Suchergebnisse - (("autoencoder application") OR ("Autoencoder publications"))*

Slow feature‐constrained decomposition autoencoder: Application to process anomaly detection and localization.

Autoren: Jia, Mingwei Jiang, Lingwei Hu, Junhao et al.

Quelle: International Journal of Adaptive Control & Signal Processing. Jul2025, Vol. 39 Issue 7, p1483-1502. 20p.

Schlagwörter: *AUTOENCODERS, *MANUFACTURING processes, *SIGNAL processing, *TREND analysis, *OUTLIER detection, *MEASUREMENT uncertainty (Statistics), *PROCESS control systems

Autoencoder Application for Artwork Authentication Fingerprinting Using the Craquelure Network

Autoren: Gianina Chirosca Roxana Radvan Matei Pop et al.

Quelle: Applied Sciences, Vol 15, Iss 16, p 9014 (2025)

Schlagwörter: craquelure detection, convolutional neural networks, image processing, 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/2076-3417/15/16/9014; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/0c411f6328ac45c29e22cd00b317ec70

Autoencoder Application for Artwork Authentication Fingerprinting Using the Craquelure Network.

Autoren: Chirosca, Gianina Radvan, Roxana Pop, Matei et al.

Quelle: Applied Sciences (2076-3417); Aug2025, Vol. 15 Issue 16, p9014, 13p

Schlagwörter: ART authentication, DEEP learning, ARTISTIC creation, IMAGE analysis, FEATURE extraction, PATTERN perception, EMBEDDINGS (Mathematics), AUTOENCODERS

Autoencoder Application for Anomaly Detection in Power Consumption of Lighting Systems

Autoren: Tomasz Śmiałkowski Andrzej Czyżewski

Quelle: IEEE Access, Vol 11, Pp 124150-124162 (2023)

Schlagwörter: autoencoder, machine learning, smart city, 11. Sustainability, road lighting systems, smart meters, Anomaly detection, Electrical engineering. Electronics. Nuclear engineering, 7. Clean energy, TK1-9971

Zugangs-URL: https://doaj.org/article/682c1dc2a6ec473983d8185865ed5f08

A Pansharpening Based on the Non-Subsampled Contourlet Transform and Convolutional Autoencoder: Application to QuickBird Imagery

Autoren: Ahmad Al Smadi Shuyuan Yang Ahed Abugabah et al.

Quelle: IEEE Access, Vol 10, Pp 44778-44788 (2022)

Schlagwörter: Pansharpening, multispectral image fusion, convolutional autoencoder, NSCT, remote sensing, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Dateibeschreibung: electronic resource

Relation: https://ieeexplore.ieee.org/document/9761938/; https://doaj.org/toc/2169-3536

Zugangs-URL: https://doaj.org/article/34ee88da446440829ab7702d7885f6da

An Invariant Representation of Coupler Curves Using a Variational Autoencoder: Application to Path Synthesis of Four-Bar Mechanisms

Autoren: Anar Nurizada Anurag Purwar

Quelle: Volume 8: 47th Mechanisms and Robotics Conference (MR).

Schlagwörter: 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Disentangled-Multimodal Adversarial Autoencoder: Application to Infant Age Prediction With Incomplete Multimodal Neuroimages

Autoren: Dan Hu Han Zhang Zhengwang Wu et al.

Quelle: IEEE Transactions on Medical Imaging. 39:4137-4149

Schlagwörter: Male, 03 medical and health sciences, 0302 clinical medicine, Brain, Humans, Infant, Neuroimaging, Magnetic Resonance Imaging, Algorithms

Zugangs-URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773223
https://pubmed.ncbi.nlm.nih.gov/32746154
https://koreauniv.pure.elsevier.com/en/publications/disentangled-multimodal-adversarial-autoencoder-application-to-in
https://pubmed.ncbi.nlm.nih.gov/32746154/
https://www.ncbi.nlm.nih.gov/pubmed/32746154
http://doi.org/10.1109/TMI.2020.3013825
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773223
https://dblp.uni-trier.de/db/journals/tmi/tmi39.html#HuZWWWSLLS20

Convolutional Autoencoder Application for Breast Cancer Classification

Autoren: Maryam Naderan Yuri Zaychenko

Quelle: 2020 IEEE 2nd International Conference on System Analysis & Intelligent Computing (SAIC). :1-4

Schlagwörter: 0301 basic medicine, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Zugangs-URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=9239139
http://xplorestaging.ieee.org/ielx7/9239099/9239115/09239139.pdf?arnumber=9239139

Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation

Autoren: An, Seunghwan Jeon, Jong-June

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, Machine Learning (stat.ML), 02 engineering and technology, 01 natural sciences, 0105 earth and related environmental sciences, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2302.11294

A Stacked Autoencoder Application for Residential Load Curve Forecast and Peak Shaving

Autoren: Xinan Wang Di Shi Yishen Wang et al.

Quelle: 2019 IEEE Power & Energy Society General Meeting (PESGM). :1-5

Schlagwörter: Signal Processing (eess.SP), FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Electrical Engineering and Systems Science - Signal Processing, 7. Clean energy

Zugangs-URL: http://arxiv.org/pdf/1902.08167
http://arxiv.org/abs/1902.08167
http://ui.adsabs.harvard.edu/abs/2019arXiv190208167W/abstract

CNN Based Autoencoder Application in Breast Cancer Image Retrieval

Autoren: Minarno, Agus Eko Ghufron, Kharisma Muzaki Sabrila, Trifebi Shina et al.

Quelle: 2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)

Verfügbarkeit: https://doi.org/10.1109/isitia52817.2021.9502205
http://xplorestaging.ieee.org/ielx7/9502176/9502189/09502205.pdf?arnumber=9502205

Autoencoders and their applications in machine learning: a survey

Autoren: Berahmand, Kamal Daneshfar, Fatemeh Salehi, Elaheh Sadat et al.

Quelle: Artificial Intelligence Review. 57

Schlagwörter: Autoencoder application, 0202 electrical engineering, electronic engineering, information engineering, Bottleneck layer, Feature extraction, Deep learning, Autoencoder, 02 engineering and technology, Reconstruction loss, Dimensionality reduction, Unsupervised learning

[Sparse Denoising Autoencoder Application in Identification of Counterfeit Pharmaceutical]

Autoren: Hui-hua, Yang Zhi-chao, Luo Shu-jie, Jiang et al.

Quelle: Guang pu xue yu guang pu fen xi = Guang pu. 36(9)

Schlagwörter: Machine Learning, Spectroscopy, Near-Infrared, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Neural Networks, Computer, 0503 education, Algorithms

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/30084593

Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems

Autoren: Chakraborty, Indrasis Chakraborty, Rudrasis Vrabie, Draguna

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, 0209 industrial biotechnology, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Statistics - Machine Learning, Machine Learning (stat.ML), 02 engineering and technology, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/1804.05320

A variational autoencoder application for real-time anomaly detection at CMS

Autoren: Valente, Lorenzo Bonacorsi, Daniele Anzalone, Luca et al.

Weitere Verfasser: Valente, Lorenzo Bonacorsi, Daniele Anzalone, Luca et al.

Schlagwörter: Machine Learning,Deep Learning,HEP,Model Compression,Quantization-Aware Training,HLS4ML,Anomaly Detection,Variational Autoencoder,FPGA, Physics [LM-DM270]

Dateibeschreibung: application/pdf

Relation: https://amslaurea.unibo.it/id/eprint/28788/1/thesisLorenzo.pdf; Valente, Lorenzo (2023) A variational autoencoder application for real-time anomaly detection at CMS. [Laurea magistrale], Università di Bologna, Corso di Studio in Physics [LM-DM270]

Verfügbarkeit: https://amslaurea.unibo.it/id/eprint/28788/
https://amslaurea.unibo.it/id/eprint/28788/1/thesisLorenzo.pdf

Disentangled-Multimodal Adversarial Autoencoder: Application to Infant Age Prediction With Incomplete Multimodal Neuroimages.

Autoren: Hu, Dan Zhang, Han Wu, Zhengwang et al.

Quelle: IEEE Transactions on Medical Imaging; Dec2020, Vol. 39 Issue 12, p4137-4149, 13p

Schlagwörter: COMPUTER-assisted image analysis (Medicine), FUNCTIONAL magnetic resonance imaging, FORECASTING, FUNCTIONAL connectivity, INFANTS, MULTIPLE imputation (Statistics)

Anomaly detection in urban lighting systems using autoencoder and transformer algorithms.

Autoren: Śmiałkowski, Tomasz Czyżewski, Andrzej

Quelle: Scientific Reports; 10/15/2025, Vol. 15 Issue 1, p1-13, 13p

Schlagwörter: AUTOENCODERS, MACHINE learning, ENERGY consumption, OUTLIER detection, MUNICIPAL lighting, ARTIFICIAL intelligence, TRANSFORMER models, REAL-time computing

Moving forward in water distribution network leak identification through an innovative features engineering step.

Autoren: Damonti, Elvio Bernasconi, Giancarlo

Quelle: Digital Signal Processing. Jan2026:Part C, Vol. 168, pN.PAG-N.PAG. 1p.

Schlagwörter: *WATER distribution, *LEAK detection, *FEATURE selection, *PATTERN perception, *AUTOENCODERS, *CONVOLUTIONAL neural networks, *OUTLIER detection

Intelligent Tumor Synthesis Based on Medical Image Knowledge for Liver Tumor Segmentation.

Autoren: Ji, Hefeng Xiao, Jing Lin, Jiefan et al.

Quelle: ACM Transactions on Multimedia Computing, Communications & Applications; Sep2025, Vol. 21 Issue 9, p1-23, 23p

Schlagwörter: COMPUTER-assisted image analysis (Medicine), LIVER tumors, CLINICAL medical education, IMAGE analysis, PRINCIPAL components analysis

Comparative Analysis of Recurrent vs. Temporal Convolutional Autoencoders for Detecting Container Impacts During Quay Crane Handling.

Autoren: Jakovlev, Sergej Eglynas, Tomas Pocevicius, Edvinas et al.

Quelle: Journal of Marine Science & Engineering; Jul2025, Vol. 13 Issue 7, p1231, 23p

Schlagwörter: AUTOENCODERS, EDGE computing, CONVOLUTIONAL neural networks, CRANES (Machinery), OUTLIER detection, SHIPPING containers, REAL-time computing