Distributed Hybrid Power State Estimation Under PMU Sampling Phase Errors

Du, Ji-Xiang; Ma, Shaodan; Wu, Yik Chung; Poor, H. Vincent

doi:10.1109/tsp.2014.2332438

Cited by 45 publications

(30 citation statements)

References 35 publications

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“…In a probabilistic graphical model, each vertex (node) represents a random variable, and there are a set of edges joining some pairs of vertices. The graph gives a visual way of understanding the joint distribution of an entire set of random variables on graph [16], [27]. Fig.…”

Section: B Primer On Probabilistic Graphical Modelmentioning

confidence: 99%

Proactive Doppler Shift Compensation in Vehicular Cyber-Physical Systems

Rao

2018

IEEE/ACM Trans. Networking

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In vehicular cyber-physical systems (CPS), safety information, including vehicular speed and location information, is shared among vehicles via wireless waves at specific frequency. This helps control vehicle to alleviate traffic congestion and road accidents. However, Doppler shift existing between vehicles with high relative speed causes an apparent frequency shift for the received wireless wave, which consequently decreases the reliability of the recovered safety information and jeopardizes the safety of vehicular CPS. Passive confrontation of Doppler shift at the receiver side is not applicable due to multiple Doppler shifts at each receiver. In this paper, we provide a proactive Doppler shift compensation algorithm based on the probabilistic graphical model. Each vehicle pre-compensates its carrier frequency individually so that there is no frequency shift from the desired carrier frequency between each pair of transceiver. The pre-compensated offset for each vehicle is computed in a distributed fashion in order to be adaptive to the distributed and dynamic topology of vehicular CPS. Besides, the updating procedure is designed in a broadcasting fashion to reduce communication burden. It is rigorously proved that the proposed algorithm is convergence guaranteed even for systems with packet drops and random communication delays. Simulations based on real map and transportation data verify the accuracy and convergence property of the proposed algorithm. It is shown that this method achieves almost the optimal frequency compensation accuracy with an error approaching the Cramér-Rao lower bound.

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Section: B Primer On Probabilistic Graphical Modelmentioning

confidence: 99%

Proactive Doppler Shift Compensation in Vehicular Cyber-Physical Systems

Rao

2018

IEEE/ACM Trans. Networking

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“…, M } denoting the set of nodes, and E Net ⊂ V × V the set of all undirect communication links in the network, i.e., if i and j are within the communication range, (i, j) ∈ E Net . The local observations, y i,j , between node i and j are modeled by the pairwise linear Gaussian model [20,21]:…”

Section: Linear Gaussian Modelmentioning

confidence: 99%

Distributed convergence verification for Gaussian belief propagation

Kar

Moura

2017

2017 51st Asilomar Conference on Signals, Systems, and Computers

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Gaussian belief propagation (BP) is a computationally efficient method to approximate the marginal distribution and has been widely used for inference with high dimensional data as well as distributed estimation in large-scale networks. However, the convergence of Gaussian BP is still an open issue. Though sufficient convergence conditions have been studied in the literature, verifying these conditions requires gathering all the information over the whole network, which defeats the main advantage of distributed computing by using Gaussian BP. In this paper, we propose a novel sufficient convergence condition for Gaussian BP that applies to both the pairwise linear Gaussian model and to Gaussian Markov random fields. We show analytically that this sufficient convergence condition can be easily verified in a distributed way that satisfies the network topology constraint.Index Termsgraphical model, belief propagation, large-scale networks, distributed inference, Markov random field.

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“…It extends classical signal processing concepts such as signals, filters, Fourier transform, frequency response, low-and highpass filtering, from signals residing on regular lattices to data residing on general graphs; for example, a graph signal models the data value assigned to each node in a graph. Recent work involves sampling for graph signals [9], [10], [11], [12], recovery for graph signals [13], [14], [15], [16], representations for graph signals [17], [18] principles on graphs [19], [20], stationary graph signal processing [21], [22], graph dictionary construction [23], graph-based filter banks [24], [25], [26], [27], denoising on graphs [24], [28], community detection and clustering on graphs [29], [30], [31], distributed computing [32], [33] and graph-based transforms [34], [35], [36]. We here consider detecting localized categorical attributes on graphs.…”

Section: Introductionmentioning

confidence: 99%

Detecting Localized Categorical Attributes on Graphs

Chen

Yang

Zong

et al. 2017

IEEE Trans. Signal Process.

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Abstract-Do users from Carnegie Mellon University form social communities on Facebook? Do signal processing researchers from tightly collaborate with each other? Do Chinese restaurants in Manhattan cluster together? These seemingly different problems share a common structure: an attribute that may be localized on a graph. In other words, nodes activated by an attribute form a subgraph that can be easily separated from other nodes. In this paper, we thus focus on the task of detecting localized attributes on a graph. We are particularly interested in categorical attributes such as attributes in online social networks, ratings in recommender systems and viruses in cyber-physical systems because they are widely used in numerous data mining applications. To solve the task, we formulate a statistical hypothesis testing problem to decide whether a given attribute is localized or not. We propose two statistics: graph wavelet statistic and graph scan statistic, both of which are provably effective in detecting localized attributes. We validate the robustness of the proposed statistics on both simulated data and two real-world applications: high air-pollution detection and keyword ranking in a co-authorship network collected from IEEE Xplore. Experimental results show that the proposed graph wavelet statistic and graph scan statistic are effective and efficient.

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Distributed Hybrid Power State Estimation Under PMU Sampling Phase Errors

Cited by 45 publications

References 35 publications

Proactive Doppler Shift Compensation in Vehicular Cyber-Physical Systems

Proactive Doppler Shift Compensation in Vehicular Cyber-Physical Systems

Distributed convergence verification for Gaussian belief propagation

Detecting Localized Categorical Attributes on Graphs

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