Search Results - "Stacked sparse autoencoder network"

An industrial process fault diagnosis method based on independent slow feature analysis and stacked sparse autoencoder network

Authors: Chang Li Chenglin Wen Zhe Zhou

Source: Journal of the Franklin Institute. 361:234-247

An Innovative PolSAR Image Classification Method Based on Non-Negative Constraints Stacked Sparse Autoencoder Network with Multi-Features Joint Representation Learning

Source: Academic Journal of Computing & Information Science. 6

Wavelet Transform and Stacked Sparse Autoencoder Network Based Location Method of Cable Incipient Fault

Authors: Xue Bai Li Shenghui Dong Henan

Source: Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019).

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://www.atlantis-press.com/proceedings/cnci-19/125906915

Parameter identification for nonlinear Hammerstein models with stacked sparse autoencoder network.

Authors: Li, Feng Song, Liexin Wang, Tianhu et al.

Source: Engineering Applications of Artificial Intelligence. Jan2026:Part 2, Vol. 163, pN.PAG-N.PAG. 1p.

Subject Terms: *HAMMERSTEIN equations, *NONLINEAR systems, *LEAST squares, *TECHNOLOGICAL innovations, *STATISTICAL models, *MATHEMATICAL analysis, *PARAMETER estimation, *ARTIFICIAL neural networks

A deep learning based malicious module identification using stacked sparse autoencoder network for VLSI circuit reliability

Authors: M. Priyatharishini M. Nirmala Devi

Source: Measurement. 194:111055

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Deep Learning Based Retrieval of Forest Aboveground Biomass from Combined LiDAR and Landsat 8 Data

Authors: Linjing Zhang Zhenfeng Shao Jianchen Liu et al.

Source: Remote Sensing, Vol 11, Iss 12, p 1459 (2019)

Subject Terms: LiDAR, Landsat 8 OLI, combined optical and LiDAR indices, synergy, Stacked Sparse Autoencoder network (SSAE), forest aboveground biomass (AGB), Science

Relation: https://www.mdpi.com/2072-4292/11/12/1459; https://doaj.org/toc/2072-4292; https://doaj.org/article/f306008bbc1849e998fd17412f06d8fe

Availability: https://doi.org/10.3390/rs11121459
https://doaj.org/article/f306008bbc1849e998fd17412f06d8fe

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)

Authors: 曾梦洁 李舜酩 李冉冉 et al.

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

Tool Wear Prediction Based on Attention Long Short-term Memory Network with Small Samples.

Authors: Weiwei Yu Hua Huang Runlan Guo et al.

Source: Sensors & Materials; 2023, Vol. 35 Issue 7, Part 2, p2321-2335, 15p

Subject Terms: GENERATIVE adversarial networks, K-nearest neighbor classification, FORECASTING, SIGNAL sampling, WIRELESS sensor networks

Intrusion Detection Model for Industrial Internet of Things Based on Improved Autoencoder.

Authors: Zhang, Wumei Zhang, Yongzhen

Source: Computational Intelligence & Neuroscience; 5/27/2022, p1-8, 8p

Subject Terms: INTERNET of things, INTRUSION detection systems (Computer security), INDUSTRIAL safety, INDUSTRIAL security, PROBLEM solving, INDUSTRIALIZATION

A hybrid stacked sparse autoencoder and LightGBM framework for high-performance intrusion detection in IoT networks.

Authors: Madhavan Nair Vasanthamma, Hari Vinayak Thankaswamy, Jarin

Source: Iran Journal of Computer Science; Dec2025, Vol. 8 Issue 4, p2175-2198, 24p

HYBRID DEEP LEARNING NETWORK-BASED APPROACH FOR AIR COMBAT MANEUVER DECISION-MAKING.

Authors: SHEEBA D. LINGESHWARI G. ANORELIN A.

Source: i-Manager's Journal on Computer Science; Sep2025, Vol. 13 Issue 2, p83-93, 11p

Subject Terms: LONG short-term memory, DEEP learning, DECISION making, METADATA, AIR warfare, ENSEMBLE learning, AUTONOMOUS robots

Hybrid twin attention based convolutional stacked sparse autoencoder for classification of defected weld images.

Authors: Srikanth, T. Mani, M. Radhika

Source: Computers & Electrical Engineering. May2025:Part A, Vol. 124, pN.PAG-N.PAG. 1p.

Subject Terms: *CONVOLUTIONAL neural networks, *WELDING defects, *JOINING processes, *MANUFACTURING processes, *DISCRETE wavelet transforms, *DEEP learning

Advanced Hybridization and Optimization of DNNs for Medical Imaging: A Survey on Disease Detection Techniques: Advanced Hybridization and Optimization...: M. K. Bohmrah, H. Kaur.

Authors: Bohmrah, Maneet Kaur Kaur, Harjot

Source: Artificial Intelligence Review; Apr2025, Vol. 58 Issue 4, p1-68, 68p

Establishment and Solution Test of Wear Prediction Model Based on Particle Swarm Optimization Least Squares Support Vector Machine.

Authors: Huang, Xiao Wang, Yongguo Mao, Yuhui

Source: Machines; Apr2025, Vol. 13 Issue 4, p290, 24p

Subject Terms: SUPPORT vector machines, PRINCIPAL components analysis, LEAST squares, MULTISENSOR data fusion, MACHINE learning

Network Intrusion Detection Based on Stacked Sparse Autoencoder and Binary Tree Ensemble Method

Authors: Yanhua Yu Baoan Zhang Jie Li

Source: 2018 IEEE International Conference on Communications Workshops (ICC Workshops). :1-6

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://ieeexplore.ieee.org/document/8403759/
https://doi.org/10.1109/ICCW.2018.8403759
https://dblp.uni-trier.de/db/conf/icc/icc2018w.html#ZhangYL18

Diverse Geographical Regions Based Biodiversity Conservation by LiDAR Image with Deep Learning Model.

Authors: Babu C. N., Gireesh Reddy, Chilukala Mahender Kumar, Mungara Kiran et al.

Source: Remote Sensing in Earth Systems Sciences; Dec2024, Vol. 7 Issue 4, p738-749, 12p

Research on milling cutter wear monitoring based on self-learning feature boundary model.

Authors: Hou, Xuchen Xia, Wei Liu, Xianli et al.

Source: International Journal of Advanced Manufacturing Technology; Nov2024, Vol. 135 Issue 3/4, p1789-1807, 19p

Subject Terms: MILLING cutters, ROOT-mean-squares, AUTODIDACTICISM, JUDGMENT (Psychology), SIGNALS & signaling

Transformers-based architectures for stroke segmentation: a review.

Authors: Zafari-Ghadim, Yalda Rashed, Essam A. Mohamed, Amr et al.

Source: Artificial Intelligence Review; Nov2024, Vol. 57 Issue 11, p1-35, 35p

Fault-MTL: A Multi-task Deep Learning Approach for Simultaneous Fault Classification and Localization in Power Systems.

Authors: Bhardwaj, Dewesh Londhe, Narendra D. Raj, Ritesh

Source: Journal of Control, Automation & Electrical Systems; Oct2024, Vol. 35 Issue 5, p884-898, 15p

Subject Terms: DEEP learning, ELECTRIC lines, TASK performance, POWER resources, JOB performance

A novel NMF-DiCCA deep learning method and its application in wind turbine blade icing failure identification.

Authors: Chuangyan Yang Peng Li Xun Lang et al.

Source: Maintenance & Reliability / Eksploatacja i Niezawodność; 2024, Vol. 26 Issue 4, p1-14, 14p

Subject Terms: MACHINE learning, SINGULAR value decomposition, LATENT variables, AUTOENCODERS, NONNEGATIVE matrices, WIND turbine blades