Due to the high-measuring cost, the monitoring of power quality (PQ) is nontrivial. This paper is aimed at reducing the cost of PQ monitoring in power network. Using a real-world PQ dataset, this paper adopts a learn-from-data approach to obtain a device latent feature model, which captures the device behavior as a PQ transition function. With the latent feature model, the power network could be modeled, in analogy, as a data-driven network, which presents the opportunity to use the well-investigated network monitoring and data estimation algorithms to solve the network quality monitoring problem in power grid. Based on this network model, algorithms are proposed to intelligently place measurement devices on suitable power links to reduce the uncertainty of PQ estimation on unmonitored power links. The meter placement algorithms use entropy-based measurements and Bayesian network models to identify the most suitable power links for PQ meter placement. Evaluation results on various simulated networks including IEEE distribution test feeder system show that the meter placement solution is efficient, and has the potential to significantly reduce the uncertainty of PQ values on unmonitored power links.Index Terms-Bayesian networks (BNs), conditional entropy (CE), Monte Carlo (MC) simulations, power quality (PQ) monitoring.

In this paper, we investigate a pure form of the topological mapping problem in mobile robotics. We consider the mapping ability of a robot navigating a graph-like world in which it is able to assign a relative ordering to the edges, leaving a vertex with reference to the edge by which it arrived but is unable to associate a unique label with any vertex or edge. Our work extends and builds upon earlier approaches in this problem domain, which are based on construction of exploration tree of plausible world models. The main contributions of the paper are improved exploration strategies that reduce model ambiguity, a new method of search through consistent models in the exploration tree that maintains a bounded set of likely hypotheses based on the principle of Occam's Razor, the incorporation of arbitrary feature vectors into the problem formulation, and an investigation of various aspects of this problem through numerical simulations.

Abstract-We consider the problem of inferring sensor positions and a topological (i.e. qualitative) map of an environment given a set of cameras with non-overlapping fields of view. In this way, without prior knowledge of the environment nor the exact position of sensors within the environment, one can infer the topology of the environment, and common traffic patterns within it. In particular, we consider sensors stationed at the junctions of the hallways of a large building. We infer the sensor connectivity graph and the travel times between sensors (and hence the hallway topology) from the sequence of events caused by unlabeled agents (i.e. people) passing within view of the different sensors. We do this based on a firstorder semi-Markov model of the agent's behavior. The paper describes a problem formulation and proposes a stochastic algorithm for its solution. The result of the algorithm is a probabilistic model of the sensor network connectivity graph and the underlying traffic patterns. We conclude with results from numerical simulations

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