Suchergebnisse - Stacked denoising sparse autoencoder~

Network Intrusion Detection Method Based on Stacked Denoising Sparse Autoencoder and Extreme Learning Machine

Autoren: Guoling Zhang Jiaxing He Rui Li et al.

Quelle: 2020 2nd International Conference on Information Technology and Computer Application (ITCA). :194-199

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

Zugangs-URL: http://xplorestaging.ieee.org/ielx7/9421350/9421928/09421975.pdf?arnumber=9421975
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=9421975

Classification of Thyroid Nodules with Stacked Denoising Sparse Autoencoder

Autoren: Zexin Li Kaiji Yang Lili Zhang et al.

Quelle: Int J Endocrinol
International Journal of Endocrinology, Vol 2020 (2020)

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, 13. Climate action, RC648-665, Diseases of the endocrine glands. Clinical endocrinology, Research Article, 3. Good health

Dateibeschreibung: text/xhtml

Zugangs-URL: https://downloads.hindawi.com/journals/ije/2020/9015713.pdf
https://pubmed.ncbi.nlm.nih.gov/33488708
https://doaj.org/article/85ee260c795a4cef89458a6f89338a3c
https://pubmed.ncbi.nlm.nih.gov/33488708/
https://downloads.hindawi.com/journals/ije/2020/9015713.pdf
http://downloads.hindawi.com/journals/ije/2020/9015713.pdf
https://europepmc.org/article/MED/33488708
https://www.hindawi.com/journals/ije/2020/9015713/
https://www.ncbi.nlm.nih.gov/pubmed/33488708

Stacked Denoising Sparse Autoencoder-Based Fuzzy Rule Classifiers

Autoren: Rahul Kumar Sevakula Nishchal K. Verma

Quelle: Studies in Computational Intelligence ISBN: 9789811950728

Unsupervised Feature Learning With Distributed Stacked Denoising Sparse Autoencoder for Abnormal Behavior Detection Using Apache Spark

Autoren: Naila Marir Huiqiang Wang Guangsheng Feng

Quelle: 2019 IEEE 2nd International Conference on Knowledge Innovation and Invention (ICKII). :473-476

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

Zugangs-URL: https://ieeexplore.ieee.org/document/9042645/
https://dblp.uni-trier.de/db/conf/ickii/ickii2019.html#MarirWF19
http://dblp.uni-trier.de/db/conf/ickii/ickii2019.html#MarirWF19

Radar Target Recognition Based on Stacked Denoising Sparse Autoencoder

Autoren: Zhao Feixiang Liu Yongxiang Huo Kai

Quelle: Leida xuebao, Vol 6, Iss 2, Pp 149-156 (2017)

Schlagwörter: Target recognition, Deep learning, Stacked denoising sparse autoencoder, Electricity and magnetism, QC501-766

Dateibeschreibung: electronic resource

Relation: http://radars.ie.ac.cn/CN/html/R16151.htm; https://doaj.org/toc/2095-283X

Zugangs-URL: https://doaj.org/article/1c2d823be05147f8a13145c2075c9645

Faulty elements diagnosis of phased array antennas using a generative adversarial learning-based stacked denoising sparse autoencoder

Autoren: Da Lin Qi Wan

Quelle: Journal of Electromagnetic Waves and Applications. 33:382-407

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

Zugangs-URL: https://www.tandfonline.com/doi/full/10.1080/09205071.2018.1553689

A Stacked Denoising Sparse Autoencoder Based Fault Early Warning Method for Feedwater Heater Performance Degradation

Autoren: Xingshuo Li Jinfu Liu Jiajia Li et al.

Quelle: Energies, Vol 13, Iss 22, p 6061 (2020)

Schlagwörter: stacked denoising sparse autoencoder, feedwater heater, fault early warning, frequent pattern model, feature reduction, Technology

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/1996-1073/13/22/6061; https://doaj.org/toc/1996-1073

Zugangs-URL: https://doaj.org/article/b7a25ca30337465a89c648d213539593

Research of stacked denoising sparse autoencoder

Autoren: Jian Zhang Shifei Ding Nan Zhang et al.

Quelle: Neural Computing and Applications. 30:2083-2100

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

Zugangs-URL: https://link.springer.com/article/10.1007/s00521-016-2790-x
https://dblp.uni-trier.de/db/journals/nca/nca30.html#MengD0018

A novel modulation classification method in cognitive radios using higher-order cumulants and denoising stacked sparse autoencoder

Autoren: Xu Zhu Takeo Fujii

Quelle: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). :1-5

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

M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders

Autoren: Conghui Lu Peng Chen Hua Zhong et al.

Quelle: Electronics; Volume 11; Issue 8; Pages: 1233

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, improved M-ary phase position shift keying (MPPSK), deep learning, autoencoders, demodulation

Dateibeschreibung: application/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

A modulation classification method in cognitive radios system using stacked denoising sparse autoencoder

Autoren: Takeo Fujii Xu Zhu

Quelle: 2017 IEEE Radio and Wireless Symposium (RWS). :218-220

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

Zugangs-URL: http://ieeexplore.ieee.org/document/7885992/
https://doi.org/10.1109/RWS.2017.7885992
http://doi.org/10.1109/RWS.2017.7885992
https://dblp.uni-trier.de/db/conf/rws/rws2017.html#ZhuF17

Radar HRRP target recognition based on stacked denosing sparse autoencoder

Autoren: Guangxing Tai Yanhua Wang Yang Li et al.

Quelle: The Journal of Engineering (2019)

Schlagwörter: learning (artificial intelligence), feature extraction, radar target recognition, neural nets, signal denoising, stacked denosing sparse autoencoder, end-to-end radar high-resolution range profile recognition method, sparse autoencoders, layer-by-layer pre-training, pre-training results, two-step training process, radar hrrp target recognition, Engineering (General). Civil engineering (General), TA1-2040

Dateibeschreibung: electronic resource

Relation: https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0741; https://doaj.org/toc/2051-3305

Zugangs-URL: https://doaj.org/article/86278ba9bb5d4cb3bc3938d6ff53d2ed

M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders.

Autoren: Lu, Conghui Chen, Peng Zhong, Hua et al.

Quelle: Electronics (2079-9292); Apr2022, Vol. 11 Issue 8, p1233-1233, 12p

Schlagwörter: PHASE shift keying, DEMODULATION, PHASE detectors, RANDOM noise theory, IMAGE denoising

Multimodal cloud resources utilization forecasting using a Bidirectional Gated Recurrent Unit predictor based on a power efficient Stacked denoising Autoencoders

Autoren: Hamzaoui Ikhlasse Duthil Benjamin Courboulay Vincent et al.

Quelle: Alexandria Engineering Journal, Vol 61, Iss 12, Pp 11565-11577 (2022)

Schlagwörter: Multimodal instances prediction, Stacked Denoising Auto Encoders, Bidirectional Gated Recurrent Unit, Time series, Power efficiency, Engineering (General). Civil engineering (General), TA1-2040

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S1110016822003295; https://doaj.org/toc/1110-0168

Zugangs-URL: https://doaj.org/article/d6a38b36e5104493bdf044e0bfa7d2ed

Research of stacked denoising sparse autoencoder.

Autoren: Meng, Lingheng Ding, Shifei Zhang, Nan et al.

Quelle: Neural Computing & Applications; Oct2018, Vol. 30 Issue 7, p2083-2100, 18p

Schlagwörter: SIGNAL denoising, ARTIFICIAL intelligence, MACHINE learning, DEEP learning, ENCODING

Faulty elements diagnosis of phased array antennas using a generative adversarial learning-based stacked denoising sparse autoencoder.

Autoren: Lin, Da Wan, Qi

Quelle: Journal of Electromagnetic Waves & Applications; Feb2019, Vol. 33 Issue 3, p382-407, 26p, 4 Black and White Photographs, 7 Diagrams, 13 Graphs

Schlagwörter: ANTENNA radiation patterns, PHASED array antennas, SIGNAL denoising, WIRELESS communications, BANDWIDTHS

Multimodal cloud resources utilization forecasting using a Bidirectional Gated Recurrent Unit predictor based on a power efficient Stacked denoising Autoencoders.

Autoren: Ikhlasse, Hamzaoui Benjamin, Duthil Vincent, Courboulay et al.

Quelle: Alexandria Engineering Journal. Dec2022, Vol. 61 Issue 12, p11565-11577. 13p.

Schlagwörter: Deep learning, Forecasting, Time series analysis

Firma/Körperschaft: Amazon Web Services Inc.

Deep Learning Approach Based on Tensor-Train for Sparse Signal Recovery

Autoren: Cong Zou Fang Yang

Quelle: IEEE Access, Vol 7, Pp 34753-34761 (2019)

Schlagwörter: Compressive sensing, deep learning, Tensor-Train, stacked denoising autoencoder, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Dateibeschreibung: electronic resource

Relation: https://ieeexplore.ieee.org/document/8663363/; https://doaj.org/toc/2169-3536

Zugangs-URL: https://doaj.org/article/7eb7a8bf675a46849f2b1b0a47dd7467

Classification of Thyroid Nodules with Stacked Denoising Sparse Autoencoder.

Autoren: Li, Zexin Yang, Kaiji Zhang, Lili et al.

Quelle: International Journal of Endocrinology; 12/7/2020, p1-8, 8p

Schlagwörter: THYROID nodules, GENE expression, CLASSIFICATION