In sensor networks,the adversaries can inject false data reports from compromised nodes.Previous approaches to filter false reports,e.g.,SEF,only verify the correctness of the message authentication code (MACs) carrie...In sensor networks,the adversaries can inject false data reports from compromised nodes.Previous approaches to filter false reports,e.g.,SEF,only verify the correctness of the message authentication code (MACs) carried in each data report on intermediate nodes,thus cannot filter out fake reports that are forged in a collaborative manner by a group of compromised nodes,even if these compromised nodes distribute in different geographical areas.Furthermore,if the adversary obtains keys from enough (e.g.,more than t in SEF) distinct key partitions,it then can successfully forge a data report without being detected en-route.A neighbor information based false report filtering scheme (NFFS) in wireless sensor networks was presented.In NFFS,each node distributes its neighbor information to some other nodes after deployment.When a report is generated for an observed event,it must carry the IDs and the MACs from t detecting nodes.Each forwarding node checks not only the correctness of the MACs carried in the report,but also the legitimacy of the relative position of these detecting nodes.Analysis and simulation results demonstrate that NFFS can resist collaborative false data injection attacks efficiently,and thus can tolerate much more compromised nodes than existing schemes.展开更多
Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor do...Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper,i.e.UKGE-MS.Specifically,UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information,and solves the problems of traditional unsupervised feature selection methods,which only focus on selecting features from a global perspective and ignoring the impact of local connection of samples.Firstly,considering the influence of local information of samples in feature correlation evaluation,a feature clustering algorithm based on average neighborhood mutual information is proposed,and the feature clusters with certain event correlation are obtained;Secondly,an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation,so as to enhance the generalization ability of the selected feature items.Finally,the events knowledge graph is constructed by means of sparse representation and l1 norm.Extensive experiments are carried out on five real datasets and synthetic datasets,and the UKGE-MS are compared with five corresponding algorithms.The experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection,and has some advantages over other methods in text event recognition and discovery.展开更多
针对现有模型对异质信息网络(heterogeneous information network, HIN)信息提取大部分依赖于元路径,缺乏元路径信息补充以及很少学习异质图中复杂的结构信息等问题,提出一种异质网中基于邻居节点和元路径的推荐算法(NMRec)。提取用户...针对现有模型对异质信息网络(heterogeneous information network, HIN)信息提取大部分依赖于元路径,缺乏元路径信息补充以及很少学习异质图中复杂的结构信息等问题,提出一种异质网中基于邻居节点和元路径的推荐算法(NMRec)。提取用户和物品邻居节点补充元路径缺失的信息,以卷积的方式捕获节点之间丰富的交互,通过注意力机制得到节点和元路径的嵌入表示,拼接用户、物品、邻居节点及元路径进行TOP-N推荐。在两个公开数据集上的实验结果表明,NMRec推荐性能良好,对推荐结果有良好的可解释性,与7种推荐基准算法相比,NMRec在评价指标Pre@10、Recall@10、NDGG@10上至少提升了0.21%、29%、1.46%。展开更多
为解决用户群体移动轨迹划分和密度峰值聚类算法自身局限性的问题,以校园轨迹为对象,考虑时间和位置语义信息层面的信息,建立网络用户间的相似性度量模型,提出一种基于共享近邻贡献度的密度峰值聚类算法(density peak clustering based ...为解决用户群体移动轨迹划分和密度峰值聚类算法自身局限性的问题,以校园轨迹为对象,考虑时间和位置语义信息层面的信息,建立网络用户间的相似性度量模型,提出一种基于共享近邻贡献度的密度峰值聚类算法(density peak clustering based on shared nearest neighbor contribution,SNNC-DPC),结合信息熵理论,通过最小化局部密度熵自适应选择截断距离;在局部密度计算上,利用共享近邻贡献度重新计算局部密度,更加全面地反映数据分布的特性;采用非线性变换方法选取决策值,解决聚类中心选取困难且方法单一的问题。在真实校园轨迹数据集上实验,验证了改进算法的有效性。展开更多
智能电表故障的准确预测对实现计量设备精准主动运维、保障电网稳定运行具有重要意义。电表各故障类型样本的出现频次不同,且不同故障类型样本在高维特征空间中的分布存在重叠,这极大增加了故障预测的难度。现有不平衡分类方法通过构建...智能电表故障的准确预测对实现计量设备精准主动运维、保障电网稳定运行具有重要意义。电表各故障类型样本的出现频次不同,且不同故障类型样本在高维特征空间中的分布存在重叠,这极大增加了故障预测的难度。现有不平衡分类方法通过构建单一样本信息与其对应类别标签的映射关系来划分样本类型,导致对具有相似表征信息的重叠区样本难以准确判别,降低了整体分类精度。该文提出一种基于多粒度近邻图的智能电表故障分类方法。首先,选择原始数据集中样本作为目标样本,以目标样本及其近邻样本作为节点、目标样本与其近邻样本连线作为边构建近邻图。根据选择的近邻样本数量不同构建多粒度近邻图,实现目标样本的信息扩充和训练样本的数量扩增,更有利于模型稳定训练。构建编码器挖掘近邻图节点特征,利用图注意力机制,根据近邻图节点编码特征和节点邻接关系将近邻样本信息自适应地聚合到目标样本,实现对相似样本差异的有效挖掘。对于给定测试样本,通过集成测试样本多粒度近邻图的分类结果,得到更精准、更鲁棒的智能电表故障预测结果。在20个KEEL(knowledge extraction based on evolutionary learning)和UCI(UC Irvine machine learning repository)不平衡分类公开数据集和智能电表实际故障数据集上的大量实验结果表明,与17种典型方法相比,该文所提算法在处理智能电表故障分类问题上具有显著优势。展开更多
针对基于RSSI和CSI的指纹定位技术易受环境干扰、定位精度较低的问题,提出了一种基于RSSI指纹和相位修正信道状态信息(phase correct based channel state information,PC-CSI)指纹的加权融合指纹定位技术。基于PC-CSI的指纹定位在传统...针对基于RSSI和CSI的指纹定位技术易受环境干扰、定位精度较低的问题,提出了一种基于RSSI指纹和相位修正信道状态信息(phase correct based channel state information,PC-CSI)指纹的加权融合指纹定位技术。基于PC-CSI的指纹定位在传统基于CSI幅值的指纹定位基础上增加相位信息对定位结果进行修正,之后对RSSI指纹和PC-CSI指纹的定位结果加权重定位。实验结果表明,提出的加权融合指纹定位算法与基于CSI的主动定位算法相比,平均定位误差(mean position error,MPE)降低了36.2%,能满足室内定位需求。展开更多
准确识别岩质高边坡结构面和获取产状统计信息是进行边坡稳定性分析的重要前提。无人机摄影测量技术为解决高边坡结构面准确勘测难题提供了可能,但缺少高效准确的影像后处理方法,且现有研究没有考虑结构面产状信息特征的不确定性,致使...准确识别岩质高边坡结构面和获取产状统计信息是进行边坡稳定性分析的重要前提。无人机摄影测量技术为解决高边坡结构面准确勘测难题提供了可能,但缺少高效准确的影像后处理方法,且现有研究没有考虑结构面产状信息特征的不确定性,致使结构面识别准确性差、效率低。针对该问题,以江西省南昌市某露天矿高边坡为研究背景,提出了融合无人机摄影、后处理算法及统计分析的一体化结构面识别与产状统计信息采集方法。首先,通过Phantom 4 Pro V2.0无人机获取边坡表面影像;其次,利用Context Capture软件进行处理,得到高密度三维点云数据;然后,采用K近邻(KNN)算法中的确定近邻点数量法构建相似点集,采用基于密度的聚类(DBSCAN)算法进行聚类分析,从而实现边坡结构面识别,获得结构面产状信息并进行统计特征分析;最后,通过现场勘测数据进行对比验证。结果表明:该方法能够快速获取完整的高密度点云数据,准确高效地识别岩质高边坡大部分结构面,识别结果与边坡工程现场实际情况基本吻合;该方法可获取高边坡结构面数量、产状信息及其统计特征,大部分结构面倾角和倾向概率分布与实测数据拟合较好,为高边坡裂隙网络模型构建及稳定性分析提供了重要数据来源。展开更多
In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is e...In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.展开更多
基金Projects(61173169,61103203,70921001)supported by the National Natural Science Foundation of ChinaProject(NCET-10-0798)supported by Program for New Century Excellent Talents in University of China
文摘In sensor networks,the adversaries can inject false data reports from compromised nodes.Previous approaches to filter false reports,e.g.,SEF,only verify the correctness of the message authentication code (MACs) carried in each data report on intermediate nodes,thus cannot filter out fake reports that are forged in a collaborative manner by a group of compromised nodes,even if these compromised nodes distribute in different geographical areas.Furthermore,if the adversary obtains keys from enough (e.g.,more than t in SEF) distinct key partitions,it then can successfully forge a data report without being detected en-route.A neighbor information based false report filtering scheme (NFFS) in wireless sensor networks was presented.In NFFS,each node distributes its neighbor information to some other nodes after deployment.When a report is generated for an observed event,it must carry the IDs and the MACs from t detecting nodes.Each forwarding node checks not only the correctness of the MACs carried in the report,but also the legitimacy of the relative position of these detecting nodes.Analysis and simulation results demonstrate that NFFS can resist collaborative false data injection attacks efficiently,and thus can tolerate much more compromised nodes than existing schemes.
基金This study was funded by the International Science and Technology Cooperation Program of the Science and Technology Department of Shaanxi Province,China(No.2021KW-16)the Science and Technology Project in Xi’an(No.2019218114GXRC017CG018-GXYD17.11),Thesis work was supported by the special fund construction project of Key Disciplines in Ordinary Colleges and Universities in Shaanxi Province,the authors would like to thank the anonymous reviewers for their helpful comments and suggestions.
文摘Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper,i.e.UKGE-MS.Specifically,UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information,and solves the problems of traditional unsupervised feature selection methods,which only focus on selecting features from a global perspective and ignoring the impact of local connection of samples.Firstly,considering the influence of local information of samples in feature correlation evaluation,a feature clustering algorithm based on average neighborhood mutual information is proposed,and the feature clusters with certain event correlation are obtained;Secondly,an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation,so as to enhance the generalization ability of the selected feature items.Finally,the events knowledge graph is constructed by means of sparse representation and l1 norm.Extensive experiments are carried out on five real datasets and synthetic datasets,and the UKGE-MS are compared with five corresponding algorithms.The experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection,and has some advantages over other methods in text event recognition and discovery.
文摘针对现有模型对异质信息网络(heterogeneous information network, HIN)信息提取大部分依赖于元路径,缺乏元路径信息补充以及很少学习异质图中复杂的结构信息等问题,提出一种异质网中基于邻居节点和元路径的推荐算法(NMRec)。提取用户和物品邻居节点补充元路径缺失的信息,以卷积的方式捕获节点之间丰富的交互,通过注意力机制得到节点和元路径的嵌入表示,拼接用户、物品、邻居节点及元路径进行TOP-N推荐。在两个公开数据集上的实验结果表明,NMRec推荐性能良好,对推荐结果有良好的可解释性,与7种推荐基准算法相比,NMRec在评价指标Pre@10、Recall@10、NDGG@10上至少提升了0.21%、29%、1.46%。
文摘为解决用户群体移动轨迹划分和密度峰值聚类算法自身局限性的问题,以校园轨迹为对象,考虑时间和位置语义信息层面的信息,建立网络用户间的相似性度量模型,提出一种基于共享近邻贡献度的密度峰值聚类算法(density peak clustering based on shared nearest neighbor contribution,SNNC-DPC),结合信息熵理论,通过最小化局部密度熵自适应选择截断距离;在局部密度计算上,利用共享近邻贡献度重新计算局部密度,更加全面地反映数据分布的特性;采用非线性变换方法选取决策值,解决聚类中心选取困难且方法单一的问题。在真实校园轨迹数据集上实验,验证了改进算法的有效性。
文摘智能电表故障的准确预测对实现计量设备精准主动运维、保障电网稳定运行具有重要意义。电表各故障类型样本的出现频次不同,且不同故障类型样本在高维特征空间中的分布存在重叠,这极大增加了故障预测的难度。现有不平衡分类方法通过构建单一样本信息与其对应类别标签的映射关系来划分样本类型,导致对具有相似表征信息的重叠区样本难以准确判别,降低了整体分类精度。该文提出一种基于多粒度近邻图的智能电表故障分类方法。首先,选择原始数据集中样本作为目标样本,以目标样本及其近邻样本作为节点、目标样本与其近邻样本连线作为边构建近邻图。根据选择的近邻样本数量不同构建多粒度近邻图,实现目标样本的信息扩充和训练样本的数量扩增,更有利于模型稳定训练。构建编码器挖掘近邻图节点特征,利用图注意力机制,根据近邻图节点编码特征和节点邻接关系将近邻样本信息自适应地聚合到目标样本,实现对相似样本差异的有效挖掘。对于给定测试样本,通过集成测试样本多粒度近邻图的分类结果,得到更精准、更鲁棒的智能电表故障预测结果。在20个KEEL(knowledge extraction based on evolutionary learning)和UCI(UC Irvine machine learning repository)不平衡分类公开数据集和智能电表实际故障数据集上的大量实验结果表明,与17种典型方法相比,该文所提算法在处理智能电表故障分类问题上具有显著优势。
文摘针对基于RSSI和CSI的指纹定位技术易受环境干扰、定位精度较低的问题,提出了一种基于RSSI指纹和相位修正信道状态信息(phase correct based channel state information,PC-CSI)指纹的加权融合指纹定位技术。基于PC-CSI的指纹定位在传统基于CSI幅值的指纹定位基础上增加相位信息对定位结果进行修正,之后对RSSI指纹和PC-CSI指纹的定位结果加权重定位。实验结果表明,提出的加权融合指纹定位算法与基于CSI的主动定位算法相比,平均定位误差(mean position error,MPE)降低了36.2%,能满足室内定位需求。
文摘准确识别岩质高边坡结构面和获取产状统计信息是进行边坡稳定性分析的重要前提。无人机摄影测量技术为解决高边坡结构面准确勘测难题提供了可能,但缺少高效准确的影像后处理方法,且现有研究没有考虑结构面产状信息特征的不确定性,致使结构面识别准确性差、效率低。针对该问题,以江西省南昌市某露天矿高边坡为研究背景,提出了融合无人机摄影、后处理算法及统计分析的一体化结构面识别与产状统计信息采集方法。首先,通过Phantom 4 Pro V2.0无人机获取边坡表面影像;其次,利用Context Capture软件进行处理,得到高密度三维点云数据;然后,采用K近邻(KNN)算法中的确定近邻点数量法构建相似点集,采用基于密度的聚类(DBSCAN)算法进行聚类分析,从而实现边坡结构面识别,获得结构面产状信息并进行统计特征分析;最后,通过现场勘测数据进行对比验证。结果表明:该方法能够快速获取完整的高密度点云数据,准确高效地识别岩质高边坡大部分结构面,识别结果与边坡工程现场实际情况基本吻合;该方法可获取高边坡结构面数量、产状信息及其统计特征,大部分结构面倾角和倾向概率分布与实测数据拟合较好,为高边坡裂隙网络模型构建及稳定性分析提供了重要数据来源。
基金Supported by the National Natural Science Foundation of China(No.62172352,61871465,42002138)the Natural Science Foundation of Hebei Province(No.F2019203157)the Science and Technology Research Project of Hebei(No.ZD2019004)。
文摘In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.
