Search Results - sparse convolutional autoencoder (SCAE)

Sparse convolutional autoencoder‐based fault location for drive circuits in nuclear reactors

Authors: Cheng Yang Yannan Yuan Fu Wang et al.

Source: Quality and Reliability Engineering International. 40:819-837

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy

SCAE—Stacked Convolutional Autoencoder for Fault Diagnosis of a Hydraulic Piston Pump with Limited Data Samples.

Authors: Eraliev, Oybek Lee, Kwang-Hee Lee, Chul-Hee

Source: Sensors (14248220). Jul2024, Vol. 24 Issue 14, p4661. 17p.

Subject Terms: *RECIPROCATING pumps, *FAULT diagnosis, *HIERARCHICAL Bayes model, *CONVOLUTIONAL neural networks, *DEEP learning, *MACHINE learning, *FEATURE extraction, *DIAGNOSIS

Sparse convolutional autoencoder‐based fault location for drive circuits in nuclear reactors.

Authors: Yang, Cheng Yuan, Yannan Wang, Fu et al.

Source: Quality & Reliability Engineering International; Mar2024, Vol. 40 Issue 2, p819-837, 19p

Subject Terms: FAULT location (Engineering), NUCLEAR reactors, NUCLEAR reactor control, FAULT diagnosis, ELECTRONIC equipment, LINE drivers (Integrated circuits)

Face Recognition Based on Stacked Convolutional Autoencoder and Sparse Representation

Authors: Hong Xu Kai Liu Jianjun Yang et al.

Source: 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP). :1-4

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Access URL: https://dblp.uni-trier.de/db/conf/icdsp/icdsp2018.html#ChangYLXLH18

Feature extraction of fields of fluid dynamics data using sparse convolutional autoencoder.

Authors: Obayashi, Wataru Aono, Hikaru Tatsukawa, Tomoaki et al.

Source: AIP Advances; Oct2021, Vol. 11 Issue 10, p1-6, 6p

Subject Terms: FLUID dynamics, FEATURE extraction, PROPER orthogonal decomposition, NAVIER-Stokes equations, CONVOLUTIONAL neural networks, UNSTEADY flow, COMPRESSIBLE flow

Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia.

Authors: Akila, Serag Mohamed Imanov, Elbrus Almezhghwi, Khaled

Source: Diagnostics (2075-4418). Jul2023, Vol. 13 Issue 13, p2199. 14p.

Subject Terms: *MACHINE learning, *PNEUMONIA, *ANOMALY detection (Computer security), *LUNG infections, *DIAGNOSIS, *KLEBSIELLA infections

A radar emitter structural identification method for complex conditions based on compressed sensing and an autoencoder.

Authors: Song, Yu Zhang, Wei Yang, Yaozu et al.

Source: Nonlinear Dynamics; Aug2025, Vol. 113 Issue 15, p19159-19178, 20p

A Novel Deep Neural Network-Based Framework for Subsurface Analysis in Non-Stationary Thermal Wave Imaging.

Authors: Swapna, M. N. Suman, M. Prasanthi, Y. N. et al.

Source: Sensing & Imaging; 9/5/2025, Vol. 26 Issue 1, p1-18, 18p

Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network †.

Authors: Lai, Trinh Thuc Tran, Tuan Phong Cho, Jaehyuk et al.

Source: Applied Sciences (2076-3417); Sep2023, Vol. 13 Issue 18, p10090, 19p

Subject Terms: WIRELESS sensor networks, DATA recovery, NOISE, NOISE control

Robust Feature Extraction for Geochemical Anomaly Recognition Using a Stacked Convolutional Denoising Autoencoder.

Authors: Xiong, Yihui Zuo, Renguang

Source: Mathematical Geosciences; Apr2022, Vol. 54 Issue 3, p623-644, 22p

Dual Autoencoder Network with Separable Convolutional Layers for Denoising and Deblurring Images.

Authors: Solovyeva, Elena Abdullah, Ali

Source: Journal of Imaging; Sep2022, Vol. 8 Issue 9, pN.PAG-N.PAG, 22p

An Integrated Hybrid Deep Learning Framework for Intrusion Detection in IoT and IIoT Networks Using CNN-LSTM-GRU Architecture.

Authors: Afraji, Doaa Mohsin Abd Ali Lloret, Jaime Peñalver, Lourdes

Source: Computation; Sep2025, Vol. 13 Issue 9, p222, 28p

Subject Terms: INTRUSION detection systems (Computer security), DEEP learning, ELECTRONIC data processing, INTERNET security, INTERNET of things

Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition.

Authors: Hossain, Prommy Sultana Chakrabarty, Amitabha Kim, Kyuheon et al.

Source: Applied Sciences (2076-3417); Jun2022, Vol. 12 Issue 11, p5463-5463, 20p

Subject Terms: MACHINE learning, SPEECH perception, SPEECH

A Fault Diagnosis Method for the Train Communication Network Based on Active Learning and Stacked Consistent Autoencoder.

Authors: Yang, Yueyi Wang, Haiquan Nie, Xiaobo et al.

Source: Symmetry (20738994); Oct2025, Vol. 17 Issue 10, p1622, 17p

Subject Terms: FAULT diagnosis, ACTIVE learning, AUTOENCODERS, PATTERN recognition systems, METADATA, RELIABILITY in engineering, RAILROAD communication systems, MACHINE learning

Structural inversion of radar emitter based on stacked convolutional autoencoder and deep neural network.

Authors: Jiang, Yilin Song, Yu Zhang, Wei et al.

Source: IET Signal Processing (Wiley-Blackwell); Feb2023, Vol. 17 Issue 2, p1-20, 20p

Subject Terms: ADDITIVE white Gaussian noise, CHROMOSOME inversions, RADAR, VIDEO coding

A New SCAE-MT Classification Model for Hyperspectral Remote Sensing Images.

Authors: Chen, Huayue Chen, Ye Wang, Qiuyue et al.

Source: Sensors (14248220); Nov2022, Vol. 22 Issue 22, p8881, 16p

Subject Terms: DEEP learning, ARTIFICIAL neural networks, FOREIGN bodies, LEARNING strategies

Improving automated latent fingerprint detection and segmentation using deep convolutional neural network.

Authors: Chhabra, Megha Ravulakollu, Kiran Kumar Kumar, Manoj et al.

Source: Neural Computing & Applications; Mar2023, Vol. 35 Issue 9, p6471-6497, 27p

Subject Terms: CONVOLUTIONAL neural networks, FORENSIC fingerprinting, DEEP learning, HUMAN fingerprints, OBJECT recognition (Computer vision), IMAGE segmentation, DATABASES

Data-Driven Method for Real-Time Reconstruction of the Structural Displacement Field.

Authors: Yan, Jun Du, Hongze Bu, Yufeng et al.

Source: Journal of Aerospace Engineering; May2024, Vol. 37 Issue 3, p1-11, 11p

Subject Terms: CONVOLUTIONAL neural networks, DEEP learning, KALMAN filtering

HRRP target recognition method based on one-dimensional stacked pooling fusion convolutional autoencoder. (English)

Authors: ZHANG Guoling WU Chongming LI Rui et al.

Source: Systems Engineering & Electronics; Dec2021, Vol. 43 Issue 12, p3533-3541, 9p

Subject Terms: FEATURE extraction, TURBO codes, AUTOMATIC target recognition, ALGORITHMS, CHANNEL coding

Pair wise training for stacked convolutional autoencoders using small scale images.

Authors: Kumar, Aiswarya S. Nair, Jyothisha J. Thampi, Sabu M. et al.

Source: Journal of Intelligent & Fuzzy Systems; 2019, Vol. 36 Issue 3, p1987-1995, 9p

Subject Terms: NATURAL language processing, COMPUTER vision, VIDEO coding