农作物冠层光谱信息的施肥管理分区研究

随着地面遥感技术的不断发展,越来越多的农作物冠层光谱检测传感器被应用到了农业生产,其中应用较为广泛的就是Greenseeker植物光谱检测仪,利用Greenseeker植物光谱检测仪可以获取农作物冠层光谱信息归一化植被指数(NDVI)数据,从而能够进行农作物的施肥管理分区的划分,依据划分好的施肥管理分区可以实现有针对性的变量施肥。模糊c-均值(FCM)算法是划分农作物施肥管理分区常用的算法,但是模糊c-均值算法具有一定的局限性,就是在计算过程中随着NDVI数据量的增加会不断进行数据的迭代计算,从而会影响施肥管理分区划分的速度。在模糊c-均值算法的基础上提出一种基于模型的模糊c-均值(MFCM)算法,基于模型的模糊c-均值算法在划分农作物施肥管理分区过程中不必在每获取一组数据时就对全部数据进行迭代计算,可有效提高划分施肥管理分区的速度。通过搭建的农作物冠层光谱信息采集平台获取大豆和玉米的NDVI数据,利用基于模型的模糊c-均值算法划分大豆和玉米的施肥管理分区,使用分区评价指标轮廓系数(SC)和调整兰德指数(ARI)评价划分施肥管理分区的效果。结果表明,随着获取的NDVI数据量的不断增加,...     

An Upper Bound on the Average Size of Silhouettes

It is a widely observed phenomenon in computer graphics that the size of the silhouette of a polyhedron is much smaller than the size of the whole polyhedron. This paper provides, for the first time, theoretical evidence supporting this for a large class of objects, namely for polyhedra or, more generally, tessellated surfaces that approximate surfaces in some reasonable way. The approximated surfaces are two-manifolds that may be nonconvex and nondifferentiable and may have boundaries. The tessellated surfaces should, roughly speaking, have no short edges, have fat faces, and the distance between the mesh and the surface it approximates should never be too large. We prove that such tessellated surfaces of complexity n have silhouettes of expected size where the average is taken over all points of view. The viewpoints can be chosen at random at infinity or at random in a bounded region.     

Innovation in the cluster validating techniques

To detect database records containing approximate and exact duplicates because of data entry error or differences in the detailed schemas of records from multiple databases or for some other reasons is an important line of research. Yet no comprehensive comparative study has been performed to evaluate the effectiveness of Silhouette width, Calinski & Harbasz index (pseudo F-statistics) and Baker & Hubert index (γ index) algorithms for exact and approximate duplicates. In this paper, a comparative study and effectiveness of these three cluster validation techniques which involve measuring the stability of a partition in a data set in the presence of noise, in particular, approximate and exact duplicates are presented. Silhouette width, Calinski & Harbasz index and Baker & Hubert index are calculated before and after inserting the exact and approximate duplicates (deliberately) in the data set. Comprehensive experiments on glass, wine, iris and ruspini database confirms that the Baker & Hubert index is not stable in the presence of approximate duplicates. Moreover, Silhouette width, Calinski and Harbasz index and Baker & Hubert indice do not exceed the original data indice in the presence of approximate duplicates.     

贝叶斯优化模糊聚类地级行政区声环境

声环境功能区划是噪声污染管理的重要手段,当前声环境功能区划研究大多是基于某个特定的地级行政区来进行的,难以反映各地级行政区声环境的异同。 该文基于134个地级行政区的人口、面积、各声环境功能区面积和及面积占比,进行地级行政区声环境表征和归一化处理。 以轮廓系数作为聚类有效性评价指标,基于贝叶斯优化模糊聚类方法对地级行政区声环境表征进行聚类分析。 通过本文方法与谱聚类、K-medoids聚类、高斯混合模型聚类的聚类性能对比,验证了该方法的有效性。 结果表明我国地级行政区声环境分为9类,城市规模和用地情况发生显著变化导致其声环境表征和归类发生变化后应重新评估当前噪声污染管理政策,并借鉴同类的地级行政区的噪声污染管理政策做出必要的调整。     

Robust cluster analysis for detecting physico-chemical typologies of freshwater from wells of the plain of Friuli (northeastern Italy)

An approach for the characterisation of the groundwater system of the southern plain of Friuli-Venezia Giulia Region (Italy) is proposed on the basis of its physico-chemical composition, in order to detect multivariate patterns for unpolluted waters typical of specific areas in the plain, as well as for eventual polluted zones. The analytical data are relative to 38 wells (depth ranging from 20 to 200 m) sampled in three different periods along a year. Ten physico-chemical parameters were determined: conductivity, temperature, dissolved oxygen, calcium, magnesium, chlorides, nitrates, sulphates, atrazine and desethylatrazine.

Cluster analysis (CA) provides the methodological bases for detecting the classes of freshwater being typical for the considered plain: partitioning around medoids (PAM) and fuzzy clustering are considered. The number of classes to be characterised and the clustering algorithm are selected by comparing the average silhouette index for models counting from 2 to 10 clusters; six classes obtained by PAM partition the data set at best.

Plotting the frequencies of cluster membership for each well on a map permits the association of the six classes of waters to five easily recognisable geographical areas and to one group of two wells that are highly polluted by nitrates and triazines.

Averages and ranges of values for physico-chemical parameters of each class can be provided according to this methodology, defining a set of values being characteristic for the composition of waters belonging to the classes of wells identified in the considered plain.