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Advances in Fog Microphysics Research in China
Duanyang Liu,Zihua Li,Wenlian Yan,Yi Li 한국기상학회 2017 Asia-Pacific Journal of Atmospheric Sciences Vol.53 No.1
Fog microphysical research in China based on field experiments obtained many important results in recent 50 years. With the fast development of China's economy, urbanization in the last 30 years, special features of fog microphysical structure also appeared, which did not appear in other countries. This article reviews the fog microphysical research around China, and introduces the effect of urbanization on fog microphysical structure and the microphysical processes as well as macroscopic conditions of radiation fog droplet spectral broadening. Urbanization led to an increase in fog droplet number concentration but decreases in fog liquid water content (LWC) and fog droplet size, as well as a decrease in visibility in large cities. Observations show that the radiation fog could be divided into wide-spectrum one, which is all extremely dense fog with the spectral width more than 40 μm, and narrow-spectrum one, most of which is dense fog with the spectral width less than 22 μm, according to droplet spectral distribution. During developing from dense fog to extremely dense fog, the widespectrum radiation fog is characterized by explosive deepening, that is, within a very short time (about 30 min), the droplet concentration increase by about one order of magnitude, droplet spectral broadening across 20 μm, generally up to 30-40 μm, or even 50 μm. As a result, water content increased obviously, visibility decreased to less than 50 m, when dense fog became extremely dense fog.
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( Si Shen ),( Guojiang Shen ),( Yang Shen ),( Duanyang Liu ),( Xi Yang ),( Xiangjie Kong ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.11
Advanced traffic signal timing method plays very important role in reducing road congestion and air pollution. Reinforcement learning is considered as superior approach to build traffic light timing scheme by many recent studies. It fulfills real adaptive control by the means of taking real-time traffic information as state, and adjusting traffic light scheme as action. However, existing works behave inefficient in complex intersections and they are lack of feasibility because most of them adopt traffic light scheme whose phase sequence is flexible. To address these issues, a novel adaptive traffic signal timing scheme is proposed. It's based on actor-critic reinforcement learning algorithm, and advanced techniques proximal policy optimization and generalized advantage estimation are integrated. In particular, a new kind of reward function and a simplified form of state representation are carefully defined, and they facilitate to improve the learning efficiency and reduce the computational complexity, respectively. Meanwhile, a fixed phase sequence signal scheme is derived, and constraint on the variations of successive phase durations is introduced, which enhances its feasibility and robustness in field applications. The proposed scheme is verified through field-data-based experiments in both medium and high traffic density scenarios. Simulation results exhibit remarkable improvement in traffic performance as well as the learning efficiency comparing with the existing reinforcement learning-based methods such as 3DQN and DDQN.
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