Suchergebnisse - stack denoising autoencoder~

Automatic fault diagnosis of rolling bearings under multiple working conditions based on unsupervised stack denoising autoencoder

Autoren: Lei Wang Hang Rao Zhengcheng Dong et al.

Quelle: Structural Health Monitoring. 23:3084-3104

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

Stack Denoising Autoencoder and State-Space Model Based Bearing RUL Prediction Method

Autoren: Lei Yang Cuiwentong Xu Rongkai Duan et al.

Quelle: 2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD). :1-6

Intelligent Pre-warning Method of Transmission Line Icing Based on Stack Denoising Autoencoder

Autoren: Chunyi Wang Wei Liu Heng Zhou et al.

Quelle: Lecture Notes in Electrical Engineering ISBN: 9789819938872

Correction to: A Multi-stack Denoising Autoencoder for QoS Prediction

Autoren: Mengwei Wu Qin Lu Yingxue Wang

Quelle: Lecture Notes in Computer Science ISBN: 9783031159305

A Multi-stack Denoising Autoencoder for QoS Prediction

Autoren: Mengwei Wu Qin Lu Yingxue Wang

Quelle: Lecture Notes in Computer Science ISBN: 9783031159305

Automatic fault diagnosis of rolling bearings under multiple working conditions based on unsupervised stack denoising autoencoder.

Autoren: Wang, Lei Rao, Hang Dong, Zhengcheng et al.

Quelle: Structural Health Monitoring; Sep2024, Vol. 23 Issue 5, p3084-3104, 21p

Schlagwörter: AUTOENCODERS, DEEP learning, ROLLER bearings, DIAGNOSIS, ALGORITHMS, FEATURE extraction

SDAE-LFM: A Latent Factor Model for Recommendation Based on Stack Denoising AutoEncoder

Autoren: Zhichun Jia Jianyan Luo Mindong Xin et al.

Quelle: Journal of Physics: Conference Series. 1646:012151

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

Zugangs-URL: http://ui.adsabs.harvard.edu/abs/2020JPhCS1646a2151L/abstract

A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA Optimization

Autoren: Ruiqin Duan Xiaosheng Peng Cong Li et al.

Quelle: IEEE Access, Vol 10, Pp 123595-123604 (2022)

Schlagwörter: bat optimization algorithm, Stack denoising autoencoder, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, 7. Clean energy, wind power prediction, TK1-9971

Zugangs-URL: https://doaj.org/article/586005aa63424fbcb4d401df2e9c8da3

Network intrusion detection based on Contractive Sparse Stacked Denoising Autoencoder

Autoren: Yang Yang Li Wencui Meng Huiping et al.

Quelle: 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). :1-6

Intelligent Fault Diagnosis Method for Blade Damage of Quad-Rotor UAV Based on Stacked Pruning Sparse Denoising Autoencoder and Convolutional Neural Network

Autoren: Pu Yang Chenwan Wen Huilin Geng et al.

Quelle: Machines, Vol 9, Iss 12, p 360 (2021)

Schlagwörter: intelligent fault diagnosis, stacked pruning sparse denoising autoencoder, convolutional neural network, anti-noise, Mechanical engineering and machinery, TJ1-1570

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2075-1702/9/12/360; https://doaj.org/toc/2075-1702

Zugangs-URL: https://doaj.org/article/57fd1ddf636d4ff7816cfd2cd17bcfcf

SDCA: a novel stack deep convolutional autoencoder – an application on retinal image denoising.

Autoren: Ghosh, Swarup Kr Biswas, Biswajit Ghosh, Anupam

Quelle: IET Image Processing (Wiley-Blackwell); Dec2019, Vol. 13 Issue 14, p2778-2789, 12p

Hyperspectral image classification using k-sparse denoising autoencoder and spectral–restricted spatial characteristics.

Autoren: Lan, Rushi Li, Zeya Liu, Zhenbing et al.

Quelle: Applied Soft Computing. Jan2019, Vol. 74, p693-708. 16p.

Schlagwörter: Hyperspectral imaging systems, Image denoising, Random noise theory, Big data, Feature extraction

A Multi-Dimensional Evaluation Method of Collaborative Learning Quality in Higher Education Based on Random Forest.

Autoren: Wang, Haozeng Li, Jiahuan

Quelle: International Journal of High Speed Electronics & Systems; Dec2025, Vol. 34 Issue 4, p1-21, 21p

Schlagwörter: ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, RANDOM forest algorithms, AUTOENCODERS, COLLABORATIVE learning

Multimodal Deep Learning for Alzheimer’s Disease Diagnosis: Integrating Neuroimaging and Genetic Data.

Autoren: Rahman, Md Jubaer Nipa, Romana Nourin

Quelle: Journal of Computer Science & Technology Studies; Mar/Apr2025, Vol. 7 Issue 2, p284-293, 10p

Schlagwörter: SINGLE nucleotide polymorphisms, ALZHEIMER'S disease, AUTOENCODERS, SUPPORT vector machines, K-nearest neighbor classification, DEEP learning

An Intelligent Fault Diagnosis Method for Reciprocating Compressors Based on LMD and SDAE

Autoren: Yang Liu Lixiang Duan Zhuang Yuan et al.

Quelle: Sensors, Vol 19, Iss 5, p 1041 (2019)

Schlagwörter: reciprocating compressor, deep learning, stack denoising autoencoder, local mean decomposition, fault diagnosis, Chemical technology, TP1-1185

Relation: http://www.mdpi.com/1424-8220/19/5/1041; https://doaj.org/toc/1424-8220; https://doaj.org/article/581b77e33d6b4e1ebc7b1007ec176339

Verfügbarkeit: https://doi.org/10.3390/s19051041
https://doaj.org/article/581b77e33d6b4e1ebc7b1007ec176339

A Deep Learning Model to Predict the ncRNA-Protein Interactions Based on Sequences Information Only.

Autoren: Sewailem, Maha FM Arif, Muhammad Alam, Tanvir

Quelle: Bioinformatics & Biology Insights; 11/10/2025, Vol. 19, p1-12, 12p

Long baseline underwater source localization based on deep K-Means++ clustering in complex underwater environments.

Autoren: Dai, Yawen Yang, Lei Cao, Yifei

Quelle: Digital Signal Processing. Sep2025, Vol. 164, pN.PAG-N.PAG. 1p.

Schlagwörter: *LEAST squares, *AUTOENCODERS, *FEATURE extraction, *LOCATION analysis, *CLUSTER analysis (Statistics)

Flight delay prediction based on deep learning and Levenberg-Marquart algorithm

Autoren: Maryam Farshchian Yazdi Seyed Reza Kamel Seyyed Javad Mahdavi Chabok et al.

Quelle: Journal of Big Data, Vol 7, Iss 1, Pp 1-28 (2020)

Schlagwörter: Deep learning, Stacked denoising autoencoders, Flight delay prediction, Big data, Computer engineering. Computer hardware, TK7885-7895, Information technology, T58.5-58.64, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: http://link.springer.com/article/10.1186/s40537-020-00380-z; https://doaj.org/toc/2196-1115

Zugangs-URL: https://doaj.org/article/b39bc4e95b1f4e4fa5a3efb13c97e0e5

Research on tool condition monitoring (TCM) using a novel unsupervised deep neural network (DNN).

Autoren: Jingjing Gao Jing Liu Xinli Yu

Quelle: Journal of Vibroengineering. Feb2024, Vol. 26 Issue 1, p193-208. 16p.

Schlagwörter: *ARTIFICIAL neural networks, *FEATURE extraction, *AUTOENCODERS, *PRINCIPAL components analysis, *LINEAR network coding

Intelligent Fault Diagnosis Method for Blade Damage of Quad-Rotor UAV Based on Stacked Pruning Sparse Denoising Autoencoder and Convolutional Neural Network.

Autoren: Yang, Pu Wen, Chenwan Geng, Huilin et al.

Quelle: Machines; Dec2021, Vol. 9 Issue 12, p360-360, 1p

Schlagwörter: CONVOLUTIONAL neural networks, FAULT diagnosis, ARTIFICIAL neural networks, DIAGNOSIS, DEEP learning, DRONE aircraft