基于蜂窝网的室内定位由于与通信网络共用基础设施,因此具有覆盖范围广、无需基础设施再投资等突出优点,已成为电信运营商级室内定位的首选,是5G通信领域的研究热点之一。在蜂窝网室内定位场景中,基站的布局将直接影响接收首径的数量、...基于蜂窝网的室内定位由于与通信网络共用基础设施,因此具有覆盖范围广、无需基础设施再投资等突出优点,已成为电信运营商级室内定位的首选,是5G通信领域的研究热点之一。在蜂窝网室内定位场景中,基站的布局将直接影响接收首径的数量、到达时间TOA(Time of Arrivaling)和测量误差等要素,从而影响定位精度。据此,文中提出一种面向室内定位的基站选择优化方法,以减小由于基站布局引入的误差。首先,引入TOA信息去除TDOA定位的虚定位点;其次,针对不同基站选择方案得到的定位结果,利用二次聚类的思想去除孤立点,并根据聚类结果中样本节点数量最多的类确定定位点的位置。实验结果表明,与其他优化方法相比,所提方法的室内定位平均误差降低了15.49%。展开更多
移动边缘计算(mobile edge computing,MEC)环境下,海量的领域服务分布在边缘服务器上,如何对大规模的边缘服务进行精确的聚类是亟需解决的重要问题之一。为此提出了一种MEC环境下多维属性感知的边缘服务二次聚类方法。该方法首先分析并...移动边缘计算(mobile edge computing,MEC)环境下,海量的领域服务分布在边缘服务器上,如何对大规模的边缘服务进行精确的聚类是亟需解决的重要问题之一。为此提出了一种MEC环境下多维属性感知的边缘服务二次聚类方法。该方法首先分析并建立了MEC环境下边缘服务二次聚类指标模型。之后,提出了一种基于密度的最小生成树启发式分段聚类算法(heuristic segmented for MST clustering based on service density,DMSC),基于DMSC算法依据一级指标对边缘服务进行一次聚类。最后,将密度峰值算法中γ值引入到层次聚类中,构建了基于密度峰值的层次聚类算法(hierarchical clustering based on density peak,HCDP),基于HCDP算法依据二级聚类指标,在一次聚类的基础上对边缘服务进行二次聚类。在人工数据集和UCI数据集上开展了大量验证实验。实验结果表明,DMSC算法与HCDP算法提高了聚类的准确率,减少了算法的平均迭代次数,增强了算法的稳定性。展开更多
Aims Inferring environmental conditions from characteristic patterns of plant co-occurrences can be crucial for the development of conservation strategies concerning secondary neotropical forests.However,nomethodologi...Aims Inferring environmental conditions from characteristic patterns of plant co-occurrences can be crucial for the development of conservation strategies concerning secondary neotropical forests.However,nomethodological agreement has been achieved so far regarding the identification and classification of characteristic groups of vascular plant species in the tropics.This study examines botanical and,in particular,statistical aspects to beconsidered in such analyses.Based on these,we propose a novel data-driven approach for the identification of characteristic plantco-oc currences in neotropical secondary mountain forests.Methods Floristic inventory data were gathered in secondary tropical mountain forests in Ecuador.Vegetation classification was performed by coupling locally adaptive isometric feature mapping,a non-linear ordination method and fuzzy-c-means clustering.This approach was designed for dealing with underlying non-linearities and uncertainties in the inventory data.Important Findings The results indicate that the applied non-linear mapping in combination with fuzzy classification of species occurrence allows an effective identification of characteristic groups of co-occurring species as fuzzy-defined clusters.The selected species indicated groups representing characteristic life-form distributions,as they correspond to various stages of forest regeneration.Combining the identified‘characteristic species groups’with meta-information derived from accompanying studies indicated that the clusters can also be related to habitat conditions.In conclusion,we identified species groups either characteristic of different stages of forest succession after clear-cutting or of impact by fire or a landslide.We expect that the proposed data-mining method will be useful for vegetation classification where no a priori knowledge is available.展开更多
文摘基于蜂窝网的室内定位由于与通信网络共用基础设施,因此具有覆盖范围广、无需基础设施再投资等突出优点,已成为电信运营商级室内定位的首选,是5G通信领域的研究热点之一。在蜂窝网室内定位场景中,基站的布局将直接影响接收首径的数量、到达时间TOA(Time of Arrivaling)和测量误差等要素,从而影响定位精度。据此,文中提出一种面向室内定位的基站选择优化方法,以减小由于基站布局引入的误差。首先,引入TOA信息去除TDOA定位的虚定位点;其次,针对不同基站选择方案得到的定位结果,利用二次聚类的思想去除孤立点,并根据聚类结果中样本节点数量最多的类确定定位点的位置。实验结果表明,与其他优化方法相比,所提方法的室内定位平均误差降低了15.49%。
文摘移动边缘计算(mobile edge computing,MEC)环境下,海量的领域服务分布在边缘服务器上,如何对大规模的边缘服务进行精确的聚类是亟需解决的重要问题之一。为此提出了一种MEC环境下多维属性感知的边缘服务二次聚类方法。该方法首先分析并建立了MEC环境下边缘服务二次聚类指标模型。之后,提出了一种基于密度的最小生成树启发式分段聚类算法(heuristic segmented for MST clustering based on service density,DMSC),基于DMSC算法依据一级指标对边缘服务进行一次聚类。最后,将密度峰值算法中γ值引入到层次聚类中,构建了基于密度峰值的层次聚类算法(hierarchical clustering based on density peak,HCDP),基于HCDP算法依据二级聚类指标,在一次聚类的基础上对边缘服务进行二次聚类。在人工数据集和UCI数据集上开展了大量验证实验。实验结果表明,DMSC算法与HCDP算法提高了聚类的准确率,减少了算法的平均迭代次数,增强了算法的稳定性。
文摘Aims Inferring environmental conditions from characteristic patterns of plant co-occurrences can be crucial for the development of conservation strategies concerning secondary neotropical forests.However,nomethodological agreement has been achieved so far regarding the identification and classification of characteristic groups of vascular plant species in the tropics.This study examines botanical and,in particular,statistical aspects to beconsidered in such analyses.Based on these,we propose a novel data-driven approach for the identification of characteristic plantco-oc currences in neotropical secondary mountain forests.Methods Floristic inventory data were gathered in secondary tropical mountain forests in Ecuador.Vegetation classification was performed by coupling locally adaptive isometric feature mapping,a non-linear ordination method and fuzzy-c-means clustering.This approach was designed for dealing with underlying non-linearities and uncertainties in the inventory data.Important Findings The results indicate that the applied non-linear mapping in combination with fuzzy classification of species occurrence allows an effective identification of characteristic groups of co-occurring species as fuzzy-defined clusters.The selected species indicated groups representing characteristic life-form distributions,as they correspond to various stages of forest regeneration.Combining the identified‘characteristic species groups’with meta-information derived from accompanying studies indicated that the clusters can also be related to habitat conditions.In conclusion,we identified species groups either characteristic of different stages of forest succession after clear-cutting or of impact by fire or a landslide.We expect that the proposed data-mining method will be useful for vegetation classification where no a priori knowledge is available.
