针对随机竞争预约机制在网络负载较重时预约性能恶化导致卫星信道利用率下降的问题,提出了一种适用于卫星网络信道预约的统计预测机制,该机制在对预约时隙(slots for reservation,SR)可用度进行多等级划分的基础上,利用一致最小方差无...针对随机竞争预约机制在网络负载较重时预约性能恶化导致卫星信道利用率下降的问题,提出了一种适用于卫星网络信道预约的统计预测机制,该机制在对预约时隙(slots for reservation,SR)可用度进行多等级划分的基础上,利用一致最小方差无偏估计和Faulkenberry理论计算下一帧SR可用度的预测区间,根据Markov模型的转移概率矩阵和SR可用度出现次数的频率估计,对下一帧内信道SR的可用度进行精确预测,用户终端根据预测结果动态调整预约请求的发送概率。理论分析与仿真结果表明,该机制具有较好的适应性和有效性,预测精度得到了较大提高,减小了信道预约冲突概率,提高了卫星信道利用率。展开更多
As the popularity of open source projects,the volume of incoming pull requests is too large,which puts heavy burden on integrators who are responsible for accepting or rejecting pull requests.An accepted pull request ...As the popularity of open source projects,the volume of incoming pull requests is too large,which puts heavy burden on integrators who are responsible for accepting or rejecting pull requests.An accepted pull request prediction approach can help integrators by allowing them either to enforce an immediate rejection of code changes or allocate more resources to overcome the deficiency.In this paper,an approach CTCPPre is proposed to predict the accepted pull requests in GitHub.CTCPPre mainly considers code features of modified changes,text features of pull requests’description,contributor features of developers’previous behaviors,and project features of development environment.The effectiveness of CTCPPre on 28 projects containing 221096 pull requests is evaluated.Experimental results show that CTCPPre has good performances by achieving accuracy of 0.82,AUC of 0.76 and F1-score of 0.88 on average.It is compared with the state of art accepted pull request prediction approach RFPredict.On average across 28 projects,CTCPPre outperforms RFPredict by 6.64%,16.06%and 4.79%in terms of accuracy,AUC and F1-score,respectively.展开更多
在基于蜂窝通信演进形成的车用无线通信技术(Cellular-Vehicle to everything,C-V2X)场景下,基站作为多接入边缘计算(Multi-access Edge Computing,MEC)边缘缓存节点可提高用户获取数据的效率,但其缓存容量有限.因此,C-V2X中如何准确预...在基于蜂窝通信演进形成的车用无线通信技术(Cellular-Vehicle to everything,C-V2X)场景下,基站作为多接入边缘计算(Multi-access Edge Computing,MEC)边缘缓存节点可提高用户获取数据的效率,但其缓存容量有限.因此,C-V2X中如何准确预测缓存请求内容成为待解决的重要问题.本文从文件请求的时变性出发,针对实际的城市场景,采用Simulation of Urban MObility(SUMO)对交通流进行建模;其次,通过采集实际网站分时分类的点击量数据,并根据各路段交通流规律进行预处理,构建用户请求模型;最后,利用Long Short-Term Memory(LSTM)深度学习模型进行训练,预测各基站的文件请求.仿真结果表明,在网易新闻流行度分布和请求间隔分布形成的文件请求下,vanillaLSTM模型对娱乐类型数据集预测时的均方根误差在1.3左右.展开更多
文摘针对随机竞争预约机制在网络负载较重时预约性能恶化导致卫星信道利用率下降的问题,提出了一种适用于卫星网络信道预约的统计预测机制,该机制在对预约时隙(slots for reservation,SR)可用度进行多等级划分的基础上,利用一致最小方差无偏估计和Faulkenberry理论计算下一帧SR可用度的预测区间,根据Markov模型的转移概率矩阵和SR可用度出现次数的频率估计,对下一帧内信道SR的可用度进行精确预测,用户终端根据预测结果动态调整预约请求的发送概率。理论分析与仿真结果表明,该机制具有较好的适应性和有效性,预测精度得到了较大提高,减小了信道预约冲突概率,提高了卫星信道利用率。
基金Project(2018YFB1004202)supported by the National Key Research and Development Program of ChinaProject(61732019)supported by the National Natural Science Foundation of ChinaProject(SKLSDE-2018ZX-06)supported by the State Key Laboratory of Software Development Environment,China
文摘As the popularity of open source projects,the volume of incoming pull requests is too large,which puts heavy burden on integrators who are responsible for accepting or rejecting pull requests.An accepted pull request prediction approach can help integrators by allowing them either to enforce an immediate rejection of code changes or allocate more resources to overcome the deficiency.In this paper,an approach CTCPPre is proposed to predict the accepted pull requests in GitHub.CTCPPre mainly considers code features of modified changes,text features of pull requests’description,contributor features of developers’previous behaviors,and project features of development environment.The effectiveness of CTCPPre on 28 projects containing 221096 pull requests is evaluated.Experimental results show that CTCPPre has good performances by achieving accuracy of 0.82,AUC of 0.76 and F1-score of 0.88 on average.It is compared with the state of art accepted pull request prediction approach RFPredict.On average across 28 projects,CTCPPre outperforms RFPredict by 6.64%,16.06%and 4.79%in terms of accuracy,AUC and F1-score,respectively.
文摘在基于蜂窝通信演进形成的车用无线通信技术(Cellular-Vehicle to everything,C-V2X)场景下,基站作为多接入边缘计算(Multi-access Edge Computing,MEC)边缘缓存节点可提高用户获取数据的效率,但其缓存容量有限.因此,C-V2X中如何准确预测缓存请求内容成为待解决的重要问题.本文从文件请求的时变性出发,针对实际的城市场景,采用Simulation of Urban MObility(SUMO)对交通流进行建模;其次,通过采集实际网站分时分类的点击量数据,并根据各路段交通流规律进行预处理,构建用户请求模型;最后,利用Long Short-Term Memory(LSTM)深度学习模型进行训练,预测各基站的文件请求.仿真结果表明,在网易新闻流行度分布和请求间隔分布形成的文件请求下,vanillaLSTM模型对娱乐类型数据集预测时的均方根误差在1.3左右.
