Suchergebnisse - "stacked denoising autoencoder"

Data Quality Improvement Method for Power Equipment Condition Based on Stacked Denoising Autoencoders Improved by Particle Swarm Optimization

Autoren: JI Rong, HOU Huijuan, SHENG Gehao, ZHANG Lijing, SHU Bo, JIANG Xiuchen

Quelle: Shanghai Jiaotong Daxue xuebao, Vol 59, Iss 6, Pp 780-788 (2025)

Schlagwörter: power equipment, status data, stacked denoising autoencoder, data cleaning, 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/2025/1006-2467/1006-2467-59-6-780.shtml; https://doaj.org/toc/1006-2467

Zugangs-URL: https://doaj.org/article/a9b9c95033014d53b01068f2d1ca54df

Prediction of psychophysiological perception of driver in road tunnel using particle swarm optimization improved stacked denoising autoencoder model

Autoren: Can Qin Bo Liang Jia'an Niu et al.

Quelle: Array, Vol 27, Iss , Pp 100475- (2025)

Schlagwörter: Lighting environment of road tunnel, Stacked denoising autoencoder, Particle swarm optimization, Information representation, Psychophysiological perception, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S259000562500102X; https://doaj.org/toc/2590-0056

Zugangs-URL: https://doaj.org/article/42d2b7245a534844bd1edcf6bbf34507

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

Traceability and analysis method for measurement laboratory testing data based on intelligent Internet of Things and deep belief network

Autoren: He PeiDong Li XiaoJun Shen WenQi et al.

Quelle: Journal of Intelligent Systems, Vol 33, Iss 1, Pp 6821-30 (2024)

Schlagwörter: intelligent iot, deep belief network, metrology laboratory, data traceability, abnormal analysis, stacked denoising autoencoder model, cosine similarity, gaussian bernoulli restricted boltzmann machine, cloud-management-edge-end, Science, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2191-026X

Zugangs-URL: https://doaj.org/article/b2a8183e0b4746a7aaeba8a4f7b0893c

Identification Method for Missing Cotter Pins in Catenary Systems Based on Stacked Denoising Autoencoder Network

Autoren: SHAN Chonghao

Quelle: Chengshi guidao jiaotong yanjiu, Vol 27, Iss 10, Pp 274-279 (2024)

Schlagwörter: metro, catenary, cotter pin, yolo v5 algorithm, stacked denoising autoencoder, Transportation engineering, TA1001-1280

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1007-869X

Zugangs-URL: https://doaj.org/article/1e4b6be3a39145f5af24cbf54545d3c4

Deep learning framework designed for high-performance lithium-ion batteries state monitoring

Autoren: Paul Takyi-Aninakwa Shunli Wang Guangchen Liu et al.

Quelle: Renewable and Sustainable Energy Reviews. 218:115803

Schlagwörter: Lithium-ion batteries, State of charge estimation, Secondary scale feature extraction, Long short-term memory, Deep-stacked denoising autoencoder

Zugangs-URL: https://rgu-repository.worktribe.com/output/2830428

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 for wind power frequency support based on modified stacked denoising autoencoder

Autoren: Tao Zhou Yalun Wang Hao Quan et al.

Quelle: Energy Reports, Vol 8, Iss, Pp 946-957 (2022)

Schlagwörter: Secondary frequency drop, Stepwise inertial control, Adamax optimization, Sparrow search algorithm, Modified stacked denoising autoencoder, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, 7. Clean energy, TK1-9971

Zugangs-URL: https://doaj.org/article/e2213d1e01814cd191a81dbf57319281

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

A Sample-Efficient OPF Learning Method Based on Annealing Knowledge Distillation

Autoren: Ziheng Dong Kai Hou Zeyu Liu et al.

Quelle: IEEE Access, Vol 10, Pp 99724-99733 (2022)

Schlagwörter: sample efficiency, 0202 electrical engineering, electronic engineering, information engineering, deep learning, focal loss function, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, annealing knowledge distillation, Optimal power flow, stacked denoising autoencoder, TK1-9971

Zugangs-URL: https://doaj.org/article/b142210c79ff426d848fe44ba55f16c4

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

Social Network Embedding Method Combining Node Attributes and Loop-Free Path

Autoren: WANG Benyu, GU Yijun, PENG Shufan

Quelle: Jisuanji kexue yu tansuo, Vol 16, Iss 11, Pp 2505-2518 (2022)

Schlagwörter: network embedding, social network, node attributes, loop-free path, stacked denoising autoencoder, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: http://fcst.ceaj.org/fileup/1673-9418/PDF/2104075.pdf; https://doaj.org/toc/1673-9418

Zugangs-URL: https://doaj.org/article/ad5e6a0570124a5aa7575f193c353cac

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

PRPI-SC: an ensemble deep learning model for predicting plant lncRNA-protein interactions

Autoren: Haoran Zhou Jael Sanyanda Wekesa Yushi Luan et al.

Quelle: BMC Bioinformatics, Vol 22, Iss S3, Pp 1-15 (2021)

Schlagwörter: lncRNA-protein, k-Mer, Stacked denoising autoencoder, Convolutional neural network, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1471-2105

Zugangs-URL: https://doaj.org/article/f9627aec7351486a98db5eef487bf704

Improved deep reinforcement learning for car-following decision-making

Autoren: Yang, Xiaoxue Zou, Yajie Zhang, Hao et al.

Quelle: Physica A. 624

Schlagwörter: Car-following model, Deep reinforcement learning, Driving behavior imitation, Stacked denoising autoencoders, Behavioral research, Decision making, Learning systems, Recurrent neural networks, Auto encoders, Car-following modeling, De-noising, Deterministics, Driving behaviour, Policy gradient, Reinforcement learnings, Stacked denoising autoencoder, Reinforcement learning

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-65535

Feature-Alignment-Based Cross-Platform Question Answering Expert Recommendation

Autoren: Bin Tang Qinqin Gao Xin Cui et al.

Quelle: Mathematics, Vol 11, Iss 9, p 2174 (2023)

Schlagwörter: community question answering, expert recommendation, cross-domain recommendation, stacked denoising autoencoder, feature alignment, Mathematics, QA1-939

Relation: https://www.mdpi.com/2227-7390/11/9/2174; https://doaj.org/toc/2227-7390; https://doaj.org/article/b0d685528ade41e1ac629af49aa9234a

Verfügbarkeit: https://doi.org/10.3390/math11092174
https://doaj.org/article/b0d685528ade41e1ac629af49aa9234a

Data augmentation versus noise compensation for x- vector speaker recognition systems in noisy environments

Autoren: Mohammadamini, Mohammad Matrouf, Driss Laboratoire Informatique d'Avignon (LIA) et al.

Weitere Verfasser: Mohammadamini, Mohammad Matrouf, Driss Laboratoire Informatique d'Avignon (LIA) et al.

Quelle: EUSIPCO ; https://hal.science/hal-02882342 ; EUSIPCO, Jan 2021, Amsterdam, Netherlands

Schlagwörter: speaker recognition, x-vector, data augmentation, noise compensation, denoising autoencoder, deep stacked denoising autoencoder, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]

Geographisches Schlagwort: Amsterdam, Netherlands

Relation: info:eu-repo/semantics/altIdentifier/arxiv/2006.15903; ARXIV: 2006.15903

Verfügbarkeit: https://hal.science/hal-02882342
https://hal.science/hal-02882342v1/document
https://hal.science/hal-02882342v1/file/Data%20augmentation%20versus%20noise%20compensation%20for%20xvector%20speaker%20recognition%20systems%20in%20noisy.pdf

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: Tensor-Train, 0202 electrical engineering, electronic engineering, information engineering, deep learning, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Compressive sensing, stacked denoising autoencoder, TK1-9971

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08663363.pdf
https://doaj.org/article/7eb7a8bf675a46849f2b1b0a47dd7467
https://ieeexplore.ieee.org/document/8663363/
https://dblp.uni-trier.de/db/journals/access/access7.html#ZouY19

Stacked Denoising Autoencoder With Density-Grid Based Clustering Method for Detecting Outlier of Wind Turbine Components

Autoren: Zexian Sun Hexu Sun

Quelle: IEEE Access, Vol 7, Pp 13078-13091 (2019)

Schlagwörter: Density-grid based clustering, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, outlier detection, unsupervised learning, 7. Clean energy, stacked denoising autoencoder, TK1-9971

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08620681.pdf
https://doaj.org/article/7cff3f08acbe49f0ac8c2422e38bde0b
https://dblp.uni-trier.de/db/journals/access/access7.html#SunS19
https://ieeexplore.ieee.org/document/8620681