Predicting Responses to Mechanical Ventilation for Preterm Infants with Acute Respiratory Illness using Artificial Neural Networks

Infants born prematurely are particularly susceptible to respiratory illness due to underdeveloped lungs, which can often result in fatality. Preterm infants in acute stages of respiratory illness typically require mechanical ventilation assistance, and the efficacy of the type of mechanical ventilation and its delivery has been the subject of a number clinical studies. With recent advances in machine learning approaches, particularly deep learning, it may be possible to estimate future responses to mechanical ventilation in real‐time, based on ventilation monitoring up to the point of analysis. In this work, recurrent neural networks are proposed for predicting future ventilation parameters due to the highly nonlinear behavior of the ventilation measures of interest and the ability of recurrent neural networks to model complex nonlinear functions. The resulting application of this particular class of neural networks shows promise in its ability to predict future responses for different ventilation modes. Towards improving care and treatment of preterm newborns, further development of this prediction process for ventilation could potentially aid in important clinical decisions or studies to improve preterm infant health

Home Economics Research at Iowa State

Are you one of those who dislike dish-washing? Being detested, let us face it; being drudgery, let us improve it, and being inevitable, let us accept it. Twenty-six and one-half million women in the United States are engaged in the profession of homemaking, according to the, last census report. Just think of the effort and time which is spent on this one task in the households which they represent

A generalized computationally efficient inverse characterization approach combining direct inversion solution initialization with gradient-based optimization

A computationally efficient gradient-based optimization approach for inverse material characterization from incomplete system response measurements that can utilize a generally applicable parameterization (e.g., finite element-type parameterization) is presented and evaluated. The key to this inverse characterization algorithm is the use of a direct inversion strategy with Gappy proper orthogonal decomposition (POD) response field estimation to initialize the inverse solution estimate prior to gradient-based optimization. Gappy POD is used to estimate the complete (i.e., all components over the entire spatial domain) system response field from incomplete (e.g., partial spatial distribution) measurements obtained from some type of system testing along with some amount of a priori information regarding the potential distribution of the unknown material property. The estimated complete system response is used within a physics-based direct inversion procedure with a finite element-type parameterization to estimate the spatial distribution of the desired unknown material property with minimal computational expense. Then, this estimated spatial distribution of the unknown material property is used to initialize a gradient-based optimization approach, which uses the adjoint method for computationally efficient gradient calculations, to produce the final estimate of the material property distribution. The three-step [(1) Gappy POD, (2) direct inversion, and (3) gradient-based optimization] inverse characterization approach is evaluated through simulated test problems based on the characterization of elastic modulus distributions with localized variations (e.g., inclusions) within simple structures. Overall, this inverse characterization approach is shown to efficiently and consistently provide accurate inverse characterization estimates for material property distributions from incomplete response field measurements. Moreover, the solution procedure is shown to be capable of extrapolating significantly beyond the initial assumptions regarding the potential nature of the unknown material property distribution

Adaptive Reduced-Basis Generation for Reduced-Order Modeling for the Solution of Stochastic Nondestructive Evaluation Problems

A novel algorithm for creating a computationally efficient approximation of a system response that is defined by a boundary value problem is presented. More specifically, the approach presented is focused on substantially reducing the computational expense required to approximate the solution of a stochastic partial differential equation, particularly for the purpose of estimating the solution to an associated nondestructive evaluation problem with significant system uncertainty. In order to achieve this computational efficiency, the approach combines reduced-basis reduced-order modeling with a sparse grid collocation surrogate modeling technique to estimate the response of the system of interest with respect to any designated unknown parameters, provided the distributions are known. The reduced-order modeling component includes a novel algorithm for adaptive generation of a data ensemble based on a nested grid technique, to then create the reduced-order basis. The capabilities and potential applicability of the approach presented are displayed through two simulated case studies regarding inverse characterization of material properties for two different physical systems involving some amount of significant uncertainty. The first case study considered characterization of an unknown localized reduction in stiffness of a structure from simulated frequency response function based nondestructive testing. Then, the second case study considered characterization of an unknown temperature-dependent thermal conductivity of a solid from simulated thermal testing. Overall, the surrogate modeling approach was shown through both simulated examples to provide accurate solution estimates to inverse problems for systems represented by stochastic partial differential equations with a fraction of the typical computational cost

Efficient Global Sensitivity Analysis of Structural Vibration for a Nuclear Reactor System Subject to Nonstationary Loading

The structures associated with the nuclear steam supply system (NSSS) of a pressurized water reactor (PWR) include significant epistemic and aleatory uncertainties in the physical parameters, while also being subject to various non-stationary stochastic loading conditions over the life of a nuclear power plant. To understand the influence of these uncertainties on nuclear reactor systems, sensitivity analysis must be performed. This work evaluates computational design of experiment strategies, which execute a nuclear reactor equipment system finite element model to train and verify Gaussian Process (GP) surrogate models. The surrogate models are then used to perform both global and local sensitivity analyses. The significance of the sensitivity analysis for efficient modeling and simulation of nuclear reactor stochastic dynamics is discussed

Simplified Automatic Fault Detection in Wind Turbine Induction Generators

This paper presents a simplified automated fault detection scheme for wind turbine induction generators with rotor electrical asymmetries. Fault indicators developed in previous works have made use of the presence of significant spectral peaks in the upper sidebands of the supply frequency harmonics; however, the specific location of these peaks may shift depending on the wind turbine speed. As wind turbines tend to operate under variable speed conditions, it may be difficult to predict where these fault‐related peaks will occur. To accommodate for variable speeds and resulting shifting frequency peak locations, previous works have introduced methods to identify or track the relevant frequencies, which necessitates an additional set of processing algorithms to locate these fault‐related peaks prior to any fault analysis. In this work, a simplified method is proposed to instead bypass the issue of variable speed (and shifting frequency peaks) by introducing a set of bandpass filters that encompass the ranges in which the peaks are expected to occur. These filters are designed to capture the fault‐related spectral information to train a classifier for automatic fault detection, regardless of the specific location of the peaks. Initial experimental results show that this approach is robust against variable speeds and further shows good generalizability in being able to detect faults at speeds and conditions that were not presented during training. After training and tuning the proposed fault detection system, the system was tested on “unseen” data and yielded a high classification accuracy of 97.4%, demonstrating the efficacy of the proposed approach

Safety in the Home.

4 p