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This paper proposes an adaptive approach to improve the performance of Target Controlled Infusion (TCI) based strategies. The method determines an adequate drug dose profile that drives the drug effect on the individual patient to a desired target in a prespecified period of time. It combines an optimal variance constrained drug dose design with a hybrid identification of the individual patient dynamics...
Deep neural networks comprise several hidden layers of units, which can be pre-trained one at a time via an unsupervised greedy approach. A whole network can then be trained (fine-tuned) in a supervised fashion. One possible pre-training strategy is to regard each hidden layer in the network as the input layer of an auto-encoder. Since auto-encoders aim to reconstruct their own input, their training...
The structural optimization problem of choosing the profile of each member belonging to a framed structure in order to minimize its weight while satisfying stress, displacement, stability, and other applicable constraints is often complicated by the requirement of considering non-linear structural behavior. The problem is further complicated if the members are to be chosen from a discrete set of commercially...
The realization of integrated frequency-based smart sensor for flow measurement requires a precise frequency to digital converter. A VHDL-based implementation of such converter for a royalty-free solution with 1 ppm resolution is reported. This work is part of a correlator under development to measure total flow of multiphase fluids. This intellectual property block can also be used with other frequency...
Timed Continuous Petri Net (TCPN) systems are piecewise linear models with input constraints that can approximate the dynamical behavior of a class of timed discrete event systems. This paper concentrates in the development of a control structure for TCPN that transfers the system from the initial state to another desired one. The resulting control law consists in a Linear Programming Problem, which...
In this paper we study fault diagnosis of systems modeled by untimed continuous Petri nets. In particular, we generalize our previous works in this framework where we solved this problem only for special classes of continuous Petri nets, namely state machines and backward conflict free nets. We show that the price to pay for this generalization is that only three diagnosis states can be defined, rather...
Continuous Petri nets can be viewed as an approximation of the classical discrete models introduced to cope with the state explosion problem typical of discrete event systems. In this paper we consider free-labeled Petri net systems, and assume that certain transitions, including all those modeling faulty behaviors, are unobservable, i.e., they are labeled with the empty word. Based on the notion...
Continuous Petri nets (conPNs) are an approximation of (discrete) Petri nets (PNs) introduced to cope with the state explosion problem typical of discrete event systems. We consider a free-labeled Petri net model and assume that certain transitions, including all that model faulty behaviors are unobservable, i.e., they are labeled with the empty word. We show how to design a marking observer that...
In this paper, we develop an automated framework for formal verification of timed continuous Petri nets (contPN). Specifically, we consider two problems: (1) given an initial set of markings, construct a set of unreachable markings, i.e., such that all trajectories starting in the initial set avoid the latter one; (2) given a linear temporal logic (LTL) formula over a set of linear predicates in the...
Fluidification constitutes a relaxation technique to study discrete event systems through a continuous approximated model, thus avoiding the state explosion problem. In this paper, the approximation by Timed Continuous Petri nets under infinite server semantics is studied. The main contribution of this work is the addition of gaussian noise in order to obtain a better (but stochastic) approximation...
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