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A fully distributed state estimator for multi-area distribution networks is described in this section.
distributed state estimators using middleware.
power models such as distributed state estimation.
Subsequently, the stability of the proposed distributed state estimation is analyzed.
This paper addresses the problem of distributed state estimation in a multi-vehicle framework.
We consider distributed state estimation over a resource-limited wireless sensor network.
We illustrate our discussion with a generic distributed state space generation algorithm.
It uses a supervised reinforcement learning approach combined with Gaussian distributed state activation.
Distributed operating systems and applications issues, emphasizing high-level protocols and distributed state sharing as the key technologies.
In this paper, distributed state estimators are designed for periodic systems with sensor nonlinearities and successive packet dropouts.
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We assume one-step first-order correlation time series structure so that HMM contains with Poisson distributed state-dependent distribution.
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