Výsledky vyhledávání - RNN-LSTM autoencoder

Variational Autoencoders for Anomaly Detection and Transfer Knowledge in Electricity and District Heating Consumption

Autoři: Shahid, Zahraa Khais Saguna, Saguna Åhlund, Christer

Zdroj: IEEE transactions on industry applications. 60(5):7437-7450

Témata: Anomaly Detection, Anomaly detection, Autoencoder, Buildings, CNNLSTM, District heating, Electricity, electricity and district heating Daily Consumption, Energy consumption, Resistance heating, RNN-LSTM, School buildings, transfer knowledge, VAE, Water heating, Pervasive Mobile Computing, Distribuerade datorsystem

Popis souboru: print

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-108412

Short-Term Fault Prediction of Wind Turbines Based on Integrated RNN-LSTM

Autoři: V Siva Brahmaiah Rama Sung-Ho Hur Jung-Min Yang

Zdroj: IEEE Access, Vol 12, Pp 22465-22478 (2024)

Témata: long short-term memory autoencoder, wind turbine, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, Extreme gradient boosting, self-organizing maps, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, 7. Clean energy, fault prediction, random forest regressor, TK1-9971

Přístupová URL adresa: https://doaj.org/article/6f3b9384d6424d1bbb9c82332b37fbef

On the Sequential Data Models in Side-Channel Analysis: RNN, LSTM, GRU Hyperparameters, Autoencoder and Embedding Layer

Autoři: Mulders, Maurits (author)

Témata: Side-channel analysis, Machine learning, deep learning, Recurrent networks, long short-term memory networks, Autoencoder, embedding, master thesis

URL: http://resolver.tudelft.nl/uuid:871da1ae-3d9f-4644-8aa3-d94bf7be94c1

Autoencoders for Anomaly Detection in Electricity and District Heating Consumption: A Case Study in School Buildings in Sweden

Autoři: Shahid, Zahraa Khais Saguna, Saguna Åhlund, Christer

Zdroj: Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, (EEEIC / I&CPS Europe 2023).

Témata: Anomaly Detection, Autoencoder, CNN-LSTM, electricity and district heating, Daily Consumption, RNN-LSTM, school buildings, Pervasive Mobile Computing, Distribuerade datorsystem

Popis souboru: print

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-101201

A Deep Convolutional Autoencoder with Metaheuristic Optimization based Feature Reduction Framework for Genetic Disorder Detection Model.

Autoři: Puvaneswari, S. Indırani, G.

Zdroj: Journal of Intelligent Systems & Internet of Things; 2026, Vol. 18 Issue 1, p361-373, 13p

Témata: GENETIC disorders, DEEP learning, DIAGNOSIS, OPTIMIZATION algorithms, FEATURE selection, METAHEURISTIC algorithms, NUCLEOTIDE sequencing, AUTOENCODERS

Deep Learning‐Based Carbon Emission Monitoring and Prediction Study for Bridge Reconstruction and Expansion Projects.

Autoři: Liu, Shuai Wang, Yangchun Zhang, Wei a další

Zdroj: Advances in Civil Engineering; 10/3/2025, Vol. 2025, p1-10, 10p

Témata: DEEP learning, ARTIFICIAL intelligence, ENVIRONMENTAL impact analysis, FORECASTING, BRIDGE maintenance & repair, AUTOENCODERS, BUILDING additions

Supervised autoencoder MLP for financial time series forecasting.

Autoři: Bieganowski, Bartosz Ślepaczuk, Robert

Zdroj: Journal of Big Data; 8/22/2025, Vol. 12 Issue 1, p1-45, 45p

Témata: ARTIFICIAL neural networks, AUTOENCODERS, FOREIGN exchange rates, RISK-return relationships, STANDARD & Poor's 500 Index, BUSINESS forecasting, DATA augmentation

Towards Intelligent Threat Detection in 6G Networks Using Deep Autoencoder.

Autoři: Mhawi, Doaa N. Oleiwi, Haider W. Al-Raweshidy, Hamed

Zdroj: Electronics (2079-9292); Aug2025, Vol. 14 Issue 15, p2983, 17p

Témata: 6G networks, INTRUSION detection systems (Computer security), FEATURE extraction, ANOMALY detection (Computer security), MACHINE learning, INTERNET security, METADATA, AUTOENCODERS

Anomalous Radio Signal Detection Based on an Adversarial Autoencoder.

Autoři: Wang, Huan Luo, Yushu Long, Fan a další

Zdroj: Electronics (2079-9292); May2025, Vol. 14 Issue 9, p1785, 19p

Témata: GENERATIVE adversarial networks, AUTOENCODERS, FREQUENCY-domain analysis, SIGNAL detection, SIGNAL reconstruction

A performance analysis of convolutional autoencoder modified WaveGAN architectures for realistic 12 lead electrocardiogram synthesis.

Autoři: Liu, Jiaqi Chui, Kwok Tai Lee, Lap-Kei a další

Zdroj: Scientific Reports; 10/17/2025, Vol. 15 Issue 1, p1-22, 22p

Témata: GENERATIVE adversarial networks, ELECTROCARDIOGRAPHY, ROUTINE diagnostic tests, FEATURE extraction, PROBABILISTIC generative models, DEEP learning, RESEARCH evaluation, DATA augmentation

Comprehensive Analysis and Improved Techniques for Anomaly Detection in Time Series Data with Autoencoder Models.

Autoři: Erniyazov, Sarvarbek Yong-Min Kim Jaleel, Mohammed Abdul a další

Zdroj: International Journal on Advanced Science, Engineering & Information Technology; 2024, Vol. 14 Issue 6, p1861-1867, 7p

Témata: LONG short-term memory, AUTOENCODERS, SYSTEM failures, TIME series analysis, DATA modeling

Noise-robust markerless video gait anomaly detection via two-stage acquisition and LSTM autoencoders.

Autoři: Yoon, Hyerin Jo, Eunah Ryu, Seungjae a další

Zdroj: Scientific Reports; 11/26/2025, Vol. 15 Issue 1, p1-18, 18p

Témata: SARCOPENIA, PARKINSON'S disease, ORTHOPEDICS, RECURRENT neural networks, GAIT disorders, MOTION analysis

An Integrated ARMA-Based Deep Autoencoder and GRU Classifier System for Enhanced Recognition of Daily Hand Activities.

Autoři: Rivera, Patricio Valarezo, Edwin Kim, Tae-Seong

Zdroj: International Journal of Pattern Recognition & Artificial Intelligence; May2021, Vol. 35 Issue 06, pN.PAG-N.PAG, 19p

Témata: HUMAN activity recognition, DEEP learning, HAND signals, HUMAN-computer interaction, AUTOREGRESSIVE models

Compression of EEG signals with the LSTM-autoencoder via domain adaptation approach.

Autoři: Liu, Yongfei Yang, Fan Wu, Binbin

Zdroj: Computer Methods in Biomechanics & Biomedical Engineering; Sep2025, Vol. 28 Issue 12, p1857-1870, 14p

Témata: ARTIFICIAL neural networks, KNOWLEDGE transfer, DATA transmission systems, COMPUTER performance

A Multi-Encoder BHTP Autoencoder for Robust Lithium Battery SOH Prediction Under Small-Sample Scenarios.

Autoři: Liu, Chang Wang, Shunli Ma, Zhiqiang a další

Zdroj: Batteries; May2025, Vol. 11 Issue 5, p180, 22p

Témata: BATTERY management systems, LITHIUM cobalt oxide, LITHIUM cells, DEEP learning, AUTOENCODERS

Comparative analysis of deep learning architectures in solar power prediction.

Autoři: Abdelsattar, Montaser Azim, Mohamed A. AbdelMoety, Ahmed a další

Zdroj: Scientific Reports; 8/28/2025, Vol. 15 Issue 1, p1-20, 20p

Témata: DEEP learning, FORECASTING, CONVOLUTIONAL neural networks, AUTOENCODERS, TRANSFORMER models, ELECTRIC power distribution grids, SMART power grids, ENERGY consumption forecasting

Research on 3D convolutional autoencoder enhanced metro abnormal behavior detection based on multi-level attentional memory.

Autoři: Ye, Run Zhang, Kun Zhang, Cheng a další

Zdroj: Multimedia Tools & Applications; Jun2025, Vol. 84 Issue 21, p23861-23879, 19p

Témata: AUTOENCODERS, DEEP learning, PASSENGER traffic, SUBWAYS, PROBLEM solving, TRAFFIC monitoring, PEDESTRIANS

Efficient Real-Time Anomaly Detection in IoT Networks Using One-Class Autoencoder and Deep Neural Network.

Autoři: Ayad, Aya G. El-Gayar, Mostafa M. Hikal, Noha A. a další

Zdroj: Electronics (2079-9292); Jan2025, Vol. 14 Issue 1, p104, 25p

Témata: ARTIFICIAL neural networks, AUTOENCODERS, ANOMALY detection (Computer security), INTERNET of things, INTRUSION detection systems (Computer security)

A novel methodology based on long short-term memory stacked autoencoders for unsupervised detection of abnormal working conditions in semiconductor manufacturing systems.

Autoři: Hosseinpour, Fatemeh Ahmed, Ibrahim Baraldi, Piero a další

Zdroj: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk & Reliability; Oct2025, Vol. 239 Issue 5, p1115-1133, 19p

Enhancing parkinson disease detection through feature based deep learning with autoencoders and neural networks.

Autoři: Valarmathi, P. Suganya, Y. Saranya, K. R. a další

Zdroj: Scientific Reports; 3/13/2025, Vol. 15 Issue 1, p1-28, 28p

Témata: ARTIFICIAL neural networks, PARKINSON'S disease, ARTIFICIAL intelligence, AUTOENCODERS, MEDICAL sciences, DEEP learning