Výsledky vyhľadávania - unsupervised sparse‐autoencoder‐based deep neural network

Robust approach for AMC in frequency selective fading scenarios using unsupervised sparse‐autoencoder‐based deep neural network

Autori: Maqsood Hussain Shah Xiaoyu Dang

Zdroj: IET Communications. 13:423-432

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

Prístupová URL adresa: https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-com.2018.5688
https://ieeexplore.ieee.org/document/8648840
https://doi.org/10.1049/iet-com.2018.5688
https://dblp.uni-trier.de/db/journals/iet-com/iet-com13.html#ShahD19
https://digital-library.theiet.org/content/journals/10.1049/iet-com.2018.5688
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-com.2018.5688

A Comparative Study of Lightweight, Sparse Autoencoder-Based Classifiers for Edge Network Devices: An Efficiency Analysis of Feed-Forward and Deep Neural Networks.

Autori: Jo, Mi Young Kim, Hyun Jung

Zdroj: Sensors (14248220); Oct2025, Vol. 25 Issue 20, p6439, 22p

Predmety: EDGE computing, AUTOENCODERS, RESEARCH evaluation, ANOMALY detection (Computer security), FEEDFORWARD neural networks, ARTIFICIAL neural networks

Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

Autori: Zhe Yang Dejan Gjorgjevikj Jianyu Long a ďalší

Zdroj: Chinese Journal of Mechanical Engineering, Vol 34, Iss 1, Pp 1-12 (2021)

Predmety: Deep learning, Fault diagnostics, Novelty detection, Multi-head deep neural network, Sparse autoencoder, Ocean engineering, TC1501-1800, Mechanical engineering and machinery, TJ1-1570

Popis súboru: electronic resource

Relation: https://doaj.org/toc/1000-9345; https://doaj.org/toc/2192-8258

Prístupová URL adresa: https://doaj.org/article/b89721cf6296483a8a2661e099c0789e

Robust approach for AMC in frequency selective fading scenarios using unsupervised sparse‐autoencoder‐based deep neural network.

Autori: Shah, Maqsood Hussain Dang, Xiaoyu

Zdroj: IET Communications (Wiley-Blackwell); Mar2019, Vol. 13 Issue 4, p423-432, 10p

Predmety: ARTIFICIAL neural networks, ARTIFICIAL intelligence, DEEP learning, ORDER statistics, SIGNAL classification

Sparse Autoencoder-Based Multi-Head Deep Neural Networks for Machinery Fault Diagnostics With Novelty Detection

Autori: Zhe Yang Dejan Gjorgjevikj Jian-Yu Long a ďalší

Prístupová URL adresa: https://europepmc.org/article/PPR/PPR251499
https://www.researchsquare.com/article/rs-122416/v1.pdf
https://www.researchsquare.com/article/rs-122416/v1

Sparse Autoencoder Based Deep Neural Network for Voxelwise Detection of Cerebral Microbleed

Autori: Yi-Ding Lv Yudong Zhang Shuihua Wang a ďalší

Zdroj: 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS). :1229-1232

Predmety: 2. Zero hunger, 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 10. No inequality

Prístupová URL adresa: https://dblp.uni-trier.de/db/conf/icpads/icpads2016.html#ZhangHLCZW16
https://ieeexplore.ieee.org/document/7823881/
https://doi.org/10.1109/ICPADS.2016.0166
http://ieeexplore.ieee.org/document/7823881/

Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties.

Autori: Yang, Zhe Gjorgjevikj, Dejan Long, Jianyu a ďalší

Zdroj: Chinese Journal of Mechanical Engineering; 6/4/2021, Vol. 34 Issue 1, p1-12, 12p

A comparative case study between shallow and deep neural networks in induction motor's fault diagnosis.

Autori: Gholaminejad, Azadeh Jorkesh, Saeid Poshtan, Javad

Zdroj: IET Science, Measurement & Technology (Wiley-Blackwell); Jul2023, Vol. 17 Issue 5, p195-207, 13p

Predmety: ARTIFICIAL neural networks, INDUCTION motors, FAULT diagnosis, INDEPENDENT component analysis, SHORT circuits, POWER resources

Sparse Representation Convolutional Autoencoder for Feature Learning of Vibration Signals and its Applications in Machinery Fault Diagnosis.

Autori: Miao, Mengqi Sun, Yuanhang Yu, Jianbo

Zdroj: IEEE Transactions on Industrial Electronics; Dec2022, Vol. 69 Issue 12, p13565-13575, 11p

Predmety: ROLLER bearings, FAULT diagnosis, ARTIFICIAL neural networks, SUPERVISED learning, DEEP learning, VIBRATION (Mechanics), MACHINERY

Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network.

Autori: Sohaib, Muhammad Kim, Jong-Myon

Zdroj: Shock & Vibration; 5/7/2018, p1-11, 11p

Predmety: ROTARY combustion engines, FAULT diagnosis, NEURAL circuitry, COST effectiveness, ALGORITHMS

A Multistage Physics-Informed Neural Network for Fault Detection in Regulating Valves of Nuclear Power Plants.

Autori: Lai, Chenyang Ahmed, Ibrahim Zio, Enrico a ďalší

Zdroj: Energies (19961073); Jun2024, Vol. 17 Issue 11, p2647, 23p

Predmety: NUCLEAR power plants, RELIEF valves, ARTIFICIAL neural networks, FLOW coefficient, VALVES, VECTOR data

Construction of Home Product Design System Based on Self-Encoder Depth Neural Network.

Autori: Lu, Guangpu

Zdroj: Computational Intelligence & Neuroscience; 4/21/2022, p1-12, 12p

Predmety: DWELLING design & construction, DEEP learning, DOMESTIC architecture, FURNITURE design, COMPUTER vision, ALGORITHMS

Hand Gesture Recognition using Sparse Autoencoder-based Deep Neural Network Based on Electromyography Measurements.

Autori: Yucheng Wang Chunhui Wang Zhonghui Wang a ďalší

Zdroj: Proceedings of SPIE; 1/26/2018, Vol. 10598, p1-7, 7p

Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition.

Autori: Zhang, Hua Gou, Ruoyun Shang, Jili a ďalší

Zdroj: Frontiers in Physiology; 3/2/2021, Vol. 11, pN.PAG-N.PAG, 13p

Predmety: CONVOLUTIONAL neural networks, ARTIFICIAL neural networks, EMOTION recognition, MACHINE learning, EMOTIONS

Deep Learning in Aircraft Design, Dynamics, and Control: Review and Prospects.

Autori: Dong, Yiqun Tao, Jun Zhang, Youmin a ďalší

Zdroj: IEEE Transactions on Aerospace & Electronic Systems; Aug2021, Vol. 57 Issue 4, p2346-2368, 23p

Predmety: DEEP learning, OBJECT recognition (Computer vision), RECURRENT neural networks, FLIGHT control systems, CONVOLUTIONAL neural networks, PROBLEM solving

Gear Fault Detection Method Based on Convex Hull Clustering of Autoencoder's Latent Space.

Autori: Batsch, Michał Kiczek, Bartłomiej

Zdroj: Applied Sciences (2076-3417); Jun2024, Vol. 14 Issue 12, p5282, 21p

Predmety: ARTIFICIAL neural networks, GENERATIVE adversarial networks, SUPERVISED learning, PRINCIPAL components analysis, SIGNAL processing, MACHINE learning

Deep autoencoder based domain adaptation for transfer learning.

Autori: Dev, Krishna Ashraf, Zubair Muhuri, Pranab K. a ďalší

Zdroj: Multimedia Tools & Applications; Jul2022, Vol. 81 Issue 16, p22379-22405, 27p

Predmety: SUPERVISED learning, TRANSFER of training, ARTIFICIAL neural networks, DEEP learning, PHYSIOLOGICAL adaptation, MACHINE learning, CONCEPT learning

ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data.

Autori: Almuqhim, Fahad Saeed, Fahad

Zdroj: Frontiers in Computational Neuroscience; 4/8/2021, Vol. 15, pN.PAG-N.PAG, 10p

Predmety: AUTISM spectrum disorders, FUNCTIONAL magnetic resonance imaging, BRAIN imaging, NEURAL development, FEATURE extraction

Deep Neural Networks for Prediction of Cardiovascualr Diseases.

Autori: Aslam, Muhammad Aqeel Munir, Muhammad Asif Ahmad, Rauf a ďalší

Zdroj: Nano Biomedicine & Engineering; 2022, Vol. 14 Issue 1, p81-89, 9p

Predmety: ARTIFICIAL neural networks, FORECASTING

Data‐driven sensor fault detection and isolation of nonlinear systems: Deep neural‐network Koopman operator.

Autori: Bakhtiaridoust, Mohammadhosein Irani, Fatemeh Negar Yadegar, Meysam a ďalší

Zdroj: IET Control Theory & Applications (Wiley-Blackwell); Jan2023, Vol. 17 Issue 2, p123-132, 10p

Predmety: NONLINEAR systems, FAULT diagnosis, LINEAR operators, INVARIANT sets, DETECTORS, SET functions