从全球导航卫星系统反射信号(Global Navigation Satellite System Reflectometry,GNSS-R)测高应用中对镜面反射点的高精度需求出发,利用GNSS-R空间几何位置关系和机器学习中的RMSprop(Root Mean Square Prop)算法对镜面反射点问题进行...从全球导航卫星系统反射信号(Global Navigation Satellite System Reflectometry,GNSS-R)测高应用中对镜面反射点的高精度需求出发,利用GNSS-R空间几何位置关系和机器学习中的RMSprop(Root Mean Square Prop)算法对镜面反射点问题进行研究,提出了一种基于RMSprop的高精度镜面反射点预测算法,该算法能够实现对学习率的自适应调整,加速梯度下降,提高算法的收敛性能。仿真结果表明,该算法收敛快,精度高。展开更多
The objective of this study is to understand the current mental status of college students in China's Mainland. In this study, 60 thousand college students' microblog content from January 2014 to June 2014 was co...The objective of this study is to understand the current mental status of college students in China's Mainland. In this study, 60 thousand college students' microblog content from January 2014 to June 2014 was collected. An emotional energy level, which was developed by a psychologist David R Hawkins, was taken as a basis for ontology database to divide the student's emotion into three parts--positive, negative and neutral status. An ontology-based semantic analysis method was used to analyze the microblog data. The result shows that 46.38% of Sina microblog data reflects positive psychological status, and the ratios of neutral and negative psychological status are 19.77% and 33.85%, respectively. It means that almost one third microblog reflects some negative mentality. The semantic analysis of the big data suggests that most students have healthy mental status, and the negative status of the students should not be ignored.展开更多
文摘从全球导航卫星系统反射信号(Global Navigation Satellite System Reflectometry,GNSS-R)测高应用中对镜面反射点的高精度需求出发,利用GNSS-R空间几何位置关系和机器学习中的RMSprop(Root Mean Square Prop)算法对镜面反射点问题进行研究,提出了一种基于RMSprop的高精度镜面反射点预测算法,该算法能够实现对学习率的自适应调整,加速梯度下降,提高算法的收敛性能。仿真结果表明,该算法收敛快,精度高。
基金Supported by the Natural Science Foundation of Hubei Province(2013CFB292)
文摘The objective of this study is to understand the current mental status of college students in China's Mainland. In this study, 60 thousand college students' microblog content from January 2014 to June 2014 was collected. An emotional energy level, which was developed by a psychologist David R Hawkins, was taken as a basis for ontology database to divide the student's emotion into three parts--positive, negative and neutral status. An ontology-based semantic analysis method was used to analyze the microblog data. The result shows that 46.38% of Sina microblog data reflects positive psychological status, and the ratios of neutral and negative psychological status are 19.77% and 33.85%, respectively. It means that almost one third microblog reflects some negative mentality. The semantic analysis of the big data suggests that most students have healthy mental status, and the negative status of the students should not be ignored.