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Large-scale dynamic models of metabolism remain an overarching ambition of efforts that aim to analyze and explain complex behavior of living entities. However, intrinsic nonlinearity of dynamics of living organisms and structural and quantitative uncertai ...
This paper introduces a dynamic model of the stochastic repayment behavior exhibited by delinquent creditcard accounts. Based on this model, we construct a dynamic collectability score (DCS) that estimates the account-specific probability of collecting a g ...
A new open-source software platform that, among others, allows to select models for inertial sensor stochastic calibration is presented in this paper. This platform consists in a package included in the statistical software R. The identification of stochas ...
The paper aims at assessing the effects of combined voltage and/or current synchrophasor measurements, and their associated uncertainties, on the accuracy of state estimators adopted in distribution systems. Such an assessment is first carried out with res ...
We propose a state estimator for linear discrete-time systems composed by coupled subsystems affected by bounded disturbances. The architecture is distributed in the sense that each subsystem is equipped with a local state estimator that exploits suitable ...
In this paper we propose a novel distributed state estimator for large-scale linear systems composed by subsystems interacting through state variables. The distributed state estimator has the following features: (i) local state estimators, each dedicated t ...
Proposed 50 years ago for studying stability of oscillators, Allan Variance (AV) was accepted by IEEE as a standard for characterizing behavior of sensors. However, the inverse mapping, i.e. the estimation of noise-parameters from Allan Variance is less st ...
This paper introduces a dynamic model of the stochastic repayment behavior exhibited by delinquent credit-card accounts. Based on this model, we construct a dynamic collectability score (DCS) which estimates the account-specific probability of collecting a ...
This paper surveys recent advances related to adaptation, learning, and optimization over networks. Various distributed strategies are discussed that enable a collection of networked agents to interact locally in response to streaming data and to continual ...
Gradient-echo echo-planar imaging (GE EPI) is the most commonly used approach to assess localized blood oxygen level dependent (BOLD) signal changes in real-time. Alternatively, real-time spin-echo single-voxel spectroscopy (SE SVS) has recently been intro ...