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检索条件"主题词=Variational autoencoder"
1569 条 记 录,以下是681-690 订阅
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Anomaly Signal Imputation Using Latent Coordination Relations
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IEEE ACCESS 2024年 12卷 117072-117089页
作者: Chalongvorachai, Thasorn Woraratpanya, Kuntpong King Mongkuts Inst Technol Ladkrabang Sch Informat Technol Bangkok 10520 Thailand
Missing data is a critical challenge in industrial data analysis, particularly during anomaly incidents caused by system equipment malfunctions or, more critically, by cyberattacks in industrial systems. It impedes ef... 详细信息
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Nonlinear Slow Feature Analysis for Oscillating Characteristics Under Deep Encoder-Decoder Framework
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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年 第7期20卷 9568-9578页
作者: Puli, Vamsi Krishna Huang, Biao Univ Alberta Dept Chem & Mat Engn Edmonton T6G 2V4 AB Canada
Slow feature analysis aims to linearly transform measured data into uncorrelated signals that vary from slow to fast. While earlier extensions successfully extracted slow features from nonlinear sequential data, they ... 详细信息
来源: 评论
Enhanced autoencoder-based fraud detection: a novel approach with noise factor encoding and SMOTE
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KNOWLEDGE AND INFORMATION SYSTEMS 2024年 第1期66卷 635-652页
作者: Cakir, Mert Yilmaz Sirin, Yahya Istanbul Sabahattin Zaim Univ Comp Sci & Engn TR-34303 Istanbul Turkiye
Fraud detection is a critical task across various domains, requiring accurate identification of fraudulent activities within vast arrays of transactional data. The significant challenges in effectively detecting fraud... 详细信息
来源: 评论
Learning-Based Image Compression With Parameter-Adaptive Rate-Constrained Loss
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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 1099-1103页
作者: Guerin Jr, Nilson D. da Silva, Renam Castro Macchiavello, Bruno Univ Brasilia Dept Comp Sci BR-70910900 Brasilia Brazil Univ Brasilia Fac UnB Gama BR-72444240 Gama Leste Brazil
In recent years, the crucial task of image compression has been addressed by end-to-end neural network methods. However, achieving fine-grained rate control in this new paradigm has presented challenges. In our previo... 详细信息
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Unsupervised learning of charge-discharge cycles from various lithium-ion battery cells to visualize dataset characteristics and to interpret model performance
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Energy and AI 2024年 第3期17卷 397-405页
作者: Akihiro Yamashita Sascha Berg Egbert Figgemeier Helmholtz Institute Münster:Ionics in Energy Storage(IMD-4/HI MS) Forschungszentrum JülichJülichGermany Institute for Power Electronics and Electrical Drives(ISEA) RWTH Aachen UniversityAachenGermany Jülich Aachen Research Alliance JARA-EnergyGermany
Machine learning (ML) is a rapidly growing tool even in the lithium-ion battery (LIB) research field. To utilize this tool, more and more datasets have been published. However, applicability of a ML model to different... 详细信息
来源: 评论
A data-driven distributed process monitoring method for industry manufacturing systems
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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2024年 第7期46卷 1296-1316页
作者: Yin, Ming Tian, Jiayi Zhu, Dan Wang, Yibo Jiang, Jijiao Northwestern Polytech Univ Sch Software Xian 710129 Shaanxi Peoples R China Iowa State Univ Debbie & Jerry Ivy Coll Business Ames IA USA Northwestern Polytech Univ Sch Management Xiaan Peoples R China
Process monitoring technology can help make the right decisions in manufacturing, but the complexity and scale of modern process industry processes render process monitoring difficult. Existing data-driven process mon... 详细信息
来源: 评论
Deep federated learning hybrid optimization model based on encrypted aligned data
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PATTERN RECOGNITION 2024年 148卷
作者: Zhao, Zhongnan Liang, Xiaoliang Huang, Hai Wang, Kun Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150080 Peoples R China Harbin Engn Univ Sch Comp Sci & Technol Harbin 150001 Peoples R China
Federated learning can achieve multi-party data-collaborative applications while safeguarding personal privacy. However, the process often leads to a decline in the quality of sample data due to a substantial amount o... 详细信息
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Discriminative multimodal learning via conditional priors in generative models
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NEURAL NETWORKS 2024年 169卷 417-430页
作者: Mancisidor, Rogelio A. Kampffmeyer, Michael Aas, Kjersti Jenssen, Robert BI Norwegian Business Sch Dept Data Sci & Analyt Nydalsveien 37 N-0484 Oslo Norway UiT Arctic Univ Norway Fac Sci & Technol Dept Phys & Technol Hansine Hansens Veg 18 N-9037 Tromso Norway Norwegian Comp Ctr POB 114 Blindern Oslo Norway
Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data, which depict an object from different viewpoints. These two learning me... 详细信息
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System Condition Monitoring Based on a Standardized Latent Space and the Nataf Transform
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IEEE ACCESS 2024年 12卷 32637-32659页
作者: Oliveira-Filho, Adaiton Zemouri, Ryad Pelletier, Francis Tahan, Antoine Ecole Technol Super Dept Mech Engn Montreal PQ H3C 1K3 Canada Res Ctr Hydroquebec Varennes PQ J3X 1S1 Canada Power Factors Brossard PQ J4Z 1A7 Canada
This work introduces a new condition monitoring approach for complex systems based on a standardized latent space representation. Latent variable models such as the variational autoencoders are widely used to analyze ... 详细信息
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Data Augmentation for Classification of Multi-Domain Tension Signals
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INFORMATICA 2024年 第4期35卷 883-908页
作者: Zvirblis, Tadas Piksrys, Armantas Bzinkowski, Damian Rucki, Miroslaw Kilikevicius, Arturas Kurasova, Olga Vilnius Univ Inst Data Sci & Digital Technol Vilnius Lithuania Vilnius Univ Inst Comp Sci Vilnius Lithuania Kazimierz Pulaski Univ Technol & Humanities Radom Fac Mech Engn Radom Poland Vilnius Gediminas Tech Univ Inst Mech Sci Vilnius Lithuania
There are different deep neural network (DNN) architectures and methods for performing augmentation on time series data, but not all the methods can be adapted for specific datasets. This article explores the developm... 详细信息
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