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Information about respiratory mechanics such as resistance, elastance, and muscular pressure is important to mitigate ventilator‐induced lung injury. Particularly during pressure support ventilation, the available options to quantify breathing effort and calculate respiratory system mechanics are often invasive or complex. We herein propose a robust and flexible estimation of respiratory effort better than current methods. We developed a method for non‐invasively estimating breathing effort using only flow and pressure signals. Mixed‐integer quadratic programming (MIQP) was employed, and the binary variables were the switching moments of the respiratory effort waveform. Mathematical constraints, based on ventilation physiology, were set for some variables to restrict feasible solutions. Simulated and patient data were used to verify our method, and the results were compared to an established estimation methodology. Our algorithm successfully estimated the respiratory effort, resistance, and elastance of the respiratory system, resulting in more robust performance and faster solver times than a previously proposed algorithm that used quadratic programming (QP) techniques. In a numerical simulation benchmark, the worst‐case errors for resistance and elastance were 25% and 23% for QP versus <0.1% and <0.1% for MIQP, whose solver times were 4.7 s and 0.5 s, respectively. This approach can estimate several breathing effort profiles and identify the respiratory system's mechanical properties in invasively ventilated critically ill patients. [ABSTRACT FROM AUTHOR]

Background: We aimed to investigate the physiological mechanism and spatial distribution of increased physiological dead-space, an early marker of ARDS mortality, in the initial stages of ARDS. We hypothesized that: increased dead-space results from the spatial redistribution of pulmonary perfusion, not ventilation; such redistribution is not related to thromboembolism (ie, areas with perfusion = 0 and infinite ventilation-perfusion ratio, V˙/Q˙ ), but rather to moderate shifts of perfusion increasing V˙/Q˙ in non-dependent regions.Methods: Five healthy anesthetized sheep received protective ventilation for 20 hours, while endotoxin was continuously infused. Maps of voxel-level lung ventilation, perfusion, V˙/Q˙ , CO2 partial pressures, and alveolar dead-space fraction were estimated from positron emission tomography at baseline and 20 hours.Results: Alveolar dead-space fraction increased during the 20 hours (+0.05, P = .031), mainly in non-dependent regions (+0.03, P = .031). This was mediated by perfusion redistribution away from non-dependent regions (-5.9%, P = .031), while the spatial distribution of ventilation did not change, resulting in increased V˙/Q˙ in non-dependent regions. The increased alveolar dead-space derived mostly from areas with intermediate V˙/Q˙ (0.5≤ V˙/Q˙ ≤10), not areas of nearly "complete" dead-space ( V˙/Q˙ >10).Conclusions: In this early ARDS model, increases in alveolar dead-space occur within 20 hours due to the regional redistribution of perfusion and not ventilation. This moderate redistribution suggests changes in the interplay between active and passive perfusion redistribution mechanisms (including hypoxic vasoconstriction and gravitational effects), not the appearance of thromboembolism. Hence, the association between mortality and increased dead-space possibly arises from the former, reflecting gas-exchange inefficiency due to perfusion heterogeneity. Such heterogeneity results from the injury and exhaustion of compensatory mechanisms for perfusion redistribution. [ABSTRACT FROM AUTHOR]

A data-based estimation of the wind-power curve in wind turbines may be a challenging task due to the presence of anomalous data, possibly due to wrong sensor reads, operation halts, malfunctions or other. In this study, the authors describe a data-based procedure to build a robust and accurate estimate of the wind-power curve. In particular, they combine a joint clustering procedure, where both the wind speeds and the power data are clustered, with an Earth Mover Distance-based Extreme Learning Machine algorithm to filter out data that poorly contribute to explain the unknown curve. After estimating the cut-in and the rated speed, they use a radial basis function neural network to fit the filtered data and obtain the curve estimate. They extensively compared the proposed procedure against other conventional methodologies over measured data of nine turbines, to assess and discuss its performance. [ABSTRACT FROM AUTHOR]

A data‐based estimation of the wind–power curve in wind turbines may be a challenging task due to the presence of anomalous data, possibly due to wrong sensor reads, operation halts, malfunctions or other. In this study, the authors describe a data‐based procedure to build a robust and accurate estimate of the wind–power curve. In particular, they combine a joint clustering procedure, where both the wind speeds and the power data are clustered, with an Earth Mover Distance‐based Extreme Learning Machine algorithm to filter out data that poorly contribute to explain the unknown curve. After estimating the cut‐in and the rated speed, they use a radial basis function neural network to fit the filtered data and obtain the curve estimate. They extensively compared the proposed procedure against other conventional methodologies over measured data of nine turbines, to assess and discuss its performance. [ABSTRACT FROM AUTHOR]

This study investigates the ability of temperature sensors installed in the traction core of trains to early detect incipient faults. For instance, the breaking of a bearing is known to be critical as it may cause an increase of the temperature in the motor compartment, that in turn may eventually lead to a winding fault in the induction motor. The technique proposed in this contribution is characterised by extreme generality, since most frequent incipient faults lead to temperature increase that, if properly analysed, can be a tool for preventive maintenance. In particular, the measured data, provided by the main Italian railway company, are processed by two different methodologies which are characterised by positive, yet different, performances. The results show that preventive maintenance with the proposed approach is feasible. [ABSTRACT FROM AUTHOR]

In this work, a new multi-objective population-based optimization algorithm is presented and tested. In this contribution, the concepts of fast non-dominating sorting and density estimation using the crowding distance are used to create a multi-objective optimization algorithm based on previous work, which is a single objective evolutionary optimization algorithm based on self-organizing maps (SOMs). The SOMs paradigm introduces a strong collaboration between neighbors solutions that improves exploitation. Furthermore, the representative power of the SOMs enhances the exploration and diversification. A state of the art benchmark approach is used to evaluate the performance of the proposed algorithm, obtaining positive results. The test problem uses an analytical model of an inductively coupled wireless power transfer system (WPT). The objective is to optimize the WPT model characteristics in order to allow simultaneous data and power transfer between the coils. The WPT design approach uses more degrees of freedom than existing techniques leading to a number of solutions where both the power signals and the data signal can coexist on the same physical channel achieving good figures of merit. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Predictive Maintenance, Prognostics and Reliability Centered Maintenance approach are becoming more and more important in the railway sector to reduce costs of operation and to increase reliability and safety. In fact, these are fundamental to optimize the maintenance process, to define new measures and algorithms which locate faults, to monitor health conditions of subsystems and to estimate residual life of components. In some cases it's possible to use existing sensors and existing processing hardware to extract new information from the existing available data. It's clear that this is usually the best option because the benefit can be achieved with little or no cost at all. This paper describes the result of a study performed with the aim of detecting arcing events without the need of additional equipment mounted on board the train. A set of data relative to voltage and current collected on high speed trains along with a set of measurements coming from photosensors are available. The data are processed by the use of an advanced classification technique, Support Vector Machines, with the aim of extracting important information such as the time coordinate related to anomalies in the overhead contact line and the status of the pantograph contact strip. [ABSTRACT FROM AUTHOR]

In this paper a method to determine the mean value and the variance of the response of a system with uncertain parameters is proposed. With the term 'response of a system' we indicate either the value of a single parameter that represents a figure of merit of a device (e.g. the efficiency of a transmission system or the band width of a communication channel) or a characteristic function of the system (e.g. the impulse or the frequency response). In the latter case we estimate the mean value and the variance at every sample of the response. The estimate is performed by using the delta method, a technique for approximating expected values of functions of random variables when the direct evaluation is not feasible. Two examples of the application of the proposed procedure are reported and the results are compared with simulations performed by a Monte Carlo analysis. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]