The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning...The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning. Four factors related to a node becoming a cluster-head are drawn by analysis, which are energy （ energy available in each node）, number （the number of neighboring nodes）, centrality （ a value to classify the nodes based on the proximity how central the node is to the cluster）, and location （the distance between the base station and the node）. The factors are as input variables of neural networks and the output variable is suitability that is the degree of a node becoming a cluster head. A group of cluster-heads are selected according to the size of network. Then the base station broadcasts a message containing the list of cluster-heads＇ IDs to all nodes. After that, each cluster-head announces its new status to all its neighbors and sets up a new cluster. If a node around it receives the message, it registers itself to be a member of the cluster. After identifying all the members, the cluster-head manages them and carries out data aggregation in each cluster. Thus data flowing in the network decreases and energy consumption of nodes decreases accordingly. Experimental results show that, compared with other algorithms, the proposed algorithm can significantly increase the lifetime of the sensor network.展开更多
随着现代通信和信息技术的飞速发展,智能交通系统(Intelligent Transportation System,ITS)逐渐成为热门研究领域,车载自组网(Vehicular Ad Hoc Network,VANET)作为其关键技术,在实时道路信息共享和车辆间通信中起重要作用.然而,现有VA...随着现代通信和信息技术的飞速发展,智能交通系统(Intelligent Transportation System,ITS)逐渐成为热门研究领域,车载自组网(Vehicular Ad Hoc Network,VANET)作为其关键技术,在实时道路信息共享和车辆间通信中起重要作用.然而,现有VANET分簇算法仍存在簇稳定性低、分簇开销大等问题.为解决这些问题,本文提出了一种端云协同的VANET分簇算法,在端云协同阶段,车辆通过路边单元(Road Side Unit,RSU)将自身特征数据上传至云,云侧根据特征变化,对车辆进行动态稳定性分类.稳定的端节点具有更高的可靠性和更长的连接持续时间.在端端协同阶段,考虑了稳定节点的相对移动性和覆盖节点数量等因素,进行簇头选举,简化簇头选举过程,提高了簇的稳定性.此外,针对控制开销大的问题,本文提出了一种邻居发现和更新机制,限制HELLO消息的转发操作,降低开销并优化资源使用.实验结果表明:本文提出的算法在簇稳定性、簇数量及分簇开销等关键性能指标上均优于基线算法,展示了其在实际交通场景中的应用潜力.展开更多
针对图神经网络在空间转录组数据聚类过程中识别的空间域不连续或存在交叉这一问题,提出一种基于多尺度图对比学习的空间转录组聚类方法mcmlST。首先,使用SCANPY和主成分分析法,对空间转录组数据进行预处理。然后对ST数据进行增强处理,...针对图神经网络在空间转录组数据聚类过程中识别的空间域不连续或存在交叉这一问题,提出一种基于多尺度图对比学习的空间转录组聚类方法mcmlST。首先,使用SCANPY和主成分分析法,对空间转录组数据进行预处理。然后对ST数据进行增强处理,形成新的视图。接着,基于图自编码器和辅助自编码器,设计双重编码结构,学习空间转录组数据的嵌入特征。最后,基于嵌入特征,使用k-means算法识别空间转录组数据中的空间域。在3个经典的空间转录组数据集(人类大脑皮层右侧背外侧前额叶皮层、人类乳腺癌Block A Section 1和STARmap)上,该方法与3个基线方法conST、CCST和DeepST相比,计算得到更高的ARI和NMI,表明该方法具有优越的空间转录组聚类性能。展开更多
基金The National Natural Science Foundation of China(No.60472053),the Natural Science Foundation of Jiangsu Province(No.BK2003055),the Specialized Research Fund for the Doctoral Pro-gram of Higher Education (No.20030286017).
文摘The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning. Four factors related to a node becoming a cluster-head are drawn by analysis, which are energy （ energy available in each node）, number （the number of neighboring nodes）, centrality （ a value to classify the nodes based on the proximity how central the node is to the cluster）, and location （the distance between the base station and the node）. The factors are as input variables of neural networks and the output variable is suitability that is the degree of a node becoming a cluster head. A group of cluster-heads are selected according to the size of network. Then the base station broadcasts a message containing the list of cluster-heads＇ IDs to all nodes. After that, each cluster-head announces its new status to all its neighbors and sets up a new cluster. If a node around it receives the message, it registers itself to be a member of the cluster. After identifying all the members, the cluster-head manages them and carries out data aggregation in each cluster. Thus data flowing in the network decreases and energy consumption of nodes decreases accordingly. Experimental results show that, compared with other algorithms, the proposed algorithm can significantly increase the lifetime of the sensor network.
文摘随着现代通信和信息技术的飞速发展,智能交通系统(Intelligent Transportation System,ITS)逐渐成为热门研究领域,车载自组网(Vehicular Ad Hoc Network,VANET)作为其关键技术,在实时道路信息共享和车辆间通信中起重要作用.然而,现有VANET分簇算法仍存在簇稳定性低、分簇开销大等问题.为解决这些问题,本文提出了一种端云协同的VANET分簇算法,在端云协同阶段,车辆通过路边单元(Road Side Unit,RSU)将自身特征数据上传至云,云侧根据特征变化,对车辆进行动态稳定性分类.稳定的端节点具有更高的可靠性和更长的连接持续时间.在端端协同阶段,考虑了稳定节点的相对移动性和覆盖节点数量等因素,进行簇头选举,简化簇头选举过程,提高了簇的稳定性.此外,针对控制开销大的问题,本文提出了一种邻居发现和更新机制,限制HELLO消息的转发操作,降低开销并优化资源使用.实验结果表明:本文提出的算法在簇稳定性、簇数量及分簇开销等关键性能指标上均优于基线算法,展示了其在实际交通场景中的应用潜力.
文摘针对图神经网络在空间转录组数据聚类过程中识别的空间域不连续或存在交叉这一问题,提出一种基于多尺度图对比学习的空间转录组聚类方法mcmlST。首先,使用SCANPY和主成分分析法,对空间转录组数据进行预处理。然后对ST数据进行增强处理,形成新的视图。接着,基于图自编码器和辅助自编码器,设计双重编码结构,学习空间转录组数据的嵌入特征。最后,基于嵌入特征,使用k-means算法识别空间转录组数据中的空间域。在3个经典的空间转录组数据集(人类大脑皮层右侧背外侧前额叶皮层、人类乳腺癌Block A Section 1和STARmap)上,该方法与3个基线方法conST、CCST和DeepST相比,计算得到更高的ARI和NMI,表明该方法具有优越的空间转录组聚类性能。
