Suchergebnisse - conventional denoising autoencoder (dae)

A Study on Denoising Autoencoder Noise Selection for Improving the Fault Diagnosis Rate of Vibration Time Series Data.

Autoren: Jang, Jun-gyo Lee, Soon-sup Hwang, Se-Yun et al.

Quelle: Applied Sciences (2076-3417); Jun2025, Vol. 15 Issue 12, p6523, 17p

Schlagwörter: NOISE control, SUPPORT vector machines, AUTOENCODERS, TIME series analysis, WAVELET transforms, RANDOM noise theory, KURTOSIS

Nonlinear Dynamic Process Monitoring Based on Discriminative Denoising Autoencoder and Canonical Variate Analysis

Autoren: Jun Liang Daoguang Liu Yinxiao Zhan et al.

Quelle: Actuators, Vol 13, Iss 11, p 440 (2024)

Schlagwörter: process monitoring, fault diagnosis, canonical variate analysis (CVA), dynamics, denoising autoencoder (DAE), nonlinear process, Materials of engineering and construction. Mechanics of materials, TA401-492, Production of electric energy or power. Powerplants. Central stations, TK1001-1841

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-0825/13/11/440; https://doaj.org/toc/2076-0825

Zugangs-URL: https://doaj.org/article/12e18d4cc1334e0ba37224c8e9190b1c

Artificial Neural Networks in the Wastewater Industry. From Conventional to Data-based Industrial Control

Autoren: Pisa Dacosta, Ivan

Thesis Advisors: Morell Pérez, Antoni, López Vicario, José, Vilanova i Arbós, Ramon

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Xarxes neuronals artificials, Redes neuronales artificiales, Artificial neural networks, Control automàtic, Control automático, Automatic control, Estacions depuradores d'aigües residuals, Estaciones depuradoras de aguas residuales, Wastewater treatment plants, Tecnologies

Time: 621.3

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/675219

A Study on Denoising Autoencoder Noise Selection for Improving the Fault Diagnosis Rate of Vibration Time Series Data

Autoren: Jun-gyo Jang Soon-sup Lee Se-Yun Hwang et al.

Quelle: Applied Sciences, Vol 15, Iss 12, p 6523 (2025)

Schlagwörter: Denoising Autoencoder, fault diagnosis, vibration signal, noise filtering, One-Class Support Vector Machine, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-3417/15/12/6523; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/37ebaef54df8464f921775e119f3fbf1

Nonlinear Dynamic Process Monitoring Based on Discriminative Denoising Autoencoder and Canonical Variate Analysis.

Autoren: Liang, Jun Liu, Daoguang Zhan, Yinxiao et al.

Quelle: Actuators; Nov2024, Vol. 13 Issue 11, p440, 22p

Schlagwörter: FISHER discriminant analysis, MANUFACTURING processes, FEATURE extraction

Correlation Distance Skip Connection Denoising Autoencoder (CDSK-DAE) for Speech Feature Enhancement

Autoren: Badi, Alzahra Park, Sangwook Han, David K. et al.

Index Begriffe: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Machine Learning, Computer Science - Sound, text

URL: http://arxiv.org/abs/1907.11361

Reducing false damage detections in guided ultrasonic wave monitoring systems using a denoising autoencoder.

Autoren: Kong Chen, Yon Bakhary, Norhisham Padil, Khairul Hazman et al.

Quelle: Nondestructive Testing & Evaluation; Apr2025, Vol. 40 Issue 4, p1610-1640, 31p

Schlagwörter: STRUCTURAL health monitoring, NUCLEAR industry, ULTRASONIC waves, AUTOENCODERS, TEMPERATURE effect

Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction

Autoren: Lin Zhang Jixin Wang Rui Chang et al.

Quelle: Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)

Schlagwörter: Hepatitis C, Autoencoder, Denoising autoencoder, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/b365e86c8a1242cf8bd98a8818eb882f

Self-supervised learning for effective denoising of flow fields.

Autoren: Yu, Linqi Yousif, Mustafa Z. Zhou, Dan et al.

Quelle: Physics of Fluids; Oct2024, Vol. 36 Issue 10, p1-18, 18p

Schlagwörter: TURBULENT flow, TURBULENCE, LAMINAR flow, CHANNEL flow, REYNOLDS number

A Novel PAPR Reduction Scheme Based on Deep Autoencoder Network for FBMC Systems

Autoren: Xing Cheng Deli Chen Shuaishuai Li et al.

Quelle: IEEE Access, Vol 13, Pp 68948-68958 (2025)

Schlagwörter: Filter bank multicarrier, peak to average power ratio, deep learning, denoising autoencoder network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Dateibeschreibung: electronic resource

Relation: https://ieeexplore.ieee.org/document/10967259/; https://doaj.org/toc/2169-3536

Zugangs-URL: https://doaj.org/article/678c4448230241fc87fe49f3e61edf69

Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction.

Autoren: Zhang, Lin Wang, Jixin Chang, Rui et al.

Quelle: Scientific Reports; 4/21/2024, Vol. 14 Issue 1, p1-12, 12p

Schlagwörter: HEPATITIS C, DEEP learning, FEATURE extraction, HEPATITIS C virus, VIRAL hepatitis, AUDITORY masking, MACHINE learning

Extraction of Flaw Signals from the Mixed 1-D Signals by Denoising Autoencoder.

Autoren: Lee, Seung-Eun Park, Jinhyun Kim, Hak-Joon et al.

Quelle: Applied Sciences (2076-3417); Mar2023, Vol. 13 Issue 6, p3534, 14p

Schlagwörter: SIGNAL denoising, ULTRASONIC testing, EDDY current testing, ARTIFICIAL neural networks, SIGNALS & signaling, PHASED array antennas

Robust and Fast Temperature Extraction for Brillouin Optical Time-Domain Analyzer by Using Denoising Autoencoder-Based Deep Neural Networks.

Autoren: Wang, Biwei Guo, Nan Wang, Liang et al.

Quelle: IEEE Sensors Journal; Apr2020, Vol. 20 Issue 7, p3614-3620, 7p

Advancing real-time validation of automotive software systems via continuous integration and intelligent failure analysis.

Autoren: Abboush, Mohammad Knieke, Christoph Rausch, Andreas

Quelle: Scientific Reports; 9/25/2025, Vol. 15 Issue 1, p1-19, 19p

Schlagwörter: FAULT diagnosis, SOFTWARE validation, MACHINE learning, QUANTITATIVE research, AUTOMOBILE software, HARDWARE-in-the-loop simulation, TEST validity

Lightweight CNN-based eye diagrams denoising autoencoder for 16-QAM IsOWC system.

Autoren: Suman, Subhash Raut, Subhradeep Mishra, Jitendra K.

Quelle: Optical Engineering; Oct2025, Vol. 64 Issue 10, p108103-108103, 1p

Environment-dependent denoising autoencoder for distant-talking speech recognition.

Autoren: Ueda, Yuma Kai, Atsuhiko Wang, Longbiao et al.

Quelle: EURASIP Journal on Advances in Signal Processing; 11/12/2015, Vol. 2015 Issue 1, p1-11, 11p

Schlagwörter: COMMUNICATIONS research -- Methodology, SIGNAL denoising, ENCODING, EQUIPMENT & supplies

Noise-Robust Spoofed Speech Detection Using Discriminative Autoencoder.

Autoren: Dişken, Gökay Tüfekçi, Zekeriya

Quelle: Celal Bayar University Journal of Science; 2023, Vol. 19 Issue 2, p167-174, 8p

Schlagwörter: SPEECH enhancement, VECTORS (Calculus), AUTOENCODERS, SIGNAL-to-noise ratio, DEEP learning, MACHINE learning, ARTIFICIAL neural networks, RESEARCH personnel, AUTOMATIC speech recognition

Deep Learning-Based Noise Type Classification and Removal for Drone Image Restoration.

Autoren: Ahmed, Waqar Khan, Sajid Noor, Adeeb et al.

Quelle: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ); Mar2024, Vol. 49 Issue 3, p4287-4306, 20p

Schlagwörter: IMAGE denoising, IMAGE reconstruction, NOISE, DEEP learning

Derivation of Kinetic Parameters and Lignocellulosic Composition From Thermogram of Biomass Pyrolysis Using Convolutional Neural Network.

Autoren: Kim, Heeyoon Jo, Hyunbin Ryu, Changkook et al.

Quelle: International Journal of Energy Research; 12/12/2024, Vol. 2024, p1-14, 14p

Schlagwörter: CONVOLUTIONAL neural networks, PYROLYSIS kinetics, LIGNOCELLULOSE, AUTOENCODERS, REGRESSION analysis

Towards the Analysis of Regularized Denoising Autoencoder for Biosignal Processing: Lasso Versus Ridge Norms.

Autoren: Dasan, Evangelin Jeyabalan, Nelson Samuel Jebastin

Quelle: Wireless Personal Communications; Jan2024, Vol. 134 Issue 1, p319-338, 20p

Schlagwörter: ARTIFICIAL implants, INTELLIGENT sensors, COST functions, NANOSENSORS, DATA compression, DATABASES