Inter-domain path computing is one big issue in multi-domain networks. The Hierarchical Path Computing Element (H-PCE) is a semi-central architecture for computing inter-domain path. To facilitate H-PCE in inter-domai...Inter-domain path computing is one big issue in multi-domain networks. The Hierarchical Path Computing Element (H-PCE) is a semi-central architecture for computing inter-domain path. To facilitate H-PCE in inter-domain path computing, this paper proposed a topology aggregation scheme to abstract the edge nodes and their connected inter-domain link as one vertex to achieve more optimal paths and confidentiality guarantee. The effectiveness of the scheme has been demonstrated on solving wavelength routing in multi-domain Wavelength Division Multiplexing (WDM) network via simulation. Simulation results show that this scheme reduces at least 10% inter-domain blocking probability, compared with the traditional Domain-to-the-Node (DtN) scheme.展开更多
船舶智能化的发展对船舶视觉感知系统实时目标检测能力提出了更高要求,YOLOv5作为YOLO(You Only Look Once)系列算法的最新成果,以良好的速度和精度被广泛应用于海上目标检测.但在实际海上航行中往往伴随着多变的自然条件和复杂的活动场...船舶智能化的发展对船舶视觉感知系统实时目标检测能力提出了更高要求,YOLOv5作为YOLO(You Only Look Once)系列算法的最新成果,以良好的速度和精度被广泛应用于海上目标检测.但在实际海上航行中往往伴随着多变的自然条件和复杂的活动场景,这使其在复杂海域中小目标检测能力和多目标分类效果并不理想.因此,为提升YOLOv5在复杂海域中目标检测能力,本文提出多路径聚合网络结构(MPANet).在自底向上特征传递过程中融合多层次特征信息以增强多尺度定位能力,同时结合SimAM注意力模块和Transformer结构增强高阶特征语义信息.在自定义数据集中实验结果表明:MPANet-YOLOv5相较于YOLOv5模型AP提升了5.4%,召回率提升了3.3%,AP0.5提升了3.3%,AP_(0.5:0.95)提升了2.2%,不同海域测试结果显示MPANet-YOLOv5海面小目标检测能力明显优于YOLOv5.展开更多
The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana ...The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.展开更多
复杂网络最短路径经典算法的处理效率较低,不适用于大规模复杂网络,而现有近似算法通用性有限,且计算准确率不理想,不能满足规模日益扩大的复杂网络中的最短路径计算需求。针对于此,提出基于 k -shell的复杂网络最短路径近似算法。算法...复杂网络最短路径经典算法的处理效率较低,不适用于大规模复杂网络,而现有近似算法通用性有限,且计算准确率不理想,不能满足规模日益扩大的复杂网络中的最短路径计算需求。针对于此,提出基于 k -shell的复杂网络最短路径近似算法。算法利用节点的k -shell值进行网络划分并引导搜索路径,利用超点聚合处理k -shell子网来降低路径搜索中节点和连边的规模,通过在路径搜索过程使用双向搜索树方法提高算法的计算效率和准确率。实验结果表明,算法通用性较好,在现实与仿真大规模复杂网络中均具有较高的计算效率和准确率。展开更多
基金Acknowledgements This work was supported by Chang Jiang Scholars Program of the Ministry of Education of China, National Science Fund for Distinguished Young Scholars under Grant No.60725104 the National Basic Research Program of China under Grant No. 2007CB310706+2 种基金 the National Natural Science Foundation of China under Ca＇ant No. 60932002, No. 60932005, No. 61071101 the Hi-Tech Research and Development Program of China under Grant No. 2009AA01Z254, No. 2009AA01Z215 NCEF Program of MoE of China, and Sichuan Youth Science and Technology Foundation under Crant No. 09ZQ026-032.
文摘Inter-domain path computing is one big issue in multi-domain networks. The Hierarchical Path Computing Element (H-PCE) is a semi-central architecture for computing inter-domain path. To facilitate H-PCE in inter-domain path computing, this paper proposed a topology aggregation scheme to abstract the edge nodes and their connected inter-domain link as one vertex to achieve more optimal paths and confidentiality guarantee. The effectiveness of the scheme has been demonstrated on solving wavelength routing in multi-domain Wavelength Division Multiplexing (WDM) network via simulation. Simulation results show that this scheme reduces at least 10% inter-domain blocking probability, compared with the traditional Domain-to-the-Node (DtN) scheme.
文摘船舶智能化的发展对船舶视觉感知系统实时目标检测能力提出了更高要求,YOLOv5作为YOLO(You Only Look Once)系列算法的最新成果,以良好的速度和精度被广泛应用于海上目标检测.但在实际海上航行中往往伴随着多变的自然条件和复杂的活动场景,这使其在复杂海域中小目标检测能力和多目标分类效果并不理想.因此,为提升YOLOv5在复杂海域中目标检测能力,本文提出多路径聚合网络结构(MPANet).在自底向上特征传递过程中融合多层次特征信息以增强多尺度定位能力,同时结合SimAM注意力模块和Transformer结构增强高阶特征语义信息.在自定义数据集中实验结果表明:MPANet-YOLOv5相较于YOLOv5模型AP提升了5.4%,召回率提升了3.3%,AP0.5提升了3.3%,AP_(0.5:0.95)提升了2.2%,不同海域测试结果显示MPANet-YOLOv5海面小目标检测能力明显优于YOLOv5.
基金supported by the Beijing Science Foundation(No.9232005)the Beijing Municipal Philosophy and Social Science Foundation of China(No.19GLB036)the Beijing Science and Technology Project(No.Z221100005822014)。
文摘The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.
文摘复杂网络最短路径经典算法的处理效率较低,不适用于大规模复杂网络,而现有近似算法通用性有限,且计算准确率不理想,不能满足规模日益扩大的复杂网络中的最短路径计算需求。针对于此,提出基于 k -shell的复杂网络最短路径近似算法。算法利用节点的k -shell值进行网络划分并引导搜索路径,利用超点聚合处理k -shell子网来降低路径搜索中节点和连边的规模,通过在路径搜索过程使用双向搜索树方法提高算法的计算效率和准确率。实验结果表明,算法通用性较好,在现实与仿真大规模复杂网络中均具有较高的计算效率和准确率。
