With the emergence of massive online courses,how to evaluate the quality of courses with different qualities to improve the discrimination between courses and recommend personalized online course learning resources fo...With the emergence of massive online courses,how to evaluate the quality of courses with different qualities to improve the discrimination between courses and recommend personalized online course learning resources for learners needs to be evaluated from all aspects.In this paper,a method of constructing an online course portrait based on feature engineering is proposed.Firstly,the framework of online course portrait is established,the related features of the portrait are extracted by feature engineering method,and then the indicator weights of the portrait are calculated by entropy weight method.Finally,experiments are designed to evaluate the performance of the algorithms,and an example of the course portrait is given.展开更多
When a mass of individual targets move closely, it is unpractical or unnecessary to localize and track every specific target in wireless sensor networks (WSN). However, they can be tracked as a whole by view of group ...When a mass of individual targets move closely, it is unpractical or unnecessary to localize and track every specific target in wireless sensor networks (WSN). However, they can be tracked as a whole by view of group target. In order to decrease the amount of energy spent on active sensing and communications, a flexible boundary detecting model for group target tracking in WSN is proposed, in which, the number of sensors involved in target tracking is adjustable. Unlike traditional one or multiple individual targets, the group target usually occupies a large area. To obtain global estimated position of group target, a divide-merge algorithm using convex hull is designed. In this algorithm, group target’s boundary is divided into several small pieces, and each one is enclosed by a convex hull which is constructed by a cluster of boundary sensors. Then, the information of these small convex hulls is sent back to a sink. Finally, big convex hull merged from these small ones is considered as the group target’s contour. According to our metric of precision evaluation, the simulation experiments confirm the efficiency and accuracy of this algorithm.展开更多
基金This work is supported by the National Key Research and Development Program of China(Grant No.2020AAA0108803).
文摘With the emergence of massive online courses,how to evaluate the quality of courses with different qualities to improve the discrimination between courses and recommend personalized online course learning resources for learners needs to be evaluated from all aspects.In this paper,a method of constructing an online course portrait based on feature engineering is proposed.Firstly,the framework of online course portrait is established,the related features of the portrait are extracted by feature engineering method,and then the indicator weights of the portrait are calculated by entropy weight method.Finally,experiments are designed to evaluate the performance of the algorithms,and an example of the course portrait is given.
文摘When a mass of individual targets move closely, it is unpractical or unnecessary to localize and track every specific target in wireless sensor networks (WSN). However, they can be tracked as a whole by view of group target. In order to decrease the amount of energy spent on active sensing and communications, a flexible boundary detecting model for group target tracking in WSN is proposed, in which, the number of sensors involved in target tracking is adjustable. Unlike traditional one or multiple individual targets, the group target usually occupies a large area. To obtain global estimated position of group target, a divide-merge algorithm using convex hull is designed. In this algorithm, group target’s boundary is divided into several small pieces, and each one is enclosed by a convex hull which is constructed by a cluster of boundary sensors. Then, the information of these small convex hulls is sent back to a sink. Finally, big convex hull merged from these small ones is considered as the group target’s contour. According to our metric of precision evaluation, the simulation experiments confirm the efficiency and accuracy of this algorithm.
