Suchergebnisse - Residual learning conventional denoising autoencoder

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

Development of machine learning-based analysis and processing tools applied to magnetization transfer MRI: Z-spectral denoising and fitting

Autoren: Mohammed Ali, Sajad Lund University, Faculty of Science, Medical Radiation Physics, Lund, Lunds universitet, Naturvetenskapliga fakulteten, Medicinsk strålningsfysik, Lund, Originator Lund University, Faculty of Science, Medical Radiation Physics, Lund, MR Physics, Lunds universitet, Naturvetenskapliga fakulteten, Medicinsk strålningsfysik, Lund, MR Physics, Originator et al.

Weitere Verfasser: Mohammed Ali, Sajad Lund University, Faculty of Science, Medical Radiation Physics, Lund, Lunds universitet, Naturvetenskapliga fakulteten, Medicinsk strålningsfysik, Lund, Originator Lund University, Faculty of Science, Medical Radiation Physics, Lund, MR Physics, Lunds universitet, Naturvetenskapliga fakulteten, Medicinsk strålningsfysik, Lund, MR Physics, Originator et al.

Schlagwörter: Medical and Health Sciences, Other Medical and Health Sciences, Other Medical and Health Sciences not elsewhere specified, Medicin och hälsovetenskap, Annan medicin och hälsovetenskap, Övrig annan medicin och hälsovetenskap, Natural Sciences, Computer and Information Sciences, Artificial Intelligence, Naturvetenskap, Data- och informationsvetenskap (Datateknik), Artificiell intelligens, Engineering and Technology, Medical Engineering, Medical Imaging, Teknik, Medicinteknik, Medicinsk bildvetenskap

Dateibeschreibung: electronic

Zugangs-URL: https://lucris.lub.lu.se/ws/files/227931572/Thesis_V5_with_cover.pdf

Dual Channel Residual Learning for Denoising Path Tracing.

Autoren: Liu, Ping Ji, Hangyu

Quelle: International Journal of Image & Graphics; Jan2025, Vol. 25 Issue 1, p1-20, 20p

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

Heart rate estimation for U-Net and LSTM models combining multiple attention mechanisms.

Autoren: Li, Ahui Cai, Jun

Quelle: Medical Engineering & Physics. Nov2025, Vol. 145, pN.PAG-N.PAG. 1p.

Schlagwörter: *LONG short-term memory, *DEEP learning, *SIGNAL processing, *HEART beat measurement, *PATIENT monitoring

RESIDUAL LEARNING BASED IMAGE DENOISING AND COMPRESSION USING DNCNN.

Autoren: Shaliniswetha, Savaram Mahaboob, Shaik Taj

Quelle: ICTACT Journal on Image & Video Processing; Nov2022, Vol. 13 Issue 2, p2817-2824, 8p

Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles.

Autoren: Tarmissi, Khaled Mengash, Hanan Abdullah Negm, Noha et al.

Quelle: AIMS Mathematics; 2024, Vol. 9 Issue 12, p1-24, 24p

Schlagwörter: OBJECT recognition (Computer vision), ARTIFICIAL intelligence, COMPUTER vision, WEATHER, MACHINE learning

Survival prediction for patients with glioblastoma multiforme using a Cox proportional hazards denoising autoencoder network.

Autoren: Ting Yan Zhenpeng Yan Lili Liu et al.

Quelle: Frontiers in Computational Neuroscience; 1/10/2023, Vol. 16, p01-12, 12p

Schlagwörter: NOMOGRAPHY (Mathematics), GLIOBLASTOMA multiforme, PROPORTIONAL hazards models, MAGNETIC resonance imaging, RECEIVER operating characteristic curves, SCATTER diagrams

Near-surface defect detection in ultrasonic testing using domain-knowledge-informed self-supervised learning.

Autoren: Jeon, Minsu Choi, Minseok Choi, Wonjae et al.

Quelle: Ultrasonics. Mar2025, Vol. 147, pN.PAG-N.PAG. 1p.

Schlagwörter: *ULTRASONIC testing, *SUPERPOSITION principle (Physics), *ARTIFICIAL intelligence, *COMPUTER vision, *AUTOENCODERS

ResDNN: deep residual learning for natural image denoising.

Autoren: Singh, Gurprem Mittal, Ajay Aggarwal, Naveen

Quelle: IET Image Processing (Wiley-Blackwell); Sep2020, Vol. 14 Issue 11, p2425-2434, 10p

Poststack Seismic Data Denoising Based on 3-D Convolutional Neural Network.

Autoren: Liu, Dawei Wang, Wei Wang, Xiaokai et al.

Quelle: IEEE Transactions on Geoscience & Remote Sensing; Mar2020, Vol. 58 Issue 3, p1598-1629, 32p

Intelligent Detection of Railway Axles Fatigue Crack Using Acoustic Emission-Stacked Denoising Autoencoders.

Autoren: Lin, Li Zhao, Shuang Tang, Xiaowen et al.

Quelle: Journal of Nondestructive Evaluation; Dec2025, Vol. 44 Issue 4, p1-22, 22p

Schlagwörter: FATIGUE cracks, ACOUSTIC emission, DEEP learning, NONDESTRUCTIVE testing, DETECTION algorithms, FEATURE extraction, AXLES

Adversarial Defense for Medical Images.

Autoren: Tsai, Min-Jen Lee, Ya-Chu Lien, Hsin-Ying et al.

Quelle: Electronics (2079-9292); Nov2025, Vol. 14 Issue 22, p4384, 46p

Schlagwörter: DEEP learning, AUTOENCODERS, ARTIFICIAL intelligence, MEDICAL photography, SIGNAL denoising, IMAGE quality in imaging systems

Advancing Self-Supervised Learning for Building Change Detection and Damage Assessment: Unified Denoising Autoencoder and Contrastive Learning Framework.

Autoren: Yang, Songxi Peng, Bo Sui, Tang et al.

Quelle: Remote Sensing; Aug2025, Vol. 7 Issue 15, p2717, 28p

Schlagwörter: FEATURE extraction, AUTOENCODERS, STRUCTURAL health monitoring, URBAN research, EMERGENCY management

A hybrid deep learning framework for denoising acoustic partial discharge signals.

Autoren: Kumar, Chandan Ganguly, Biswarup Dey, Debangshu et al.

Quelle: Signal, Image & Video Processing; Dec2025, Vol. 19 Issue 12, p1-17, 17p

SPARSE-OTFS-Net: A Sparse Robust OTFS Signal Detection Algorithm for 6G Ubiquitous Coverage.

Autoren: Ling, Yunzhi Xu, Jun

Quelle: Electronics (2079-9292); Sep2025, Vol. 14 Issue 17, p3532, 17p

Schlagwörter: 6G networks, SIGNAL detection, DEEP learning, INTERFERENCE suppression, ELECTRONIC modulation, DOPPLER effect

Overview of Research on Digital Image Denoising Methods.

Autoren: Mao, Jing Sun, Lianming Chen, Jie et al.

Quelle: Sensors (14248220); Apr2025, Vol. 25 Issue 8, p2615, 30p

Schlagwörter: ARTIFICIAL neural networks, IMAGE denoising, ARTIFICIAL intelligence, IMAGE segmentation, DEEP learning, RESEARCH personnel

Physics-Informed SDAE-Based Denoising Model for High-Impedance Fault Detection.

Autoren: Lin, Jianxin Wang, Xuchang Wang, Huaiyuan

Quelle: Processes; Nov2025, Vol. 13 Issue 11, p3673, 23p

Schlagwörter: FAULT diagnosis, SIGNAL denoising, SIGNAL integrity (Electronics), ELECTRIC power distribution, SIGNAL processing, DISCRETE wavelet transforms

An Advanced Immersion Level Prediction Using Ensemble Classifier with Heuristic Search Algorithm in 3D Games Content Generated Virtual Environments.

Autoren: Sundararajan, Kamalanathan Santhanam, Prasanna

Quelle: Fusion: Practice & Applications; 2026, Vol. 21 Issue 1, p277-292, 16p

Schlagwörter: ARTIFICIAL intelligence, METAHEURISTIC algorithms, LONG short-term memory, AUTOENCODERS, HEURISTIC

A Deep Learning Approach for Real-Time Intrusion Mitigation in Automotive Controller Area Networks.

Autoren: Kousar, Anila Ahmed, Saeed Khan, Zafar A.

Quelle: World Electric Vehicle Journal; Sep2025, Vol. 16 Issue 9, p492, 49p

Schlagwörter: DEEP learning, CONTROLLER area network (Computer network), CYBERTERRORISM, ARTIFICIAL intelligence, AUTOMOBILE security measures, INTERNET security, AUTOMOTIVE engineering