Ricardo Dunia

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Inferential sensors, or soft sensors, refer to a modeling approach to estimating hard-to-measure process variables from other easy-to-measure, on-line sensors. Since many sensors are used as input variables to estimate the output, the probability of one of the sensors fails increases signiicantly. In this paper, we propose a self-validating inferential(More)
Fault detection and process monitoring using principal component analysis (PCA) have been studied intensively and applied to industrial processes. PCA is used to deene an orthogonal partition of the measurement space into two orthogonal subspaces: a principal component sub-space (PCS), and a residual subspace (RS). In this paper, each process fault is also(More)
Input and output time delays in continuous-time state-space systems are treated separately as their effects are encountered before and after the state dynamics. Existing discretization techniques for such systems usually consider the delays to be integer multiples of the sampling time. This work develops a discretization procedure for multi-variable(More)
Tumor growth models subject to virotherapy treatment are analyzed and compared in this paper. Tumor growth conditions are obtained for each model type based on the virus infection rate and immune suppressive drug delivery. Equilibrium conditions resulted into quadratic functions for which the tumor radius remained constant during virotherapy. An irrigation(More)
A general mathematical model of viral infections inside a spherical organ is presented. Transported quantities are used to represent external cells or viral particles that penetrate the organ surface to either promote or combat the infection. A diffusion mechanism is considered for the migration of transported quantities to the organ inner tissue. Cases(More)