Robust Geometric Formation Control of Multiple Autonomous

©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the American Control Conference (ACC 2013), 17-19 June 2013, Washington, D.C.This paper develops a robust controller for autonomous underwater vehicles with bounded time delays, so that the AUVs form and keep a desired formation shape and track a desired trajectory. We use a six-degree-of-freedom dynamic model for each AUV to describe its motions in the three-dimensional space. We design an orientation controller based on feedback linearization, so that the orientation of each AUV converges to its desired value. We derive formation dynamics of AUVs and decouple the dynamics into a formation shape and a formation center, using the Jacobi transform. We treat couplings in the formation dynamics as perturbations and design a robust formation-keeping controller to tolerate both the perturbations and the time delays. We demonstrate the effectiveness of our controller in simulations

Estimating the contribution of local primary emissions to particulate pollution using high-density station observations

Local primary emission, transport, and secondary formation of aerosols constitute the major atmospheric particulate matter (PM) over a certain region. To identify and quantify major sources of ambient PM is important for pollution mitigation strategies, especially on a city scale. We developed two source apportionment methods to make the first‐order estimates of local primary contribution ratio (LCR) of PM_(2.5) (PM with diameter less than 2.5 μm) using the high‐density (about 1/km^2) network observations with high sampling frequency (about 1 hr). Measurements of PM_(2.5) mass concentration from 169 sites within a 20 km × 20 km domain are analyzed. The two methods developed here are mainly based on the spatial and temporal variations of PM_(2.5) within an urban area. The accuracy of our developed methods is subject to the assumptions on the spatial heterogeneity of primary and secondary formed aerosols as well as those from long‐range transport to a city. We apply these two methods to a typical industrial city in China in winter of 2015 with frequent severe haze events. The local primary pollution contributions calculated from the two methods agree with each other that they are often larger than 0.4. The LCR range is from 0.4 to 0.7, with an average value of 0.63. Our study indicates the decisive role of locally emitted aerosols in the urban severe haze formation during the winter time. It further suggests that reductions of local primary aerosol emissions are essential to alleviate the severe haze pollution, especially in industrial cities

Warming effect of dust aerosols modulated by overlapping clouds below

Due to the substantial warming effect of dust aerosols overlying clouds and its poor representation in climate models, it is imperative to accurately quantify the direct radiative forcing (DRF) of above-cloud dust aerosols. When absorbing aerosol layers are located above clouds, the warming effect of aerosols strongly depends on the cloud macro- and micro-physical properties underneath, such as cloud optical depth and cloud fraction at visible wavelength. A larger aerosol-cloud overlap is believed to cause a larger warming effect of absorbing aerosols, but the influence of overlapping cloud fraction and cloud optical depth remains to be explored. In this study, the impact of overlapping cloud properties on the shortwave all-sky DRF due to springtime above-cloud dust aerosols is quantified over northern Pacific Ocean based on 10-year satellite measurements. On average, the DRF is roughly 0.62 Wm^(−2). Furthermore, the warming effect of dust aerosols linearly increases with both overlapping cloud fraction and cloud optical depth. An increase of 1% in overlapping cloud fraction will amplify this warming effect by 1.11 Wm^(−2)τ^(−1). For the springtime northern Pacific Ocean, top-of-atmosphere cooling by dust aerosols turns into warming when overlapping cloud fraction is beyond 0.20. The variation of critical cloud optical depth beyond which dust aerosols switch from exerting a net cooling to a net warming effect depends on the concurrent overlapping cloud fraction. When the overlapping cloud coverage range increases from 0.2 to –0.4 to 0.6–0.8, the corresponding critical cloud optical depth reduces from 6.92 to 1.16. Our results demonstrate the importance of overlapping cloud properties for determining the springtime warming effect of dust aerosols

Estimating the contribution of local primary emissions to particulate pollution using high-density station observations

Local primary emission, transport, and secondary formation of aerosols constitute the major atmospheric particulate matter (PM) over a certain region. To identify and quantify major sources of ambient PM is important for pollution mitigation strategies, especially on a city scale. We developed two source apportionment methods to make the first‐order estimates of local primary contribution ratio (LCR) of PM_(2.5) (PM with diameter less than 2.5 μm) using the high‐density (about 1/km^2) network observations with high sampling frequency (about 1 hr). Measurements of PM_(2.5) mass concentration from 169 sites within a 20 km × 20 km domain are analyzed. The two methods developed here are mainly based on the spatial and temporal variations of PM_(2.5) within an urban area. The accuracy of our developed methods is subject to the assumptions on the spatial heterogeneity of primary and secondary formed aerosols as well as those from long‐range transport to a city. We apply these two methods to a typical industrial city in China in winter of 2015 with frequent severe haze events. The local primary pollution contributions calculated from the two methods agree with each other that they are often larger than 0.4. The LCR range is from 0.4 to 0.7, with an average value of 0.63. Our study indicates the decisive role of locally emitted aerosols in the urban severe haze formation during the winter time. It further suggests that reductions of local primary aerosol emissions are essential to alleviate the severe haze pollution, especially in industrial cities

Distribution of hepatitis C virus in eastern China from 2011 to 2020: a Bayesian spatiotemporal analysis

ObjectiveThis study aimed to evaluate the spatiotemporal distribution of patients with hepatitis C virus (HCV) and the factors influencing this distribution in Jiangsu Province, China, from 2011 to 2020.MethodsThe incidence of reported HCV in Jiangsu Province from 2011 to 2020 was obtained from the Chinese Information System for Disease Control and Prevention (CISDCP). R and GeoDa software were used to visualize the spatiotemporal distribution and the spatial autocorrelation of HCV. A Bayesian spatiotemporal model was constructed to explore the spatiotemporal distribution of HCV in Jiangsu Province and to further analyze the factors related to HCV.ResultsA total of 31,778 HCV patients were registered in Jiangsu Province. The registered incidence rate of HCV increased from 2.60/100,000 people in 2011 to 4.96/100,000 people in 2020, an increase of 190.77%. Moran's I ranged from 0.099 to 0.354 (P < 0.05) from 2011 to 2019, indicating a positive spatial correlation overall. The relative risk (RR) of the urbanization rate, the most important factor affecting the spread of HCV in Jiangsu Province, was 1.254 (95% confidence interval: 1.141–1.376), while other factors had no significance.ConclusionThe reported HCV incidence rate integrally increased in the whole Jiangsu Province, whereas the spatial aggregation of HCV incidence was gradually weakening. Our study highlighted the importance of health education for the floating population and reasonable allocation of medical resources in the future health work