A Client-Cloud-Chain Data Annotation System of Internet of Things for Semi-Supervised Missing Data
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Li, Yang & Zhang, Meng & Chen, Chen, 2022. "A Deep-Learning intelligent system incorporating data augmentation for Short-Term voltage stability assessment of power systems," Applied Energy, Elsevier, vol. 308(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
- Manuel Dario Jaramillo & Diego Francisco Carrión & Jorge Paul Muñoz, 2023. "A Novel Methodology for Strengthening Stability in Electrical Power Systems by Considering Fast Voltage Stability Index under N − 1 Scenarios," Energies, MDPI, vol. 16(8), pages 1-23, April.
- Xie, Haonan & Jiang, Meihui & Zhang, Dongdong & Goh, Hui Hwang & Ahmad, Tanveer & Liu, Hui & Liu, Tianhao & Wang, Shuyao & Wu, Thomas, 2023. "IntelliSense technology in the new power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 177(C).
- Oludamilare Bode Adewuyi & Komla A. Folly & David T. O. Oyedokun & Emmanuel Idowu Ogunwole, 2022. "Power System Voltage Stability Margin Estimation Using Adaptive Neuro-Fuzzy Inference System Enhanced with Particle Swarm Optimization," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
- Shi, Zhongtuo & Yao, Wei & Zhao, Yifan & Ai, Xiaomeng & Wen, Jinyu & Cheng, Shijie, 2024. "Two-stage weakly supervised learning to mitigate label noise for intelligent identification of power system dominant instability mode," Applied Energy, Elsevier, vol. 359(C).
- Li, Yang & Wang, Ruinong & Li, Yuanzheng & Zhang, Meng & Long, Chao, 2023. "Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach," Applied Energy, Elsevier, vol. 329(C).
- Li, Yang & Han, Meng & Shahidehpour, Mohammad & Li, Jiazheng & Long, Chao, 2023. "Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response," Applied Energy, Elsevier, vol. 335(C).
- Zhan, Xianwen & Han, Song & Rong, Na & Cao, Yun, 2023. "A hybrid transfer learning method for transient stability prediction considering sample imbalance," Applied Energy, Elsevier, vol. 333(C).
- Xiaoying Ren & Yongqian Liu & Fei Zhang & Lingfeng Li, 2024. "A Deep Learning Quantile Regression Photovoltaic Power-Forecasting Method under a Priori Knowledge Injection," Energies, MDPI, vol. 17(16), pages 1-25, August.
- Li, Yang & Cao, Jiting & Xu, Yan & Zhu, Lipeng & Dong, Zhao Yang, 2024. "Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
More about this item
Keywords
distributed learning; graph convolutional networks; incomplete data; data annotation;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:21:p:4543-:d:1273909. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.