Rainfall forecasting by technological machine learning models

Accurate forecasting of rainfall has been one of the most important issues in hydrological research. Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. Recurrent artificial neural networks (RNNS) have played a crucial role in forecasting rainfall data. Meanwhile, support vector machines (SVMs) have been successfully employed to solve nonlinear regression and time series problems. This investigation elucidates the feasibility of hybrid model of RNNs and SVMs, namely RSVR, to forecast rainfall depth values. Moreover, chaotic particle swarm optimization algorithm (CPSO) is employed to choose the parameters of a SVR model. Subsequently, example of rainfall values during typhoon periods from Northern Taiwan is used to illustrate the proposed RSVRCPSO model. The empirical results reveal that the proposed model yields well forecasting performance, RSVRCPSO model provides a promising alternative for forecasting rainfall values.     

氯苯基硅油润滑球轴承在真空环境下摩擦性能与泄漏量研究

阐明氯苯基硅油润滑球轴承的润滑机理,对不同真空度和转速下的轴承摩擦力矩进行研究.在真空度3×10 ^-5Pa条件下,选用FY-3卫星“紫外臭氧垂直探测仪”光学调制器电机样机和进口电机进行8个月工程试验后,利用高倍显微镜分析试验轴承表观状态,并分析氯苯基硅油的泄漏量.结果表明:在3×10^-5Pa下,所用试验轴承的摩擦力矩值随着转速的增大而趋向稳定;氯苯基硅油润滑球轴承摩擦力矩的变化量为9.8×10^-4Nm,小于采用Maplub101润滑的进口电机轴承摩擦力矩变化量;试验期间氯苯基硅油的泄漏量为1.92mg,证明采用氯苯基硅油润滑适合光学调制器在真空环境下长期运转