CDSTTN: A Data Imputation Method for Cyber-Physical Systems by Causal Dense Spatial–Temporal Transformer Network-Reference-Cited by-同舟云学术

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CDSTTN: A Data Imputation Method for Cyber-Physical Systems by Causal Dense Spatial–Temporal Transformer Network

Published:2023-09 Issue:3 Volume:13 Page:851-860
ISSN:2156-3357
Container-title:IEEE Journal on Emerging and Selected Topics in Circuits and Systems
language:
Short-container-title:IEEE J. Emerg. Sel. Topics Circuits Syst.

Author:

Qiu Chenmeng¹,Li Yuzhi¹,Kang Mingyu²,Chen Duxin¹^ORCID,Yu Wenwu³^ORCID

Affiliation:

1. Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, School of Mathematics, Southeast University, Nanjing, China

2. Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, School of Cyber Science and Engineering, Southeast University, Nanjing, China

3. Frontiers Science Center for Mobile Information Communication and Security, School of Mathematics, Southeast University, Nanjing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Jiangsu Provincial Key Laboratory of Networked Collective Intelligence

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/5503868/10251074/10124764.pdf?arnumber=10124764

Reference38 articles.

1. From networks to optimal higher-order models of complex systems

2. Robust Multi-View Clustering With Incomplete Information

3. Toward Causal Representation Learning

4. A reinforcement learning-based approach for imputing missing data

5. Design and Analysis of CMOS-Based Terahertz Integrated Circuits by Causal Fractional-Order RLGC Transmission Line Model

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2. Guest Editorial Complex Cyber-Multitudinal-Physical Systems: Analysis, Decision-Making, and AI Applications;IEEE Journal on Emerging and Selected Topics in Circuits and Systems;2023-09

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