Magnetohydrodynamic Simulation of Electromagnetic Pump in TC-1

The pilot molten lead-bismuth target circuit (TC-1) in university of Nevada Las Vegas (UNLV) was designed for beam power of 1 MW accelerator driven system (ADS). The TC-1 is a liquid lead-bismuth eutectic (LBE) circulation loop. Circulation of the liquid alloy is driven by an annular linear induction pump (ALIP). Experimental measurements of system parameters have yielded a surprisingly low pump efficiency of less than 1%. A numerical study of the pump efficiency is being conducted to determine which operational parameters are responsible for this low efficiency and to give insight into future EM pump design. The numerical study will first entail calculating the EM phenomena such as the induced current distribution, magnetic field and electromagnetic body forces using both analytic and numerical methods. These calculated EM forces will be incorporated into fluid flow calculations using a commercial code such as FEMLab and/or Fluent. Parametric studies of the EM and fluid flow phenomena in the pump will be carried out

Which Echo Chamber? Regions of Attraction in Learning with Decision-Dependent Distributions

As data-driven methods are deployed in real-world settings, the processes that generate the observed data will often react to the decisions of the learner. For example, a data source may have some incentive for the algorithm to provide a particular label (e.g. approve a bank loan), and manipulate their features accordingly. Work in strategic classification and decision-dependent distributions seeks to characterize the closed-loop behavior of deploying learning algorithms by explicitly considering the effect of the classifier on the underlying data distribution. More recently, works in performative prediction seek to classify the closed-loop behavior by considering general properties of the mapping from classifier to data distribution, rather than an explicit form. Building on this notion, we analyze repeated risk minimization as the perturbed trajectories of the gradient flows of performative risk minimization. We consider the case where there may be multiple local minimizers of performative risk, motivated by real world situations where the initial conditions may have significant impact on the long-term behavior of the system. As a motivating example, we consider a company whose current employee demographics affect the applicant pool they interview: the initial demographics of the company can affect the long-term hiring policies of the company. We provide sufficient conditions to characterize the region of attraction for the various equilibria in this settings. Additionally, we introduce the notion of performative alignment, which provides a geometric condition on the convergence of repeated risk minimization to performative risk minimizers

Energy Disaggregation via Adaptive Filtering

The energy disaggregation problem is recovering device level power consumption signals from the aggregate power consumption signal for a building. We show in this paper how the disaggregation problem can be reformulated as an adaptive filtering problem. This gives both a novel disaggregation algorithm and a better theoretical understanding for disaggregation. In particular, we show how the disaggregation problem can be solved online using a filter bank and discuss its optimality.Comment: Submitted to 51st Annual Allerton Conference on Communication, Control, and Computin

Blind Identification via Lifting

Blind system identification is known to be an ill-posed problem and without further assumptions, no unique solution is at hand. In this contribution, we are concerned with the task of identifying an ARX model from only output measurements. We phrase this as a constrained rank minimization problem and present a relaxed convex formulation to approximate its solution. To make the problem well posed we assume that the sought input lies in some known linear subspace.Comment: Submitted to the IFAC World Congress 2014. arXiv admin note: text overlap with arXiv:1303.671