The Use of Multivariate Analyses to Eliminate Redundant Variables when Evaluating Potatoes for Chip Quality

Selection for good chipping clones in potato breeding often takes place during the middle and late selection stages. This is due to the considerable resources needed for the assessment of several characteristics. Objective multivariate methods for the elimination of redundant variables were applied to eight standardized chipping quality traits recorded on advanced clones over 15 years at two locations in New Brunswick, Canada. The clones were evaluated of specific gravity and the following chipping scores: direct from the field in August; after 10 days storage at 13 °C; from 4 °C storage in February; from 4 °C storage in February with 1 week, 2 weeks, 3 weeks and 4 weeks reconditioning at 16 °C. Five elimination methods produced a similar deletion order. In every subset obtained, specific gravity and direct chipping from the field were always retained. Reconditioning treatments from one to four weeks after cold storage were shown to be the most redundant. The reduction in the number of traits from eight to four by every technique could recover more than 80 % of the original variation, showing that redundant variables were effectively discarded.     

Genotype by environment effects and selection for drought tolerance in tropical maize. II. Three-mode pattern analysis

A selection program in three tropical maize populations aimed to improve tolerance of mid-season to late season drought environments while maintaining grain yield (GY) potential. The selection process employed other attributes that included maintaining a constant anthesis date (AD) and, under drought, shortening the anthesis-silking interval (ASI) and increasing ear number per plant (EPP). Three-mode (genotypes × environments × attributes) pattern analysis, which consists of clustering and ordination, should be able to collectively interpret these changes from ten evaluation trials. Mixture maximum likelihood clustering identified four groups that indicated the populations' performance had changed with selection. Groups containing the advanced cycles of selection were higher yielding in most environments and had lower ASI and higher EPP, particularly in drought environments. Check entries with no selection for drought tolerance remained grouped with the initial cycles of selection. A 3 × 2 × 3 (genotypes by environments by attributes) principal component model explained 70% of the variation. For the first environmental component, ASI was shown to be highly negatively correlated with both GY and EPP while anthesis date (AD) was virtually uncorrelated with other traits. The second environmental component (explaining 10% of the variation) contrasted droughted and well-watered environments and showed that EPP and GY were better indicators of this contrast (in terms of changes in population performance) than were AD or ASI. Three-mode analysis demonstrated that improvements with selection occurred in both droughted and well-watered environments and clearly summarised the overall success of the breeding program. This revised version was published online in July 2006 with corrections to the Cover Date.     

基于模态叠加法的曲轴动态特性研究与结构优化

为了计算得到曲轴动态特性参数,并使用模态贡献因子理论对动态特性进行研究,基于研究结果能够更加有效的实现曲轴结构参数的优化,首先基于多体动力学理论建立了某V12型曲轴的刚柔耦合多体动力学模型。基于模态贡献因子理论对曲轴动态特性进行分析,将前15阶模态缩聚为前5阶模态以减少计算规模,最大应力值的误差为0.9%,扭振角位移的误差为0.16%。利用BP(back propagation)神经网络建立了曲轴动静特性与结构参数之间的数学模型,进行了最大应力值和扭振角位移的线性回归,其输出响应的复相关系数都在0.95以上,表明此网络的泛化能力和预测性能都很好。对神经网络建立的数学模型使用遗传算法进行优化,优化后此曲轴的扭振角位移减小了2.63%,最大应力值减小了3.98%。结果表明神经网络结合遗传算法的优化方法对曲轴结构参数的动态特性和静态特性的联合优化能够满足设计预期,并且具有高效性和可行性。     

黑土区基准坡长和LS算法对地形因子的影响

该文结合小区实测坡长资料及Mc Cool数据,对地形因子算法中涉及的基准坡长影响和不同算法计算值的特征进行了讨论。研究表明,在坡长较小的情况下,20和22.13 m基准坡长计算得到的坡长因子相差不大,但随着坡度增加,在坡长指数的复合影响下,两者的差异渐趋增大。理论分析表明,坡长指数采用与坡度相关的变值更为合理。通过与实测数据对比发现,修正版通用土壤侵蚀模型(RUSLE)算法中的坡长因子与其更为接近,结合与Mc Cool数据的对比分析表明,研究区坡长指数更适合细沟和细沟间侵蚀比率中等的情形,或者采用RUSLE模型算法计算得到。同时研究发现,张宪奎算法、Moore算法、Desmet算法和B?hner算法计算得到的LS因子值都要比参照值小,而且4种算法与参照值的接近程度依次降低。空间分布特征而言,栅格累计算法总体较为破碎,并在平直坡面出现较多平行分布条带,而含有面积指标的其他3种算法则总体呈现光滑连续特性,但有无数据区域的存在。该结果对于研究区土壤侵蚀模型地形因子算法选择具有一定的现实意义。     

基于显著和模糊检测的浅景深作物病害图像分割

大多数现有的基于图像的作物病害诊断方法往往对输入图像的质量具有很高的要求,例如要求背景简单、大景深等等。因此这些方法的预处理过程中需要去除复杂背景,然而这个预处理较难获得理想的结果。此外,当作物病斑面积较小时,会使得获取的图像景深较浅,也导致了这些方法难以抽取精确的病斑区域。为了解决上述问题,该文提出一种利用目标检测来分割病斑图像的方法。首先,该方法对抽取的结构特征和颜色特征进行整合并对特征空间进行量化,从而得到作物病害图像的显著区域。该方法不需要进行去除复杂背景的预处理过程即可得到病斑区域的图像;同时,为了处理浅景深的病害图像,引入了模糊检测方法用以进一步过滤背景和模糊区域的图像。试验中利用多种黄瓜和水稻病害的图片,将该方法与阈值法、图切割法进行了对比,结果表明该方法在效率不明显降低时,其分割效果明显优于阈值法;在分割效果差异不大时,其运行效率明显高于图切割方法;同时,该方法能够对浅景深的作物病害图像的病斑区域进行有效的分割。     

基于HJ-CCD数据和随机森林算法的小麦叶面积指数反演

为给小麦长势的遥感监测提供技术支持,该文运用随机森林回归(RF,random forest)算法建立小麦叶面积指数(LAI)遥感反演模型。首先基于2010-2013年江苏地区小麦环境减灾卫星HJ-CCD的影像数据,提取拔节、孕穗和开花3个生育期的卫星植被指数,进而根据各生育期植被指数和相应实测LAI数据,利用RF算法构建各期小麦LAI反演模型,并以人工神经网络(ANN,artificial neural network)模型为参比模型进行预测精度的比较。结果表明:RF算法模型在3个生育期的预测结果均好于同期的ANN模型。拔节、孕穗和开花3个生育期RF模型预测值与地面实测值的R2分别为0.79,0.67和0.59,对应的RMSE分别为0.57,0.90和0.78;ANN模型的R2分别为0.67,0.31和0.30,对应的RMSE分别为0.82,1.94和1.43。该研究结果为提高大田尺度下的小麦LAI遥感预测精度提供了技术和方法。     

压力输水树状管网遗传优化布置和神经网络优化设计

分别应用单亲遗传算法和Hopfield神经网络方法进行机压式树状管网的优化布置和优化设计,能够在管网投资最小的最优树状管网布置形式上,获得年费用最低的最优设计方案。研究表明:该方法是可行的和有效的,能减小管网工程投资,提高设计水平和设计效率     

基于多目标粒子群的土地整理项目选址模型

土地整理项目选址实质上是一个多目标空间优化问题,将传统选址方法用于解决多目标土地整理项目选址存在明显不足。该文提出了一种基于多目标粒子群的土地整理项目选址模型,归纳了土地整理项目选址的选址规则,提取3个能够体现土地整理项目选址目的及意义的目标函数,即新增耕地潜力最高、空间分区集中连片、土地适宜性评价指数最高,同时考虑了土地整理项目最小新增耕地率、实施规模2类约束条件,并详细阐述了算法的核心思想、矢量编码策略、状态更新机制等内容,最后选取湖北省嘉鱼县为试验区,验证了模型的可行性和有效性,结果表明通过对目标的权重调控可以得到不同目标偏好的土地整理项目选址方案,该文所构建模型在土地整理实践中具有可操作性,提供的选址方案科学合理,为土地利用规划和土地利用调控管理提供支持。     

喷雾机喷杆结构形状及截面尺寸优化与试验

针对喷雾机喷杆结构动力学特性原始设计缺陷,该文提出了一种基于遗传算法的多变量优化方法。以平面十八杆桁架单元为基础结构,在Abaqus平台中建立了单侧喷杆参数化有限元模型。基于Isight多学科多目标优化平台提取模型设计变量组及优化目标,采用多岛遗传算法(multi-island genetic algorithm,MIGA)对喷杆桁架结构形状及杆件截面尺寸进行优化,得到了一阶固有频率为10.87 Hz,质量为35.82 kg,幅宽为5 m的单侧喷杆结构模型。根据优化后模型的结构参数制造喷杆样品,通过对其进行模态试验验证了数值模型的准确性。针对优化前、后喷杆模型进行了以位移功率谱密度(power spectrum density,PSD)函数为激励的随机振动仿真,结果表明,优化后喷杆模型的质量下降并没有明显增加其在竖直方向上振动位移响应量。该文研究结果可为进一步研究喷雾机喷杆结构的动力学性能优化提供依据。     

基于灰色聚类法的乌鲁木齐市空气质量状况研究

采用灰色聚类分析方法,对乌市2000～2004年和2005年12个月的大气污染物进行多因子污染等级评价。结果表明2000年至2004年乌市大气质量状况变化总体较稳定并向清洁程度发展;2005年12个月的大气质量状况受到采暖期和非采暖期的影响较大。