改性玉米淀粉黏结剂对种绳捻制质量的影响

为了提高水稻种绳的抗拉强度和穴距准确性、防止种子在种绳捻制和直播过程中脱落,提出了在包容材料上涂刷黏结剂的方法并配制了一种改性玉米淀粉黏结剂。试验结果表明,配制的改性玉米淀粉黏结剂及其涂刷量和黏度对水稻出苗率影响不显著。通过二次回归正交旋转组合试验,研究了改性玉米淀粉黏结剂的涂刷量和黏度与水稻种绳的抗拉强度、穴距准确性和每穴粒数合格率的关系,建立了相应的数学模型。利用多目标非线性优化方法,对黏结剂的涂刷量和黏度进行了参数优化。确定的最优参数组合是黏结剂涂刷量为80%、黏度为7 792 mPa·s。在此条件下,种绳的抗拉强度达到18.84 N,穴距变异系数为3.25%,穴粒数合格率为91.05%。研究结果证明,在种绳捻制过程中涂刷适量的改性玉米淀粉黏结剂,对提高种绳抗拉强度和穴距准确性具有重要作用,对水稻出苗和土壤环境没有影响。这为水稻种绳捻制机的改进设计提供了重要参考。     

TRMM降水数据在复杂山地的精度评估——以重庆市为例

在地形复杂的重庆地区,利用研究区内34个气象站点实测数据,分别从年、季、月3个尺度,对2000-2011年间TRMM 3B43降水数据精度进行了验证,并分析了高程和坡度对月尺度验证结果的影响,同时利用主成分分析法比较了高程与坡度对TRMM 3B43降水数据的影响程度。研究表明:(1) 年尺度上,TRMM 3B43年降水数据普遍高于气象站点的实测结果(平均偏高5.86%),渝西、渝南的结果比渝东北的准确。季尺度上,秋季拟合效果高于其它3个季节。月尺度上,相关系数R=0.85,两者之间存在显著相关性。(2) 逐站点验证,研究区TRMM 3B43月降水数据具有较高精度(相关系数均大于0.80)。(3) 随着海拔的升高,相关系数呈"增加-减少-增加"的变化趋势,绝对偏差呈减小趋势;随着坡度的升高,绝对偏差呈"增加-减少-增加"的变化趋势,绝对偏差呈线性增加的趋势。(4) 利用主成分分析方法得出,高程对数据精度的影响大于坡度。     

多光谱遥感结合随机森林算法反演耕作土壤有机质含量

土壤有机质（Soil Organic Matter,SOM）遥感反演一般以单期影像作为输入量,为研究多时相影像遥感结合随机森林提高SOM遥感反演精度的可能性,该研究以黑龙江省农垦总局胜利农场耕地范围为研究区,以Sentinel-2A和Landsat 8影像作为数据源,获取两期裸土遥感影像,构建光谱指数,以随机森林算法筛选波段和光谱指数作为输入量,构建SOM反演模型。结果表明：1）两期影像的SOM反射光谱响应波段包括二者共有的中心波长：约560、660、850 nm,以及Sentinel-2A特有的中心波长740 nm 4个波段;2）基于单期影像最佳波段和光谱指数,Sentinel-2A影像SOM最优反演模型R2为0.913,均方根误差为0.860 g/kg,精度高于Landsat 8影像反演模型;3）单期影像最佳波段引入光谱指数,相比以最佳波段作为输入量,使SOM最优反演模型的均方根误差分别提高了28.867%和8.722%;4）引入时相信息,基于单期和两期影像波段和光谱指数,SOM最优反演模型精度由高到低为两期影像（R2为0.938,均方根误差1.329 g/kg）、Sentinel-2A影像（R2为0.935,均方根误差为1.944 g/kg）、Landsat 8影像（R2为0.922,均方根误差2.022 g/kg）,两期影像SOM最优反演模型的稳定性和精度略高于单期影像。研究结果证明了Sentinel-2A影像数据以及多时相裸土影像反演SOM的优势。     

全极化SAR图像的煤矿区土地覆盖信息分层提取方法研究

为弥补光学遥感在煤矿区资源与生态环境监测中应用的不足, 研究应用全极化SAR图像有效提取煤矿区土地覆盖信息的方法具有重要意义。针对全极化SAR图像极化信息丰富、斑点噪声多、局部异质性大等特点, 提出采用面向对象的影像分类方法对其进行分层土地覆盖信息提取。以徐州市西南部的煤矿区为研究区, 选取Radarsat-2的全极化SAR图像, 分析了研究区内全极化SAR图像中典型地物的灰度特征, 提出面向对象分类方法所涉及的最优分割尺度选择法, 给出全极化SAR图像分割对象后向散射特征的计算方法。对研究区的SAR图像进行试验, 首先对SAR图像进行多尺度分割, 选择各土地覆盖类型的最优分割尺度, 然后在该尺度下计算出土地覆盖类型的后向散射特征指数, 最后采用模糊逻辑分类法分层提取出研究区内的土地覆盖信息。结果表明: 在适于各土地覆盖类型提取的最优分割尺度下, 充分利用分割对象的灰度、形状、纹理以及类间相关特征, 并综合应用隶属函数法和最邻近分类法, 能有效地提取煤矿区的农田、道路、塌陷地、建筑物、山林这5类土地覆盖信息。与最大似然分类法相比, 该方法能够较好地消除"椒盐现象", 各种土地覆盖类型的提取精度都有所提高, 其总体分类精度可提高38.3%。     

Receiver-operating characteristic curves and likelihood ratios: improvements over traditional methods for the evaluation and application of veterinary clinical pathology tests

Receiver-operating characteristic (ROC) curves provide a cutoff-independent method for the evaluation of continuous or ordinal tests used in clinical pathology laboratories. The area under the curve is a useful overall measure of test accuracy and can be used to compare different tests (or different equipment) used by the same tester, as well as the accuracy of different diagnosticians that use the same test material. To date, ROC analysis has not been widely used in veterinary clinical pathology studies, although it should be considered a useful complement to estimates of sensitivity and specificity in test evaluation studies. In addition, calculation of likelihood ratios can potentially improve the clinical utility of such studies because likelihood ratios provide an indication of how the post-test probability changes as a function of the magnitude of the test results. For ordinal test results, likelihood ratios can be calculated on a category-specific basis from the empirical data or by using the slope of the line joining adjacent category limits on the ROC curve. For continuous test results, data need to be categorized into intervals for estimation of likelihood ratios, or they can be calculated as the slope (tangent) to the ROC curve at a unique test value. We use ROC analysis and calculate likelihood ratios to evaluate the performance of tests reported in 2 articles previously published in this journal.     

SIEMENS系统数控铣床对于空间斜面上孔槽的加工应用

针对零件具有空间结构,且加工精度高,不易改变装夹基准的情况下,根据数控铣床的优点,解决现实的加工难题。     

气磁轴承主轴系统误差补偿技术的研究

误差补偿技术是提高精密机床主轴回转精度的有效途径.分析了气磁轴承主轴系统的回转误差和误差分离方法的原理,并对主轴的主动控制进行了方案设计.     

Optimization of Structure Parameters in Ultra-precision Plane Honing

The structure parameters of honing wheel have effects on machining accuracy in ultra-precision plane honing. This paper put forward to the optimization of structure parameters. By calculating the machining accuracy coefficient and wear coefficient of triangle radial structure honing wheels, it is obvious that the suitable triangle radial structure honing wheel can improve machining accuracy of workpiece.     

On the interpretation of test sensitivity in the two-test two-population problem: Assumptions matter

Bayesian analyses of diagnostic test accuracy often require the assumption of constant test accuracy among populations to ensure model identifiability. In a prior study (Toft, N., Jørgensen, E., Højsgaard, S., 2005. Diagnosing diagnostic tests: evaluating the assumptions underlying the estimation of sensitivity and specificity in the absence of a gold standard. Prev. Vet. Med. 68, 19–33), the sensitivity estimate from a two-test two-population model was shown to be weighted toward the population with the higher prevalence of infection. In the present study, we provided analytical formulae that give insight into the effect of assuming constant sensitivity when this assumption was false. To further investigate the effect of failure of the assumption of constant sensitivity, we also simulated several data sets under the assumption that the first test's sensitivity varied in the two populations. Bayesian conditional independence models that presumed constant sensitivities were implemented in WinBUGS and posterior estimates (mean and 95% probability intervals) were evaluated based on the known true values of the parameters. Findings from the Bayesian analyses of several scenarios indicated that the posterior mean was a good estimate of the weighted mean of the sensitivities in the two populations, when one test was perfectly specific. When neither test was perfectly specific, the Bayesian posterior mean for test 1 sensitivity was either greater than the larger of the two true sensitivities, or smaller than both, and estimates of prevalence and the second test's specificity were incorrect. The implication is that estimates of some parameters will be biased if test sensitivities are not constant across populations. Without a perfectly specific test, and if the assumption of constant sensitivity fails, the only solution we are aware of would involve incorporating prior information on at least two parameters.     

MODIS土地利用/覆被多时相多光谱决策树分类

利用MODIS多时相与多光谱结合,尝试探讨低成本、高精度的北京土地利用/覆被实时获取方法。首先根据归一化植被指数（NDVI）的均值、标准差建立了研究区各地类的典型NDVI时间序列曲线,进而提取了6个可以反映区域物候模式、植被生长速率等信息的分类参数;然后对反映地表土壤信息较多的3月份多光谱影像进行主成分变换,选取第一主成分（PC1）作为辅助分类参数;最后基于分类回归树（CART）算法进行监督决策树分类。经SPOT-5影像验证,分类总体精度达到83%,Kappa系数为0.769,PC1辅助分类后总体精度提高