Suchergebnisse - "sparse stacked denoising autoencoder"

An Anomaly Detection Method for Nonlinear Industrial Process Using Sparse Stacked Denoising Autoencoder

Autoren: Mingwei Yang YanHua Liu Hong Chen et al.

Quelle: 2023 9th International Conference on Systems and Informatics (ICSAI). :1-6

A novel multimodal framework for automatic recognition of individual cattle based on hybrid features using sparse stacked denoising autoencoder and group sparse representation techniques

Autoren: Santosh Kumar Sunil Kumar Mehak Shafi et al.

Quelle: Multimedia Tools and Applications. 81:31075-31106

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Network intrusion detection based on Contractive Sparse Stacked Denoising Autoencoder

Autoren: Yang Yang Li Wencui Meng Huiping et al.

Quelle: 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). :1-6

A Nonnegativity-Constraint Sparse Stacked Denoising Autoencoder for Anomaly Detection in Electric Power Communication Network

Autoren: Zhuo Tao Yuanyuan Wang Yang Yang et al.

Quelle: 2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). :1-6

Schlagwörter: 9. Industry and infrastructure, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings

Autoren: Ping Liu Peng Fan Hui Wang et al.

Quelle: Measurement. 146:305-314

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://ui.adsabs.harvard.edu/abs/2019Meas..146..305S/abstract
https://www.sciencedirect.com/science/article/pii/S0263224119305950

A novel multimodal framework for automatic recognition of individual cattle based on hybrid features using sparse stacked denoising autoencoder and group sparse representation techniques.

Autoren: Kumar, Santosh Kumar, Sunil Shafi, Mehak et al.

Quelle: Multimedia Tools & Applications; Sep2022, Vol. 81 Issue 21, p31075-31106, 32p

Schlagwörter: ENDANGERED species, FEATURE extraction, FACE, EXTRACTION techniques

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

Learning social representations with deep autoencoder for recommender system.

Autoren: Pan, Yiteng He, Fazhi Yu, Haiping

Quelle: World Wide Web; Jul2020, Vol. 23 Issue 4, p2259-2279, 21p

Schlagwörter: SOCIAL learning, COLLECTIVE representation, RECOMMENDER systems, MATRIX decomposition, SOCIAL influence, SOCIAL interaction, SOCIAL problems

A Robust Rolling Bearing Fault Diagnosis Approach Using Hybrid Red Deer Optimization Based 1D-CNN.

Autoren: Joseph, Thomas U., Sudeep K., Keerthi Krishnan et al.

Quelle: International Journal of Acoustics & Vibration; Sep2025, Vol. 30 Issue 3, p272-283, 12p

Schlagwörter: ROLLER bearings, FAULT diagnosis, SIGNAL separation, SIGNAL processing, METAHEURISTIC algorithms, SIGNAL denoising, FRACTAL analysis, CONVOLUTIONAL neural networks

A Fault Diagnosis Framework for Waterjet Propulsion Pump Based on Supervised Autoencoder and Large Language Model.

Autoren: Liu, Zhihao Xiao, Haisong Zhang, Tong et al.

Quelle: Machines; Aug2025, Vol. 13 Issue 8, p698, 18p

Schlagwörter: FAULT diagnosis, AUTOENCODERS, VIBRATION measurements, LANGUAGE models, DIAGNOSIS, FEATURE extraction, PROPULSION systems

Unsupervised Machine Learning Methods for Anomaly Detection in Network Packets.

Autoren: Park, Hyoseong Shin, Dongil Park, Chulgyun et al.

Quelle: Electronics (2079-9292); Jul2025, Vol. 14 Issue 14, p2779, 14p

Schlagwörter: ANOMALY detection (Computer security), INTRUSION detection systems (Computer security), TRAFFIC monitoring, AUTOENCODERS, COMPUTER network security, LOSS functions (Statistics), MACHINE learning

Exploring the evolution, progress, and future of point-of-interest recommendation over location-based social network: a comprehensive review.

Autoren: Acharya, Malika Mohbey, Krishna Kumar

Quelle: GeoInformatica; Jul2025, Vol. 29 Issue 3, p305-350, 46p

Schlagwörter: RECOMMENDER systems, GEOSPATIAL data, TOURIST attractions, SOCIAL networks, CONSUMER preferences

Blind Denoising Autoencoder.

Autoren: Majumdar, Angshul

Quelle: IEEE Transactions on Neural Networks & Learning Systems; Jan2019, Vol. 30 Issue 1, p312-317, 6p

Schlagwörter: SIGNAL denoising, MACHINE learning

RETRACTED: A bearing fault diagnosis method based on LSTM-GAN and convolutional neural network under small sample variable working conditions.

Autoren: Rong, Mansong Wei, Yuan Xiao, Zhijun et al.

Quelle: Journal of Intelligent & Fuzzy Systems; 2025Supplement3, Vol. 48, p653-667, 15p

Schlagwörter: CONVOLUTIONAL neural networks, RETRACTION of scholarly articles, FAULT diagnosis, DIAGNOSIS

Multibolt looseness monitoring of steel structure based on multitask active sensing method and substructure cross-domain transfer learning.

Autoren: Chen, Yixuan Wei, Jingyi Gao, Zhennan et al.

Quelle: Structural Health Monitoring; Nov2024, Vol. 23 Issue 6, p3486-3504, 19p

Schlagwörter: BOLTED joints, STRESS waves, STRUCTURAL frames, THEORY of wave motion, SERVICE life, SPACE frame structures

Residual Adversarial Subdomain Adaptation Network Based on Wasserstein Metrics for Intelligent Fault Diagnosis of Bearings.

Autoren: Cai, Haichao Yang, Bo Xue, Yujun et al.

Quelle: Applied Sciences (2076-3417); Oct2024, Vol. 14 Issue 19, p9057, 14p

Schlagwörter: FAULT diagnosis, DEEP learning, DIAGNOSIS, LOCAL government

Automatic fault diagnosis of rolling bearings under multiple working conditions based on unsupervised stack denoising autoencoder.

Autoren: Wang, Lei Rao, Hang Dong, Zhengcheng et al.

Quelle: Structural Health Monitoring; Sep2024, Vol. 23 Issue 5, p3084-3104, 21p

Schlagwörter: AUTOENCODERS, DEEP learning, ROLLER bearings, DIAGNOSIS, ALGORITHMS, FEATURE extraction

NFHP-RN: A Method of Few-Shot Network Attack Detection Based on the Network Flow Holographic Picture-ResNet.

Autoren: Tao Yi Xingshu Chen Mingdong Yang et al.

Quelle: Computer Modeling in Engineering & Sciences (CMES); 2024, Vol. 140 Issue 1, p929-955, 27p

Schlagwörter: DEEP learning, MACHINE learning, FEATURE extraction

A clustering multi-stage training transfer learning framework for cross simulation domain and experimental domain fault diagnosis.

Autoren: Yu, Shubo Liu, Zhansheng Zhao, Chen et al.

Quelle: Measurement Science & Technology; Jun2024, Vol. 35 Issue 6, p1-16, 16p

Schlagwörter: TRANSFER of training, FAULT diagnosis, DEEP learning, ROLLER bearings, LEARNING strategies, ROTATING machinery, DIAGNOSIS

基于自编码的长流程造纸过程 断纸故障识别. (Chinese)

Alternate Title: Paper Break Fault Recognition in Long Process Papermaking Process Based on Autoencoder. (English)

Autoren: 陈国健 李继庚 陈 波 et al.

Quelle: China Pulp & Paper; 2024, Issue 3, p113-120, 9p