Suchergebnisse - "sparse denoising autoencoder"

Flatness pattern recognition based on stacked sparse denoising autoencoder and improved Osprey optimisation algorithm kernel-extreme learning machine

Autoren: Yaluo Zhou Shaochuan Zhang Wenguang Liu et al.

Quelle: Ironmaking & Steelmaking: Processes, Products and Applications.

Fault diagnosis of aircraft engines based on multi-sensor fusion sparse denoising autoencoder

Autoren: ShiJie He Zhi-Ming Wang Kun-Zhi Liu et al.

Quelle: 2024 43rd Chinese Control Conference (CCC). :4835-4840

Residential Electricity Behavior Classification Model Based on Sparse Denoising Autoencoder And K-Means

Autoren: Zhengnan Yao Feishen Wei Yifan Huang

Quelle: 2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC). :506-510

Early fault detection method for nuclear power plants based on sparse denoising autoencoder and kernel principal component analysis

Autoren: Wenzhe Yin Hong Xia Xueying Huang et al.

Quelle: Annals of Nuclear Energy. 220:111460

Toward Robust Fault Identification of Complex Industrial Processes Using Stacked Sparse-Denoising Autoencoder With Softmax Classifier

Autoren: Jinping Liu Longcheng Xu Yongfang Xie et al.

Quelle: IEEE Transactions on Cybernetics. 53:428-442

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

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/34550897
https://ieeexplore.ieee.org/document/9546667

SupRes: Facial Image Upscaling Using Sparse Denoising Autoencoder

Autoren: Manan Agrawal Mohd Ayaan Anwar Nakul Saroha et al.

Quelle: 2023 7th International Conference on Computing Methodologies and Communication (ICCMC). :541-546

Classification model of electricity consumption behavior based on sparse denoising autoencoder feature dimensionality reduction and spectral clustering

Autoren: Yifan Huang Zhengnan Yao Qifeng Xu

Quelle: International Journal of Electrical Power & Energy Systems, Vol 158, Iss , Pp 109960- (2024)

Schlagwörter: Electricity consumption behavior classification, Feature extraction, Autoencoder, Clustering, Secondary classification, Production of electric energy or power. Powerplants. Central stations, TK1001-1841

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S0142061524001819; https://doaj.org/toc/0142-0615

Zugangs-URL: https://doaj.org/article/0a1872f2827a46cdba596421ba762779

Suspect Multifocus Image Fusion Based on Sparse Denoising Autoencoder Neural Network for Police Multimodal Big Data Analysis

Autoren: Jin Wang Yanfei Gao

Quelle: Scientific Programming. 2021:1-12

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 16. Peace & justice

Dateibeschreibung: text/xhtml

Zugangs-URL: https://downloads.hindawi.com/journals/sp/2021/6614873.pdf
http://downloads.hindawi.com/journals/sp/2021/6614873.pdf
https://dblp.uni-trier.de/db/journals/sp/sp2021.html#WangG21
https://downloads.hindawi.com/journals/sp/2021/6614873.pdf
https://www.hindawi.com/journals/sp/2021/6614873/

A Double-Weighted Deterministic Extreme Learning Machine Based on Sparse Denoising Autoencoder and Its Applications

Autoren: Liang Luo Bolin Liao Cheng Hua et al.

Quelle: Journal of Computer and Communications. 10:138-153

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

A novel stacked sparse denoising autoencoder for mammography restoration to visual interpretation of breast lesion

Autoren: Swarup Kr Ghosh Biswajit Biswas Anupam Ghosh

Quelle: Evolutionary Intelligence. 14:133-149

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

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

Autoren: Rushi Lan Zeya Li Zhenbing Liu et al.

Quelle: Applied Soft Computing. 74:693-708

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

Zugangs-URL: https://dblp.uni-trier.de/db/journals/asc/asc74.html#LanLLGL19
https://www.sciencedirect.com/science/article/pii/S1568494618306057
https://doi.org/10.1016/j.asoc.2018.08.049

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

Spatio-temporal-Aware Sparse Denoising Autoencoder Neural Network for Air Quality Prediction

Autoren: Yatian Shen Daojun Han Yan Li et al.

Quelle: 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS). :96-100

Schlagwörter: 13. Climate action, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 01 natural sciences, 0105 earth and related environmental sciences

Zugangs-URL: https://dblp.uni-trier.de/db/conf/ccis/ccis2018.html#LiSHSS18

Low-level structure feature extraction for image processing via stacked sparse denoising autoencoder

Autoren: Zunlin Fan Duyan Bi Linyuan He et al.

Quelle: Neurocomputing. 243:12-20

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

Zugangs-URL: https://dl.acm.org/doi/10.1016/j.neucom.2017.02.066
https://www.sciencedirect.com/science/article/pii/S0925231217303909
http://www.sciencedirect.com/science/article/pii/S0925231217303909
https://dblp.uni-trier.de/db/journals/ijon/ijon243.html#FanBHMGL17

Noise reduction in low-dose ct using a 3D multiscale sparse denoising autoencoder

Autoren: Frank Schebesch Mariappan S. Nadar Florin C. Ghesu et al.

Quelle: 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). :1-6

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://dblp.uni-trier.de/db/conf/mlsp/mlsp2017.html#MentlMGSHMN17
https://ieeexplore.ieee.org/document/8168176/
https://doi.org/10.1109/MLSP.2017.8168176
http://ieeexplore.ieee.org/document/8168176/

Classification method for uncertain data based on sparse denoising autoencoder neural network

Autoren: Chunying Liu Jijiang Yu Huang Yuwen

Quelle: Advances in Modelling and Analysis B. 60:210-223

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

Zugangs-URL: http://iieta.org/sites/default/files/Journals/AMA/AMA_B/60.01_13.pdf

Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder

Autoren: Juying Dai Jian Tang Faming Shao et al.

Quelle: Applied Sciences, Vol 9, Iss 13, p 2743 (2019)

Schlagwörter: IMFs, multiscale permutation entropy, stacked sparse denoising autoencoder, fault diagnosis, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-3417/9/13/2743; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/513f8bf811eb491dab82fad9ce1ea4c2

Radar HRRP recognition based on sparse denoising autoencoder and multi-layer perceptron deep model

Autoren: Zenghui Zhang Wenxian Yu Gang Xiong et al.

Quelle: 2016 Fourth International Conference on Ubiquitous Positioning, Indoor Navigation and Location Based Services (UPINLBS). :283-288

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

Zugangs-URL: https://dblp.uni-trier.de/db/conf/upinlbs/upinlbs2016.html#YanZXY16
https://ieeexplore.ieee.org/document/7809986/

Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder

Autoren: Changmiao Wang Ahmed Elazab Fucang Jia et al.

Quelle: BioMedical Engineering OnLine, Vol 17, Iss 1, Pp 1-19 (2018)

Schlagwörter: Chest screening, Computer aided diagnosis, Deep learning, Autoencoder, Receiver operating characteristic, Medical technology, R855-855.5

Dateibeschreibung: electronic resource

Relation: http://link.springer.com/article/10.1186/s12938-018-0496-2; https://doaj.org/toc/1475-925X

Zugangs-URL: https://doaj.org/article/0651ceb9a36e483794499d2f0818af09

Modified metaheuristics with stacked sparse denoising autoencoder model for cervical cancer classification

Autoren: Thavavel Vaiyapuri Haya Alaskar Liyakathunisa Syed et al.

Quelle: Computers and Electrical Engineering. 103:108292

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology