yu Zhichao Li Yi Duan et al.

Quelle: Optics for Arts, Architecture, and Archaeology (O3A) IX. :7

GPR B-Scan Image Denoising via Multi-Scale Convolutional Autoencoder with Data Augmentation

Autoren: Jiabin Luo Wentai Lei Feifei Hou et al.

Quelle: Electronics
Volume 10
Issue 11

Schlagwörter: ground-penetrating radar (GPR), image denoising, generative adversarial network, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, convolutional autoencoder, data augmentation

Dateibeschreibung: application/pdf

Zugangs-URL: https://www.mdpi.com/2079-9292/10/11/1269/pdf
https://www.mdpi.com/2079-9292/10/11/1269
https://www.mdpi.com/2079-9292/10/11/1269/pdf

Rolling Bearing Health Indicator Extraction and RUL Prediction Based on Multi-Scale Convolutional Autoencoder

Autoren: Zijian Ye Qiang Zhang Siyu Shao et al.

Quelle: Applied Sciences, Vol 12, Iss 11, p 5747 (2022)

Schlagwörter: MSCAE, RUL, rolling bearing, health indicator, 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/2076-3417/12/11/5747; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/cb10e7f308674a2981f86b4bf22b7786

An optimized multi-scale convolutional autoencoder for efficient abnormal event detection using rgb, depth and optical flow data.

Autoren: Alqahtani, Abdullah

Quelle: Multimedia Tools & Applications; Aug2025, Vol. 84 Issue 28, p34401-34435, 35p

Schlagwörter: ANOMALY detection (Computer security), DEEP learning, OPTICAL flow, VIDEO surveillance, OBJECT recognition (Computer vision), LONG short-term memory

RAECAE: A Multi-Scale Attention Residual Convolutional Autoencoder-Based Method for Denoising Vibration Signals of Wind Turbine Rolling Bearings

Autoren: Zhou, Xinxin Zhang, Jinye Zhang, Hongrui et al.

Quelle: 2025 International Joint Conference on Neural Networks (IJCNN) ; page 1-8

Verfügbarkeit: https://doi.org/10.1109/ijcnn64981.2025.11228738
http://xplorestaging.ieee.org/ielx8/11227166/11227148/11228738.pdf?arnumber=11228738