Graph-Frequency Domain Kalman Filtering for Industrial Pipe Networks Subject to Measurement Outliers-Reference-Cited by-同舟云学术

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Graph-Frequency Domain Kalman Filtering for Industrial Pipe Networks Subject to Measurement Outliers

Published:2024-05 Issue:5 Volume:20 Page:7977-7985
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Su Lei¹^ORCID,Han Zhongyang¹^ORCID,Zhao Jun¹^ORCID,Wang Wei¹^ORCID

Affiliation:

1. School of Control Science and Engineering, Dalian University of Technology, Dalian, China

Funder

National Natural Science Foundation of China

LiaoNing Revitalization Talents Program

Sci-Tech Talent Innovation Support Program of Dalian

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10521545/10462617.pdf?arnumber=10462617

Reference32 articles.

1. Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach

2. Spatiotemporal Prediction for Energy System of Steel Industry by Generalized Tensor Granularity Based Evolving Type-2 Fuzzy Neural Network

3. Outlier Detection for Process Monitoring in Industrial Cyber-Physical Systems

4. Robust Online Sequential RVFLNs for Data Modeling of Dynamic Time-Varying Systems With Application of an Ironmaking Blast Furnace

5. Data-driven Soft Sensors in the process industry

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. GSP-KalmanNet: Tracking Graph Signals via Neural-Aided Kalman Filtering;IEEE Transactions on Signal Processing;2024

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