控制工程造价不是盲目压价

本文对工程造价管理角度进行了论述,从不同的角度分析了控制工程造价的方法,阐明如 何科学地控制工程造价。     

基于MCMC模拟的MGPD模型及其在地质灾害风险度量中的应用

基于马尔科夫链蒙特卡洛(简记为MCMC)模拟的参数贝叶斯估计,对改进的广义帕累托分布(简记为MGPD)模型进行了优化,并利用该模型得到了地质灾害损失的在险损失值(简记为VaR)和条件损失值(简记为CVaR).以湖南娄底市地质灾害损失数据进行实证分析及模型适应性检验,结果表明:优化后的模型不仅具有很好的极值数据描述能力,而且具有较强的适用性.     

西北旱区喷灌条件下紫花苜蓿生长特征与品质指标的关系

西北旱区由于独特的气候条件,生产的苜蓿干草品质好,产量高。如何根据苜蓿的田间指标估测干草品质参数,对于苜蓿草产品生产销售具有重要意义。研究在喷灌条件下不同灌溉量对紫花苜蓿的生长品质的影响,并利用生长指标对紫花苜蓿的品质进行预测。试验于2014至2015年于中国农业大学石羊河流域生态节水试验站进行为期2年的田间试验。试验共设3个灌溉处理:A₁灌溉量为100%紫花苜蓿蒸腾蒸散量(ETc),A₂,A₃的灌溉量分别为A₁处理的66%,33%,以及一个不灌溉处理A₄。结果表明,苜蓿生长指标如株高(H)和茎叶比(SLR)随生育期的推进呈上升趋势,而鲜干比(FDR)呈下降趋势;所有生长指标随着灌溉量的减少均呈下降趋势。在品质方面,随着生育期的推进,所有处理的粗蛋白(CP)呈下降趋势,而中性洗涤纤维(NDF)和酸性洗涤纤维(ADF)则呈现上升趋势;随着灌溉量的降低,NDF与ADF呈下降趋势,而CP则呈上升趋势。各生长指标与品质指标的拟合结果表明,H和SLR均与各品质指标显著线性相关(P<0.001),且H与品质拟合结果相关系数最高(R²>0.900),而FDR则与各品质指标不显著。这些结果表明,在田间管理较好,长势良好,杂草与病虫害发生较少的情况下,可以用株高对苜蓿干草品质做出预测。     

农田平地机导航侧滑估计与自适应控制方法

针对农田平地机在无人驾驶作业时因受地面侧滑影响而导致路径追踪控制精度降低的问题,提出一种基于牵引式农田平地机的导航侧滑估计与自适应控制方法。根据路径追踪时平地铲与拖拉机的位置关系,建立了牵引式农田平地机的运动学模型;基于运动学模型,提出了一种侧滑估计器,用来预测导航控制中车轮因侧滑产生的侧滑角,并计算出拖拉机的前轮转角控制率;采用改进的非线性函数构造方法对PID控制器的参数KP、KI、KD进行实时整定,实现了农田平地机自适应导航控制。田间试验表明,该方法使平地铲路径追踪的平均绝对偏差减小了6.78 cm,标准偏差减小了10.1 cm。与无侧滑估计和参数自适应的导航控制相比,本文方法提高了农田平地机的路径追踪精度,增强了导航控制系统对平地机的适应性和鲁棒性。     

中国农业面源污染排放的空间差异及其动态演变

以2003—2014年为研究时段,在测算农业面源污染排放强度的基础上,综合运用基尼系数和非参数估计方法,研究我国农业面源污染的空间差异及其动态演变,结果表明:1)我国农业面源污染排放总体下降,且表现出明显的空间差异,东部和中部地区的排放强度较高,西部和东北地区则相对较低。2)2003—2014年,中国农业面源污染排放强度的区域差异略微扩大,地区间差异是其总体差异的主要来源。3)核密度估计结果表明,中国农业面源污染排放总体差异表现为"下降-上升-下降"的波动变化趋势。4)马尔科夫链分析表明,中国农业面源污染在不同类型间的相互流动较为微弱,但从长期来看,存在向两极分化发展的趋势特征。     

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.     

长白和大白猪主要生长性状的遗传参数估计

旨在分析母猪的出生年份、出生季节、初生重、开测日龄等固定效应对长白、大白猪主要生长性状的影响,并对目标生长性状进行遗传参数估计（遗传力、遗传方差、表型相关和遗传相关）,为猪的遗传改良提供基本依据。本试验利用GLM模型分析试验猪群（398头长白猪和1 176头大白猪）的固定效应对猪生长性状的影响,并采用多性状动物模型对目标性状进行遗传参数估计。目标生长性状包括达100 kg体重日龄（age to 100 kg,AGE）、达100 kg背膘厚（backfat to 100 kg,BF）、100 kg平均日增重（average daily gain to 100 kg,ADG）。研究表明,在大白和长白猪中,猪的出生年、出生季、初生重以及开测日龄对生长性状均具有极显著的影响（P<0.001）;长白猪的AGE、ADG和BF的遗传力分别为0.321、0.327和0.324,大白猪对应性状的遗传力分别为0.454、0.469和0.408;长白猪的ADG和AGE之间的遗传相关、表型相关分别为-0.990、-0.995,大白猪的ADG和AGE之间的遗传相关、表型相关分别为-0.993、-0.998,均呈现较强的负相关。长白、大白猪的生长性状（AGE、ADG、BF）均属于中等遗传力性状,其出生年份、出生季节、初生重和开测日龄对猪的生长性状影响较大。在遗传参数估计分析时,提高样本数量并提升表型数据质量,可以增加遗传参数估计的可靠性。本研究中的生长性状遗传参数估计结果较为可靠,可为后续的遗传改良提供参考。     

计算机视觉技术在智慧羊场中的研究进展

智慧羊场具有科学化、标准化管理的优点,是羊场转型发展的趋势。计算机视觉技术是人工智能的核心,可以实现非接触性、自动化、实时监测羊的个体信息。本文总结并分析了计算机视觉在羊场的个体识别方法、行为识别方法、体尺与体重估测、疾病监测的研究现状,并指出计算机视觉在智慧羊场的进一步发展趋势。 [关键词] 计算机视觉|智慧羊场|个体识别|行为识别|体尺与体重估测|疾病监测     

Genetic analysis for sow stayability at different parities in purebred Landrace and Large White pigs

Genetic parameters for sow stayability were estimated from farrowing records of 10,295 Landrace sows and 8192 Large White sows. The record for sow stayability from parity k to parity k + 1 (k = 1, …, 6) was 0 when a sow had a farrowing record at parity k but not at parity k + 1, and 1 when a sow had both records. Heritability was estimated by using single-trait linear and threshold animal models. Genetic correlations among parities were estimated by using two-trait linear–linear and single-trait random regression linear animal models. Genetic correlations with litter traits at birth were estimated by using a two-trait linear–linear animal model. Heritability estimates by linear model analysis were low (0.065–0.119 in Landrace & 0.061–0.157 in Large White); those by threshold model analysis were higher (0.136–0.200 & 0.110–0.283). Genetic correlations among parities differed between breeds and models. Genetic correlation between sow stayability and number born alive was positive in many cases, implying that selection for number born alive does not reduce sow stayability. The results seem to be affected by decisions on culling made by farmers.     

AN ACCELERATION SEARCH METHOD FOR UNCONSTRAINED OPTIMIZATION AND ITS APPLICATION IN MODEL PARAMETER ESTIMATION

In order to improve the convergence rate of the algorithms, an acceleration search method along a straight or curve is proposed,and the specific calculation steps of the algorithms are presented. The numerical results in model parameter estimation show that the convergence rate is improved and the calculation accords with the practical demands.