Výsledky vyhledávání - stacked convolutional autoencoder network*

FA-SconvAE-LSTM: Feature-Aligned Stacked Convolutional Autoencoder with Long Short-Term Memory Network for Soft Sensor Modeling

Autoři: Ping Wu Zengdi Miao Ke Wang a další

Zdroj: Engineering Applications of Artificial Intelligence. 150:110535

An Improved Data Compression Framework for Wireless Sensor Networks Using Stacked Convolutional Autoencoder (S-CAE)

Autoři: Lithin Kumble Kiran Kumari Patil

Zdroj: SN Computer Science. 4

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

Autoři: Yilin Jiang Yu Song Wei Zhang a další

Zdroj: IET Signal Processing. 17

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

Gait Recognition Method of Underground Coal Mine Personnel Based on Densely Connected Convolution Network and Stacked Convolutional Autoencoder

Autoři: Xiaoyang Liu Jinqiang Liu

Zdroj: Entropy, Vol 22, Iss 6, p 695 (2020)

Témata: underground coal mine personnel, gait recognition, similarity learning, densely connected convolution network, stacked convolutional autoencoder, Two-Stream neural network, Science, Astrophysics, QB460-466, Physics, QC1-999

Popis souboru: electronic resource

Relation: https://www.mdpi.com/1099-4300/22/6/695; https://doaj.org/toc/1099-4300

Přístupová URL adresa: https://doaj.org/article/20cf7dcdd76d4b939451b71332a43bdc

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

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

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

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

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

Autoři: Yihui Xiong Renguang Zuo

Zdroj: Mathematical Geosciences. 54:623-644

Témata: 0101 mathematics, 01 natural sciences, 0105 earth and related environmental sciences

Přístupová URL adresa: https://link.springer.com/article/10.1007/s11004-021-09935-z

Gait recognition method of coal mine personnel based on Two-Stream neural network

Autoři: Liu Xiaoyang Liu Jinqiang Zheng Haolin

Zdroj: 矿业科学学报, Vol 6, Iss 2, Pp 218-227 (2021)

Témata: underground coal mine personnel, gait recognition, stacked convolutional autoencoder, residual neural network, two-stream neural network, Engineering geology. Rock mechanics. Soil mechanics. Underground construction, TA703-712, Mining engineering. Metallurgy, TN1-997

Popis souboru: electronic resource

Relation: https://doaj.org/toc/2096-2193

Přístupová URL adresa: https://doaj.org/article/b612e7d6f7fd46ffb1f98d612ae18269

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

Autoři: Megha Chhabra Kiran Kumar Ravulakollu Manoj Kumar a další

Zdroj: Neural Computing and Applications. 35:6471-6497

Témata: 0209 industrial biotechnology, Latent fingerprints, Segmentation, 0202 electrical engineering, electronic engineering, information engineering, Convolutional neural network, Autoencoder, 02 engineering and technology, Classification

Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder

Autoři: Yi Zhu Xinke Zhou Xindong Wu

Zdroj: Applied Sciences, Vol 13, Iss 1, p 481 (2022)

Témata: domain adaptation, convolutional autoencoder, sparse autoencoder, 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/2076-3417/13/1/481; https://doaj.org/toc/2076-3417

Přístupová URL adresa: https://doaj.org/article/491024e2261147178e7df1a59356e59a

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

Autoři: Trinh Thuc Lai Tuan Phong Tran Jaehyuk Cho a další

Zdroj: Applied Sciences, Vol 13, Iss 18, p 10090 (2023)

Témata: wireless sensor network, noise reduction, weather effect, data reconstruction, convolutional autoencoder, 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/2076-3417/13/18/10090; https://doaj.org/toc/2076-3417

Přístupová URL adresa: https://doaj.org/article/289f15b5b08f4ab286c61a43296b825a

Intelligent Optimization of Latent Fingerprint Image Segmentation using Stacked Convolutional Autoencoder.

Autoři: Chhabra, Megha Shukla, Manoj Kumar Ravulakollu, Kiran Kumar

Zdroj: International Journal of Performability Engineering. Apr2021, Vol. 17 Issue 4, p379-393. 15p.

Témata: Forensic fingerprinting, Image segmentation, Human fingerprints, Smart structures, Feature selection, Law enforcement agencies

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

Autoři: Jiang, Yilin Song, Yu Zhang, Wei a další

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

Témata: ADDITIVE white Gaussian noise, CHROMOSOME inversions, RADAR, VIDEO coding

Optimizing dotted Arabic expiration date recognition with ARABEX: a convolutional autoencoder with bidirectional LSTM and CRNN approach.

Autoři: Zaki, Hozaifa Soliman, Ghada

Zdroj: International Journal on Document Analysis & Recognition; Dec2025, Vol. 28 Issue 4, p555-572, 18p

SCADefender: An Autoencoder-Based Defense for CNN-Based Image Classifiers.

Autoři: Nguyen, Duc-Anh Do Minh, Kha Nhu, Ngoc Nguyen a další

Zdroj: International Journal of Pattern Recognition & Artificial Intelligence; Sep2023, Vol. 37 Issue 12, p1-26, 26p

Témata: CONVOLUTIONAL neural networks, IMAGE recognition (Computer vision), SPACE surveillance, DEEP learning

Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury

Autoři: Hakseung Kim Dong-Joo Kim Frederick A. Zeiler a další

Přispěvatelé: Hakseung Kim Dong-Joo Kim Frederick A. Zeiler a další

Zdroj: Journal of Neurosurgery. 132:1952-1960

Témata: 03 medical and health sciences, 0302 clinical medicine, traumatic brain injury, intracranial pressure, convolutional neural network, stacked convolutional autoencoder, cerebral hypoperfusion

Popis souboru: application/octet-stream

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/31075774
https://koreauniv.pure.elsevier.com/en/publications/artifact-removal-from-neurophysiological-signals-impact-on-intrac
https://thejns.org/view/journals/j-neurosurg/132/6/article-p1952.xml
https://www.ncbi.nlm.nih.gov/pubmed/31075774
https://pubmed.ncbi.nlm.nih.gov/31075774/
https://www.repository.cam.ac.uk/handle/1810/291453
https://doi.org/10.3171/2019.2.jns182260
https://doi.org/10.17863/cam.38621

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

Autoři: Xiong, Yihui Zuo, Renguang

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

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

Autoři: Lai, Trinh Thuc Tran, Tuan Phong Cho, Jaehyuk a další

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

Témata: WIRELESS sensor networks, DATA recovery, NOISE, NOISE control

A hybrid 1DCNN-GRU deep learning framework for classifying caprine granulosa cell fertility potential using single-cell transcriptomics.

Autoři: Sananmuang, Thanida Puthier, Denis Chokeshaiusaha, Kaj

Zdroj: Veterinary World. Jul2025, Vol. 18 Issue 7, p1922-1935. 14p.

Témata: Granulosa cells, Fertility, Convolutional neural networks, Transcriptomes, Deep learning, Recurrent neural networks, Goats, Biomarkers

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

Autoři: Huayue Chen Ye Chen Qiuyue Wang a další

Zdroj: Sensors, Vol 22, Iss 8881, p 8881 (2022)

Témata: stacked convolutional autoencoder network, transfer learning, feature extraction, small samples, HRSI, Chemical technology, TP1-1185

Relation: https://www.mdpi.com/1424-8220/22/22/8881; https://doaj.org/toc/1424-8220; https://doaj.org/article/6e5c53c528b04a9a934f22282bdde353

Dostupnost: https://doi.org/10.3390/s22228881
https://doaj.org/article/6e5c53c528b04a9a934f22282bdde353

Stacked Convolutional Autoencoder for Detecting Animal Images in Cluttered Scenes with a Novel Feature Extraction Framework

Autoři: S. Divya Meena L. Agilandeeswari

Zdroj: Advances in Intelligent Systems and Computing ISBN: 9789811501838

Přístupová URL adresa: https://link.springer.com/chapter/10.1007/978-981-15-0184-5_44
https://dblp.uni-trier.de/db/conf/socpros/socpros2018-2.html#MeenaA18