Suchergebnisse - stack autoencoder (SAE)

A new Stack Autoencoder: Neighbouring Sample Envelope Embedded Stack Autoencoder Ensemble Model

Autoren: Zhou, Chuanyan Ma, Jie Li, Fan et al.

Index Begriffe: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/2210.14956

Power Load Forecasting System of Iron and Steel Enterprises Based on Deep Kernel–Multiple Kernel Joint Learning.

Autoren: Zhang, Yan Wang, Junsheng Sun, Jie et al.

Quelle: Processes; Feb2025, Vol. 13 Issue 2, p584, 9p

Schlagwörter: DEEP learning, AUTOENCODERS, TIME series analysis, FORECASTING, STEEL

A Hybrid Forecasting System Based on Comprehensive Feature Selection and Intelligent Optimization for Stock Price Index Forecasting.

Autoren: He, Xuecheng Wang, Jujie

Quelle: Mathematics (2227-7390); Dec2024, Vol. 12 Issue 23, p3778, 25p

Schlagwörter: STOCK price forecasting, STOCK price indexes, STANDARD deviations, KRIGING, AUTOENCODERS

Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery

Autoren: Eftychios Protopapadakis Anastasios Doulamis Nikolaos Doulamis et al.

Quelle: Remote Sensing, Vol 13, Iss 3, p 371 (2021)

Schlagwörter: semi-supervised learning, deep learning, stack autoencoders, building detection, remote sensing, semantic segmentation, Science

Relation: https://www.mdpi.com/2072-4292/13/3/371; https://doaj.org/toc/2072-4292; https://doaj.org/article/cd15bfe4145a455bbb00617947fb93f9

Verfügbarkeit: https://doi.org/10.3390/rs13030371
https://doaj.org/article/cd15bfe4145a455bbb00617947fb93f9

A Hybrid Forecasting System Based on Comprehensive Feature Selection and Intelligent Optimization for Stock Price Index Forecasting

Autoren: Xuecheng He Jujie Wang

Quelle: Mathematics, Vol 12, Iss 23, p 3778 (2024)

Schlagwörter: stock index forecasting, recursive feature elimination with cross-validation, stack autoencoder, interval prediction, Mathematics, QA1-939

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2227-7390/12/23/3778; https://doaj.org/toc/2227-7390

Zugangs-URL: https://doaj.org/article/c11685e85fda44d78e1f29f5d6421bb6

Short-Term Streamflow Forecasting Using the Feature-Enhanced Regression Model

Autoren: Bai, Yun Bezak, Nejc Lebar, Klaudija et al.

Quelle: Water Resources Management. 33:4783-4797

Schlagwörter: dolgotrajni kratkoročni spomin, hidrotehnika, forecast, 0208 environmental biotechnology, 0207 environmental engineering, stack autoencoder, 02 engineering and technology, feature enhanced, daily reservoir inflow, napovedi, 6. Clean water, avtoenkoder, 13. Climate action, dnevni vtok v rezervoar, vhodna spremenljivka, long short-term memory

Dateibeschreibung: application/pdf; text/url

Zugangs-URL: https://repozitorij.uni-lj.si/Dokument.php?id=126546&dn=
https://ideas.repec.org/a/spr/waterr/v33y2019i14d10.1007_s11269-019-02399-1.html
https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002218111023615
https://link.springer.com/article/10.1007/s11269-019-02399-1

基于内积矩阵及卷积自编码器的 螺栓松动状态监测. (Chinese)

Alternate Title: BOLT LOOSENING STATE MONITORING BASED ON INNER PRODUCT MATRIX AND CONVOLUTIONAL AUTOENCODER. (English)

Autoren: 张敏照 王 乐 田鑫海

Quelle: Engineering Mechanics / Gongcheng Lixue; 2022, Vol. 39 Issue 12, p222-231, 10p

Rolling Bearing Fault Diagnosis Based on Stacked Autoencoder Network with Dynamic Learning Rate.

Autoren: Pan, Hong Tang, Wei Xu, Jin-Jun et al.

Quelle: Advances in Materials Science & Engineering; 12/28/2020, p1-12, 12p

Schlagwörter: ROLLER bearings, FAULT diagnosis, SAMPLE size (Statistics), SAMPLING (Process), ELECTRONIC data processing

基于 SAE-ConvLSTM 深度学习模型的 多站城轨短时客流预测.

Alternate Title: Prediction of short-time passenger flow on multi-station urban rail based on SAE-ConvLSTM deep learning model.

Autoren: 李 莎 王秋雯 陈彦如 et al.

Quelle: Application Research of Computers / Jisuanji Yingyong Yanjiu. Jul2022, Vol. 39 Issue 7, p2025-2031. 7p.

Schlagwörter: *DEEP learning, *PARTICLE swarm optimization, *STANDARD deviations, *CONVOLUTIONAL neural networks, *SIMULATED annealing, *FEATURE extraction

Digital Twin Inspired Intelligent Bearing Fault Diagnosis Method Based on Adaptive Correlation Filtering and Improved SAE Classification Model

Autoren: Wenhua Zhang Zhifeng Liu Zhiqiang Liao

Quelle: Mathematical Problems in Engineering. 2022:1-17

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 01 natural sciences, 0104 chemical sciences

Dateibeschreibung: text/xhtml

SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG

Autoren: Xiaofen Xing Zhenqi Li Tianyuan Xu et al.

Quelle: Frontiers in Neurorobotics, Vol 13 (2019)

Schlagwörter: EEG, emotion recognition, neural network, Stack AutoEncoder, LSTM, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

Dateibeschreibung: electronic resource

Relation: https://www.frontiersin.org/article/10.3389/fnbot.2019.00037/full; https://doaj.org/toc/1662-5218

Zugangs-URL: https://doaj.org/article/7d468ff791c647fa9967ff5afb66b408

A Sintering State Recognition Framework to Integrate Prior Knowledge and Hidden Information Considering Class Imbalance.

Autoren: Wang, Dingxiang Zhang, Xiaogang Chen, Hua et al.

Quelle: IEEE Transactions on Industrial Electronics; Aug2021, Vol. 68 Issue 8, p7400-7411, 12p

Schlagwörter: PRIOR learning, SINTERING, FEATURE extraction, KERNEL functions

Intrusion Detection Model Based on SAE and BALSTM

Autoren: Fan Jiajia Zhang Junfeng Xu Jiangfeng

Quelle: 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). :1192-1197

Zugangs-URL: https://ieeexplore.ieee.org/abstract/document/9498102

Deep Feature Learning Architectures for Daily Reservoir Inflow Forecasting.

Autoren: Li, Chuan Bai, Yun Zeng, Bo

Quelle: Water Resources Management; Nov2016, Vol. 30 Issue 14, p5145-5161, 17p

Schlagwörter: RESERVOIRS, BODIES of water, HYDRAULIC structures, ARTIFICIAL neural networks, MATHEMATICAL models

Short-term streamflow forecasting using the feature-enhanced regression model

Autoren: Bai, Yun Bezak, Nejc Lebar, Klaudija et al.

Quelle: Water resources management, vol. 33, no. nov., pp. 4783-4797, 2019. ; ISSN: 0920-4741

Schlagwörter: long short-term memory, stack autoencoder, feature enhanced, daily reservoir inflow, forecast, hidrotehnika, dolgotrajni kratkoročni spomin, avtoenkoder, vhodna spremenljivka, dnevni vtok v rezervoar, napovedi, info:eu-repo/classification/udc/626/627

Dateibeschreibung: application/pdf; text/url

Relation: info:eu-repo/grantAgreement/ARRS//P2-0180; https://plus.cobiss.net/cobiss/si/sl/bib/8969057

Verfügbarkeit: https://repozitorij.uni-lj.si/IzpisGradiva.php?id=114378
https://repozitorij.uni-lj.si/Dokument.php?id=126546&dn=
https://repozitorij.uni-lj.si/Dokument.php?id=126623&dn=
https://plus.cobiss.net/cobiss/si/sl/bib/8969057
https://hdl.handle.net/20.500.12556/RUL-114378

Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery.

Autoren: Protopapadakis, Eftychios Doulamis, Anastasios Doulamis, Nikolaos et al.

Quelle: Remote Sensing; Feb2021, Vol. 13 Issue 3, p371, 1p

Schlagwörter: REMOTE sensing, OPTICAL radar, LIDAR, INFRARED imaging, SUPERVISED learning, ESTIMATION theory, SECURE Sockets Layer (Computer network protocol)

Geografische Kategorien: GERMANY

RDCSAE-RKRVFLN: A unified deep learning framework for robust and accurate DOA estimation.

Autoren: Raiguru, Priyadarshini Swain, Bhanja Kishor Rout, Susanta Kumar et al.

Quelle: Applied Soft Computing. Sep2024, Vol. 162, pN.PAG-N.PAG. 1p.

Schlagwörter: Convolutional neural networks, Standard deviations, Supervised learning, Direction of arrival estimation, Field programmable gate arrays, Deep learning

SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG.

Autoren: Xing, Xiaofen Li, Zhenqi Xu, Tianyuan et al.

Quelle: Frontiers in Neurorobotics; 6/12/2019, pN.PAG-N.PAG, 14p

Schlagwörter: BRAIN-computer interfaces, EMOTION recognition, EMOTIONAL conditioning, ELECTROENCEPHALOGRAPHY, SIGNAL classification, RECURRENT neural networks, SHORT-term memory

A Deep-Learning-Based Health Indicator Constructor Using Kullback–Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures.

Autoren: Nguyen, Tuan-Khai Ahmad, Zahoor Kim, Jong-Myon

Quelle: Sensors (14248220); May2022, Vol. 22 Issue 10, p3687-3687, 14p

Schlagwörter: DETERIORATION of concrete, ACOUSTIC emission, CONCRETE beams, CONCRETE, REMAINING useful life

Damage characterization using CNN and SAE of broadband Lamb waves.

Autoren: Gao, Fei Hua, Jiadong

Quelle: Ultrasonics. Feb2022, Vol. 119, pN.PAG-N.PAG. 1p.

Schlagwörter: *LAMB waves, *DEEP learning, *FEATURE extraction, *STRUCTURAL health monitoring, *CONVOLUTIONAL neural networks, *MACHINE learning