Search for: All records

The tools and techniques such as imaging and machine learning used in the measurement of many material and microstructural properties are rapidly evolving. In metals, the grain size is routinely measured to estimate the yield strength. This paper describes some of the algorithms used in processing the microstructures to conduct quantitative measurements. The image processing methods provide the possibility to go beyond calculating the ASTM grain size number and calculate the actual surface area of each grain, grain boundary length, and the shape of the grains. The image analysis methods can be very helpful in conducting detailed quantitative analysis with greater accuracy than many labour-intensive manual methods currently in use. The work describes the complexities in applying the imaging methods and approaches in the metallurgical and materials fields. Successful application of such methods can reduce the time and effort required to characterise microstructures and can provide more precise information. 

Abstract Neutrinos are one of the most promising messengers for signals of new physics Beyond the Standard Model (BSM). On the theoretical side, their elusive nature, combined with their unknown mass mechanism, seems to indicate that the neutrino sector is indeed opening a window to new physics. On the experimental side, several long-standing anomalies have been reported in the past decades, providing a strong motivation to thoroughly test the standard three-neutrino oscillation paradigm. In this Snowmass21 white paper, we explore the potential of current and future neutrino experiments to explore BSM effects on neutrino flavor during the next decade. 

Cameras are everywhere, and are increasingly coupled with video analytics software that can identify our face, track our mood, recognize what we are doing, and more. We present the results of a 10-day in-situ study designed to understand how people feel about these capabilities, looking both at the extent to which they expect to encounter them as part of their everyday activities and at how comfortable they are with the presence of such technologies across a range of realistic scenarios. Results indicate that while some widespread deployments are expected by many (e.g., surveillance in public spaces), others are not, with some making people feel particularly uncomfortable. Our results further show that individuals’ privacy preferences and expectations are complicated and vary with a number of factors such as the purpose for which footage is captured and analyzed, the particular venue where it is captured, and whom it is shared with. Finally, we discuss the implications of people’s rich and diverse preferences on opt-in or opt-out rights for the collection and use (including sharing) of data associated with these video analytics scenarios as mandated by regulations. Because of the user burden associated with the large number of privacy decisions people could be faced with, we discuss how new types of privacy assistants could possibly be configured to help people manage these decisions. 

In this work, we present a Linear Matrix Inequality (LMI) based method to synthesize an optimal H1 estimator for a large class of linear coupled partial differential equations (PDEs) utilizing only finite dimensional measurements. Our approach extends the newly developed framework for representing and analyzing distributed parameter systems using operators on the space of square integrable functions that are equipped with multipliers and kernels of semi-separable class. We show that by redefining the state, the PDEs can be represented using operators that embed the boundary conditions and input-output relations explicitly. The optimal estimator synthesis problem is formulated as a convex optimization subject to LMIs that require no approximation or discretization. A scalable algorithm is presented to synthesize the estimator. The algorithm is illustrated by suitable examples. 

In this paper, we consider input-output properties of linear systems consisting of PDEs on a finite domain coupled with ODEs through the boundary conditions of the PDE. This framework can be used to represent e.g. a lumped mass fixed to a beam or a system with delay. This work generalizes the sufficiency proof of the KYP Lemma for ODEs to coupled ODE-PDE systems using a recently developed concept of fundamental state and the associated boundary-condition-free representation. The conditions of the generalized KYP are tested using the PQRS positive matrix parameterization of operators resulting in a finite-dimensional LMI, feasibility of which implies prima facie provable passivity or L2-gain of the system. No discretization or approximation is involved at any step and we use numerical examples to demonstrate that the bounds obtained are not conservative in any significant sense and that computational complexity is lower than existing methods involving finite-dimensional projection of PDEs. 

Natural disasters, such as hurricanes, large wind and ice storms, typically require the repair of a large number of components in electricity distribution networks. Since power cannot be restored before the completion of repairs, optimally scheduling the available crews to minimize the cumulative duration of the customer interruptions reduces the harm done to the affected community. We have previously proposed approximation algorithms to schedule post-disaster repairs in electricity distribution networks with complete damage information [1]. In this paper, we extend our previous work to the case with incomplete damage information. We model this problem as scheduling a set of jobs with stochastic processing times on parallel identical machines in order to minimize the total weighted energization time. A linear programming (LP) based list scheduling policy is proposed and then analyzed in terms of theoretical performance.