In order to improve the English online courses teaching scene adaptability, optimize the online English teaching mode, improve the quality of English teaching, an English online course teaching platform is proposed ba...In order to improve the English online courses teaching scene adaptability, optimize the online English teaching mode, improve the quality of English teaching, an English online course teaching platform is proposed based on scene simulation. B/S client communication protocol is constructed in network transport layer, and the scene simulation model of English Online Course Teaching is established, online English teaching platform designed in this paper is constructed based on the sensing layer, network layer and application layer. A data processing center is constructed in the B/S structure and the network environment, Open Core kernel is used to realize the optimization of teaching model of English online courses, improve the teaching platform of online courses, and improve the scene adaptation ability. The test results show that the platform for online English teaching has better independent learning ability, the effectiveness of online English course teaching is better, test objects have higher satisfaction with English Online Course Teaching.展开更多
This paper studies the multi-objective optimization of space station short-term mission planning(STMP), which aims to obtain a mission-execution plan satisfying multiple planning demands. The planning needs to allocat...This paper studies the multi-objective optimization of space station short-term mission planning(STMP), which aims to obtain a mission-execution plan satisfying multiple planning demands. The planning needs to allocate the execution time effectively, schedule the on-board astronauts properly, and arrange the devices reasonably. The STMP concept models for problem definitions and descriptions are presented, and then an STMP multi-objective planning model is developed. To optimize the STMP problem, a Non-dominated Sorting Genetic Algorithm II(NSGA-II) is adopted and then improved by incorporating an iterative conflict-repair strategy based on domain knowledge. The proposed approach is demonstrated by using a test case with thirty-five missions, eighteen devices and three astronauts. The results show that the established STMP model is effective, and the improved NSGA-II can successfully obtain the multi-objective optimal plans satisfying all constraints considered. Moreover, through contrast tests on solving the STMP problem, the NSGA-II shows a very competitive performance with respect to the Strength Pareto Evolutionary Algorithm II(SPEA-II) and the Multi-objective Particle Swarm Optimization(MOPSO).展开更多
文摘In order to improve the English online courses teaching scene adaptability, optimize the online English teaching mode, improve the quality of English teaching, an English online course teaching platform is proposed based on scene simulation. B/S client communication protocol is constructed in network transport layer, and the scene simulation model of English Online Course Teaching is established, online English teaching platform designed in this paper is constructed based on the sensing layer, network layer and application layer. A data processing center is constructed in the B/S structure and the network environment, Open Core kernel is used to realize the optimization of teaching model of English online courses, improve the teaching platform of online courses, and improve the scene adaptation ability. The test results show that the platform for online English teaching has better independent learning ability, the effectiveness of online English course teaching is better, test objects have higher satisfaction with English Online Course Teaching.
基金supported by the National Natural Science Foundation of China(Grant No.11402295)the Science Project of National University of Defense Technology(Grant No.JC14-01-05)the Hunan Provincial Natural Science Foundation of China(Grant No.2015JJ3020)
文摘This paper studies the multi-objective optimization of space station short-term mission planning(STMP), which aims to obtain a mission-execution plan satisfying multiple planning demands. The planning needs to allocate the execution time effectively, schedule the on-board astronauts properly, and arrange the devices reasonably. The STMP concept models for problem definitions and descriptions are presented, and then an STMP multi-objective planning model is developed. To optimize the STMP problem, a Non-dominated Sorting Genetic Algorithm II(NSGA-II) is adopted and then improved by incorporating an iterative conflict-repair strategy based on domain knowledge. The proposed approach is demonstrated by using a test case with thirty-five missions, eighteen devices and three astronauts. The results show that the established STMP model is effective, and the improved NSGA-II can successfully obtain the multi-objective optimal plans satisfying all constraints considered. Moreover, through contrast tests on solving the STMP problem, the NSGA-II shows a very competitive performance with respect to the Strength Pareto Evolutionary Algorithm II(SPEA-II) and the Multi-objective Particle Swarm Optimization(MOPSO).
