Suchergebnisse - stacked denoising autoencoder(sage)

Health condition identification for rolling bearing using a multi-domain indicator-based optimized stacked denoising autoencoder

Autoren: Xiaoan Yan Minping Jia Ying Liu

Quelle: Structural Health Monitoring. 19:1602-1626

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

Zugangs-URL: https://journals.sagepub.com/doi/10.1177/1475921719893594

Fault diagnosis method of rotating machinery based on stacked denoising autoencoder

Autoren: Zhouliang Chen Zhinong Li

Quelle: Journal of Intelligent & Fuzzy Systems. 34:3443-3449

Schlagwörter: 0301 basic medicine, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs169524
https://dblp.uni-trier.de/db/journals/jifs/jifs34.html#ChenL18b

Floating offshore wind turbine fault diagnosis using stacked denoising autoencoder with temporal information

Autoren: Xujie Zhang Ping Wu Jiajun He et al.

Quelle: Transactions of the Institute of Measurement and Control.

Schlagwörter: 13. Climate action, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy

Zugangs-URL: http://journals.sagepub.com/doi/pdf/10.1177/01423312211057994

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

Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review.

Autoren: Kalu, Kingsley Ataguba, Grace Onifade, Oyepeju et al.

Quelle: Nutrients. Oct2025, Vol. 17 Issue 20, p3223. 33p.

A fault diagnosis framework for rotating machinery of marine equipment: A semi-supervised learning framework based on contractive stacked autoencoder

Autoren: Penghao Pan Dong Zhao Yueyang Li

Quelle: Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment. 237:625-636

Schlagwörter: 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

A fault diagnosis framework for rotating machinery of marine equipment: A semi-supervised learning framework based on contractive stacked autoencoder.

Autoren: Pan, Penghao Zhao, Dong Li, Yueyang

Quelle: Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment (Sage Publications, Ltd.); Aug2023, Vol. 237 Issue 3, p625-636, 12p

Automated Video-EEG Analysis in Epilepsy Studies: A Narrative Review of Advances and Challenges.

Autoren: Zuev VA Salmagambetova EG Djakov SN et al.

Quelle: Journal of medical systems [J Med Syst] 2025 Oct 20; Vol. 49 (1), pp. 142. Date of Electronic Publication: 2025 Oct 20.

Publikationsart: Journal Article; Review

Info zur Zeitschrift: Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 7806056 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-689X (Electronic) Linking ISSN: 01485598 NLM ISO Abbreviation: J Med Syst Subsets: MEDLINE

MeSH-Schlagworte: Electroencephalography*/methods , Epilepsy*/diagnosis , Epilepsy*/physiopathology , Video Recording*/methods, Humans ; Machine Learning

Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection.

Autoren: Ahmed, Imtiaz Galoppo, Travis Hu, Xia et al.

Quelle: IEEE Transactions on Pattern Analysis & Machine Intelligence; Aug2022, Vol. 44 Issue 8, p4110-4124, 15p

Schlagwörter: ANOMALY detection (Computer security), GENERATIVE adversarial networks, EUCLIDEAN metric, GEODESIC distance, EUCLIDEAN distance, SPANNING trees, GEODESICS

Intelligent fault prediction of rolling bearing based on gate recurrent unit and hybrid autoencoder.

Autoren: Che, Changchang Wang, Huawei Fu, Qiang et al.

Quelle: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (Sage Publications, Ltd.); Mar2021, Vol. 235 Issue 6, p1106-1114, 9p

μPIX: leveraging generative AI for enhanced, personalized and sustainable microscopy.

Autoren: Bon, Gabriel Sapède, Daniel Matthews, Cédric et al.

Quelle: Methods in Microscopy; Jul2025, Vol. 2 Issue 2, p215-233, 19p

Schlagwörter: FLUORESCENCE microscopy, GENERATIVE adversarial networks, IMAGE denoising, DEEP learning, SUPERVISED learning

ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning.

Autoren: Jahangir, Raiyan Islam, Muhammad Nazrul Islam, Md. Shofiqul et al.

Quelle: BMC Cardiovascular Disorders; 4/7/2025, Vol. 25 Issue 1, p1-14, 14p

Schlagwörter: BOOSTING algorithms, ARTIFICIAL intelligence, MACHINE learning, PRINCIPAL components analysis, SURVIVAL rate

A sequential cross-product knowledge accumulation, extraction and transfer framework for machine learning-based production process modelling.

Autoren: Xie, Jiarui Zhang, Chonghui Sage, Manuel et al.

Quelle: International Journal of Production Research; Jun2024, Vol. 62 Issue 12, p4181-4201, 21p

Schlagwörter: MACHINE tools, MANUFACTURING processes, AUXETIC materials, FEATURE selection, GAS turbines

Semi-Supervised Deep Learning Framework for Predictive Maintenance in Offshore Wind Turbines †.

Autoren: Barnabei, Valerio F. Ancora, Tullio C. M. Delibra, Giovanni et al.

Quelle: International Journal of Turbomachinery, Propulsion & Power; Sep2025, Vol. 10 Issue 3, p14, 20p

Schlagwörter: DEEP learning, LONG short-term memory, CONDITION-based maintenance, SUPERVISORY control & data acquisition systems, OFFSHORE wind power plants, SUPERVISED learning, OUTLIER detection

Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction.

Autoren: Hajim, Wesam Ibrahim Zainudin, Suhaila Daud, Kauthar Mohd et al.

Quelle: PeerJ Computer Science; Dec2024, p1-36, 36p

Schlagwörter: CONVOLUTIONAL neural networks, LONG short-term memory, DEEP learning, GOLDEN eagle, AUTOENCODERS

A Survey on Variational Autoencoders in Recommender Systems.

Autoren: Liang, Shangsong Pan, Zhou liu, wei et al.

Quelle: ACM Computing Surveys; Oct2024, Vol. 56 Issue 10, p1-40, 40p

Schlagwörter: ARTIFICIAL neural networks, REINFORCEMENT learning, DEEP reinforcement learning, GRAPH neural networks, FEEDFORWARD neural networks, DEEP learning, LATENT variables

Boosting sparsity-induced autoencoder: A novel sparse feature ensemble learning for image classification.

Autoren: Rui Shi Jian Ji Chunhui Zhang et al.

Quelle: International Journal of Advanced Robotic Systems; May/Jun2019, Vol. 16 Issue 3, p1-9, 9p

Schlagwörter: IMAGE reconstruction algorithms, COMPUTER vision, BACK propagation, IMAGE processing, IMAGE denoising, CLASSIFICATION, DEEP learning

A review of hyperspectral image classification based on graph neural networks.

Autoren: Zhao, Xiaofeng Ma, Junyi Wang, Lei et al.

Quelle: Artificial Intelligence Review; Jun2025, Vol. 58 Issue 6, p1-56, 56p

Deep Learning-Based Sleep Stage Classification: A Systematic Review.

Autoren: Satapathy, Santosh Kumar Patel, Tapan Modi, Madhuram et al.

Quelle: Journal of the Institution of Engineers (India): Series B; Dec2025, Vol. 106 Issue 6, p2087-2109, 23p

LiteFallNet: A lightweight deep learning model for efficient real-time fall detection.

Autoren: Owusu, Emmanuel Acquah, Isaac Asare, Michael Asiedu et al.

Quelle: Digital Health; 10/9/2025, Vol. 11, p1-20, 20p