Data‐based design of robust fault detection and isolation residuals via LASSO optimization and Bayesian filtering

In this paper, a data‐based approach for the design of structured residual subsets for the robust isolation of sensor faults is proposed. Linear regression models are employed to estimate faulty signals and to build a set of primary residuals. L₁‐regularized least squares estimation is used to identify model parameters and to enforce sparsity of the solutions by increasing the regularization weight. In this way, it is possible to generate a set of residuals generators with different fault sensitivity. Then, a residual selection procedure based on fault sensitivity maximization is proposed to extract a minimum size subset of structured residuals that allows for isolation of the faulty sensor. To overcome modelling uncertainty, a robust recursive Bayesian Filter has been employed to process, online, the distance of the residuals from nominal fault directions, providing a fault probability for each sensor. The proposed method has been validated by designing and testing a fault isolation scheme for six aircraft sensors using multi‐flight experimental data of a P92 Tecnam aircraft.     

Boosting modern and historical handwritten text recognition with deformable convolutions

International Journal on Document Analysis and Recognition (IJDAR) - Handwritten Text Recognition (HTR) in free-layout pages is a challenging image understanding task that can provide a relevant...     

A Learning Strategy for the Autonomous Control of Type 1 Diabetes

This article proposes a learning strategy for the control of the blood glucose in type 1 diabetes based on continuous subcutaneous glucose measurement and subcutaneous insulin administration. The method relies on an Iterative Learning Control strategy that exploits the approximated repetitiveness of the daily feeding habits of a patient. The administration strategy for the insulin is based on a mixed feedback and feedforward law whose parameters are tuned through a learning process based on the day-by-day analysis of the glucose response to the infusion of exogenous insulin. The proposed scheme is fully autonomous in the sense that it does not require any a priori information on the insulin/glucose response of the patient, on the amount of ingested carbohydrates, and on the announcement of the mealtimes. A novel filtering strategy of the subcutaneous glucose signal is proposed to provide a robust detection of the meal occurrence despite the significant noise introduced by the subcutaneous glucose sensor. A specific module is proposed to detect and prevent possible hypoglycemia events. Considering a prototype diabetic virtual patient it was showed that, thanks to the learning mechanism, the scheme in a few days is able to bring and to maintain the blood glucose in the normoglycemia region and that the control performance can improve over time. Long-run simulation studies have also shown the robustness of the learning scheme in the presence of realistic uncertainties and interpatient variability.