Hierarchical self-localization of underwater wireless sensor network nodes

The follow-up application of underwater wireless sensor network is influenced by accuracy of self-localization of nodes. The self-localization of nodes is discussed in this paper. First of all, nodes of underwater wireless sensor network are classified into several levels according to the accuracy of position of nodes and the levels are from the first to the fifth in accordance with accuracy of nodes from high to low respectively. Secondly, the level of anchor nodes can be known by those unknown nodes from the information given by the anchor nodes themselves, At the same time the unknown nodes are able to be located in the area controlled by the first level of anchor nodes that are as the aggregation. Then the positioning algorithm is designed correspondingly in accordance with the accuracy level of nodes. Finally, the positioning algorithm is simulated and analyzed. The result shows that the unknown nodes can be located effectively by hierarchical control.     

基于机器学习的高光谱湿地植被分类研究

为实现湿地植被的精细分类和高精度制图,为湿地管理部门提供准确的决策依据,以美国加州萨克拉门托—圣华金水域的典型湿地植被为研究对象,以高光谱影像为数据源,结合野外GPS采样点,对典型湿地植被的光谱反射率作一阶导数和二阶导数处理,基于均值置信区间原理筛选特征波段,基于单因素分析法筛选能够明显区分植被类型的植被指数。联合特征波段和植被指数构建特征集,利用机器学习C5.0决策树生成知识规则并提取湿地植被信息。结果表明,基于机器学习C5.0决策树的湿地植被提取总体精度为80.09%,Kappa系数为0.792,与最大似然法比较,总体精度提升10.79%,Kappa系数提升0.105,说明基于机器学习的C5.0决策树法能够实现植被的精细分类,方法切实可行。     

基于遥感影像的昆明中心城区绿地信息提取

以昆明中心城区遥感影像为研究数据,采用基于面向对象分类的研究方法,按照监督分类的思想,将地物分为城市绿地、林地、裸地、水体、建筑和道路6种类型,运用支持向量机的分类器对绿地信息进行提取,并根据总体分类精度和kappa系数的大小对分类结果进行验证,结果表明:城市绿地信息提取精度良好,符合城市绿地调查的精度标准,认为此种绿地信息提取方法值得推广运用。     

一种基于花授粉算法的WRF风速集合预报新模型

基于不同水平分辨率和边界层参数化方案的集合预报思路,应用花授粉算法与不限制负值的约束理论(FPA-NNCT)进行权重平均,提出一种新的风速集合预报模型(FPA-NNCT-WRF-E).利用山东省代表山地和海滨下垫面的2个风电场风速实测数据,将新模型与传统算术集合模型(M-WRF-E)以及FPA模型(FPA-WRF-E)的风速预报结果进行对比评估.结果表明:FPA-NNCT-WRF-E预报明显优于M-WRF-E和FPA-WRF-E的风速预报,与M-WRF-E相比, FPA-WRF-E将风速平均绝对误差(MAE)减小了20%以上,而新模型FPA-NNCT-WRF-E将MAE减小了38%以上.预报的准确性得到了提高.     

Mixed-effect non-linear modelling for diameter estimation along the stem of Tectona grandis in mid-western Brazil

This study evaluated the efficiency of taper functions and the application of mixed-effect modelling for diameter estimation along the stems of Tectona grandis. We sampled 266 trees of Tectona grandis, measuring the diameter at relative heights for volume determination, grouping the data according to three form-factor classes. Six taper functions were fitted, selecting the function with better fit performance. Six taper functions were fitted, selecting the function with better fit performance. The selected function was fitted in its basic formulation, and with the mixed non-linear modelling technique in different scenarios, and for the stem stratified in three portions of the total height. The precision and selection of the adjusted models were evaluated regarding the coefficient of determination, standard error of estimate, the Akaike information criterion, bias, quadratic error and absolute bias. According to the statistical criteria used, the model of Kozak was selected for the adjustments. For diameter estimation, the scenario with two coefficients as random effects provided an accuracy increase of 11.91%, and the mixed non-linear modelling better estimated the stem diameter for the stratified stems. In conclusion, the model of Kozak can be used to describe the stem shape of Tectona grandis, and the mixed-effect non-linear model approach was the best technique to estimate diameter along the stem of Tectona grandis.     

龙门式点样仪精度的研究

为了实现龙门式点样仪的高精度运动控制,采用一种消除反向误差方法,消除双输出轴电机驱动设备两侧松紧程度不同的同步带轮运动过程中,设备返回启动位置时,连接两侧运动机构的桥梁与启动时产生的夹角,提高了设备的重复定位精度;改进了一种标定方法,提高了设备的定位精度。通过基于ARM9的微控制器对是否采用上述方法进行对比实验,利用激光跟踪仪测量了设备的重复定位精度以及定位精度。实验结果表明:未采用上述方法,设备重复定位精度为0.09 mm,定位精度为0.07 mm。采用上述方法,设备的重复定位精度可达0.01 mm,定位精度可达0.03 mm。     

Investigating the persistence of accuracy of genomic predictions over time in broilers

Accuracy of genomic predictions is an important component of the selection response. The objectives of this research were: 1) to investigate trends for prediction accuracies over time in a broiler population of accumulated phenotypes, genotypes, and pedigrees and 2) to test if data from distant generations are useful to maintain prediction accuracies in selection candidates. The data contained 820K phenotypes for a growth trait (GT), 200K for two feed efficiency traits (FE1 and FE2), and 42K for a carcass yield trait (CY). The pedigree included 1,252,619 birds hatched over 7 years, of which 154,318 from the last 4 years were genotyped. Training populations were constructed adding 1 year of data sequentially, persistency of accuracy over time was evaluated using predictions from birds hatched in the three generations following or in the years after the training populations. In the first generation, before genotypes became available for the training populations (first 3 years of data), accuracies remained almost stable with successive additions of phenotypes and pedigree to the accumulated dataset. The inclusion of 1 year of genotypes in addition to 4 years of phenotypes and pedigree in the training population led to increases in accuracy of 54% for GT, 76% for FE1, 110% for CY, and 38% for FE2; on average, 74% of the increase was due to genomics. Prediction accuracies declined faster without than with genomic information in the training populations. When genotypes were unavailable, the average decline in prediction accuracy across traits was 41% from the first to the second generation of validation, and 51% from the second to the third generation of validation. When genotypes were available, the average decline across traits was 14% from the first to the second generation of validation, and 3% from the second to the third generation of validation. Prediction accuracies in the last three generations were the same when the training population included 5 or 2 years of data, and a decrease of ~7% was observed when the training population included only 1 year of data. Training sets including genomic information provided an increase in accuracy and persistence of genomic predictions compared with training sets without genomic data. The two most recent years of pedigree, phenotypic, and genomic data were sufficient to maintain prediction accuracies in selection candidates. Similar conclusions were obtained using validation populations per year.     

张家港市农田土壤重金属含量高光谱遥感监测模型构建

以张家港农田土壤作为研究对象,在实验室测定土壤重金属元素As、Cd、Cr、Cu、Zn、Ni、Pb、Hg的含量,并与土壤可见近红外高光谱数据建立土壤重金属含量的定量估测模型,以快速获取研究区农田的土壤重金属含量。为保证模型预测的精度和稳定性,首先,对原始高光谱数据进行平滑处理,并进行一阶导数、倒数一阶导数、倒数的对数一阶导数、平方根一阶导数和连续统去除等形式的光谱变换;然后,提取不同变换光谱的特征波段进行相关性分析;最后,通过逐步回归法建立重金属含量的定量估算模型。结果表明:张家港市农田土壤中Cd、Hg、Cu、Zn存在一定的污染风险。在高光谱的不同变换形式中,一阶导数和连续统去除与重金属含量的相关系数高于其他变换形式。基于8种土壤重金属含量与高光谱数据建立的定量估算模型具有良好的预测精度。Cd、Hg、Cr、As、Cu、Zn、Ni、Pb估算模型的实际值与验证值的拟合度分别为0.874、0.879、0.800、0.646、0.513、0.655、0.603和0.542,可用于预测张家港市的农田土壤重金属含量。     

小麦赤霉病自动监测预警系统应用效果评价

2018年在江苏、陕西、河南、湖北、安徽共18个县(市)安装了小麦赤霉病预报器,在周边设置未防治麦田进行赤霉病调查,并与预警软件平台预测结果相比较,评价小麦赤霉自动监测预警系统的准确性。评价结果表明,2018年该系统预测的准确性达71.8%。结合陕西省植保总站、西安市植保站、渭南华州区植保站、商洛洛南县植保站、安徽凤台县植保站2016年-2018年对该系统的评价结果,证实该系统预测准确性较高,系统工作稳定,自动化程度高,可为小麦赤霉病的科学防控提供重要的参考依据,具有一定的应用前景。