Fiber Bragg grating (FBG) is used to construct large sensor network. In order to increase the number of sensors, FBG will be engraved as much as possible under the condition of limited bandwidth, which will lead to th...
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(纸本)9789881563972
Fiber Bragg grating (FBG) is used to construct large sensor network. In order to increase the number of sensors, FBG will be engraved as much as possible under the condition of limited bandwidth, which will lead to the overlapping of part of the spectrum and reduces the demodulation accuracy of FBG central wavelength. An distributed estimation algorithm (EDA) is proposed to improve the demodulation accuracy of the center wavelength in this paper. The overlapping spectrum model is constructed by the combined spectral shape multiplexing technique. and the stress experimental system is built to obtain the overlapping spectrum information. The proposed algorithm is used to demodulate the overlapping spectral model and compare it with the existing optimization algorithm. The simulation and experimental results show that when the spectrum of the FBG sensor network partially overlaps or completely overlaps, the demodulation error is less than 3 pin compared with the existing technology. It is of great significance to improve the measurement accuracy of the overlapping spectrum of FBG sensor networks.
This paper proposes a distributed joint parameter and state variables estimationalgorithm for large-scale state-space interconnected systems. In this distributedestimation scheme, each interconnected sub-system is d...
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This paper proposes a distributed joint parameter and state variables estimationalgorithm for large-scale state-space interconnected systems. In this distributedestimation scheme, each interconnected sub-system is described by a linear discrete-time state space mathematical model. Each sub-system is supposed to be controlled by an intelligent controller that can communicate with its interconnected neighbors and exchange information, such as state variables. The proposed approach comprises two recursive estimationalgorithms, a parameter estimationalgorithm considering the state space model and a distributed Kalman filter for state variables estimation. It is a fully distributed cooperative approach that allows to reduce complexity and saves computational and communication resources. Theoretical analysis and numerical examples are provided to prove the feasibility and effectiveness of this joint estimationalgorithm.
Sensor networks are an indispensable part of the Internet of Things (IoT), where sensors perform data acquisition and information processing tasks to obtain the parameters of interest so that IoT-based monitoring, dia...
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Sensor networks are an indispensable part of the Internet of Things (IoT), where sensors perform data acquisition and information processing tasks to obtain the parameters of interest so that IoT-based monitoring, diagnosis and other systems respond quickly to the changing conditions, instantaneous faults, etc. distributed estimation algorithms are usually employed to estimate the parameters of interest in these IoT-based applications. However, when sensor networks have highly correlated input signals and nonstationary behavior in which the parameters of interest are time-varying, conventional distributed estimation algorithms suffer from severely degraded learning performance due to the large eigenvalue spread in the covariance matrix of the input signals and the random perturbation of the parameters of interest. To address these problems, this paper proposes two diffusion Bayesian subband adaptive filter (DBSAF) algorithms from a Bayesian learning perspective. As the highly-correlated input signal is whitened in a multiband structure and an estimate of the uncertainty in the parameters of interest is obtained by performing Bayesian inference, the proposed DBSAF algorithms are able to achieve better learning performance in comparison with the competing diffusion algorithms. The transient and steady-state mean square error performance of the proposed DBSAF algorithms are analyzed, and are verified by numerical simulations. A lower bound on the time-varying step-size is derived to maintain the optimal steady-state performance in nonstationary scenarios. A new method for the estimation of the noise variance is also proposed. Numerical simulations demonstrate the excellent learning performance of the proposed algorithms in comparison with benchmark algorithms.
The process of enhancing the ability of a complex network against various malicious attacks through link addition/rewiring has been the subject of extensive interest and research. The performance of existing methods o...
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The process of enhancing the ability of a complex network against various malicious attacks through link addition/rewiring has been the subject of extensive interest and research. The performance of existing methods often highly depends on full knowledge about the network topology. In this study, the authors devote ourselves to developing new distributed strategies to perform link manipulation sequentially using only local accessible topology information. This strategy is concerned with a matrix-perturbation-based approximation of the network-based optimisation problems and a distributedalgorithm to compute eigenvectors and eigenvalues of graph matrices. In addition, the development of a distributed stopping criterion, which provides the desired accuracy on the distributed estimation algorithm, enables us to solve the link-operation problem in a finite-time manner. Finally, all results are illustrated and validated using numerical demonstrations and examples.
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