Determining the influence and effects of manufacturing variables on sulfur dioxide cells

A survey of the Li/SO2 manufacturing community was conducted to determine where variability exists in processing. The upper and lower limits of these processing variables might, by themselves or by interacting with other variables, influence safety, performance, and reliability. A number of important variables were identified and a comprehensive design experiment is being proposed to make the proper determinations

Effect of Mn and Mg dopants on vacancy defect formation in ammonothermal GaN

We have applied positron annihilation spectroscopy to study the formation of Ga vacancy related defects in Mg and Mn doped bulk GaN crystals grown by the ammonothermal method. We show that Mn doping has little or no effect on the formation of Ga vacancies, while Mg doping strongly suppresses their formation, in spite of both dopants leading to highly resistive material. We suggest the differences are primarily due to the hydrogen-dopant interactions. Further investigations are called for to draw a detailed picture of the atomic scale phe-nomena in the synthesis of ammonothermal GaN.Peer reviewe

Robustness Verification of Support Vector Machines

We study the problem of formally verifying the robustness to adversarial examples of support vector machines (SVMs), a major machine learning model for classification and regression tasks. Following a recent stream of works on formal robustness verification of (deep) neural networks, our approach relies on a sound abstract version of a given SVM classifier to be used for checking its robustness. This methodology is parametric on a given numerical abstraction of real values and, analogously to the case of neural networks, needs neither abstract least upper bounds nor widening operators on this abstraction. The standard interval domain provides a simple instantiation of our abstraction technique, which is enhanced with the domain of reduced affine forms, which is an efficient abstraction of the zonotope abstract domain. This robustness verification technique has been fully implemented and experimentally evaluated on SVMs based on linear and nonlinear (polynomial and radial basis function) kernels, which have been trained on the popular MNIST dataset of images and on the recent and more challenging Fashion-MNIST dataset. The experimental results of our prototype SVM robustness verifier appear to be encouraging: this automated verification is fast, scalable and shows significantly high percentages of provable robustness on the test set of MNIST, in particular compared to the analogous provable robustness of neural networks

Structural Damage Detection and Reliability Estimation using a Multidimensional Monitoring Approach

Many structural elements are exposed to load conditions that are difficult to model during the design stage, such as environmental uncertainties, random impacts and overloading amongst others, thus, increasing un programmed maintenance and reducing..