Search Results - denoising autoencoder network

Conditioned fully convolutional denoising autoencoder for multi-target NILM

Authors: García, Diego Pérez, Daniel Papapetrou, Panagiotis et al.

Source: Neural Computing & Applications. 37(17):10491-10505

Subject Terms: Non-intrusive Load Monitoring, Energy Efficiency, Deep Convolutional Neural Networks, Interpretability, Multi-target NILM models, data- och systemvetenskap, Computer and Systems Sciences

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-237967
https://doi.org/10.1007/s00521-024-10552-0

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

Authors: Xing Cheng Deli Chen Shuaishuai Li et al.

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

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

Access URL: https://doaj.org/article/678c4448230241fc87fe49f3e61edf69

Identification Method for Missing Cotter Pins in Catenary Systems Based on Stacked Denoising Autoencoder Network

Authors: SHAN Chonghao

Source: Chengshi guidao jiaotong yanjiu, Vol 27, Iss 10, Pp 274-279 (2024)

Subject Terms: Transportation engineering, yolo v5 algorithm, TA1001-1280, catenary, metro, cotter pin, stacked denoising autoencoder

Access URL: https://doaj.org/article/1e4b6be3a39145f5af24cbf54545d3c4

Wind turbine generator early fault diagnosis using LSTM-based stacked denoising autoencoder network and stacking algorithm

Authors: Junshuai Yan Yongqian Liu Hang Meng et al.

Source: International Journal of Green Energy. 21:2477-2492

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

Authors: Ting Yan Zhenpeng Yan Lili Liu et al.

Source: Front Comput Neurosci
Frontiers in Computational Neuroscience, Vol 16 (2023)

Subject Terms: 0301 basic medicine, glioblastoma multiforme, 0303 health sciences, 03 medical and health sciences, denoising autoencoder, deep learning, Neurosciences. Biological psychiatry. Neuropsychiatry, time-dependent ROC curve, Cox proportional hazard regression, survival prediction, RC321-571, Neuroscience, 3. Good health

Access URL: https://pubmed.ncbi.nlm.nih.gov/36704230
https://doaj.org/article/1b901e4cde1f4d28b246bcd3028ee0b6

Evaluation Algorithm of Teaching Work Quality in Colleges and Universities Based on Deep Denoising Autoencoder Network

Authors: Yu Liu

Source: Mobile Information Systems. 2021:1-7

Subject Terms: 4. Education, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

File Description: text/xhtml

Access URL: https://downloads.hindawi.com/journals/misy/2021/8161985.pdf
https://downloads.hindawi.com/journals/misy/2021/8161985.pdf
https://www.hindawi.com/journals/misy/2021/8161985/
https://doi.org/10.1155/2021/8161985
https://dblp.uni-trier.de/db/journals/mis/mis2021.html#Liu21e

U-Net-Based Denoising Autoencoder Network for De-Speckling in Fetal Ultrasound Images

Authors: S. Satish N. Herald Anantha Rufus M. Antony Freeda Rani et al.

Source: Lecture Notes in Networks and Systems ISBN: 9789819970926

Stacked Denoising Autoencoder network for short-term prediction of electrical Algerian load

Authors: Chelabi, Hiba Khadir, Mohamed Tarek Chikhaoui, Belkacem

Source: 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT). :189-194

Subject Terms: 13. Climate action, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy

File Description: pdf

Access URL: https://r-libre.teluq.ca/1966/
https://dblp.uni-trier.de/db/conf/codit/codit2020.html#HibaTB20

Variational Type Graph Autoencoder for Denoising on Event Recommendation.

Authors: Zhang, Shuo Meng, Xiangwu Zhang, Yujie

Source: ACM Transactions on Information Systems. Jan2025, Vol. 43 Issue 1, p1-27. 27p.

Subject Terms: *Artificial intelligence, Autoencoder, Latent variables, Signal denoising, Random graphs

Infrared Dim and Small Target Detection Based on Denoising Autoencoder Network

Authors: Manshu Shi Huan Wang

Source: Mobile Networks and Applications. 25:1469-1483

Subject Terms: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://dblp.uni-trier.de/db/journals/monet/monet25.html#ShiW20
https://link.springer.com/10.1007/s11036-019-01377-6
https://link.springer.com/article/10.1007/s11036-019-01377-6
https://doi.org/10.1007/s11036-019-01377-6

Partial Discharge Pattern Recognition Based on Stacked Denoising Autoencoder Network: Computer and AI Applications in Power Industry

Authors: Suya Li Bin Wei Jin He et al.

Source: Proceedings of the 2nd International Conference on Information Technologies and Electrical Engineering. :1-6

Subject Terms: 0301 basic medicine, 03 medical and health sciences, 13. Climate action, 0502 economics and business, 05 social sciences

Molecular Dynamics Properties without the Full Trajectory: A Denoising Autoencoder Network for Properties of Simple Liquids

Authors: Alireza Moradzadeh N. R. Aluru

Source: The Journal of Physical Chemistry Letters. 10:7568-7576

Subject Terms: 0103 physical sciences, 01 natural sciences

Access URL: https://pubmed.ncbi.nlm.nih.gov/31738568
https://www.ncbi.nlm.nih.gov/pubmed/31738568
https://pubs.acs.org/doi/full/10.1021/acs.jpclett.9b02820
https://par.nsf.gov/servlets/purl/10146996
https://par.nsf.gov/biblio/10146996-molecular-dynamics-properties-without-full-trajectory-denoising-autoencoder-network-properties-simple-liquids
https://pubs.acs.org/doi/10.1021/acs.jpclett.9b02820

Deep Denoising Autoencoder Networks for Hyperspectral Unmixing

Authors: Keyao Wen Shunmin Zhao Kedi Hu et al.

Source: Lecture Notes in Electrical Engineering ISBN: 9789811584107

Access URL: https://link.springer.com/chapter/10.1007/978-981-15-8411-4_62

A novel malicious domain detection approach based on multi-perspective spatiotemporal alignment learning.

Authors: JIN, Xueqi XU, Hongquan HUANG, Yinqiang et al.

Source: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Aug2025, Vol. 47 Issue 8, p1417-1424. 8p.

Subject Terms: *CONVOLUTIONAL neural networks, *AUTOENCODERS, *CLASSIFICATION, *PROTOTYPES, *ENCODING

一种基于降噪自编码神经网络的积雪产品去云方法.

Alternate Title: Cloud removal for snow products based on denoising autoencoder artificial neural network.

Authors: 张永宏 陈帅 王剑庚 et al.

Source: Journal of Nanjing University of Information Science & Technology (Natural Science Edition) / Nanjing Xinxi Gongcheng Daxue Xuebao (ziran kexue ban). 2023, Vol. 15 Issue 2, p169-179. 11p.

Visual tracking based on stacked Denoising Autoencoder network with genetic algorithm optimization

Authors: Dejun Mu Dawei Guo Hang Liu et al.

Source: Multimedia Tools and Applications. 77:4253-4269

Subject Terms: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://dblp.uni-trier.de/db/journals/mta/mta77.html#HuaMGL18
https://link.springer.com/article/10.1007/s11042-017-4702-1
https://doi.org/10.1007/s11042-017-4702-1

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

Authors: Shuang Mei Yudan Wang Guojun Wen

Source: Sensors, Vol 18, Iss 4, p 1064 (2018)

Subject Terms: fabric defect detection, unsupervised learning, deep neural network, convolutional denoising autoencoder, Gaussian pyramid, Chemical technology, TP1-1185

File Description: electronic resource

Relation: http://www.mdpi.com/1424-8220/18/4/1064; https://doaj.org/toc/1424-8220

Access URL: https://doaj.org/article/5e389f2bca1348e780347c16d402a82d

基于CBDAE 和 TCN-Transformer 的工业传感器时间序列预测.

Alternate Title: Industrial sensor time series prediction based on CBDAE and TCN-Transformer.

Authors: 许涛 南新元 蔡鑫 et al.

Source: Journal of Nanjing University of Information Science & Technology (Natural Science Edition) / Nanjing Xinxi Gongcheng Daxue Xuebao (ziran kexue ban). Jul2025, Vol. 17 Issue 4, p455-466. 12p.

Subject Terms: *Transformer models, Process capability, Autoencoder, Time series analysis, Data scrubbing

Temporal convolutional denoising autoencoder network for air pollution prediction with missing values

Authors: Santos Kumar Das K. Krishna Rani Samal Korra Sathya Babu

Source: Urban Climate. 38:100872

Subject Terms: 13. Climate action, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 01 natural sciences, 3. Good health, 0105 earth and related environmental sciences

Access URL: https://www.sciencedirect.com/science/article/pii/S2212095521001024

Conditioned Fully Convolutional Denoising Autoencoder for Energy Disaggregation

Authors: García, Diego Pérez, Daniel Papapetrou, Panagiotis et al.

Source: Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG International Workshops IFIP Advances in Information and Communication Technology. :421-433

Subject Terms: energy dissagregation, NILM, convolutional neural networks, data- och systemvetenskap, Computer and Systems Sciences

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-225171