Search Results - "Multi-scale convolutional autoencoder"

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

Authors: Abdullah Alqahtani

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

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

Authors: Mengxue Lu Zi Ye Longye Qiao et al.

Source: Physics of Fluids. 37

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

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

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

Subject Terms: 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)

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

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

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

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

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

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

Authors: Zihao Lei Hongguang Yun Feiyu Tian et al.

Source: Applications of Generative AI ISBN: 9783031462375

Dynamic Texture Feature Extraction Based on multi-scale Convolutional Autoencoder

Authors: Ziqi Zhu Yangwei Gu Huimin Yi

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

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

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

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

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

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

Authors: Kai Yu Zhichao Li Yi Duan et al.

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

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

Authors: Zijian Ye Qiang Zhang Siyu Shao et al.

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

Subject Terms: 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

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/12/11/5747; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/cb10e7f308674a2981f86b4bf22b7786

A deep feature extraction approach for bearing fault diagnosis based on multi-scale convolutional autoencoder and generative adversarial networks

Authors: Zhiyong Hu Taorui Han Jun Bian et al.

Source: Measurement Science and Technology. 33:065013

Subject Terms: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

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

Authors: Jiabin Luo Wentai Lei Feifei Hou et al.

Source: Electronics
Volume 10
Issue 11

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

File Description: application/pdf

Access 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

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

Authors: Alqahtani, Abdullah

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

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

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

Authors: Lu, Mengxue Ye, Zi Qiao, Longye et al.

Source: Physics of Fluids; Jan2025, Vol. 37 Issue 1, p1-12, 12p

Subject Terms: PROPER orthogonal decomposition, UNSTEADY flow, DEGREES of freedom, TURBULENT flow, CHANNEL flow

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

Authors: Ye, Zijian Zhang, Qiang Shao, Siyu et al.

Source: Applied Sciences (2076-3417); Jun2022, Vol. 12 Issue 11, p5747-5747, 20p

Subject Terms: ROLLER bearings, HEALTH status indicators, VIBRATION tests, ROTATIONAL motion, ROTATING machinery

Multi-scale convolutional autoencoder network-based implicit information mining of cultural relics.

Authors: Yu, Kai Li, Zhichao Duan, Yi et al.

Source: Proceedings of SPIE; 8/11/2023, Vol. 12620, p1262003-1262003, 1p

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

Authors: Luo, Jiabin Lei, Wentai Hou, Feifei et al.

Source: Electronics (2079-9292); Jun2021, Vol. 10 Issue 11, p1269, 1p

Subject Terms: DATA augmentation, IMAGE denoising, GENERATIVE adversarial networks, GROUND penetrating radar, SIGNAL-to-noise ratio

Distribution transformer mechanical faults diagnosis method incorporating cross-domain feature extraction and recognition of unknown-type faults.

Authors: Huang, Haiyan Gao, Wei Yang, Gengjie

Source: Measurement (02632241). Oct2024, Vol. 238, pN.PAG-N.PAG. 1p.

Subject Terms: *CONVOLUTIONAL neural networks, *OPTIMIZATION algorithms, *TRANSFORMER models, *FAULT diagnosis, *FEATURE extraction

A limited annotated sample fault diagnosis algorithm based on nonlinear coupling self-attention mechanism.

Authors: Luo, Shuyang Zhang, Dong Wu, Jinhong et al.

Source: Engineering Failure Analysis. Jun2025, Vol. 174, pN.PAG-N.PAG. 1p.

Subject Terms: *MACHINE learning, *FAULT diagnosis, *AUTOENCODERS, *MULTISCALE modeling, *ALGORITHMS

Anomaly detection for real-world electric vehicle charging data using a convolutional autoencoder with multiscale convolution, attention mechanism, and BiLSTM.

Authors: Liu, Zhibin Li, Lei Ding, Xiaoyin et al.

Source: Energy. Nov2025, Vol. 337, pN.PAG-N.PAG. 1p.

Subject Terms: *ELECTRIC vehicle charging stations, *AUTOENCODERS, *CONVOLUTIONAL neural networks, *OUTLIER detection, *LONG short-term memory, *MACHINE learning, *TIME series analysis, *PREDICTION models

Intelligent Identification of Hidden Defects in Asphalt Roads Using GPR Based on Loss Function and Anchor Box Optimization.

Authors: Wang, Peng Zhang, Lei Xing, Chao et al.

Source: Journal of Performance of Constructed Facilities; Dec2025, Vol. 39 Issue 6, p1-16, 16p

Subject Terms: GROUND penetrating radar, ASPHALT pavements, IMAGE analysis, LOSS functions (Statistics), DETECTION algorithms, OBJECT recognition (Computer vision)