Distributed and decentralized state estimation in gas networks as distributed parameter systems

Behrooz, Hesam Ahmadian; Boozarjomehry, Ramin Bozorgmehry

doi:10.1016/j.isatra.2015.06.001

Cited by 22 publications

(7 citation statements)

References 30 publications

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“…Furthermore, the destruction of the BBB increases cerebral blood flow, leading to further ischemic damage. [21,22] In the reperfusion phase, the destruction of the BBB is further exacerbated. On one hand, reperfusion further exacerbates the destruction of the BBB, [22] and on the other hand, cerebral I/R injury can be alleviated by agents that protect the BBB.…”

Section: Discussionmentioning

confidence: 99%

“…[21,22] In the reperfusion phase, the destruction of the BBB is further exacerbated. On one hand, reperfusion further exacerbates the destruction of the BBB, [22] and on the other hand, cerebral I/R injury can be alleviated by agents that protect the BBB. For example, in a cerebral I/R animal model, giving melatonin reduced the destruction of the BBB and cerebral edema.…”

Section: Discussionmentioning

confidence: 99%

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Netrin‐1 protects blood–brain barrier (BBB) integrity after cerebral ischemia‐reperfusion by activating the Kruppel‐like factor 2 (KLF2)/occludin pathway

Li,

Liu,

Chen

et al. 2024

J Biochem & Molecular Tox

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Ischemia/reperfusion (I/R)‐induced neural damage and neuroinflammation have been associated with pathological progression during stroke. Netrin‐1 is an important member of the family of laminin‐related secreted proteins, which plays an important role in governing axon elongation. However, it is unknown whether Netrin‐1 possesses a beneficial role in stroke. Here, we employed the middle cerebral artery occlusion (MCAO) model to study the function of Netrin‐1 in alleviating brain injuries. Our results demonstrate that Netrin‐1 rescued poststroke neurological deficits and inhibited production of the inflammatory cytokines such as interleukin 6 (IL‐6) and endothelial chemokine (C‐X‐C motif) ligand 1 (Cxcl1). Importantly, Netrin‐1 protected against MCAO‐induced dysfunction of the blood–brain barrier (BBB) in mice and a reduction in the expression of the tight junction (TJ) protein occludin. Additionally, we report that Netrin‐1 could ameliorate oxygen‐glucose deprivation/reoxygenation (OGD/R)‐induced injury and prevent aggravation in endothelial monolayer permeability in bEnd.3 human brain microvascular endothelial cells (HBMVECs). Mechanistically, Netrin‐1 ameliorated OGD/R‐induced decrease in occludin and Kruppel‐like factor 2 (KLF2) in HBMVECs. Notably, silencing of KLF2 abolished the beneficial effects of Netrin‐1 in protecting endothelial permeability and occludin expression, suggesting that these effects are mediated by KLF2. In conclusion, our findings suggest that Netrin‐1 could constitute a novel therapeutic strategy for ischemic stroke.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Netrin‐1 protects blood–brain barrier (BBB) integrity after cerebral ischemia‐reperfusion by activating the Kruppel‐like factor 2 (KLF2)/occludin pathway

Li,

Liu,

Chen

et al. 2024

J Biochem & Molecular Tox

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“…Most existing studies have been concerned with obtaining state estimates to perform gas leak detection. Among these studies, estimation methods used include adaptive particle filters, transfer functions, , and Kalman filters. − The drawbacks of these approaches, when contrasted with optimization-based approaches such as moving-horizon estimation, is well-documented and understood, , particularly in the context of large-scale systems . To the best of our knowledge, optimization-based estimation approaches for natural gas networks have not been studied previously.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

An Optimization-Based State Estimation Framework for Large-Scale Natural Gas Networks

Jalving

Zavala

2018

Ind. Eng. Chem. Res.

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We propose an optimization-based state estimation framework to track internal space−time flow and pressure profiles of natural gas networks during dynamic transients. We find that the estimation problem is ill-posed (because of the infinite-dimensional nature of the states) and that this leads to instability of the estimator when short estimation horizons are used. To circumvent this issue, we propose moving horizon strategies that incorporate prior information.In particular, we propose a strategy that initializes the prior using steady-state information and compare its performance against a strategy that does not initialize the prior. We find that both strategies are capable of tracking the state profiles but we also find that superior performance is obtained with steady-state prior initialization. We also find that, under the proposed framework, pressure sensor information at junctions is sufficient to track the state profiles. We also derive approximate transport models and show that some of these can be used to achieve significant computational speed-ups without sacrificing estimation performance. We show that the estimator can be easily implemented in the graph-based modeling framework Plasmo.jl and use a multipipeline network study to demonstrate the developments.

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“…However, because of the increasing size and complexity of industrial processes and applications (power networks, sensor networks etc. ), such centralized estimation approaches require a large computational load and cannot provide a satisfactory performance [22][23][24][25][26][27][28]. To overcome these drawbacks, recent research on modelling and control theories often resorts to decomposing large-scale systems into a group of interconnected sub-systems and focusing on distributed and decentralized estimation methods as alternative approaches [29][30][31][32][33][34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Distributed parameter identification algorithm for large‐scale interconnected systems

Hamdi,

Idomhgar,

Kamoun

et al. 2023

IET Control Theory & Appl

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This paper deals with parameter estimation problem of large‐scale systems. A recursive distributed parameter estimation algorithm, based on the minimization of the prediction estimation error method, is developed. Specifically, the class of large‐scale systems that are composed of several interconnected sub‐systems is considered. Each interconnected sub‐system is modelled by a linear discrete‐time state space mathematical model with unknown parameters. The convergence analysis is then achieved using the Lyapunov approach. The theoretical analysis and simulation results prove the effectiveness of the proposed algorithm.

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Distributed and decentralized state estimation in gas networks as distributed parameter systems

Cited by 22 publications

References 30 publications

Netrin‐1 protects blood–brain barrier (BBB) integrity after cerebral ischemia‐reperfusion by activating the Kruppel‐like factor 2 (KLF2)/occludin pathway

Netrin‐1 protects blood–brain barrier (BBB) integrity after cerebral ischemia‐reperfusion by activating the Kruppel‐like factor 2 (KLF2)/occludin pathway

An Optimization-Based State Estimation Framework for Large-Scale Natural Gas Networks

Distributed parameter identification algorithm for large‐scale interconnected systems

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