Pub Date : 2024-06-24DOI: 10.1016/j.ifacsc.2024.100270
Lars van de Kamp , Bram Hunnekens , Nathan van de Wouw , Tom Oomen
Estimation of the breathing effort and relevant lung parameters of a ventilated patient is essential to keep track of a patient’s clinical condition. The aim of this paper is to increase estimation accuracy through experiment design. The main method is an experiment design approach across multiple breaths within a linear regression framework to accurately identify the patient’s condition. Identifiability and persistence of excitation are used to formulate an estimation problem with a unique solution. Furthermore, Fisher information is used for assessing the parameters sensitivity to slight changes of the ventilator settings to improve the variance of the estimation. The estimation method is applied to simulated patients who breathe regularly but also to patients who have variable breathing patterns. A virtual experiment is conducted for both situations to generate estimation results. The results are analyzed using mathematical tools and show that uniquely estimating the lung parameters and breathing effort over multiple breaths for both regularly and variably breathing patients is possible in the presented framework. The proposed estimation method obtains clinically relevant estimates for a large set of breathing disturbances from the simulation case-study.
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The use of near-Earth space is currently complicated by presence of space debris objects in Earth’s orbit, which include spent stages of launch vehicles, inoperative satellites, and other large and small artificial objects. One approach to solving the problem of space debris involves docking and capturing a non-cooperative space object or spacecraft (target) with a so-called service spacecraft (chaser) for further actions to repair, refuel or change its orbit. Rendezvous and docking are complicated by the rotation of uncontrolled space objects caused by various factors. To perform this task, it is necessary to know the parameters of the orbital, rotational and relative motion of the target. The parameters of the orbital motion of such objects are usually known quite accurately from measurements from the Earth. This paper examines the case of a tumbling non-cooperative target located in an elliptical orbit. It is assumed that the target relative position and orientation are measured by the computer vision system (CVS) of the chaser. In this case, the position and orientation of the graphical reference frame (GRF) associated with the known 3-D graphical model of the target are determined relative to the reference frame associated with the chaser. The specific type of CVS is not considered. It is assumed that the chaser can carry out some maneuvers near the target and all parameters of the chaser angular motion are known. Thus, the attitude of the GRF relative to inertial reference frame (IRF) can be determined. The measured parameters are not enough to ensure safe rendezvous and docking with the target. To complete this task, it is necessary to determine all kinematic and dynamic parameters of the relative motion between the spacecraft. The rest of the required parameters are estimated. Orientation and rotation parameterization is done using quaternions. The angular motion equation of the target is considered in the GRF. This makes the angular velocity estimation faster and the inertia tensor estimation more stable. Stochastic characteristics of measurement errors are considered to be unknown and are not used. The only information about the errors is the bounds of their values. To determine the relative motion parameters, we use a new dynamic set-membership filter with ellipsoidal estimates. The filter can be successfully implemented on low-power on-board processors. The properties of the proposed algorithm are demonstrated using numerical simulation. The results obtained are expected to be used in the development of a navigation system for the rendezvous and docking, developed by a group of Ukrainian space industry enterprises under the leadership of the LLC “Kurs-orbital” (https://kursorbital.com/).
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Pub Date : 2024-06-04DOI: 10.1016/j.ifacsc.2024.100266
Christopher Yew Shuen Ang , Yeong Shiong Chiew , Xin Wang , Ean Hin Ooi , Mohd Basri Mat Nor , Matthew E. Cove , J. Geoffrey Chase
<div><h3>Background and Objective:</h3><p>Patient–ventilator asynchrony (PVA) is prevalent in mechanical ventilation (MV) for critically ill patients and has been associated with adverse patient outcomes. However, studies investigating the associations between PVA and patient outcomes employ differing time windows for PVA evaluation. In this study, machine learning methods are used to quantify the prevalence and magnitude of asynchrony at different time windows, as well as its temporal trends. The study aims to identify the optimal time window for assessing the temporal changes in the asynchrony index (AI) and magnitude of asynchrony (<span><math><msub><mrow><mi>M</mi></mrow><mrow><mi>a</mi><mi>s</mi><mi>y</mi><mi>n</mi><mo>,</mo><mi>a</mi><mi>v</mi><mi>g</mi></mrow></msub></math></span>).</p></div><div><h3>Methods:</h3><p>This study uses Convolutional Neural Networks (CNN) and Convolutional Autoencoders (CAE) to detect incidences of PVA and quantify its severity in 30 MV respiratory failure patients with 2722 h of respiratory data. The frequency of PVA and the breath-average magnitude were determined over different time periods, <em>t</em>, where <span><math><mrow><mi>t</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>5</mn></mrow></math></span>, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 45, 60 min and throughout MV. The AI for the patients was determined using the CNN model. Given an asynchronous breath, the CAEs were used to reconstruct asynchrony-free waveforms. The <span><math><msub><mrow><mi>M</mi></mrow><mrow><mi>a</mi><mi>s</mi><mi>y</mi><mi>n</mi><mo>,</mo><mi>a</mi><mi>v</mi><mi>g</mi></mrow></msub></math></span> was quantified as the difference between the two waveforms. The change in AI (<span><math><mi>Δ</mi></math></span>AI) and the change in <span><math><msub><mrow><mi>M</mi></mrow><mrow><mi>a</mi><mi>s</mi><mi>y</mi><mi>n</mi><mo>,</mo><mi>a</mi><mi>v</mi><mi>g</mi></mrow></msub></math></span>