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The Optimum Sowing Time for Plastic-film Corn and the Application of Two Related Theories

Agricultural Sciences in China 2002年第5期1卷 508-515页

作者： WU Rui-xiang, LIU Rong-quan, LU Cui-ling, LU Yong-lai, LI Hua, ZHANG Li, LU Xiu-zhi, YU Shu-ping, WU Xiu-yan and WANG Xin-huan(ningcheng Agro-technique Extension Centre , Inner Mongolia Autonomous Region , Tianyi 024200 , P. R. China ningcheng seed company , Inner Mongolia Autonomous Region , Tianyi 024200 , P. R . China) ningcheng Agro-technique Extension Centre Inner Mongolia Autonomous Region Tianyi 024200 P. R. China ningcheng seed company Inner Mongolia Autonomous Region Tianyi 024200 P. R. China

There are two main theories, the 'temperature-raising' theory and the 'precipitation-based regulation' theory, which guide the optimum sowing time of the plastic-film corn. The former was applied in the humid or semi-humid ecotope and on irrigated or half-shaded land in the arid and semi-arid ecotopes, while the latter was suitable for the dry-farming land in the semi-arid ecotope. The results of experiments and investigations for many years showed that the corn output was increased by 69.2% when the former theory was applied to guide the optimum sowing time for plastic-film corn in the semi-humid ecotope, and by 60. 0% when the latter theory was applied in the semi-arid ecotope. In the semi-arid ecotope, however, the output was increased only by 15.7% when the former theory was applied, and even dropped by 14.4% when the latter theory was applied.

关键词： Plastic-film corn Division of climatic ecology Optimum sowing time 'Temperature-raising' theory 'Precipitation-based regulation' theory

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A DRL-Based Container Placement Scheme with Auxiliary Tasks

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Computers, Materials & Continua 2020年第9期64卷 1657-1671页

作者： ningcheng Yuan Chao Jia Jizhao Lu Shaoyong Guo Wencui Li Xuesong Qiu Lei Shi Beijing University of Posts and Telecommunications Beijing100876China China Electronics Standardization Institute BeijingChina Communication Operation Center State Grid Henan Electric Power Company Information&Telecommunication CompanyZhengzhouChina Institute of Technology Carlow CarlowIreland

Container is an emerging virtualization technology and widely adopted in the cloud to provide services because of its lightweight,flexible,isolated and highly portable *** services are often instantiated as clusters of interconnected *** to the stochastic service arrival and complicated cloud environment,it is challenging to achieve an optimal container placement(CP)*** propose to leverage Deep Reinforcement Learning(DRL)for solving CP problem,which is able to learn from experience interacting with the environment and does not rely on mathematical model or prior ***,applying DRL method directly dose not lead to a satisfying result because of sophisticated environment states and huge action *** this paper,we propose UNREAL-CP,a DRL-based method to place container instances on servers while considering end to end delay and resource utilization *** proposed method is an actor-critic-based approach,which has advantages in dealing with the huge action ***,the idea of auxiliary learning is also included in our *** design two auxiliary learning tasks about load balancing to improve algorithm *** to other DRL methods,extensive simulation results show that UNREAL-CP performs better up to 28.6%in terms of reducing delay and deployment cost with high training efficiency and responding speed.

关键词： Container placement deep reinforcement learning auxiliary learning

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