In this paper, we conduct research on the teaching evaluation system based on the feedback tracking technology under the cloud computing environment. Teaching evaluation is an important work of teaching management dep...In this paper, we conduct research on the teaching evaluation system based on the feedback tracking technology under the cloud computing environment. Teaching evaluation is an important work of teaching management department. According to the traditional mode of work, must first into classroom, through questionnaire survey, fill out a form scale, obtain first-hand teaching evaluation data, and then input the data into the computer, after check again the data statistics and analysis which is not efficient. To optimize the procedure, we use the cloud computing to assist finalizing the general evaluation steps which is innovative and meaningful.展开更多
To date, Wald sequential probability ratio test(WSPRT) has been widely applied to track management of multiple hypothesis tracking(MHT). But in a real situation, if the false alarm spatial density is much larger than ...To date, Wald sequential probability ratio test(WSPRT) has been widely applied to track management of multiple hypothesis tracking(MHT). But in a real situation, if the false alarm spatial density is much larger than the new target spatial density, the original track score will be very close to the deletion threshold of the WSPRT. Consequently, all tracks, including target tracks, may easily be deleted, which means that the tracking performance is sensitive to the tracking environment. Meanwhile, if a target exists for a long time, its track will have a high score, which will make the track survive for a long time even after the target has disappeared. In this paper, to consider the relationship between the hypotheses of the test, we adopt the Shiryayev SPRT(SSPRT) for track management in MHT. By introducing a hypothesis transition probability, the original track score can increase faster, which solves the first problem. In addition, by setting an independent SSPRT for track deletion, the track score can decrease faster, which solves the second problem. The simulation results show that the proposed SSPRT-based MHT can achieve better tracking performance than MHT based on the WSPRT under a high false alarm spatial density.展开更多
文摘In this paper, we conduct research on the teaching evaluation system based on the feedback tracking technology under the cloud computing environment. Teaching evaluation is an important work of teaching management department. According to the traditional mode of work, must first into classroom, through questionnaire survey, fill out a form scale, obtain first-hand teaching evaluation data, and then input the data into the computer, after check again the data statistics and analysis which is not efficient. To optimize the procedure, we use the cloud computing to assist finalizing the general evaluation steps which is innovative and meaningful.
基金supported by National Natural Science Foundation of China (Grant Nos. 61471019, 61501011)
文摘To date, Wald sequential probability ratio test(WSPRT) has been widely applied to track management of multiple hypothesis tracking(MHT). But in a real situation, if the false alarm spatial density is much larger than the new target spatial density, the original track score will be very close to the deletion threshold of the WSPRT. Consequently, all tracks, including target tracks, may easily be deleted, which means that the tracking performance is sensitive to the tracking environment. Meanwhile, if a target exists for a long time, its track will have a high score, which will make the track survive for a long time even after the target has disappeared. In this paper, to consider the relationship between the hypotheses of the test, we adopt the Shiryayev SPRT(SSPRT) for track management in MHT. By introducing a hypothesis transition probability, the original track score can increase faster, which solves the first problem. In addition, by setting an independent SSPRT for track deletion, the track score can decrease faster, which solves the second problem. The simulation results show that the proposed SSPRT-based MHT can achieve better tracking performance than MHT based on the WSPRT under a high false alarm spatial density.
