Application of the general linear model for smoothing gas exchange data

The precision of an interpretation of gas exchange records in progressive exercise is limited by the typical breath-to-breath variation in the data. Recently, two procedures have been proposed for minimizing the "noise" in the estimates of alveolar gas exchange time series data. One approach utilizes an estimate of pulmonary blood flow (Q) for smoothing purposes. The other approach utilizes an estimate of effective lung volume (V'L) for smoothing purposes. In this paper, we formulate the smoothing problem as a general linear model and demonstrate the concurrent estimates of both V'L and Q. Furthermore, we investigate the interaction between V'L and Q. Specifically, when a high value of lung volume is used (such as the subject's resting functional residual capacity) in the alveolar gas exchange algorithm, the estimate of Q is biased low and the result is a less effective smoothing of the data. In addition, we demonstrate how the Q estimate can be improved by utilizing more appropriate estimates of arterial carbon dioxide tension.     

Acute cardio-respiratory changes induced by hyperpnoea using a respiratory muscle trainer

The aim of this study was to examine the cardio-respiratory effects of voluntary hyperpnoea using a respiratory muscle trainer (RMT) with three different sized rebreathing bags. In particular, the effects of hyperpnoea on inspired and end-tidal gas concentrations were determined. Seven adult males completed three 30 min bouts of hyperpnoea using optimal, oversized and undersized rebreathing bags. Inspired (F(I)) and expired end-tidal (F(ET)) O2 and CO2 concentrations, arterial O2 saturation (S(AO2)) and heart rate were measured during hyperpnoea. Before and after a bout of hyperpnoea, pulmonary function and blood pressure (BP) were assessed. Data were analysed using a two-way repeated-measures ANOVA, with p < 0.05 considered significant. Three subjects experienced discomfort during hyperpnoea and stopped after 20 min. During hyperpnoea, the F(ETCO2) was maintained at 4.6 +/- 0.7% irrespective of bag size. The increase in F(ICO2) over time reached 0.5 +/- 0.5% at 20 min. The F(IO2) fell to 19.4 +/- 0.8% at 20 min, and S(AO2) decreased to 97%. Heart rate and systolic BP increased slightly, but independently of rebreathing bag volume. No changes in pulmonary function or diastolic BP were found. It is concluded that the RMT maintained a constant F(ETCO2) at the expense of a mild hypoxia. The acute effects of hyperpnoea on the cardio-respiratory system are generally mild, but not always tolerable for 30 min.     

Noise-induced bias in last principal component modeling of linear system

Last principal component (LPC) modeling relies on principal component transformation, and utilizes the eigenvectors associated with the last (smallest) principal components. When applied to experimental data, it may be considered an alternative to least squares based estimation of model parameters. Experimental results in the literature (cited in the body of the paper) suggest that LPC modeling is inferior to LS, in terms of estimation bias, in the presence of noise. Other results show that LPC produces unbiased estimates only in a very special case. In this paper, we derive explicit expressions for noise-induced bias in LPC-based identification. We investigate static systems with input actuator and measurement noise, and discrete dynamic systems with output measurement noise. We show that, indeed, LPC-based estimates are biased even when LS-based ones are not, and when the LS estimate is also biased, the LPC estimate has the LS bias plus an additional term. The theoretical results are supported by simulation studies.     

CO2 elimination as predicted by augmented dispersion and convection in an asymmetrical dog airway model

We have developed a computer model of the dog lung based upon an asymmetrically branching network of tubes to describe gas exchange during high-frequency oscillations. Impedances to oscillatory flows were calculated for each airway segment and used to determine flow distributions in all airway generations. Gas exchange was assumed to occur by convective and augmented dispersive mechanisms. Also included in the model were features commonly found in animal studies including the effects of bias flow of fresh gas at the airway opening and equipment dead-space volume. The magnitude of CO2 elimination predicted by the model closely resembled actual experimental data. Specifically, it predicted that CO2 elimination is proportional to frequency to the 0.82 power and tidal volume to the 1.25 power. It also indicated that the bias flow rate and equipment dead-space volume influence gas exchange characteristics and thus these variables should be considered when comparing data from different studies.     

基于VSG微网储能变流器无缝切换控制策略研究

针对微网储能变流器采用状态跟随控制器进行切换的过程中存在微小电压、电流扰动等问题,本文提出了 一种基于线性自抗扰控制器(LADRC)的无缝切换控制策略.在采用P/Q控制和VSG控制相互切换方法的基础上,将电流内环控制器改成线性自抗扰控制器.通过设计一个状态观测器LESO实时在线观测估计输出变量直轴、交轴电流id、iq以...     

Towards improving the accuracy of opium yield estimates with remote sensing

Yearly estimates of illicit opium production are key metrics for assessing the effectiveness of the counter-narcotics policy in Afghanistan. Poor security often prevents access to sample locations and puts pressure on field surveyors, resulting in biased sampling and errors in data recording. Supportive methods using aerial digital photography for improving yield estimates were investigated in the UK in 2004, 2005, and 2010. There were good empirical relationships between normalized difference vegetation index and poppy yield indicators (mature capsule volume and dry capsule yield) for individual fields. The results suggested a good generalized relationship across all sampled fields and years (R² > 0.70) during the 3–4 week period including poppy flowering. Regression estimates using this relationship with the imagery counteracted bias in the sample estimate of yield, reduced sample error, and enabled the production of detailed maps showing the poppy yield distribution. The application of this approach using very-high-resolution satellite imagery was investigated in the context of the annual opium survey in Afghanistan. Initial results indicated the potential for bias correction of yield estimates using a smaller and targeted collection of ground observations as an alternative to random sampling.     

Covariance matrix estimation for left-censored data

Multivariate methods often rely on a sample covariance matrix. The conventional estimators of a covariance matrix require complete data vectors on all subjects—an assumption that can frequently not be met. For example, in many fields of life sciences that are utilizing modern measuring technology, such as mass spectrometry, left-censored values caused by denoising the data are a commonplace phenomena. Left-censored values are low-level concentrations that are considered too imprecise to be reported as a single number but known to exist somewhere between zero and the laboratory’s lower limit of detection. Maximum likelihood-based covariance matrix estimators that allow the presence of the left-censored values without substituting them with a constant or ignoring them completely are considered. The presented estimators efficiently use all the information available and thus, based on simulation studies, produce the least biased estimates compared to often used competing estimators. As the genuine maximum likelihood estimate can be solved fast only in low dimensions, it is suggested to estimate the covariance matrix element-wise and then adjust the resulting covariance matrix to achieve positive semi-definiteness. It is shown that the new approach succeeds in decreasing the computation times substantially and still produces accurate estimates. Finally, as an example, a left-censored data set of toxic chemicals is explored.     

Performance Recovery of Feedback-Linearization-Based Designs

We consider a tracking problem for a partially feedback linearizable nonlinear system with stable zero dynamics. The system is uncertain and only the output is measured. We use an extended high-gain observer of dimension $n+1$, where $n$ is the relative degree. The observer estimates $n$ derivatives of the tracking error, of which the first $(n-1)$ derivatives are states of the plant in the normal form and the $n$th derivative estimates the perturbation due to model uncertainty and disturbance. The controller cancels the perturbation estimate and implements a feedback control law, designed for the nominal linear model that would have been obtained by feedback linearization had all the nonlinearities been known and the signals been available. We prove that the closed-loop system under the observer-based controller recovers the performance of the nominal linear model as the observer gain becomes sufficiently high. Moreover, we prove that the controller has an integral action property in that it ensures regulation of the tracking error to zero in the presence of constant nonvanishing perturbation.      

Approximate Homogenization of Fully Nonlinear Elliptic PDEs: Estimates and Numerical Results for Pucci Type Equations

We are interested in the shape of the homogenized operator \(\overline{F}(Q)\) for PDEs which have the structure of a nonlinear Pucci operator. A typical operator is \(H^{a_1,a_2}(Q,x) = a_1(x) \lambda _{\min }(Q) + a_2(x)\lambda _{\max }(Q)\). Linearization of the operator leads to a non-divergence form homogenization problem, which can be solved by averaging against the invariant measure. We estimate the error obtained by linearization based on semi-concavity estimates on the nonlinear operator. These estimates show that away from high curvature regions, the linearization can be accurate. Numerical results show that for many values of Q, the linearization is highly accurate, and that even near corners, the error can be small (a few percent) even for relatively wide ranges of the coefficients.     

CO2 and O2 concentrations in integral motorcycle helmets

Inhaling air which contains excess CO2 and/or is oxygen-deficient is known to present health risks and to diminish human cognitive abilities. The average CO2 concentrations relevant to a motorcyclist wearing an integral helmet were measured 20 years ago and found to be alarmingly large. The purpose of the present study was to examine gas concentrations typically inhaled by a motorcyclist. Average concentrations of CO2 near the upper lip for persons (n = 4) wearing integral motorcycle helmets were measured in the laboratory and the field to facilitate comparison to previous work, and similarly high average concentrations were found: above 2% when stationary, well below 1% for speeds of 50 km/h or more. Very good agreement was obtained between laboratory and field measurements. Detailed measurements of the time-dependent CO2 concentrations passing through a mouthpiece for mouth-breathing showed inhaled levels slightly over half of the corresponding average concentrations, including 1.3+/-0.3% at standstill, though higher concentrations (4% or more) were inhaled at the beginning of each breath. Opening the visor at standstill had on average no effect. At a speed of 50 km/h the inhaled CO2 concentration resembles that for a person without a helmet in still air, at about 0.2%. The oxygen deficiency is generally equal to the CO2 concentration, and could also contribute negatively to a motorcyclist's cognitive abilities.     

Alveolar flooding: a computer simulation

A validated computer simulation of pulmonary microvascular exchange (J.L. Bert and K.L. Pinder, Microvasc. Res., 27 (1984) 51-70) has been extended to include exchange with the air space (alveoli). Equations which hypothetically describe characteristics associated with this additional compartment and the exchange of both fluid and plasma proteins between the lung tissue and the alveolar space are presented. These are incorporated into the simulation which has been used to predict the behavior of the pulmonary microvascular exchange system including alveolar flooding. The predicted trends associated with alveolar flooding are reasonable. However, due to the lack of specific experimental or clinical findings, the simulation remains essentially unvalidated. The effect on alveolar flooding and interstitial edema of the parameters associated with the additional relationships is presented and discussed. Primarily, results for perturbations in circulatory pressure are presented. Additionally, changes in permeability characteristics are shown and discussed.     

Using SAS to conduct nonparametric residual bootstrap multilevel modeling with a small number of groups

In multilevel modeling, researchers often encounter data with a relatively small number of units at the higher levels. As a result, of this and/or non-normality of the residuals, model parameter estimates, particularly the variance components and standard errors of parameter estimates at the group level, may be biased, thus the corresponding statistical inferences may not be trustworthy. This problem can be addressed by using bootstrap methods to estimate the standard errors of the parameter estimates for significance testing. This study illustrates how to use statistical analysis system (SAS) to conduct nonparametric residual bootstrap multilevel modeling. Specific SAS programs for such modeling are provided.     

Tidal breathing model describing end-tidal, alveolar, arterial and mixed venous CO2 and O2

Thermodilution is the current standard for determination of cardiac output. The method is invasive and constitutes a risk for the patient. As an alternative CO₂ rebreathing allows non-invasive cardiac output estimation using Ficks principle. The method relies on estimation of arterial CO₂ partial pressure from end-tidal CO₂ pressure and estimation of mixed venous CO₂ partial pressure from end-tidal CO₂ during rebreathing. Presumably the oxygenation of blood in the lung capillaries increases lung capillary CO₂ pressure due to the Haldane effect, which during rebreathing may result in overestimation of the mixed venous CO₂ pressure. However, the Haldane effect is not discussed in the current literature describing cardiac output estimation using CO₂ rebreathing. The purpose of this study is to construct and verify a compartmental tidal breathing lung model to investigate the physiological mechanisms that influence the CO₂ rebreathing technique. The model simulations show agreement with previous studies describing end-tidal to arterial differences in CO₂ pressure and rebreathing with high and low O₂ fractions in the rebreathing bag. In conclusion the simulations show that caution has to be taken when using end-tidal measurements to estimate CO₂ pressures, especially during rebreathing where the Haldane effect causes mixed venous CO₂ partial pressure to be substantially overestimated.     

CO2气氛中界限电流型氧传感器输出特性的研究

本文对钇稳定ZrO2界限电流型氧传感器在高CO2低O2浓度气氛中的输出特性进行了研究.研究结果显示: 在高CO2低O2 浓度气氛中,给界限电流型氧传感器施加0.4～0.7 V范围工作电压时,输出电流出现了第一饱和输出平台,且饱和输出电流与氧浓度的关系符合经典的理论公式;但当工作电压增加后,输出电流急剧增加,并在1.1～1.3 V和1.5～1.7 V工作电压范围内出现了第二和第三饱和输出平台.为此,本文尝试采用新的假设机理:在较高工作电压下,气体中的CO2在氧传感器Pt电极表面,气体中的CO2可能催化分解产生出附加新的氧分子浓度的设想的假设机理对试验数据进行了合理解释.同时也发现,由于N2的存在,会提高延迟了工作电压促使CO2产生分解的电压的作用.     

Neural‐Adaptive Control and Nonlinear Observer for Waste‐To‐Energy Boilers

This paper looks at the problem of controlling an incinerator that burns waste gas to generate power. The system is modelled as a standard utility boiler using one known and one unknown (waste) fuel input. Standard linear controls have trouble dealing with large variations in the waste input, and in practice boiler shutdowns can occur. In this work, a nonlinear adaptive control design accounts for uncertainty in the plant parameters, and an adaptive neural‐network estimates the effect of the waste input. Since a linear observer design cannot guarantee convergence away from a set point, a novel nonlinear observer design provides estimates of the states. The observer design uses fictitious states to estimate nonlinear terms in the observer dynamics. The analysis guarantees Lyapunov stability, thus the observer bounds depend on the accuracy of the observer initial conditions. Simulation results show the proposed method can obtain accurate performance and stability, improving over results obtained withproportional–integral control.     

Fusion of possibly biased location estimates using Gaussian mixture models

A probabilistic framework for fusing location estimates, which may be biased and inconsistent, is presented. The proposed method, involving Gaussian mixture models (GMMs), utilizes prior information regarding the sensor bias, firstly, to reduce errors in the fused location estimate, and secondly, to produce a fused covariance matrix that better reflects the expected location error. Simulations are used to evaluate performance, relative to other techniques, such as the covariance union (CU) method. A passive geolocation application involving an airborne electronic support (ES) system is considered.     

Accuracy of forest mapping based on Landsat TM data and a kNN-based method

A multi-source forest inventory (MSFI) method has been developed for use in the Norwegian National Forest Inventory (NFI). The method is based on a k-nearest neighbour rule and uses field plots from the NFI, land cover maps, and satellite image data from Landsat Thematic Mapper. The inventory method is used to produce maps of selected forest variables and to estimate the selected forest variables for large areas such as municipalities. In this study, focus has been on the qualitative variables ‘dominating species group’ and ‘development class’ because these variables are of central interest to forest managers. A mid-summer Landsat 5 TM scene was used as image data, and all NFI plots inside the scene were used as a reference dataset. The relationship between the spectral bands and the forest variables was analysed, and it was found that the levels of association were low. A leave-one-out method based on the reference dataset was used to estimate the pixel-level accuracies. They were found to be relatively low with 63% agreement for species groups. An independent control survey was available for a municipality and estimates from the MSFI were compared to it. The levels of error were quite high. It was concluded that the large area estimates were biased by the reference dataset.     

Designing priors for robust Bayesian optimal experimental design

Building mathematical models is a common task in process systems engineering, which requires estimation of model parameters as the final step of modeling exercise. Model based experimental design has evolved as a potential statistical tool for reducing uncertainties in parameter estimates. Often a huge volume of process information is generated as an end result of an experimental design. Designing optimal experiments based on current or prior process knowledge is still an open research problem. This paper deals with how information, available a priori, can be organized and systematically used for designing robust Bayesian dynamic experiments, in the presence of process constraints. The designed experiments are ‘robust’ to a poor choice of nominal parameter values. Several novel techniques for organizing a priori process knowledge are explored from a theoretical view point. The influence of proposed prior designs on parameter estimates is demonstrated on a semi-continuous baker's yeast fermenter problem.