A note on the effect of material uncertainty on acoustic source localization error in anisotropic plates.

The uncertainty in material properties of an anisotropic plate may influence the acoustic source localization process undertaken for the plate. To study this effect of material uncertainty, the two moduli of elasticity of an orthotropic plate material are considered in this note as independent random variables and the propagation of this material uncertainty through the wave front shape-based acoustic source localization approach is investigated. Assuming lognormal probability distributions for the two random variables, several design points in lognormal spaces are picked using Latin Hypercube Sampling. Finite element analysis is performed for each design point to simulate the elastic wave propagation due to an acoustic event and wave front shape-based approach is applied to estimate the source location. The time-of-arrivals and source localization errors obtained for each design point are considered as separate response functions at that design point and regression kriging metamodels through the responses at the design points are constructed. Monte Carlo simulations are carried out using these metamodels to obtain the distribution parameters (i.e., ranges, means and standard deviations) of the time-of-arrivals and localization errors. A global sensitivity analysis is performed to estimate the effect of each random variable on the localization errors. It is observed that for lognormally distributed moduli of elasticity with same coefficients of variation, uncertainty in the modulus of elasticity in the major direction affects the source localization accuracy more compared to the uncertainty in the modulus of elasticity in the minor direction, particularly when the ellipse-based technique is used.

Woodwind instrument design optimization based on impedance characteristics with geometric constraints.

Computational optimization algorithms coupled with acoustic models of wind instruments provide instrument makers with an opportunity to explore new designs. Specifically, they enable the automatic discovery of geometries exhibiting desired resonance characteristics. In this paper, the design optimization of woodwind instruments with complex geometrical features (e.g., non-cylindrical bore profile and side holes with various radii and chimney heights) is investigated. Optimal geometric designs are searched so that their acoustic input impedance has peaks with specific target frequencies and amplitudes. However, woodwind instruments exhibit complex input impedance whose features, such as resonances, might have non-smooth evolution with respect to design variables, thus hampering gradient-based optimization. For this reason, this paper introduces new formulations of the impedance characteristics (resonance frequencies and amplitudes) using a regularized unwrapped angle of the reflection function. The approach is applied to an illustrative instrument subjected to geometric constraints similar to the ones encountered by manufacturers (a key-less pentatonic clarinet with two-registers). Three optimization problems are considered, demonstrating a strategy to simultaneously adjust several impedance characteristics on all fingerings.

Construction of a risk model through the fusion of experimental data and finite element modeling: Application to car crash-induced TBI.

This article introduces a new approach for the construction of a risk model for the prediction of Traumatic Brain Injury (TBI) as a result of a car crash. The probability of TBI is assessed through the fusion of an experiment-based logistic regression risk model and a finite element (FE) simulation-based risk model. The proposed approach uses a multilevel framework which includes FE simulations of vehicle crashes with dummy and FE simulations of the human brain. The loading conditions derived from the crash simulations are transferred to the brain model thus allowing the calculation of injury metrics such as the Cumulative Strain Damage Measure (CSDM). The framework is used to propagate uncertainties and obtain probabilities of TBI based on the CSDM injury metric. The risk model from FE simulations is constructed from a support vector machine classifier, adaptive sampling, and Monte-Carlo simulations. An approach to compute the total probability of TBI, which combines the FE-based risk assessment as well as the risk prediction from the experiment-based logistic regression model is proposed. In contrast to previous published work, the proposed methodology includes the uncertainty of explicit parameters such as impact conditions (e.g., velocity, impact angle), and material properties of the brain model. This risk model can provide, for instance, the probability of TBI for a given assumed crash impact velocity.

Fusion of clinical and stochastic finite element data for hip fracture risk prediction.

Hip fracture affects more than 250,000 people in the US and 1.6 million worldwide per year. With an aging population, the development of reliable fracture risk models is therefore of prime importance. Due to the complexity of the hip fracture phenomenon, the use of clinical data only, as it is done traditionally, might not be sufficient to ensure an accurate and robust hip fracture prediction model. In order to increase the predictive ability of the risk model, the authors propose to supplement the clinical data with computational data from finite element models. The fusion of the two types of data is performed using deterministic and stochastic computational data. In the latter case, uncertainties in loading and material properties of the femur are accounted for and propagated through the finite element model. The predictive capability of a support vector machine (SVM) risk model constructed by combining clinical and finite element data was assessed using a Women׳s Health Initiative (WHI) dataset. The dataset includes common factors such as age and BMD as well as geometric factors obtained from DXA imaging. The fusion of computational and clinical data systematically leads to an increase in predictive ability of the SVM risk model as measured by the AUC metric. It is concluded that the largest gains in AUC are obtained by the stochastic approach. This gain decreases as the dimensionality of the problem increases: a 5.3% AUC improvement was achieved for a 9 dimensional problem involving geometric factors and weight while a 1.3% increase was obtained for a 20 dimensional case including geometric and conventional factors.

Optimal SVM parameter selection for non-separable and unbalanced datasets.

This article presents a study of three validation metrics used for the selection of optimal parameters of a support vector machine (SVM) classifier in the case of non-separable and unbalanced datasets. This situation is often encountered when the data is obtained experimentally or clinically. The three metrics selected in this work are the area under the ROC curve (AUC), accuracy, and balanced accuracy. These validation metrics are tested using computational data only, which enables the creation of fully separable sets of data. This way, non-separable datasets, representative of a real-world problem, can be created by projection onto a lower dimensional sub-space. The knowledge of the separable dataset, unknown in real-world problems, provides a reference to compare the three validation metrics using a quantity referred to as the "weighted likelihood". As an application example, the study investigates a classification model for hip fracture prediction. The data is obtained from a parameterized finite element model of a femur. The performance of the various validation metrics is studied for several levels of separability, ratios of unbalance, and training set sizes.

Three dimensional active contours for the reconstruction of abdominal aortic aneurysms.

An aneurysm is a gradual and progressive ballooning of a blood vessel due to wall degeneration. Rupture of abdominal aortic aneurysm (AAA) constitutes a significant portion of deaths in the US. In this study, we describe a technique to reconstruct AAA geometry from CT images in an inexpensive and streamlined fashion. A 3D reconstruction technique was implemented with a GUI interface in MATLAB using the active contours technique. The lumen and the thrombus of the AAA were segmented individually in two separate protocols and were then joined together into a hybrid surface. This surface was then used to obtain the aortic wall. This method can deal with very poor contrast images where the aortic wall is indistinguishable from the surrounding features. Data obtained from the segmentation of image sets were smoothed in 3D using a Support Vector Machine technique. The segmentation method presented in this paper is inexpensive and has minimal user-dependency in reconstructing AAA geometry (lumen and wall) from patient image sets. The AAA model generated using this segmentation algorithm can be used to study a variety of biomechanical issues remaining in AAA biomechanics including stress estimation, endovascular stent-graft performance, and local drug delivery studies.