为了解决在线课程(Massive open online course, MOOC)授课过程中,缺乏对于学生学习情况的跟踪与教学效果评估问题,本文依据视频信息对学生行为进行建模,提出了一种评判学生听课专心程度的行为自动分析算法.该算法能够有效跟踪学生的学...为了解决在线课程(Massive open online course, MOOC)授课过程中,缺乏对于学生学习情况的跟踪与教学效果评估问题,本文依据视频信息对学生行为进行建模,提出了一种评判学生听课专心程度的行为自动分析算法.该算法能够有效跟踪学生的学习状态,提取学生的行为特征参数,并对这些参数进行D-S融合判决,以获得学生的听课专注度.经过多次实验的结果表明,本文采用的方法能够有效评判学生在授课期间的专心程度,在数据融合上,与贝叶斯推理方法相比,采用D-S融合方法能有效提高实验结果的准确性和可靠性.展开更多
A forecasting model of the monthly crude oil price is investigated using the data between 1988 and 2009 from U. S. Energy Information Administration. First generalized auto-regressive condi- tional beteroskedasticity ...A forecasting model of the monthly crude oil price is investigated using the data between 1988 and 2009 from U. S. Energy Information Administration. First generalized auto-regressive condi- tional beteroskedasticity （GARCH） is applied to a state space model, a hybrid model （SS-GARCH） is proposed. Afterwards by computing a special likelihood function with two weak assumptions, model parameters are estimated by means of a faster algorithm. Based on the SS-GARCH model with the identified parameters, oil prices of next three months are forecasted by applying a Kalman filter. Through comparing the results between the SS-GARCH model and an econometric structure model, the SS-GARCH method is shown that it improves the forecasting accuracy by decreasing the index of mean absolute error （ RMSE ） from 7. 09 to 2.99, and also decreasing the index of MAE from 3. 83 to 1.69. The results indicate that the SS-GARCH model can play a useful role in forecasting short-term crude oil prices.展开更多
文摘为了解决在线课程(Massive open online course, MOOC)授课过程中,缺乏对于学生学习情况的跟踪与教学效果评估问题,本文依据视频信息对学生行为进行建模,提出了一种评判学生听课专心程度的行为自动分析算法.该算法能够有效跟踪学生的学习状态,提取学生的行为特征参数,并对这些参数进行D-S融合判决,以获得学生的听课专注度.经过多次实验的结果表明,本文采用的方法能够有效评判学生在授课期间的专心程度,在数据融合上,与贝叶斯推理方法相比,采用D-S融合方法能有效提高实验结果的准确性和可靠性.
基金Supported by Program for Changjiang Scholars and Innovative Research Team in University（ IRT1208 ）
文摘A forecasting model of the monthly crude oil price is investigated using the data between 1988 and 2009 from U. S. Energy Information Administration. First generalized auto-regressive condi- tional beteroskedasticity （GARCH） is applied to a state space model, a hybrid model （SS-GARCH） is proposed. Afterwards by computing a special likelihood function with two weak assumptions, model parameters are estimated by means of a faster algorithm. Based on the SS-GARCH model with the identified parameters, oil prices of next three months are forecasted by applying a Kalman filter. Through comparing the results between the SS-GARCH model and an econometric structure model, the SS-GARCH method is shown that it improves the forecasting accuracy by decreasing the index of mean absolute error （ RMSE ） from 7. 09 to 2.99, and also decreasing the index of MAE from 3. 83 to 1.69. The results indicate that the SS-GARCH model can play a useful role in forecasting short-term crude oil prices.
