Suchergebnisse - "stacked denoising autoencoder(sdae)"

Convergence Technology Opportunity Discovery for Firms Based on Technology Portfolio Using the Stacked Denoising AutoEncoder (SDAE)

Autoren: Deuksin Kwon So Young Sohn

Quelle: IEEE Transactions on Engineering Management. 71:1804-1818

Schlagwörter: 0502 economics and business, 05 social sciences

High-Resolution Remote Sensing Image Zero-Watermarking Algorithm Based on Blockchain and SDAE

Autoren: Dingjie Xu Na Ren Changqing Zhu

Quelle: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 323-339 (2024)

Schlagwörter: InterPlanetary file system (IPFS), Ocean engineering, Blockchain, high-resolution remote sensing images (HRRS), QC801-809, Geophysics. Cosmic physics, stacked denoising autoencoder (SDAE), 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, copyright protection, 02 engineering and technology, TC1501-1800, Hyperledger Fabric

Zugangs-URL: https://doaj.org/article/23bb2a27515440dbac1fedfec1aa0a53

Physics-Informed SDAE-Based Denoising Model for High-Impedance Fault Detection

Autoren: Jianxin Lin Xuchang Wang Huaiyuan Wang

Quelle: Processes ; Volume 13 ; Issue 11 ; Pages: 3673

Schlagwörter: high-impedance fault (HIF), distribution network, denoise, stacked denoising autoencoder (SDAE), attention mechanism, energy proportion guidance

Geographisches Schlagwort: agris

Dateibeschreibung: application/pdf

Relation: Energy Systems; https://dx.doi.org/10.3390/pr13113673

Verfügbarkeit: https://doi.org/10.3390/pr13113673

An Analog Circuit Fault Diagnosis Method Incorporating Multi-Objective Selection of Measurement Nodes

Autoren: Yating Chang Xianglian Xu Kaitian Deng et al.

Quelle: Electronics ; Volume 14 ; Issue 8 ; Pages: 1528

Schlagwörter: analog circuit, fault diagnosis, measurement node selection, stacked denoising autoencoder (SDAE), multiple objective beluga whale optimization (MOBWO), adaptive particle swarm optimization (APSO)

Dateibeschreibung: application/pdf

Relation: Power Electronics; https://dx.doi.org/10.3390/electronics14081528

Verfügbarkeit: https://doi.org/10.3390/electronics14081528

Stepwise Inertial Intelligent Control of Wind Power for Frequency Regulation Based on Stacked Denoising Autoencoder and Deep Neural Network

Autoren: WANG Yalun, ZHOU Tao, CHEN Zhong, WANG Yi, QUAN Hao

Quelle: Shanghai Jiaotong Daxue xuebao, Vol 57, Iss 11, Pp 1477-1491 (2023)

Schlagwörter: stepwise inertial control (sic), secondary frequency drop (sfd), sparrow search algorithm (ssa), stacked denoising autoencoder (sdae), deep neural network (dnn), Engineering (General). Civil engineering (General), TA1-2040, Chemical engineering, TP155-156, Naval architecture. Shipbuilding. Marine engineering, VM1-989

Dateibeschreibung: electronic resource

Relation: https://xuebao.sjtu.edu.cn/article/2023/1006-2467/1006-2467-57-11-1477.shtml; https://doaj.org/toc/1006-2467

Zugangs-URL: https://doaj.org/article/5703db5dde17408889132f952768f68b

Blink talk: A machine learning-based method for women safety using EEG and eye blink signals

Autoren: K. Shanmuga Priya S. Vasanthi Nithyanandhan R et al.

Quelle: Measurement: Sensors, Vol 28, Iss , Pp 100810- (2023)

Schlagwörter: Women safety, Machine learning, Stacked denoising autoencoder (SDAE), Multiclass support vector machine (MSVM), EEG, Discrete wavelet transform, Electric apparatus and materials. Electric circuits. Electric networks, TK452-454.4

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2665917423001460; https://doaj.org/toc/2665-9174

Zugangs-URL: https://doaj.org/article/6d1e72d7592c4f238b048b9f82002cd7

An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder

Autoren: Shenghan Zhou Zhao He Xu Chen et al.

Quelle: Aerospace, Vol 11, Iss 5, p 393 (2024)

Schlagwörter: anomaly detection, wavelet decomposition, stacked denoising autoencoder (SDAE), UAV flight data, Motor vehicles. Aeronautics. Astronautics, TL1-4050

Relation: https://www.mdpi.com/2226-4310/11/5/393; https://doaj.org/toc/2226-4310; https://doaj.org/article/b2f8b29b20824d4b8e1f34bc50790953

Verfügbarkeit: https://doi.org/10.3390/aerospace11050393
https://doaj.org/article/b2f8b29b20824d4b8e1f34bc50790953

Automatic Classification of CAD ECG Signals With SDAE and Bidirectional Long Short-Term Network

Autoren: Eric Ke Wang Xun Zhang Leyun Pan

Quelle: IEEE Access, Vol 7, Pp 182873-182880 (2019)

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, bidirectional long short-term term network (Bi-LSTM), stacked denoising autoencoder (SDAE), 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, cost-sensitive learning, denoise, Arrythmia, electrocardiogram (ECG), TK1-9971

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08807206.pdf
https://doaj.org/article/ac9888bad691427cbb2f66a0271862af
https://ieeexplore.ieee.org/document/8807206
https://dblp.uni-trier.de/db/journals/access/access7.html#WangZP19
https://doaj.org/article/ac9888bad691427cbb2f66a0271862af
https://doi.org/10.1109/ACCESS.2019.2936525

Two-Stream Deep Architecture for Hyperspectral Image Classification

Autoren: Siyuan Hao Wei Wang Yuanxin Ye et al.

Quelle: IEEE Transactions on Geoscience and Remote Sensing. 56:2349-2361

Schlagwörter: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, Class-specific fusion, convolutional neural networks (CNNs), deep learning, Feature extraction, hyperspectral image (HSI) classification, Hyperspectral imaging, Machine learning, remote sensing, stacked denoising autoencoder (SdAE), Training, two-stream architecture, Electrical and Electronic Engineering, Earth and Planetary Sciences (all), 02 engineering and technology

Dateibeschreibung: application/pdf

Zugangs-URL: https://dblp.uni-trier.de/db/journals/tgrs/tgrs56.html#HaoWYNB18
https://ui.adsabs.harvard.edu/abs/2018ITGRS..56.2349H/abstract
http://ieeexplore.ieee.org/document/8240968/
https://ieeexplore.ieee.org/document/8240968/

An Intelligent Recommendation System Utilizing a Hybrid Deep Learning Method.

Autoren: Tholib, Abu Widiyaningtyas, Triyanna Prasetya, Didik Dwi

Quelle: Engineering, Technology & Applied Science Research; Aug2025, Vol. 15 Issue 4, p25971-25977, 7p

Schlagwörter: RECOMMENDER systems, LONG short-term memory, MATRIX decomposition, DATA quality, USER experience, ENSEMBLE learning, AUTOENCODERS

DC Disturbance Classification Method Based on Compressed Sensing and Encoder.

Autoren: Yu, Huanan Zhang, Xiang Wang, Jian

Quelle: Energy Engineering; 2025, Vol. 122 Issue 12, p5055-5071, 17p

Forecasting regional carbon prices in china with a hybrid model based on quadratic decomposition and comprehensive feature screening.

Autoren: Yi, Yaoyang

Quelle: PLoS ONE; 6/30/2025, Vol. 20 Issue 6, p1-34, 34p

Schlagwörter: LONG short-term memory, HILBERT-Huang transform, FLEXIBLE work arrangements, STANDARD deviations, CARBON offsetting, CARBON pricing

A Novel Aerosol Optical Depth Retrieval Method Based on SDAE from Himawari-8/AHI Next-Generation Geostationary Satellite in Hubei Province.

Autoren: Deng, Shiquan Bai, Ting Chen, Zhe et al.

Quelle: Remote Sensing; Apr2025, Vol. 17 Issue 8, p1396, 18p

Schlagwörter: EXTREME learning machines, ATMOSPHERIC aerosols, GEOSTATIONARY satellites, AUTOENCODERS, SUPPORT vector machines

An adaptive hierarchical hybrid kernel ELM optimized by aquila optimizer algorithm for bearing fault diagnosis.

Autoren: Yan, Hao Shang, Liangliang Chen, Wan et al.

Quelle: Scientific Reports; 4/8/2025, Vol. 15 Issue 1, p1-26, 26p

Schlagwörter: EXTREME learning machines, MACHINE learning, ARTIFICIAL intelligence, FAULT diagnosis, AUTOENCODERS

Analysis and Review of Extraordinary Machine Learning Approaches

Autoren: Tyagi, Nitin Gupta, Sandeep Pratap Srivastava, Arun et al.

Quelle: International Journal of Engineering and Technology; Vol. 7 No. 4.39 (2018): Special Issue 39; 915-920 ; 2227-524X ; 10.14419/ijet.v7i4.39

Schlagwörter: Random Recursive Support Vector Machine (R2SVM), Stacked Sparse autoencoder (SSAE), Stacked Denoising Autoencoder (SDAE), Convolutional Neural Network (CNN), Support Vector Machine (SVM)

Dateibeschreibung: application/pdf

Relation: http://www.sciencepubco.com/index.php/IJET/article/view/27728/14398; http://www.sciencepubco.com/index.php/IJET/article/view/27728

Verfügbarkeit: http://www.sciencepubco.com/index.php/IJET/article/view/27728
https://doi.org/10.14419/ijet.v7i4.39.27728

Accurate estimation of state of charge and state of health of electric vehicle power batteries based on dynamic channel pruning informer-stacked denoising autoencoder model.

Autoren: Peng, Ziran Wang, Shunhao Zhang, Shengchun et al.

Quelle: Electrical Engineering. Jan2026, Vol. 108 Issue 1, p1-19. 19p.

Research on Fault Prediction of Nuclear Safety-Class Signal Conditioning Module Based on Improved GRU.

Autoren: Chen, Zhi Dai, Miaoxin Liu, Jie et al.

Quelle: Energies (19961073); Aug2024, Vol. 17 Issue 16, p4063, 16p

Schlagwörter: DIGITAL control systems, STANDARD deviations, RECURRENT neural networks, OPTIMIZATION algorithms, PREDICTION models, FEATURE extraction

An Improved Transformer Model for Remaining Useful Life Prediction of Lithium-Ion Batteries under Random Charging and Discharging.

Autoren: Zhang, Wenwen Jia, Jianfang Pang, Xiaoqiong et al.

Quelle: Electronics (2079-9292); Apr2024, Vol. 13 Issue 8, p1423, 20p

Schlagwörter: REMAINING useful life, TRANSFORMER models, ARTIFICIAL neural networks, DEEP learning, LITHIUM-ion batteries, ARTIFICIAL intelligence, ELECTRIC charge

Hybrid Deep Learning Model for Accurate Short-Term Electricity Price Forecasting

Autoren: Moradzadeh, A. Mouhammadpourfard, M. Weng, Y. et al.

Index Begriffe: Conference Paper

URL: http://hdl.handle.net/20.500.11937/97501

Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model.

Autoren: Obayya, Marwa Arasi, Munya A. Almalki, Nabil Sharaf et al.

Quelle: Cancers; Oct2023, Vol. 15 Issue 20, p5016, 17p

Schlagwörter: DEEP learning, DIGITAL image processing, RESEARCH evaluation, EARLY detection of cancer, ARTIFICIAL intelligence, INTERNET of things, SKIN tumors, DESCRIPTIVE statistics, RESEARCH funding, SENSITIVITY & specificity (Statistics), DATA analysis software, ALGORITHMS