Suchergebnisse - stacked sparse autoencoder data construction*

Research on Deep Adaptive Clustering Method Based on Stacked Sparse Autoencoders for Concrete Truck Mixers Driving Conditions

Autoren: Ying Huang Fachao Jiang Haiming Xie

Quelle: World Electric Vehicle Journal, Vol 16, Iss 10, p 581 (2025)

Schlagwörter: concrete truck mixers, stacked sparse autoencoder, deep features extraction, adaptive clustering algorithm, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Transportation engineering, TA1001-1280

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2032-6653/16/10/581; https://doaj.org/toc/2032-6653

Zugangs-URL: https://doaj.org/article/438be03da83f4e4b8087e7267170ff5e

An Innovative Study for Tool Wear Prediction Based on Stacked Sparse Autoencoder and Ensemble Learning Strategy.

Autoren: He, Zhaopeng Shi, Tielin Chen, Xu

Quelle: Sensors (14248220); Apr2025, Vol. 25 Issue 8, p2391, 14p

Schlagwörter: ARTIFICIAL neural networks, MACHINE learning, ENSEMBLE learning, NUMERICAL control of machine tools, AUTOENCODERS, DEEP learning

GRU‐based stacked sparse autoencoder with attention mechanism for process monitoring.

Autoren: Miao, Zengdi Wu, Ping Wu, Zhenquan et al.

Quelle: Canadian Journal of Chemical Engineering; Aug2025, Vol. 103 Issue 8, p3767-3785, 19p

Schlagwörter: RECURRENT neural networks, AUTOENCODERS, BLAST furnaces, MANUFACTURING processes, INDUSTRIAL safety, DEEP learning

Intelligent evaluation method for identifying favorable shale oil areas based on improved stacked sparse autoencoder.

Autoren: Rui Xu Tie Yan Shihui Sun et al.

Quelle: Oil Shale; 2025, Vol. 42 Issue 1, p79-114, 36p

Schlagwörter: SHALE oils, SUPERVISED learning, PETROLEUM prospecting, AUTOENCODERS, MACHINE learning

Web-S4AE: a semi-supervised stacked sparse autoencoder model for web robot detection.

Autoren: Jagat, Rikhi Ram Sisodia, Dilip Singh Singh, Pradeep

Quelle: Neural Computing & Applications; Aug2023, Vol. 35 Issue 24, p17883-17898, 16p

Schlagwörter: BLOGS, ROBOTS, DEEP learning, COMPUTER software, RANDOM forest algorithms, DECISION trees, SUPERVISED learning

Stacked sparse autoencoder with PCA and SVM for data-based line trip fault diagnosis in power systems.

Autoren: Wang, Yixing Liu, Meiqin Bao, Zhejing et al.

Quelle: Neural Computing & Applications. Oct2019, Vol. 31 Issue 10, p6719-6731. 13p.

Schlagwörter: *FAULT diagnosis, *DEEP learning, *MULTIPLE correspondence analysis (Statistics), *SUPPORT vector machines, *DATA mining, *ELECTRIC lines

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network.

Autoren: Zhou, Juan Zhang, Shun Wang, Peng

Quelle: Processes; Apr2024, Vol. 12 Issue 4, p816, 19p

Schlagwörter: CAPSULE neural networks, FAULT diagnosis, ELECTRIC vehicle batteries, ELECTRIC vehicles, ELECTRIC power failures, ELECTRIC fault location, FAILURE mode & effects analysis

Deep learning with leagues championship algorithm based intrusion detection on cybersecurity driven industrial IoT systems.

Autoren: Alotaibi, Saud S. Alghamdi, Turki Ali

Quelle: Scientific Reports; 8/19/2025, Vol. 15 Issue 1, p1-17, 17p

Schlagwörter: DEEP learning, INTRUSION detection systems (Computer security), AUTOENCODERS, ALGORITHMS, CYBER physical systems, INTERNET security, CYBERTERRORISM, FEATURE selection

SSAE‐MLP: Stacked sparse autoencoders‐based multi‐layer perceptron for main bearing temperature prediction of large‐scale wind turbines.

Autoren: Xiao, Xiaocong Liu, Jianxun Liu, Deshun et al.

Quelle: Concurrency & Computation: Practice & Experience; 9/10/2021, Vol. 33 Issue 17, p1-15, 15p

Schlagwörter: DEEP learning, WIND turbines, SUPERVISED learning, MACHINE learning, SUPERVISORY control systems, MAINTENANCE costs, GREEDY algorithms

Online prediction of composite material drilling quality based on multi-sensor fusion.

Autoren: Liu, Wei Cui, ·Jiacheng Lu, Yongkang et al.

Quelle: Journal of Intelligent Manufacturing; Dec2025, Vol. 36 Issue 8, p5889-5901, 13p

Schlagwörter: COMPOSITE materials, MULTISENSOR data fusion, INDUSTRIAL applications, MANUFACTURING process management, MACHINING, PREDICTION models, FORECASTING, BAYESIAN analysis

Stacked sparse autoencoders monitoring model based on fault-related variable selection.

Autoren: Yin, Jie Yan, Xuefeng

Quelle: Soft Computing - A Fusion of Foundations, Methodologies & Applications; Mar2021, Vol. 25 Issue 5, p3531-3543, 13p

Schlagwörter: ELECTRONIC data processing, INFORMATION processing, CHEMICAL industry, ACQUISITION of data, DATA modeling, EUCLIDEAN distance

基于堆叠稀疏判别自编码的滚动轴承 智能故障诊断方法. (Chinese)

Alternate Title: Intelligent Fault Diagnosis Method for Rolling Bearings Based on Stacked Sparse Discriminative Autoencoders. (English)

Autoren: 曾梦洁 李舜酩 李冉冉 et al.

Quelle: Bearing; Mar2024, Issue 3, p77-98, 8p

Building Extraction Based on an Optimized Stacked Sparse Autoencoder of Structure and Training Samples Using LIDAR DSM and Optical Images.

Autoren: Yiming Yan Zhichao Tan Nan Su et al.

Quelle: Sensors (14248220); Sep2017, Vol. 17 Issue 9, p1957, 15p

Schlagwörter: LIDAR, DIGITAL elevation models, ARTIFICIAL neural networks, OPTICAL images, REMOTE sensing, EXTRACTION techniques

An intelligent fusion-based transfer learning model with artificial protozoa optimiser for enhancing gesture recognition to aid visually impaired people.

Autoren: Mengash, Hanan Abdullah Alqadi, Basma S. Marzouk, Radwa

Quelle: Scientific Reports; 11/21/2025, Vol. 15 Issue 1, p1-20, 20p

Schlagwörter: PEOPLE with visual disabilities, ARTIFICIAL intelligence, HUMAN-computer interaction, IMAGE processing, SIGN language, DEEP learning, TRANSFER of training, HUMAN activity recognition

Latent Relationship Guided Stacked Sparse Autoencoder for Hyperspectral Imagery Classification.

Autoren: Liu, Li Wang, Yuebin Peng, Junhuan et al.

Quelle: IEEE Transactions on Geoscience & Remote Sensing; May2020, Vol. 58 Issue 5, p3711-3725, 15p

Schlagwörter: CLASSIFICATION, LEARNING ability, MATHEMATICAL regularization, SPARSE approximations

Research on Diesel Engine Fault Diagnosis Method Based on Stacked Sparse Autoencoder and Support Vector Machine.

Autoren: Bai, Huajun Zhan, Xianbiao Yan, Hao et al.

Quelle: Electronics (2079-9292); Jul2022, Vol. 11 Issue 14, pN.PAG-N.PAG, 18p

Schlagwörter: FAULT diagnosis, SUPPORT vector machines, DIESEL motors, DIAGNOSIS, WAVE analysis, SPECTRUM analysis

Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder.

Autoren: Wang, Shui-Hua Satapathy, Suresh Chandra Zhou, Qinghua et al.

Quelle: Journal of Grid Computing; Mar2022, Vol. 20 Issue 1, p1-16, 16p

Six Phase Transmission Line Protection Using Bat Algorithm Tuned Stacked Sparse Autoencoder.

Autoren: Rao Althi, Tirupathi Koley, Ebha Ghosh, Subhojit et al.

Quelle: Electric Power Components & Systems; 2023, Vol. 51 Issue 2, p113-130, 18p

Schlagwörter: ELECTRIC lines, ALGORITHMS, DIGITAL computer simulation, ELECTRIC fault location

Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Autoencoder with Multilayer Self-Learning.

Autoren: Ma, Jian Su, Hua Zhao, Wan-lin et al.

Quelle: Complexity; 7/30/2018, p1-13, 13p

Schlagwörter: AIRPLANE motors, LOGISTIC regression analysis

Deep network approach with stacked sparse autoencoders in detection of DDoS attacks on SDN‐based VANET.

Autoren: Polat, Huseyin Turkoglu, Muammer Polat, Onur

Quelle: IET Communications (Wiley-Blackwell); Dec2020, Vol. 14 Issue 22, p4089-4100, 12p