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Dave Deriso

Publications and source records attributed to Dave Deriso.

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Project 1000 x 1000: Centrifugal melt spinning for distributed manufacturing of N95 filtering facepiece respirators

The COVID-19 pandemic has caused a global shortage of personal protective equipment. While existing supply chains are struggling to meet the surge in demand, the limited supply of N95 filtering facepiece respirators (FFRs) has placed healthcare workers at risk. This paper presents a method for scalable and distributed manufacturing of FFR filter material based on a combination of centrifugal melt spinning utilizing readily available cotton candy machines as an example. The proposed method produces nonwoven polypropylene fabric material with filtering efficiency of up to 96% for particles 0.30-0.49 {\mu}m in diameter. We additionally demonstrate a scalable means to test for filtration efficiency and pressure drop to ensure a standardized degree of quality in the output material. We perform preliminary optimization of relevant parameters for scale-up and propose that this is a viable method to rapidly produce up to one million N95 FFRs per day in distributed manner with just six machines per site operating across 200 locations. We share this work as a starting point for others to rapidly construct, replicate and develop their own affordable modular processes aimed at producing high quality filtration material to address the current FFR shortage globally.

physics.app-ph

A General Optimization Framework for Dynamic Time Warping

The goal of dynamic time warping is to transform or warp time in order to approximately align two signals together. We pose the choice of warping function as an optimization problem with several terms in the objective. The first term measures the misalignment of the time-warped signals. Two additional regularization terms penalize the cumulative warping and the instantaneous rate of time warping; constraints on the warping can be imposed by assigning the value +inf to the regularization terms. Different choices of the three objective terms yield different time warping functions that trade off signal fit or alignment and properties of the warping function. The optimization problem we formulate is a classical optimal control problem, with initial and terminal constraints, and a state dimension of one. We describe an effective general method that minimizes the objective by discretizing the values of the original and warped time, and using standard dynamic programming to compute the (globally) optimal warping function with the discretized values. Iterated refinement of this scheme yields a high accuracy warping function in just a few iterations. Our method is implemented as an open source Python package GDTW.

cs.LG