A Q-Learning Approach to Flocking With UAVs in a Stochastic Environment.

In the past two decades, unmanned aerial vehicles (UAVs) have demonstrated their efficacy in supporting both military and civilian applications, where tasks can be dull, dirty, dangerous, or simply too costly with conventional methods. Many of the applications contain tasks that can be executed in parallel, hence the natural progression is to deploy multiple UAVs working together as a force multiplier. However, to do so requires autonomous coordination among the UAVs, similar to swarming behaviors seen in animals and insects.

Evaluation of a novel personal nanoparticle sampler.

This work investigated the performance in terms of collection efficiency and aspiration efficiency of a personal sampler capable of collecting ultrafine particles (nanoparticles) in the occupational environment. This sampler consists of a cyclone for respirable particle classification, micro-orifice impactor stages with an acceleration nozzle to achieve nanoparticle classification and a backup filter to collect nanoparticles. Collection efficiencies of the cyclone and impactor stages were determined using monodisperse polystyrene latex and silver particles, respectively.

Novel active personal nanoparticle sampler for the exposure assessment of nanoparticles in workplaces.

A novel active personal nanoparticle sampler (PENS), which enables the collection of both respirable particulate mass (RPM) and nanoparticles (NPs) simultaneously, was developed to meet the critical demand for personal sampling of engineered nanomaterials (ENMs) in workplaces. The PENS consists of a respirable cyclone and a micro-orifice impactor with the cutoff aerodynamic diameter (d(pa50)) of 4 μm and 100 nm, respectively. The micro-orifice impactor has a fixed micro-orifice plate (137 nozzles of 55 μm in the inner diameter) and a rotating, silicone oil-coated Teflon filter substrate at 1 rpm to achieve a uniform particle deposition and avoid solid particle bounce.