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Neural state classification (NSC) is a recently proposed method for runtime predictive monitoring of hybrid automata (HA) using deep neural networks (DNNs). NSC trains a DNN as an approximate reachability predictor that labels an HA state x as positive if an unsafe state is reachable from x within a given time bound, and labels x as negative otherwise. NSC predictors have very high accuracy, yet are prone to prediction errors that can negatively impact reliability. To overcome this limitation, we present neural predictive monitoring (NPM), a technique that complements NSC predictions with estimates of the predictive uncertainty. These measures yield principled criteria for the rejection of predictions likely to be incorrect, without knowing the true reachability values. We also present an active learning method that significantly reduces the NSC predictor’s error rate and the percentage of rejected predictions. We develop two versions of NPM based, respectively, on the use of frequentist and Bayesian techniques to learn the predictor and the rejection rule. Both versions are highly efficient, with computation times on the order of milliseconds, and effective, managing in our experimental evaluation to successfully reject almost all incorrect predictions. In our experiments on a benchmark suite of six hybrid systems, we found that the frequentist approach consistently outperforms the Bayesian one. We also observed that the Bayesian approach is less practical, requiring a careful and problem-specific choice of hyperparameters.

In this paper, we investigate the transmission-power assignment problem for κ-connected Mobile Ad hoc NET-works (MANETs), the problem of optimizing the lifetime of a MANET at a given degree κ of connectivity by minimizing power consumption. Our proposed solution is fully distributed and uses a model-based transmission power adaptation strategy based on model-predictive control. Specifically, a stochastic model of the network is used by a state estimator to predict the network's future degree of connectivity and remaining energy. The predicted states are used by an optimizer to derive an optimal transmission power assignment sequence which tracks the desired connectivity level κ. while minimizing energy consumption. Our experimental results on a simulated wireless sensor network comprising 100 mobile nodes reveals that our localized topology control algorithm provides an almost identical control policy to that of a globalized scheme which is solving a fully observable problem. The difference, of course, is in the scalability of our localized solution, which requires much less communication bandwidth and energy than the globalized approach. © 2008 IEEE.

This paper presents a formal framework, experimental infrastructure, and computational environment for modeling, analyzing and regulating the behavior of cardiac tissues. Based on the theory of hybrid automata, we aim at providing suitable tools to be used in devising strategies for the pharmacological or other forms of treatment of cardiac electrical disturbances.