为分析自然灾害风险防控研究发展状况及趋势,以2003-2017年Web of Science核心合集中SCI-EXPANDED数据库收录的1 159篇自然灾害风险防控研究文献作为数据基础,利用VOSviewer和Bibexcel软件,采用共引分析、耦合分析、共著分析、词频统计...为分析自然灾害风险防控研究发展状况及趋势,以2003-2017年Web of Science核心合集中SCI-EXPANDED数据库收录的1 159篇自然灾害风险防控研究文献作为数据基础,利用VOSviewer和Bibexcel软件,采用共引分析、耦合分析、共著分析、词频统计和共现分析等文献计量可视化分析方法,通过文献发表的时间分布、区域分布、学科和期刊分布、文献代表作者、合作机构、研究热点等方面的分析,综述了自然灾害风险防控研究进展。结果表明:近年来,自然灾害风险防控研究热度上升迅速;美国在该领域的发文量明显领先,中国则具备了赶超的潜力;各研究机构中,北京师范大学、中国科学院、悉尼大学、京都大学等是该领域的领军机构;从发展趋势上看,该领域已形成了"水旱灾害管理""灾害风险防控""灾害脆弱性""灾害影响"4个重要的研究热点和前沿方向,其中"灾害风险防控"把其他3类热点紧密关联起来,形成了一个结构紧凑的自然灾害风险防控研究知识图谱系统。上述结果揭示了自然灾害风险防控研究的演变特征与发展趋势,有助于加深对自然灾害风险防控研究领域的整体认识。展开更多
In order to improve the learning ability of robots, we present a reinforcement learning approach with a knowledge base for mapping natural language instructions to executable action sequences. A simulated platform wit...In order to improve the learning ability of robots, we present a reinforcement learning approach with a knowledge base for mapping natural language instructions to executable action sequences. A simulated platform with physical engine is built as interactive environment. Based on the knowledge base, a reward function with immediate rewards and delayed rewards is designed to handle sparse reward problems. Also, a list of object states is produced by retrieving the knowledge base, as a standard to define the quality of action sequences. Experimental results demonstrate that our approach yields good performance on accuracy of action sequences production.展开更多
文摘为分析自然灾害风险防控研究发展状况及趋势,以2003-2017年Web of Science核心合集中SCI-EXPANDED数据库收录的1 159篇自然灾害风险防控研究文献作为数据基础,利用VOSviewer和Bibexcel软件,采用共引分析、耦合分析、共著分析、词频统计和共现分析等文献计量可视化分析方法,通过文献发表的时间分布、区域分布、学科和期刊分布、文献代表作者、合作机构、研究热点等方面的分析,综述了自然灾害风险防控研究进展。结果表明:近年来,自然灾害风险防控研究热度上升迅速;美国在该领域的发文量明显领先,中国则具备了赶超的潜力;各研究机构中,北京师范大学、中国科学院、悉尼大学、京都大学等是该领域的领军机构;从发展趋势上看,该领域已形成了"水旱灾害管理""灾害风险防控""灾害脆弱性""灾害影响"4个重要的研究热点和前沿方向,其中"灾害风险防控"把其他3类热点紧密关联起来,形成了一个结构紧凑的自然灾害风险防控研究知识图谱系统。上述结果揭示了自然灾害风险防控研究的演变特征与发展趋势,有助于加深对自然灾害风险防控研究领域的整体认识。
基金supported by National Natural Science Foundation of China (No. 61773239)Shenzhen Future Industry Special Fund (No. JCYJ20160331174814755)
文摘In order to improve the learning ability of robots, we present a reinforcement learning approach with a knowledge base for mapping natural language instructions to executable action sequences. A simulated platform with physical engine is built as interactive environment. Based on the knowledge base, a reward function with immediate rewards and delayed rewards is designed to handle sparse reward problems. Also, a list of object states is produced by retrieving the knowledge base, as a standard to define the quality of action sequences. Experimental results demonstrate that our approach yields good performance on accuracy of action sequences production.
基金the Key Project of National Natural Science Foundation of China under Grant No.60496321( 国家自然科学基金重大项目)the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No.20050183065( 高等学校博士学科点专项科研基金).
