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Jin-Soo Park

Publications and source records attributed to Jin-Soo Park.

2 recordsLinked to original sources

Coarse particulate matter air quality in East Asia: implications for fine particulate nitrate

Coarse particulate matter (PM) is a serious air pollution problem in East Asia. Analysis of air quality network observations in the North China Plain and the Seoul Metropolitan Area shows that it is mainly anthropogenic and has decreased by 21% over 2015-2019. This anthropogenic coarse PM is generally not included in air quality models but scavenges nitric acid to suppress the formation of fine particulate (PM2.5) nitrate, a major contributor to PM2.5 pollution. Including it in the GEOS-Chem model decreases simulated PM2.5 nitrate to improve agreement with observations. Decreasing anthropogenic coarse PM over 2015-2019 directly increases PM2.5 nitrate in summer, offsetting the effect of other emission controls, while in winter it increases the sensitivity of PM2.5 nitrate to ammonia and sulfur dioxide emissions. Our work implies the need for stronger ammonia and nitrogen oxides emission controls to improve PM2.5 air quality as coarse PM continues to decrease.

physics.ao-ph

Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways

While current systems for autonomous robot navigation can produce safe and efficient motion plans in static environments, they usually generate suboptimal behaviors when multiple robots must navigate together in confined spaces. For example, when two robots meet each other in a narrow hallway, they may either turn around to find an alternative route or collide with each other. This paper presents a new approach to navigation that allows two robots to pass each other in a narrow hallway without colliding, stopping, or waiting. Our approach, Perceptual Hallucination for Hallway Passing (PHHP), learns to synthetically generate virtual obstacles (i.e., perceptual hallucination) to facilitate passing in narrow hallways by multiple robots that utilize otherwise standard autonomous navigation systems. Our experiments on physical robots in a variety of hallways show improved performance compared to multiple baselines.

cs.RO