Switched Integral Suboptimal Second-Order Sliding Mode Control

This paper presents a switched formulation of the suboptimal second-order integral sliding mode control law that has recently appeared in the literature. The integral approach maintains the good properties of the Suboptimal Second Order Sliding Mode (SSOSM) algorithm in terms of chattering alleviation, but, in addition avoids the reaching phase and keeps the controlled system trajectory on the sliding manifold since the initial time instant. Besides these features, the switched formulation adapts the control gains in different regions of the state space, providing the flexibility needed to accommodate different design objectives when moving towards the desired equilibrium. The paper discusses the properties of the proposed algorithm on a realistic example, that is the lateral dynamics control of a ground vehicle in which the yaw-rate tracking is typically made difficult by parametric uncertainties and nonlinear effects arising with large steering angles

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

''Sviluppo e analisi di metodi per il controllo attivo delle vibrazioni, con riferimento al problema della riduzione del livello vibratorio negli elicotteri''

Tesi di Dottorato Relatore: Prof. S. Bittanti, Politecnico di Milano Correlatore: Ing. B. Lovera, Agusta Sp

Sliding Mode Control for LPV Systems

LPV models are extremely appealing, as they allow describing the dynamics of many physical systems that are of interest in various engineering applications. For such systems, dedicated control approaches have been proposed, which rely on the measurement of the scheduling variables and exploit such information for improving the closed-loop performance with respect to fixed-structure, possibly robust, solutions. Unfortunately, such control techniques are often not so simple to tune and design, especially when also parametric uncertainties affect the system, thus requiring LPV-robust control techniques. In this work we explore the advantages offered by sliding mode (SM) algorithms for the control of LPV systems, showing that a fixed-structure SM approach can outperform genuine LPV solutions in the case of parametric uncertainties on the system model without additional tuning and design needs. A case study considering the control of lateral vehicle dynamics is used to investigate the performance of the different approaches, showing promising results for extending SM controllers to cope with additional uncertainties affecting LPV systems, that may be difficult to act upon with traditional methods

Claim risk prediction in the automobile insurance industry: a statistical data-mining approach

This paper considers the problem of assessing claim risk in the automobile insurance industry. A statistical data mining approach based on categorical data analysis is proposed. The most relevant features are searched for by either independence or conditional independence analysis. The latter aims at finding the so-called Markov Blanket of the “claim” variable, that is the minimal set of variables that renders the remaining variables superfluous for what concerns claim prediction. The proposed methodology was applied to an extensive data set provided by a primary Italian insurance company. The most relevant features turned out to be “risk class” and “fraction” (whether the premium is paid yearly or not). On a testing dataset, the predictor based on these two features performed better than classification trees. Copyright © 2007 IFAC Keywords: automobile insurance data, risk analysis data-mining, statistical methods, classification, predictio

On claim probability prediction from motor vehicle insurance data: a statistical nonparametric approach

Abstract: This paper considers the problem of assessing claim risk in the automobile insurance industry. A statistical data mining approach based on categorical data analysis is proposed. The most relevant features are searched for by either independence or conditional independence analysis. The latter aims at finding the so-called Markov Blanket of the “claim” variable, that is the minimal set of variables that renders the remaining variables superfluous for what concerns claim prediction. The proposed methodology was applied to an extensive data set provided by a primary Italian insurance company. The most relevant features turned out to be “risk class” and “fraction” (whether the premium is paid yearly or not). On a testing dataset, the predictor based on these two features performed better than classification trees. Copyright © 2007 IFA

MIRACLE: MInd ReAding CLassification Engine

Brain-computer interfaces (BCIs) have revolutionized the way humans interact with machines, particularly for patients with severe motor impairments. EEG-based BCIs have limited functionality due to the restricted pool of stimuli that they can distinguish, while those elaborating event-related potentials up to now employ paradigms that require the patient&#x2019;s perception of the eliciting stimulus. In this work, we propose MIRACLE: a novel BCI system that combines functional data analysis and machine-learning techniques to decode patients&#x2019; minds from the elicited potentials. MIRACLE relies on a hierarchical ensemble classifier recognizing 10 different semantic categories of imagined stimuli. We validated MIRACLE on an extensive dataset collected from 20 volunteers, with both imagined and perceived stimuli, to compare the system performance on the two. Furthermore, we quantify the importance of each EEG channel in the decision-making process of the classifier, which can help reduce the number of electrodes required for data acquisition, enhancing patients&#x2019; comfort