Výsledky vyhľadávania - Distributed variational autoencoder

Feasibility of a Specklegram-Based Quasi-Distributed Temperature Sensor With Principal Component Analysis and Variational Autoencoder

Autori: Yue, Shichao Lu, Huizhen Li, Boyi a ďalší

Zdroj: IEEE Sensors Journal. 24(14):22410-22418

Predmety: Temperature sensors, Sensors, Adaptive optics, Optical fiber sensors, Optical variables control, Optical refraction, Optical fibers, Distributive temperature sensor, optical fiber, variational autoencoder

Popis súboru: print

Prístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-354598

A Novel Distributed Fault Detection Approach Based on the Variational Autoencoder Model

Autori: Chenghong Huang Yi Chai Zheren Zhu a ďalší

Zdroj: ACS Omega, Vol 7, Iss 3, Pp 2996-3006 (2022)

Predmety: Chemistry, QD1-999

Popis súboru: electronic resource

Relation: https://doaj.org/toc/2470-1343

Prístupová URL adresa: https://doaj.org/article/8ccc022014884859a95a37d5f8c8ef78

METHODS OF TRAINING VARIATIONAL AUTOENCODERS TO RECOGNIZE ANOMALOUS DATA IN DISTRIBUTED SYSTEMS

DistVAE: Distributed Variational Autoencoder for sequential recommendation

Autori: Li Li Jianbing Xiahou Fan Lin a ďalší

Zdroj: Knowledge-Based Systems. 264:110313

Predmety: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Attribute-relevant distributed variational autoencoder integrated with LSTM for dynamic industrial soft sensing

Autori: Yan-Lin He Xing-Yuan Li Jia-Hui Ma a ďalší

Zdroj: Engineering Applications of Artificial Intelligence. 119:105737

Predmety: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors.

Autori: Sengodan, Boopathi Chettiagounder Stanislaus, Prince Mary Arumugam, Sivakumar Sabapathy a ďalší

Zdroj: Sensors (14248220); Sep2024, Vol. 24 Issue 17, p5630, 19p

Predmety: DATA compression, DATA transmission systems, DISTRIBUTED sensors, COMPRESSED sensing, ENERGY consumption, WIRELESS sensor networks, DEEP learning, AUTOENCODERS

Variational autoencoder enhanced analysis of energy metabolism and autophagy in exercising cardiomyocytes

Autori: Liquan Chen Yun Yang

Zdroj: Experimental Biology and Medicine, Vol 250 (2025)

Predmety: myocardial cell, energy autophagy, variational autoencoder, metabolite concentration, gene expression, feature extraction and analysis, Biology (General), QH301-705.5, Medicine

Popis súboru: electronic resource

Relation: https://www.ebm-journal.org/articles/10.3389/ebm.2025.10489/full; https://doaj.org/toc/1535-3699

Prístupová URL adresa: https://doaj.org/article/d6fb0dadf10e4dca89bd3751b91e264f

Distributed temporal–spatial neighbourhood enhanced variational autoencoder for multiunit industrial plant‐wide process monitoring.

Autori: Yao, Zongyu Jiang, Qingchao Gu, Xingsheng a ďalší

Zdroj: Canadian Journal of Chemical Engineering; May2024, Vol. 102 Issue 5, p1917-1931, 15p

Predmety: MANUFACTURING processes, NEIGHBORHOODS, SEWAGE disposal plants

Early damage detection in bridges using a variational autoencoder-based hybrid unsupervised learning framework.

Autori: Pakzad SS Masoodi AR

Zdroj: Scientific reports [Sci Rep] 2025 Dec 04. Date of Electronic Publication: 2025 Dec 04.

Spôsob vydávania: Journal Article

Informácie o časopise: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Robust Synthetic Data Generation for Sequential Financial Models Using Hybrid Variational Autoencoder–Markov Chain Monte Carlo Architectures.

Autori: Bruni Prenestino, Francesco Barbierato, Enrico Gatti, Alice

Zdroj: Future Internet. Feb2025, Vol. 17 Issue 2, p95. 31p.

Predmety: *Machine learning, Markov chain Monte Carlo, Generative adversarial networks, Data privacy, Time series analysis

Textual Adversarial Training Method Based on Distributed Perturbation

Autori: Zhidong SHEN, Hengxian YUE

Zdroj: Jisuanji gongcheng, Vol 49, Iss 9, Pp 16-22 (2023)

Predmety: textual adversarial distribution, adversarial training(at), variational autoencoder, gradient descent, monte carlo sampling, Computer engineering. Computer hardware, TK7885-7895, Computer software, QA76.75-76.765

Popis súboru: electronic resource

Relation: https://www.ecice06.com/fileup/1000-3428/PDF/20230902.pdf; https://doaj.org/toc/1000-3428

Prístupová URL adresa: https://doaj.org/article/757aa41623054630a47e99e42e96d5d0

A Novel Distributed Fault Detection Approach Based on the Variational Autoencoder Model.

Autori: Huang, Chenghong Chai, Yi Zhu, Zheren a ďalší

Zdroj: ACS Omega. 1/25/2022, Vol. 7 Issue 3, p2996-3006. 11p.

Handling partial occlusions in facial expression recognition with variational autoencoder.

Autori: Kemmou, Abdelaali El Makrani, Adil El Azami, Ikram a ďalší

Zdroj: Bulletin of Electrical Engineering & Informatics; Oct2025, Vol. 14 Issue 5, p3600-3610, 11p

Predmety: EMOTION recognition, HUMAN-computer interaction, AUTOENCODERS

Variational AutoEncoders-LSTM based fault detection of time-dependent high dimensional processes.

Autori: Maged, Ahmed Lui, Chun Fai Haridy, Salah a ďalší

Zdroj: International Journal of Production Research; Feb2024, Vol. 62 Issue 4, p1092-1107, 16p

Predmety: DEEP learning, QUALITY control charts, MANUFACTURING processes, FALSE alarms, INDUSTRIAL costs, INTELLIGENT tutoring systems

Implementing machine learning in cyber security-based IoT for botnets security detection by applying recurrent variational autoencoder.

Autori: Tawfeek, Zaki Saeed Al-Hamami, Alaa Hussein Alshami, Ahmed L. a ďalší

Zdroj: AIP Conference Proceedings; 2024, Vol. 3207 Issue 1, p1-7, 7p

Predmety: COMPUTER network traffic, COMPUTER network security, BOTNETS, DENIAL of service attacks, INTERNET security

Robust Synthetic Data Generation for Sequential Financial Models Using Hybrid Variational Autoencoder–Markov Chain Monte Carlo Architectures

Autori: Francesco Bruni Prenestino Enrico Barbierato Alice Gatti

Zdroj: Future Internet, Vol 17, Iss 2, p 95 (2025)

Predmety: synthetic data, Markov chain Monte Carlo, variational autoencoders, Information technology, T58.5-58.64

Popis súboru: electronic resource

Relation: https://www.mdpi.com/1999-5903/17/2/95; https://doaj.org/toc/1999-5903

Prístupová URL adresa: https://doaj.org/article/eb3b5a2cbd054826a85a39f7c573d611

Botnet Detection Using Recurrent Variational Autoencoder

Autori: Kim, Jeeyung Sim, Alex Kim, Jinoh a ďalší

Predmety: botnet detection, anomaly scoring, Recurrent Neural Network, Variational Autoencoder, network security

Prístupová URL adresa: https://escholarship.org/uc/item/9r97h4rn

Small sample pipeline DR defect detection based on smooth variational autoencoder and enhanced detection head faster RCNN.

Autori: Zhang, Ting You, Tianyang Liu, Zhaoying a ďalší

Zdroj: Applied Intelligence; Jul2025, Vol. 55 Issue 10, p1-29, 29p

Predmety: AUTOENCODERS, SAMPLE size (Statistics), CLASSIFICATION, CALIBRATION

ScInfoVAE: interpretable dimensional reduction of single cell transcription data with variational autoencoders and extended mutual information regularization

Autori: Weiquan Pan Faning Long Jian Pan

Zdroj: BioData Mining, Vol 16, Iss 1, Pp 1-16 (2023)

Predmety: Unsupervised clustering, Deep neural network, Variational autoencoder, Mutual information, scRNA-seq data, Computer applications to medicine. Medical informatics, R858-859.7, Analysis, QA299.6-433

Popis súboru: electronic resource

Relation: https://doaj.org/toc/1756-0381

Prístupová URL adresa: https://doaj.org/article/28f52efd4ab645139ef703efade90824

Leveraging Variational Autoencoder with Hippopotamus Optimizer-Based Dimensionality Reduction Model for Attention Deficit Hyperactivity Disorder Diagnosis Data.

Autori: Deepaletchumi, N. Mala, R.

Zdroj: Journal of Intelligent Systems & Internet of Things; 2025, Vol. 16 Issue 1, p75-85, 11p

Predmety: DRUG side effects, AUTOENCODERS, ATTENTION-deficit hyperactivity disorder, DEEP learning, MACHINE learning, FEATURE selection