规模化水产养殖环境因子监控系统的设计

为实现精确的水质环境监控,设计了基于ZigBee无线传感器网络的水产养殖环境因子监控系统。该系统对测量的水质环境因子采用自适应加权融合算法和模糊综合评判法进行两级数据的融合分析,判断当前的水质环境是否有利于养殖对象的生长并由判断结果给出控制决策。实验数据和分析结果表明,该系统具有较强的容错性,可弥补系统单因子单阀值控制的不足,提高了系统的精确性和可靠性。     

基于灵敏度分析的病死猪搬运车转盘结构多目标优化设计

【目的】改善病死猪搬运车的工作性能,降低成本,对其转盘结构进行多目标优化设计。【方法】采用Creo Simulate三维设计软件,对病死猪搬运车的转盘结构进行了静力学有限元分析以及灵敏度分析,建立多目标优化设计模型。采用模糊物元分析法和加权平均法对设计方案进行优选。【结果】建立了优化设计数学模型,得到了6组非劣解;通过评估优选,获得了病死猪搬运车转盘结构的最优化设计方案。优化设计前后的参数对比分析结果表明,优化后的病死猪搬运车转盘质量为20.90 kg,比原始方案(25.94 kg)减小了19.43%;最大等效应力为1.44 MPa,比原始方案(1.50 MPa)减少了4.00%。【结论】基于灵敏度分析的病死猪搬运车转盘结构多目标优化设计是可行的,可以为转盘结构的改进提供理论指导。     

材积方程中异方差的检验和校正

本文提出了通过检验剩余绝对值和自变量的等级相关系数,检验材积方程模型异方差性的方法。用加权最小二乘法估计异方差的材积方程参数,有效地克服了村积的异方差性,提高了材积方程的适用精度。     

Estimating forest carbon fluxes for large regions based on process-based modelling, NFI data and Landsat satellite images

The aim of this study was to develop and evaluate a new approach for estimating forest carbon fluxes for large regions based on climate-sensitive process-based model, national forest inventory (NFI) data and satellite images. The approach was tested for Central Finland and Lapland with NFI field data and daily weather data from 2004 to 2008.The approach combines (1) a light use efficiency (LUE) model, (2) a process-based summary model for estimating gross primary production (GPP) and net primary production (NPP), and (3) the Yasso07 soil carbon model, which together allow the estimation of net ecosystem exchange (NEE). Landsat TM 5 satellite images were utilized to generalize the carbon fluxes obtained for field sample plots for all forested areas using the k-NN imputation method. The accuracy of the imputations was examined by leave-one-out cross validation and by comparing the imputed and simulated values with Eddy covariance (EC) measurements.RMSE of the k-NN imputations was slightly better in Central Finland than in Lapland, the bias staying at a similar level. Based on the EC comparisons, the approach seemed to work rather well with GPP estimates in both areas, but in the north the NEE estimates were remarkably biased. The main advantages of the approach include its applicability to basic NFI data and a high output resolution (30 m).The method proved to be a promising way to produce carbon flux estimates based on large-scale forest inventory data and could therefore be easily applied to the whole of Northern Europe. However, there are still drawbacks to the approach, such as lacking parameters for peat lands. One of the future goals is to integrate the approach with an interactive mapping framework, which could thereafter be utilized, for example, in climate change research.     

The k-nearest neighbor technique with local linear regression

In a standard k-nearest neighbor (kNN) technique, imputations of unit-level values in the variables of interest (Y) are based on the k-nearest neighbors in a set of reference units. Nearest is defined with respect to a distance metric in the space of auxiliary variables (X). This study evaluates kNN imputations of Y with a selection, by the same distance metric, of k-nearest locally weighted regression models. Imputations are obtained as predictions using the X values of the k-nearest neighbors in the population. In simulated random sampling from three artificial multivariate populations and two actual univariate populations and sampling units composed of a single population element or a cluster of four elements, the new kNN technique: (1) improved the correlation between an imputation and its actual value; (2) lowered the root mean square error (RMSE) of imputations; (3) increased the slope in regressions of actual y values regressed against their imputed values; (4) performed relatively best with k values of 4 and sample sizes of 200 or greater; (5) compared favorably with a recently proposed kNN calibration procedure; and (6) had a higher (15–28%) RMSE than with a simple local linear regression. Distribution matching had a consistent negative effect (+10%) on RMSE.     

思茅松天然林单木生物量地理加权回归模型构建

通过调查云南省思茅区思茅松天然林63株思茅松单木的地上部分干、枝、叶生物量数据,并测定其中30株的根系生物量数据。基于普通最小二乘模型选型,采用地理加权回归的方法构建思茅松单木树干生物量、树枝生物量、树叶生物量和地上部分生物量,以及根系生物量和整株生物量模型。结果表明:(1)地理加权回归模型(GWR)的决定系数(R2)大于普通最小二乘(OLS)模型,且GWR模型拟合的R2值除树叶生物量模型外,其余生物量维量模型均大于0.950;Akaike信息指数(AIC)值小于普通最小二乘(OLS)模型,平均相对误差(EE)和平均相对误差绝对值(RMA)的绝对值除树枝生物量外均小于OLS模型,说明GWR模型拟合效果优于OLS模型;(2)地理加权回归模型拟合在一定程度上克服了OLS在拟合生物量模型中存在的异方差问题。     

基于GIS的混合加权模式在天津城郊土壤重金属污染评价中的应用

应用混合加权模式对天津城郊农田土壤的重金属污染进行评价,并与内梅罗指数法计算的综合污染指数进行对比,以GIS输出表达,认为混合加权法对环境质量具有较好的灵敏性,能将环境质量的差别较好的区分开来。根据混合加权的计算结果,利用地统计方法,在GIS的支持下,得出天津城郊混合加权的分析结果。     

一种新的合并估计法

将一些统计量的不同估计利用加权平均构造一个新的估计法,能提高估计量的渐近效率。本文仅对服从正态分布的标准差利用这一新法,有效地改进了平均一阶连续差及平均二阶连续差的估计效率,前者的相对效率提高了28.45%,后者提高了41.85%。     

改进加权FCM用于裂纹蛋2D图像分割的研究

解决裂纹鸡蛋图像灰度直方图目标与背景区域分布模糊、图像分割效果差的问题.通过将包含空间信息的二维直方图和改进特征加权FCM算法有机结合,迭代寻求最佳聚类有效性函数和加权矩阵,实现鸡蛋图像缺陷分割.同时,对经典FCM和改进特征加权FCM算法的性能进行了分析比较.结果表明:提出的算法更接近于真实聚类中心,目标函数值亦得到改善;二维直方图的改进特征加权模糊聚类算法更好地提取了裂纹鸡蛋图像的细节信息,图像分割效果好.     

Improving the precision of cotton performance trials conducted on highly variable soils of the southeastern USA coastal plain

Reliable agronomic and fibre quality data generated in Upland cotton (Gossypium hirsutum L.) cultivar performance trials are highly valuable. The most common strategy used to generate reliable performance trial data uses experimental design to minimize experimental error resulting from spatial variability. However, an alternative strategy uses a posteriori statistical procedures to account for spatial variability. In this study, the efficiency of the randomized complete block (RCB) design and nearest neighbour adjustment (NNA) were compared in a series of cotton performance trials conducted in the southeastern USA to identify the efficiency of each in minimizing experimental error for yield, yield components and fibre quality. In comparison to the RCB, relative efficiency of the NNA procedure varied amongst traits and trials. Results show that experimental analyses, depending on the trait and selection intensity employed, can affect cultivar or experimental line selections. Based on this study, we recommend researchers conducting cotton performance trials on variable soils consider using NNA or other spatial methods to improve trial precision.