Distributed Filtering for Complex Networks Under Multiple Event-Triggered Transmissions Within Node-Wise Communications

Liu, Yang; Wang, Zidong; Zou, Lei; Hu, Jun; Dong, Hongli

doi:10.1109/tnse.2022.3164833

Cited by 14 publications

(2 citation statements)

References 55 publications

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“…Experiment results have shown that the proposed DOTLF outperforms some selected PLD methods under both normal and fault conditions. In the future, we aim to 1) optimize the source domain data to efficiently transfer detection knowledge [10], [19], [48], [55], [57]; and 2) design an advanced control strategy to further improve the detection accuracy and detection rate of the TL-LSTM-Ri-based PLD method [18], [21], [28], [50], [60].…”

Section: Discussionmentioning

confidence: 99%

A Novel Deep Offline-to-Online Transfer Learning Framework for Pipeline Leakage Detection With Small Samples

Wang

Liu

et al. 2023

IEEE Trans. Instrum. Meas.

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In this paper, a two-stage deep offline-to-online transfer learning framework (DOTLF) is proposed for longdistance pipeline leakage detection (PLD). At the offline training stage, a feature transfer-based long short-term memory network with regularization information (TL-LSTM-Ri) is developed where a maximum mean discrepancy regularization term is employed to extract domain-invariant features and an adjacentbias-corrected regularization term is introduced to extract early fault features from pipeline samples under different scenarios. At the online detection stage, the trained TL-LSTM-Ri is employed for motion prediction so as to monitor the operating condition of the pipeline in real time. To demonstrate its application potential, the DOTLF is successfully applied to handle the PLD problem on the long-distance oil-gas pipeline data. Experimental results demonstrate the effectiveness of the proposed DOTLF for realtime PLD under real-world scenarios.

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Section: Discussionmentioning

confidence: 99%

A Novel Deep Offline-to-Online Transfer Learning Framework for Pipeline Leakage Detection With Small Samples

Wang

Liu

et al. 2023

IEEE Trans. Instrum. Meas.

Self Cite

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“…Note that the fault estimation problem in the above works is considered with the assumption that the information of the output vectors is required for each sampling instant, which may not be effective in saving communication resources. As reported by Huong et al (2020, 2022), Huong and Trinh (2022), Liu et al (2021), Wang et al (2022), Zhao et al (2022), Li et al (2021), Liu and Huang (2018, 2020) and Zuo et al (2019), event-triggered observer and event-triggered control are useful in eliminating unnecessary sampling and transmission. As a result, fault estimators based on event-triggered observers may be useful in saving communication resources.…”

Section: Introductionmentioning

confidence: 93%

Event-triggered state and fault simultaneous estimation for nonlinear systems with time delays

Huong

2023

Transactions of the Institute of Measurement and Control

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This paper addresses the problem of event-triggered robust state and fault simultaneous estimation for nonlinear time-delay systems subject to actuator and sensor unknown disturbances. Based on a fault decomposition technique and some basic mathematical transformations, we obtain an augmented system where the state vector consists of the variable of the original system and the fault. Then a novel event-triggered state observer for the augmented system is proposed to robustly estimate the variable of the original system and the fault. We next established a sufficient condition for the existence of such an observer. We translated it into a linear matrix inequality (LMI), which can be effectively solved using the MATLAB LMI Control Toolbox. Finally, an illustrative example is applied to test the proposed method.

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Recursive state estimation for delayed complex networks with random link failures and stochastic inner coupling under cyber attacks

Qi,

Wu,

Zheng

2025

Digital Signal Processing

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Distributed Filtering for Complex Networks Under Multiple Event-Triggered Transmissions Within Node-Wise Communications

Cited by 14 publications

References 55 publications

A Novel Deep Offline-to-Online Transfer Learning Framework for Pipeline Leakage Detection With Small Samples

A Novel Deep Offline-to-Online Transfer Learning Framework for Pipeline Leakage Detection With Small Samples

Event-triggered state and fault simultaneous estimation for nonlinear systems with time delays

Recursive state estimation for delayed complex networks with random link failures and stochastic inner coupling under cyber attacks

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