WE say capitalism is notgood, but when it comes to discovering and using talents, it iscertainly very bold. It has a characteristic, which is taken for granted, that no priority is given to seniority, and that anyone ...WE say capitalism is notgood, but when it comes to discovering and using talents, it iscertainly very bold. It has a characteristic, which is taken for granted, that no priority is given to seniority, and that anyone suit-展开更多
The progress of modern industry has given rise to great requirements for network transmission latency and reliability in domains such as smart grid and intelligent driving.To address these challenges,the concept of Ti...The progress of modern industry has given rise to great requirements for network transmission latency and reliability in domains such as smart grid and intelligent driving.To address these challenges,the concept of Time-sensitive networking(TSN)is proposed by IEEE 802.1TSN working group.In order to achieve low latency,Cyclic queuing and forwarding(CQF)mechanism is introduced to schedule Timetriggered(TT)flows.In this paper,we construct a TSN model based on CQF and formulate the flow scheduling problem as an optimization problem aimed at maximizing the success rate of flow scheduling.The problem is tackled by a novel algorithm that makes full use of the characteristics and the relationship between the flows.Firstly,by K-means algorithm,the flows are initially partitioned into subsets based on their correlations.Subsequently,the flows within each subset are sorted by a new special criteria extracted from multiple features of flow.Finally,a flow offset selecting method based on load balance is used for resource mapping,so as to complete the process of flow scheduling.Experimental results demonstrate that the proposed algorithm exhibits significant advantages in terms of scheduling success rate and time efficiency.展开更多
针对室内兴趣点(point of interest,POI)显著度评价问题,提出了一种基于数据平衡的随机森林(random forest,RF)模型。鉴于现有模型在处理数据不均衡及模拟显著度与影响指标之间复杂非线性关系方面的局限性,聚焦视觉、语义和结构三大维度...针对室内兴趣点(point of interest,POI)显著度评价问题,提出了一种基于数据平衡的随机森林(random forest,RF)模型。鉴于现有模型在处理数据不均衡及模拟显著度与影响指标之间复杂非线性关系方面的局限性,聚焦视觉、语义和结构三大维度,构建了包含34项特征的指标体系。通过合成少数类过采样技术(synthetic minority over-sampling technique,SMOTE)有效缓解数据不平衡和基于重要性权重的特征优化,形成了随机森林评价模型。实验结果表明,该模型在室内POI数据集上展现出卓越的性能,其准确率、精确度、召回率、加权F1分数和曲线下面积分别达到了0.987、0.984、0.987、0.987和0.999;与未进行数据均衡处理的RF模型相比,性能提升了一倍;与其他模型(如支持向量机、遗传规划算法)相比,性能分别提升了15%和5%,且在测试集上也显示出了良好的泛化性能。展开更多
文摘WE say capitalism is notgood, but when it comes to discovering and using talents, it iscertainly very bold. It has a characteristic, which is taken for granted, that no priority is given to seniority, and that anyone suit-
基金supported by Science and Technology Project of State Grid Corporation Headquarters under Grant 5108-202218280A-2-170-XG(Development and Application of Power Time-Sensitive Network Switching Chip。
文摘The progress of modern industry has given rise to great requirements for network transmission latency and reliability in domains such as smart grid and intelligent driving.To address these challenges,the concept of Time-sensitive networking(TSN)is proposed by IEEE 802.1TSN working group.In order to achieve low latency,Cyclic queuing and forwarding(CQF)mechanism is introduced to schedule Timetriggered(TT)flows.In this paper,we construct a TSN model based on CQF and formulate the flow scheduling problem as an optimization problem aimed at maximizing the success rate of flow scheduling.The problem is tackled by a novel algorithm that makes full use of the characteristics and the relationship between the flows.Firstly,by K-means algorithm,the flows are initially partitioned into subsets based on their correlations.Subsequently,the flows within each subset are sorted by a new special criteria extracted from multiple features of flow.Finally,a flow offset selecting method based on load balance is used for resource mapping,so as to complete the process of flow scheduling.Experimental results demonstrate that the proposed algorithm exhibits significant advantages in terms of scheduling success rate and time efficiency.
