灌浆压力测量误差的正交试验分析

灌浆规范中的灌浆压力是作用在灌浆岩体上的孔内灌浆压力。由于工艺的约束,现行灌浆监控过程常用孔口压力表示孔内灌浆压力,造成了灌浆压力的测量误差。针对非循环灌浆工艺,通过建立流体管道流动模型开展数值计算,采用多因素多水平正交法探究灌浆压力误差受浆液配比、浆液流速、灌浆孔深的相对影响,并采用统计法来分析不同工况下的显著影响因素。研究结果表明：灌浆压力较小时,灌浆压力误差普遍较大;若此时灌浆孔深且流速较大时,灌浆压力的测量误差则会超过灌浆压力仪表0.5%的精度要求,须进行误差补偿。其次,极差分析结果表明灌浆孔深是造成测量误差的主要因素,灌浆流速是第二个主要因素。而综合所有试验结果,浆液流速和灌浆孔深接近时,浆液配比越小,绝对偏差越大。结合上述正交试验和数值模型获取了不同工况下孔内灌浆压力值。     

农作物产量综合预测的数学模型

介绍了农作物产量综合预测的建模思路以及建模各阶段的主要算法。包含农作物产量预测的趋势因子和趋势产量的分离;趋势产量的模拟;趋势产量的外延;波动产量的预测;预测误差的修正;多个预测结果的综合集成的主要数学模型和算法。文中所涉及的数学模型和算法均经多年的应用检验,并证明其行之有效。     

农业类科技论文英文摘要标点符号错误和句子成分残缺剖析

结合例子针对性分析农业类科技论文英文摘要的2种典型错误--标点符号错误和句子成分残缺,指出需要结合作者、审稿专家和编辑的共同努力来杜绝此类错误的发生,旨在为相关作者和编辑人员提供参考,提高农业类科技期刊论文英文摘要的写作质量,加快期刊的国际化发展步伐。     

农业科技期刊论文编校常见差错分析

为了探索提高科技期刊编校质量的有效途径,以农业科技论文中的常见差错为例,从文字、外文字符、标点符号、单位和数值、参考文献等5个方面分析了产生差错的原因,并从编辑角度提出了一些建议。     

农业科技期刊英文参考文献著录常见差错分析

通过对10种农业学术类期刊英文参考文献进行核查,总结归纳了各刊英文参考文献著录上最常见的差错,分析了出现这些问题的原因,并对提高农业科技期刊英文参考文献著录的准确性提出了建议。     

大跨径桥梁挠度测量新方法研究

提出了一种以测量机器人为采集器,通过获取合作目标的几何信息进行桥梁挠度测量的新方法,从测量系统误差、偶然误差两个方面分析了测量机器人的测角及测距误差,并探讨了竖直角、视距对挠度测量误差的影响,同时依据最小二乘原理分析了该方法的测量精度.经分析得知:如果仪器至后视点距离小于150 m,至目标点距离小于200 m,挠度测量精度将优于±1 mm.在实际工程中,该方法测量的挠度值与电子水准仪测量的挠度值差异小于0.22 mm,相对误差小于3.3%,表明该方法在大跨径桥梁检测中完全能满足测试要求.     

Nonlinear Predictive Control of Small Sag cable with Axial Displacement

An approach for the control of a small sag cable is presented.The approach is based on a nonlinear predictive control scheme that determines the required axial displacement input so that the predicted response match the desired Results.It is accomplished by minimizing the norm squared local errors between the predicted and desired values.Finally simulation results that use the new control strategy to demonstrate the effective are show.     

回归系数的联合回归估计

In this paper, a new biased estimate, which is called the united regression estimate, is proposed for the regression coeffients. Under the given conditions, the proposed estimate, which is found to be superior to the least squares estimate, the principal     

Modeling grain-size dependent bias in estimating forest area: a regional application

A better understanding of scaling-up effects on estimating important landscape characteristics (e.g. forest percentage) is critical for improving ecological applications over large areas. This study illustrated effects of changing grain sizes on regional forest estimates in Minnesota, Wisconsin, and Michigan of the USA using 30-m land-cover maps (1992 and 2001) produced by the National Land Cover Datasets. The maps were aggregated to two broad cover types (forest vs. non-forest) and scaled up to 1-km and 10-km resolutions. Empirical models were established from county-level observations using regression analysis to estimate scaling effects on area estimation. Forest percentages observed at 30-m and 1-km land-cover maps were highly correlated. This intrinsic relationship was tested spatially, temporally, and was shown to be invariant. Our models provide a practical way to calibrate forest percentages observed from coarse-resolution land-cover data. The models predicted mean scaling effects of 7.0 and 12.0% (in absolute value with standard deviations of 2.2 and 5.3%) on regional forest cover estimation (ranging from 2.3 and 2.5% to 11.1 and 23.7% at the county level) with standard errors of model estimation 3.1 and 7.1% between 30 m and 1 km, and 30 m and 10 km, respectively, within a 95% confidence interval. Our models improved accuracy of forest cover estimates (in terms of percent) by 63% (at 1-km resolution) and 57% (at 10-km resolution) at the county level relative to those without model adjustment and by 87 and 84% at the regional level in 2001. The model improved 1992 and 2001 regional forest estimation in terms of area for 1-km maps by 15,141 and 7,412 km² (after area weighting of all counties) respectively, compared to the corresponding estimates without calibration using 30 m-based regional forest areas as reference.