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— This paper introduces a new method for reducing large directed graphs to simpler graphs with fewer nodes. The reduction is carried out through node and edge aggregation, where the simpler graph is representative of the original large graph. Representativeness is measured using a metric defined herein, which is motivated by thermodynamic free energy and(More)
In this technical note, we consider the dynamic coverage control problem from a clustering perspective, to which we apply control-theoretic methods to identify and track the cluster center dynamics. To the authors’ knowledge, this is the first work to consider tracking cluster centers when the dynamics of the system elements involve acceleration fields. We(More)
This paper proposes a state transition probability model for an elementary traffic network with four intersections, which is substantially the extension of the state transition probability model for a link based on a queue dynamic model. The state of this model is the combination of states of roads between these four intersections, so as the reward of each(More)
— Analysis, prediction and control of parametric generative models for stochastic processes arise in numerous applications, such as in biology, telecommunications, geography, seismology and finance. In many of these applications, it is desirable to obtain an aggregated behavior from an underlying network of stochastic interactions. This paper focuses on the(More)
Power plant is a complex and nonstationary system for which the traditional machine learning modeling approaches fall short of expectations. The ensemble-based online learning methods provide an effective way to continuously learn from the dynamic environment and autonomously update models to respond to environmental changes. This paper proposes such an(More)
Data-driven predictive analytics are in use today across a number of industrial applications, but further integration is hindered by the requirement of similarity among model training and test data distributions. This paper addresses the need of learning from possibly nonstationary data streams, or under concept drift, a commonly seen phenomenon in(More)
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