In order to avoid severe performance degradation led by the inter-cell interference (ICI) in orthogonal frequency division multiple access (OFDMA) systems with a frequency reused factor (FRF) of 1,distributed schedule...In order to avoid severe performance degradation led by the inter-cell interference (ICI) in orthogonal frequency division multiple access (OFDMA) systems with a frequency reused factor (FRF) of 1,distributed schedule algorithm (DS-OCS) and distributed proportional fairness schedule algorithm (DPFS-OCS) based on orthogonal complement space (OCS) were proposed. The first right and left singular vectors of the channel that the user experienced were selected as the transmitting and receiving beamforming vectors. An interference space was spanned by the left singular vectors of the entire interference users in the same channel. The most suitable user lay in the OCS of the interference space was scheduled to avoid suffering interference from neighboring cells based on the criterion of system capacity maximizing and proportional fairness. The simulation results show that the average system capacity can be improved by 2%-4% compared with the DS-OCS algorithm with the Max C/I algorithm,by 6%-10% compared with the DPFS-OCS algorithm with the PF algorithm.展开更多
为加强综合能源系统(integrated energy system,IES)的设备间配合和能源站的站间配合,实现能源的高效生产与灵活分配,构建了一种集中-分布式能源站结构,并在此基础上提出了考虑多品位能源互补的IES分级规划策略。首先,基于能源站分工的...为加强综合能源系统(integrated energy system,IES)的设备间配合和能源站的站间配合,实现能源的高效生产与灵活分配,构建了一种集中-分布式能源站结构,并在此基础上提出了考虑多品位能源互补的IES分级规划策略。首先,基于能源站分工的思想,建立集中-分布式能源站结构,并在其中引入热电解耦的改进燃气轮机线性化模型;然后,基于集中-分布式能源站结构和能源枢纽(energy hub,EH)理论,建立考虑多品位能源互补的IES能量耦合模型;最后,以最小化系统总体成本为目标函数,建立IES分级规划模型,确定系统中各能源站的设备配置情况与线路规划情况。算例分析表明:所提的集中-分布式能源站结构可以更好地发挥IES多能耦合的优势,由IES分级规划得到的系统拥有更好的经济性与更低的能源传输损耗。展开更多
目前,大部分将知识图谱引入推荐系统的方法只是将已知的表层知识图谱实体进行引入,没有对图谱的内在关系进行预测和挖掘,因此无法利用知识图谱中的隐藏关系。针对上述问题,提出联合学习推荐模型E-TUP(enhance towards understanding of ...目前,大部分将知识图谱引入推荐系统的方法只是将已知的表层知识图谱实体进行引入,没有对图谱的内在关系进行预测和挖掘,因此无法利用知识图谱中的隐藏关系。针对上述问题,提出联合学习推荐模型E-TUP(enhance towards understanding of user preference),使用E-CP(enhance canonical polyadic)进行知识图谱补全并将完整信息进行传递。利用储存空间负采样方法,将优质负例三元组进行存储,并随训练过程进行更新,以提高知识图谱补全中负例三元组的质量。链接预测实验结果显示,储存空间方法使E-TUP模型链接预测准确率对比现有模型最高提升10.3%。在MovieLens-1m和DBbook2014数据集上进行推荐实验,在多个评价指标上取得最佳结果,对比现有模型实现最高5.5%的提升,表明E-TUP可以有效利用知识图谱中的隐藏关系提高模型推荐准确率。基于汽车维修数据进行推荐实验,结果表明E-TUP可以有效推荐相关知识。展开更多
基金Projects(2009ZX03003-003, 2009ZX03003-004) supported by the Major National Science & Technology ProgramProject(B08038) supported by the "111" Project+1 种基金Project(HX0109012417) supported by Huawei Technologies Co., Ltd, ChinaProject(IRT0852) supported by Program for Changjiang Scholars and Innovative Research Team in Chinese University
文摘In order to avoid severe performance degradation led by the inter-cell interference (ICI) in orthogonal frequency division multiple access (OFDMA) systems with a frequency reused factor (FRF) of 1,distributed schedule algorithm (DS-OCS) and distributed proportional fairness schedule algorithm (DPFS-OCS) based on orthogonal complement space (OCS) were proposed. The first right and left singular vectors of the channel that the user experienced were selected as the transmitting and receiving beamforming vectors. An interference space was spanned by the left singular vectors of the entire interference users in the same channel. The most suitable user lay in the OCS of the interference space was scheduled to avoid suffering interference from neighboring cells based on the criterion of system capacity maximizing and proportional fairness. The simulation results show that the average system capacity can be improved by 2%-4% compared with the DS-OCS algorithm with the Max C/I algorithm,by 6%-10% compared with the DPFS-OCS algorithm with the PF algorithm.
文摘为加强综合能源系统(integrated energy system,IES)的设备间配合和能源站的站间配合,实现能源的高效生产与灵活分配,构建了一种集中-分布式能源站结构,并在此基础上提出了考虑多品位能源互补的IES分级规划策略。首先,基于能源站分工的思想,建立集中-分布式能源站结构,并在其中引入热电解耦的改进燃气轮机线性化模型;然后,基于集中-分布式能源站结构和能源枢纽(energy hub,EH)理论,建立考虑多品位能源互补的IES能量耦合模型;最后,以最小化系统总体成本为目标函数,建立IES分级规划模型,确定系统中各能源站的设备配置情况与线路规划情况。算例分析表明:所提的集中-分布式能源站结构可以更好地发挥IES多能耦合的优势,由IES分级规划得到的系统拥有更好的经济性与更低的能源传输损耗。
文摘目前,大部分将知识图谱引入推荐系统的方法只是将已知的表层知识图谱实体进行引入,没有对图谱的内在关系进行预测和挖掘,因此无法利用知识图谱中的隐藏关系。针对上述问题,提出联合学习推荐模型E-TUP(enhance towards understanding of user preference),使用E-CP(enhance canonical polyadic)进行知识图谱补全并将完整信息进行传递。利用储存空间负采样方法,将优质负例三元组进行存储,并随训练过程进行更新,以提高知识图谱补全中负例三元组的质量。链接预测实验结果显示,储存空间方法使E-TUP模型链接预测准确率对比现有模型最高提升10.3%。在MovieLens-1m和DBbook2014数据集上进行推荐实验,在多个评价指标上取得最佳结果,对比现有模型实现最高5.5%的提升,表明E-TUP可以有效利用知识图谱中的隐藏关系提高模型推荐准确率。基于汽车维修数据进行推荐实验,结果表明E-TUP可以有效推荐相关知识。
