流域尺度土壤厚度的模糊聚类与预测制图研究

基于土壤厚度与景观位置和特征之间的关系,运用模糊c均值聚类(FCM)方法对西苕溪流域的土壤厚度分布进行了空间预测。选取高程、坡度、平面曲率、剖面曲率、径流强度系数和地形湿度指数6个地形因子进行模糊聚类,根据相应的聚类参数将流域地形组合分为8类。利用部分调查获得的土壤剖面数据,结合样点属性和专家经验为典型区赋值,最后由加权平均得到流域土壤厚度预测图。验证结果表明,FCM方法可以对地形因子组合进行有效合理的分级,其预测精度较高,模型的稳定性较好,是一种低成本高效率的制图方法。该方法在土壤厚度预测方面具有一定的可靠性。     

CFG桩复合地基承载力模糊能度可靠性分析方法

在分析CFG桩复合地基承载力计算方法的基础上，根据其影响因素的区间性与模糊性特点，提出了采用三角模糊数表示计算参数取值，建立出CFG桩复合地基承载力模糊分析模型.通过探讨三角模糊数构造方法，建立出CFG桩复合地基承载力计算参数三角模糊确定方法，然后，基于给定安全系数与承载力设计构建功能函数，引入截集理论与区间运算规则，建立CFG桩复合地基承载力模糊能度可靠性分析方法.工程实例分析表明了该方法对CFG桩复合地基承载力进行分析计算的合理性与易操作性.在勘察数据与设计资料有限，不能直接采用概率可靠性分析方法的情况下，该方法为CFG桩复合地基承载能力可靠性分析与评价提供了一种精度较高的方法.     

The NURBS human body modeling using local knot removal

These days consumers’ various demands are accelerating research on apparel manufacturing system including automatic measurement, pattern generation, and clothing simulation. Accordingly, methods of reconstructing human body from point-clouds measured using a three dimensional scanning device are required for apparel CAD system to support these functions. In particular, we present in this study a human body reconstruction method focused on two issues, which are the decision of the number of control point for each sectional curve with error bound and the local knot removal for reducing the unusual concentration of control points. The approximation of sectional curves with error bounds as an approximation criterion leads all sectional curves to their own particular shapes apart from the number of control points. In addition, the application of the local knot removal to construction of human body sectional curves reduces the unusual concentration of control points effectively. The results may be used to produce an apparel CAD system as an automatic pattern generation system and a clothing simulation system through the low level control of NUBS or NURBS.     

复杂景观环境下土壤厚度分布规则提取与制图

复杂景观环境下,土壤—环境关系知识的获取是预测性土壤制图的基础。为了探究复杂景观下土壤厚度分布与环境条件的关系,该文以黑河上游祁连山区典型小流域为研究区,应用模糊c均值聚类(fuzzy C-means cluster,FCM)和决策树(decision Tree,DT)方法,建立了一套获取土壤厚度分布与环境间关系知识的方法。利用2种方法结合获得流域内土壤厚度各分布等级的环境要素关键阈值与土壤-环境关系知识集,将所得环境阈值和知识集进行预测性制图,并通过野外独立样点对制图结果进行精度评价。结果表明:土壤厚度图的总体精度为74.2%,Kappa系数为0.659。该研究将2种方法结合获得了土壤厚度分布对应的土壤环境关键阈值和土壤-环境关系知识集,为复杂景观环境下土壤厚度的预测性制图提供了一种有效的解决方案。     

基于模糊集理论的土壤重金属污染空间预测

基于54个土壤表层样品重金属全量浓度实验室测定数据,应用模糊c-均值算法对南京城市边缘带化工园附近20km^2样区内土壤重金属浓度进行了连续分类,对样点土壤的隶属度进行空间普通克里格插值,实现样区土壤重金属浓度和污染状况的空间预测.结果表明,样区土壤中重金属Cu、Zn、Cr、As和Cd的浓度均低于国家土壤环境质量标准,未发生污染;受工业、交通排放的影响,样区左侧和南部Cu、Zn、Cr、As较高,个别区域有Zn、Cr富集现象.Hg为样区主要的土壤重金属污染元素,土壤Hg污染主要发生在蔬菜基地及滁河流经区,较为严重的Hg污染土壤集中分布在样区中部的蔬菜基地.与利用样点测定数据直接插值的空间预测方法相比,基于模糊集理论的土壤污染空间预测方法可获得较好的预测效果.     

基于模糊逻辑的北京城市边缘区土壤重金属污染空间预测

Fuzzy classification combined with spatial prediction was used to assess the state of soil pollution in the peri-urban Beijing area. Total concentrations of As, Cr, Cd, Hg, and Pb were determined in 220 topsoil samples （0-20 cm） collected using a grid design in a study area of 2 600 kin2. Heavy metal concentrations were grouped into three classes according to the optimum number of classes and fuzziness exponent using the fuzzy comean （FCM） algorithm. Membership values were interpolated using ordinary kriging. The polluted soils of the study area induced by the measured heavy metals were concentrated in the northwest corner and eastern part, especially the southeastern part close to the urban zone, whereas the soils free of pollution were mainly distributed in the southwestern part. The soils with potential risk of heavy metal pollution were located in isolated spots mainly in the northern part and southeastern corner of the study region. The FCM algorithm combined with geostatistical techniques, as compared to conventional single geostatistical kriging methods, could produce a prediction with a quantitative uncertainty evaluation and higher reliability. Successful prediction of soil pollution achieved with FCM algorithm in this study indicated that fuzzy set theory had great potential for use in other areas of soil science.     

基于多源数据的盐碱地精确农作管理分区研究

为了便于对盐碱地实施变量管理和精确农作，以海涂围垦区盐碱土为研究对象，以NDVI数据、盐分数据以及作物产量数据作为分区变量，对一面积为15 hm²的盐碱地农田进行了基于多个数据源的精确农作管理分区研究。利用模糊c均值聚类方法进行分类分区，引入了模糊聚类指数(FPI)和归一化分类熵(NCE)作为最佳分区数目的判断标准，通过单项方差分析对分区结果进行比较和评价。研究发现，对本研究区，最佳的分区数目为三个。不同管理分区之间土壤化学性质(EC1:5,有机质，速效磷，速效钾，全氮，碱解氮以及阳离子交换量)的均值都存在着统计意义上的显著差异性，其中子区3具有最高的肥力水平和作物生产能力而子区1最低。利用所选取的三个变量，模糊c均值聚类算法可以较好地进行精确农作管理分区划分。分区结果不但可以指导采样, 而且可以作为变量管理的决策单元用于田间变量管理作业中,为精确农业变量投入的实施提供有效手段和决策依据。     

基于参数优化改进型可能聚类的遥感图像分割

可能聚类算法(PCA)和可能C-均值聚类算法(PCM)对初始值非常敏感,容易产生一致性聚类。改进型可能C-均值聚类算法(IPCM)能解决PCM的问题,然而IPCM的执行更依赖于参数。IPCM必须计算参数两次,因此聚类时间长。为了克服PCA和IPCM的缺点,进而应用于复杂的遥感图像分割,将PCA和IPCM相结合,提出了一种基于参数优化的改进型可能聚类算法(IPCAOP)。实验表明,IPCAOP在处理遥感图像分割方面明显优于模糊C-均值聚类(FCM)和IPCM。     

基于多重分形理论的油菜缺素叶片特征提取

为描述油菜缺素叶片图像的特征,该文提出了一种基于多重分形去趋势波动分析方法,即局部多重分形去趋势波动分析。该方法确定的hij(q)指数能有效刻画叶片图像每个像素点的多重分形特征,并以所有像素点hij(q)的平均值Lhq表征每幅图像的多重分形特征。选取4种油菜缺素叶片图像进行试验,结果表明所提取局部多重分形去趋势波动平均指数Lhq能很好地区分叶片,并通过方差分析指出当q={-10, -9, -8, -7, -6}时的Lhq区分效果最好。最后基于每个像素点的hij(q)指数利用模糊C均值聚类对缺镁油菜叶片图像进行模糊分割,并与传统的灰度值分割及经典的基于容量测度的指数分割进行了对比试验,结果表明以上述hij(q)为特征具有最佳的分割效果。     

Distribution patterns of leptocephali in the Kuroshio–Oyashio transitional region of the western North Pacific

Leptocephalus assemblages and distribution patterns were studied in the Kuroshio–Oyashio transitional region of the western North Pacific. A total of 479 leptocephali, representing at least 21 species (types) including eight undescribed types, were collected during a cruise of the T/V Hokuho Maru from 8 to 30 May 2002. The most abundant leptocephali were the congrids Gnathophis nystromi nystromi and Gnathophis nystromi ginanago. The assemblages at each station were clustered into four groups (one group in the Kuroshio region and three in the transition zone) using the ordination and the fuzzy c‐means method based on species composition similarities between stations. Each leptocephalus species was also clustered by the same method into one of three groups. The first group had the greatest numbers of species and mainly occurred at the station group in the Kuroshio region, sharing a similar distribution to G. n. nystromi. The second group had the greatest abundance, especially in the transition zone, but contained only one species, G. n. ginanago, which also occurred at all stations. Moreover discrimination of station groups in the transition zone corresponded well with the assemblage pattern of this species group. The last species group comprised rare and low abundance species. Most leptocephali in the study area were reported from the Kuroshio upstream region, so the discrimination of leptocephali assemblages in the study area may reflect their different ecology. Furthermore, the analytical methods used in this study yielded more meaningful and clearer groups than the other usual methods, validating application of this analysis to complex areas such as the transition zone.