超级电容动态容值测量与容值函数参数估计

针对目前的超级电容参数在线识别方法动态参数测量困难,通用性差,精度低的缺点,提出一种超级电容动态容值测量与参数识别方法。该方法中提出了动态电容值测量模型,通过电荷关系式与能量关系式联立推导动态电容值测量方程组,并采用限定记忆最小二乘法进行参数识别,应用时域仿真对该方法进行了验证。仿真结果表明,该文提出的电容值测量与参数识别方法适用于多种不同的容值函数。当测量信号存在噪声时仍可对电容测量,平均误差低于1%,参数估计相对误差低于20%,可以应用于超级电容动态容值测量。     

A Calibration Experiment in a Longitudinal Survey With Errors-in-Variables

The National Resources Inventory (NRI) is a large-scale longitudinal survey conducted to assess trends and conditions of nonfederal land. A key NRI estimate is year-to-year change in acres of developed land, where developed land includes roads and urban areas. In 2003, a digital data collection procedure was implemented replacing a map overlay. Data from an NRI calibration experiment are used to estimate the relationship between data collected under the old and new protocols. A measurement error model is postulated for the relationship, where duplicate measurements are used to estimate the error variance of the new procedure. If any significant discrepancy is detected between new and old measures, some parameters that govern the algorithm for the new protocol can be changed to alter the relationship. Parameters were initially calibrated so overall averages nearly match for the new and old protocols. Analyses on the data after initial parameter calibration suggest that a line with an intercept of 0 and a slope of 1 is an acceptable representation for the relationship between the two determinations. Estimation of the measurement error variances as functions of the proportion of developed land are also given.     

离心泵内部流动高速摄像测量及误差分析

为了提高离心泵内部流动测量的精度,阐述离心泵内部流动高速摄像系统,研究主要拍摄参数的确定方法,分析速度误差产生的原因以及泵内速度误差的大小,提出控制误差的方法,对离心泵内部流动进行了高速摄像测量。结果表明,选择合适的拍摄距离和拍摄频率,提高图像的分辨率和判读点的识别精度,能有效控制速度误差。分析叶轮和蜗壳内的流动时,应选择合适的图像判读间隔,以协调点输入造成的误差和弧弦差造成的误差,当拍摄频率为2 000帧/s,判读间隔为6幅时,可达到最小误差0.41%;分析泵进口和出口直管段内的流动时,可忽略弧弦差造成的误差。叶轮半径从0.125 m减小到0.02 m时,测量误差从0.41%增加到2.48%,随着叶轮半径的减小,测量误差增大。在分析不同叶轮半径位置处的速度时,为了减小测量误差,应选择不同的图像判读间隔。研究结果可为提高旋转机械内部流动测量精度提供参考。     

A comparison of parametric and non-parametric methods for modelling a coregionalization

Two methods for modelling a coregionalization were compared, the traditional parametric linear model of coregionalization (LMCR) and a non-parametric method based on a Fourier transform of the empirical (cross-) correlogram maps. The methods were compared in terms of how well they fit the experimental correlograms, and the prediction quality of their co-kriged estimates. Three datasets were compared, each representing different situations where we might use co-kriging.We found that both methods were somewhat restricted in how well they could represent the experimental correlograms because of the constraint that any coregionalization model must be positive-definite. There was little to distinguish between both methods in terms of how well the models fitted the raw correlogram data.The cokriged estimates from both methods were very similar in terms of their accuracy however the kriging variances from the LMCR were a better reflection of the prediction error. The non-parametric modelling is substantially faster than modelling the LMCR so if the only interest is in obtaining cokriged estimates then it should seriously be considered. In cases where the kriging variances are of interest then the LMCR should be used.     

Dry column-thermal energy analyzer method for determining N-nitrosopyrrolidine in fried bacon: collaborative study

A dry column method for isolating N-nitrosopyrrolidine (NPYR) from fried, cure-pumped bacon and detection by gas chromatography-thermal energy analyzer (TEA) was studied collaboratively. Testing the results obtained from 11 collaborators for homogeneous variances among samples resulted in splitting the nonzero samples into 2 groups of sample levels, each with similar variances. Outlying results were identified by AOAC-recommended procedures, and laboratories having outliers within a group were excluded. Results from the 9 collaborators remaining in the low group yielded coefficients of variation (CV) of 6.00% and 7.47% for repeatability and reproducibility, respectively, and the 8 collaborators remaining in the high group yielded CV values of 5.64% and 13.72%, respectively. An 85.2% overall average recovery of the N-nitrosoazetidine internal standard was obtained with an average laboratory CV of 10.5%. The method has been adopted official first action as an alternative to the mineral oil distillation-TEA screening procedure.     

Tolerance intervals for unbalanced one-way random effects models with covariates and heterogeneous variances

Tolerance intervals are useful in practice to help determine limits for detection monitoring or assessment monitoring of factors that may impact the environment, ecological systems, or other biological processes. This article provides a procedure for construction of one-sided and two-sided tolerance intervals for a normally distributed random variable when the mean and variance of its distribution are estimated using data following an unbalanced one-way random effects model with covariates under heterogeneous error variances. The procedure developed here is based on the concept of a generalized pivotal quantity which has been frequently used to obtain confidence intervals in situations where conventional methods are difficult to apply or fail to provide s satisfactory solutions. For the one-sided case, the generalized pivotal quantity approach yields an exact solution. On the other hand, the method leads to good approximate intervals for the two-sided case. This is confirmed by a detailed simulation study, showing that the method may be recommended for practical use. Two real-data examples are given to illustrate the applicability of the proposed procedures.     

Analyzing highly variable potency data using a linear mixed-effects measurement error model

A house standard lot is tested along with experimental samples in a variable TCID₅₀ assay in order to monitor and control assay performance. Instead of being simply a control, it is proposed to use this lot as a calibration standard to reduce the systematic variability in the assay caused by acknowledged sources of variability such as the age of the cells used in the assay and interlaboratory differences. Because of this new proposal, the consistency of the relationship between the test sample and the house standard is assessed within the acceptance range of the house standard. A linear mixed-effects measurement error model is proposed for the data. The slope curve is then used to assess the dynamic relationship between the sample and the house standard within the house standard range. It is shown with these analyses that the sample and the house standard have uniformly good agreement within the house standard range.     

不同样本尺度和分区方案的粮食产量空间化及误差修正

粮食产量数据空间化有助于粮食产量数据与其他自然、人文数据进行综合分析,但空间化过程中必然会产生误差。该文按照3种分区方案(全国不分区、全国分为7个区以及按省分区),选择3种尺度上(县级、地市级和公里网格)的总产及平均产量数据(即4种样本:县级粮食总产、县级平均粮食产量、地市级粮食总产、地市级平均粮食产量)分别为因变量,以对应的3种农田类型(水田、水浇地、旱地)面积数据为自变量,利用多元线性回归分析方法,得到15种空间化模型。采用两阶段误差分析方法,选取2个模型误差评价因子和5个空间化结果误差评价因子,对模型和空间化结果进行误差分析。结果表明:1)空间化过程中,模型精度与空间化结果的精度存在不一致性;2)对于采用同一样本的模型(常数项为0)而言,空间化结果精度随着分区方案的细化先提高再降低,而对于采用同一样本的模型(常数项非0)而言,空间化结果精度随着分区方案的细化而降低;3)在全国不分区和分为7个区2种情况下,空间化结果精度随着分析样本尺度的细化(从地市级到县级再到公里网格)先提高后降低。根据上述分析结果,最终以县级粮食总产为样本、常数项为0、全国分7个区建模的方案实现全国粮食产量数据空间化,并通过修正,得到2005年中国粮食产量公里网格分布图。该研究弥补了粮食产量空间化误差分析的不足,探寻了不同样本尺度和分区方案与空间化误差的关系,提高了空间化精度,同时对其他类型的社会经济统计数据空间化研究具有一定的参考价值。     

Model‐based analysis using REML for inference from systematically sampled data on soil

The general linear model encompasses statistical methods such as regression and analysis of variance (anova ) which are commonly used by soil scientists. The standard ordinary least squares (OLS) method for estimating the parameters of the general linear model is a design‐based method that requires that the data have been collected according to an appropriate randomized sample design. Soil data are often obtained by systematic sampling on transects or grids, so OLS methods are not appropriate. Parameters of the general linear model can be estimated from systematically sampled data by model‐based methods. Parameters of a model of the covariance structure of the error are estimated, then used to estimate the remaining parameters of the model with known variance. Residual maximum likelihood (REML) is the best way to estimate the variance parameters since it is unbiased. We present the REML solution to this problem. We then demonstrate how REML can be used to estimate parameters for regression and anova ‐type models using data from two systematic surveys of soil. We compare an efficient, gradient‐based implementation of REML (ASReml) with an implementation that uses simulated annealing. In general the results were very similar; where they differed the error covariance model had a spherical variogram function which can have local optima in its likelihood function. The simulated annealing results were better than the gradient method in this case because simulated annealing is good at escaping local optima.     

An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed γ‐ray spectrometer data

The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time‐consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy‐to‐measure ancillary data into models have improved the efficiency of spatial prediction of soil CEC. However, mapping uncertainty has not been evaluated. In this study, we use an error budget procedure to quantify the relative contributions that model, input and covariate error make to prediction error of a digital map of CEC using gamma‐ray (γ‐ray) spectrometry and apparent electrical conductivity (EC_a) data. The error budget uses empirical best linear unbiased prediction (E‐BLUP) and conditional simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models (LMMs) estimated by residual maximum likelihood (REML) are used to create the prediction models. The combined error of model [5.07 cmol(+)/kg] and input error [12.88 cmol(+)/kg] is ~12.93 cmol(+)/kg, which is twice as large as the standard deviation of CEC [6.8 cmol(+)/kg]. The individual covariate errors caused by the γ‐ray [9.64 cmol(+)/kg] and EM error [8.55 cmol(+)/kg] were large. Preprocessing techniques to improve the quality of the γ‐ray data could be considered, whereas the EM error could be reduced by the use of a smaller sampling interval in particular near the edges of the study area and at pedoderm boundaries.     

基于激光三角法的地表粗糙度测试仪的研制

针对接触法测试地表粗糙度需要人工参与、测试时间长等问题,该文提供了一种快速的地表粗糙度测试的仪器及测试方法。基于激光三角原理,将激光投影到待测目标上,摄像机接收反射光的图像,通过图像处理算法及坐标转换得到被测区域的三维坐标,再根据粗糙度计算公式得到地表粗糙度参数的结果。将该仪器分别进行了精度及野外测试试验,精度试验测量结果的平均相对误差最大值为2.93%,仪器典型分辨率在垂直仪器扫描方向为0.78 mm,仪器扫描方向为1 mm,垂直大地方向为0.83 mm。试验结果表明,农田表面空间自相关系数的测量结果反映出了农田的垄结构特征,验证了该文仪器及方法的可行性及有效性。     

基于MA-ARIMA-GASVR的食用菌温室温度预测

食用菌温室温度具有时变、非线性、多耦合特性,准确预测对稳定食用菌生产具有重要意义。本研究从挖掘温室历史温度数据时序信息角度出发,提出一种MA-ARIMA-GASVR组合方法建立温度预测模型,利用移动平均方法将历史温度序列分解成线性序列和残差序列,然后采用移动平均差分自回归模预测线性序列的趋势,再将移动平均差分自回归预测值、历史残差数据、历史温度数据作为支持向量回归模型的输入,并结合遗传算法优化支持向量回归模型参数改善其性能,从而获得更符合实际情况的温度预测值。最后选取实测温度数据作为训练集,对未来2d的温度进行预测验证。结果显示,MA-ARIMA-GASVR组合方法能更好地拟合原始温度数据,间隔1h的均方误差、平均绝对误差和平均绝对百分误差分别为0.18、0.36和1.34,均显示本研究方法预测精度优于支持向量回归、遗传算法优化的支持向量回归单一模型,也优于未经移动平均以及未经遗传算法优化的组合模型;此外,间隔6h的均方误差、平均绝对误差和平均绝对百分误差为0.29、0.52和1.95,说明本研究方法还能满足6 h以内的多步预测,为食用菌生产者预留更多调整时间。     

Simultaneous determination of saccharin and aspartame in commercial noncaloric sweeteners using the PLS-2 multivariate calibration method and validation by capillary electrophoresis

A new method to determine a mixture for sweetener sodium saccharin and aspartame in commercial noncaloric sweeteners is proposed. A classical full factorial design for standards was used in the calibration step to build the partial least-squares (PLS-2) model. Instrumental data were obtained by means of UV-visible spectrophotometry. Salicylic acid was used as an internal standard to evaluate the adjustment of the real samples to the PLS model. The concentration of analytes in the commercial samples was evaluated using the obtained model by UV spectral data. The PLS-2 method was validated by capillary zone electrophoresis (CZE), finding in all cases a relative error of less than 11% between the PLS-2 and the CZE methods. The proposed procedure was applied successfully to the determination of saccharin and aspartame in noncaloric commercial sweeteners.     

Modeling yield sustainability for different rotations in long-term barley trials

In crop rotation trials, the errors arising over time on the same plot are correlated. Often-used analyses assume zero or constant correlation between the errors while the actual covariance structure for the plot errors might be very different. The objective of this study was to identify the most suitable covariance structure and incorporate the error structure in assessment of the crop rotations in terms of yield productivity and yield sustainability. A set of five covariance structures were examined for barley yield data from a 14-year, two-course barley rotation trial conducted at two locations in northern Syria. Selection of the covariance structures was based on the Akaike information criterion (AIC, Akaike 1974) (a function of penalized log-likelihood) obtained from fitting the structure. Covariance structure with heterogeneous variances and with constant correlation between errors over cycles within the same plot was found to account for most variability in grain and straw yields at both locations. Modeling data with this covariance structure, the legume rotations gave higher productivity as well as higher annual increases compared with the continuous barley system. This implies that an agricultural production system based on a legume following a cereal is likely to be more sustainable for cereal production compared to cereal followed by cereals.     

基于改进型支持度函数的畜禽养殖物联网数据融合方法

物联网技术已广泛应用在畜禽养殖中,针对畜禽养殖物联网中数据异常实时检测以及多源感知数据融合的需求,该文提出了一种畜禽养殖物联网数据融合模型。首先对传感器采集到的原始数据进行一致性检测,确保数据准确性;其次针对来自同类型传感器的多源同构数据,采用基于改进型支持度函数的加权算法进行数据融合处理,提高融合数据准确度;最后根据畜禽养殖物联网编码规则和数据组织格式,对畜禽养殖过程中的异构感知数据进行统一描述并转换为标准数据格式,为数据分析和应用提供数据基础。该文采用实际生产中的生猪养殖物联网数据进行试验,结果表明:在数据一致性检测阶段,异常数据检测率为96.67%,保证了数据质量;在多源同构数据融合计算中,该文提出的改进型支持度函数与高斯型、新型2种支持度函数相比融合方差最小,为0.192 5,能够有效提高数据融合准确度,满足畜禽养殖物联网数据分析要求。     

THE CONSTRUCTION OF CALIBRATION CURVES FOR DETERMINING WATER CONTENT FROM RADIATION COUNTS

The application of regression theory to the construction of calibration curves for the estimation of water content from an observed radiation count is possible only when the relationship between count and water content is linear, a homogeneous error applies to the dependent variate, count, whatever its value, and the independent variate, water content, is determined free of error. Determination of the errors of estimation is further complicated by the necessity to fit the regression of count, as the dependent variate, on water content and then to invert the equation in order to estimate moisture content from count. It is shown that these requirements can be fulfilled by an initial transformation of the data and by repeated sub-sampling for the determination of water content. A method is given for calculating errors of estimation. Experimentally, it was found that a transformation (logarithmic) was necessary when calibrating a beta-gauge for leaf moisture measurement. When calibrating a neutron probe for soil-moisture measurement no transformation was required, but it was necessary to take repeated sub-samples for water content determinations.     

Statistical power for detecting trends with applications to seabird monitoring

Power analysis is helpful in defining goals for ecological monitoring and evaluating the performance of ongoing efforts. I examined detection standards proposed for population monitoring of seabirds using two programs (MONITOR and TRENDS) specially designed for power analysis of trend data. Neither program models within- and among-years components of variance explicitly and independently, thus an error term that incorporates both components is an essential input. Residual variation in seabird counts consisted of day-to-day variation within years and unexplained variation among years in approximately equal parts. The appropriate measure of error for power analysis is the standard error of estimation (S.E._est) from a regression of annual means against year. Replicate counts within years are helpful in minimizing S.E._est but should not be treated as independent samples for estimating power to detect trends. Other issues include a choice of assumptions about variance structure and selection of an exponential or linear model of population change. Seabird count data are characterized by strong correlations between S.D. and mean, thus a constant CV model is appropriate for power calculations. Time series were fit about equally well with exponential or linear models, but log transformation ensures equal variances over time, a basic assumption of regression analysis. Using sample data from seabird monitoring in Alaska, I computed the number of years required (with annual censusing) to detect trends of −1.4% per year (50% decline in 50 years) and −2.7% per year (50% decline in 25 years). At α=0.05 and a desired power of 0.9, estimated study intervals ranged from 11 to 69 years depending on species, trend, software, and study design. Power to detect a negative trend of 6.7% per year (50% decline in 10 years) is suggested as an alternative standard for seabird monitoring that achieves a reasonable match between statistical and biological significance.     

Inferring Upon Heterogeneous Associations in Dairy Cattle Performance Using a Bivariate Hierarchical Model

Multivariate hierarchical Bayesian models provide a flexible framework for comprehensive study of biological systems with more than one outcome. Recent methodological developments facilitate modeling of heterogeneous associations between outcomes by specifying a linear mixed model on (co)variances at different levels of the data structure. Motivated by previous evidence for heterogeneous correlations in animal agriculture, we apply the proposed hierarchical Bayesian models to study the nature of the correlations between key performance outcomes in dairy cattle production systems, namely milk yield and reproduction. That is, the association between these outcomes might depend upon various fixed and random effect sources of heterogeneity both at the individual cow (residual) level as well as the herd (cluster) level. We thus propose a sequential modeling approach based on the deviance information criterion to select relevant explanatory variables on both types of associations. Furthermore, we extend the proposed methodology to accommodate right-censored outcomes, as common for dairy reproduction data, and use it to analyze field data from the Michigan dairy industry. The nature of the associations between milk production and reproduction in dairy cattle was inferred to be strongly heterogeneous and driven by multiple farm management practices and herd attributes, as well as by random clustering effects, at both cow and herd levels, thereby suggesting potential between-herd and within-herd intervention strategies to optimize performance of dairy production systems. Supplementary materials are available online.     

拖拉机驱动轮滑转率估算法与验证

拖拉机作业环境恶劣,测量信号容易受到噪声干扰,其滑转率的计算过程对于输入信号的相对误差有极强的放大作用,因而造成其滑转率难以精确测量。该文提出带噪声观测器的变结构并行自适应数据融合算法,对轮速传感器、角加速传感器、车身加速度计和全球定位系统的信号进行融合,在不需要先验误差统计规律的前提下实现了对拖拉机驱动轮滑转率的在线精确估计。仿真测试结果证明:采用信息融合方法求得的驱动轮滑转率信号几乎与理论值曲线重合且鲁棒性好,平均误差为中值滤波的1/10左右,为卡尔曼滤波的1/5;算法的噪声观测器能够实时估算测量信号的白噪声方差,求得的稳态平均方差与已知精确先验误差的卡尔曼数据融合算法无明显差异;在从动轮速度信号受到有色随机噪声干扰的特殊工况下,算法的信息融合机制能够补偿大部分由有色噪声干扰造成的误差。实测试验证明:在拖拉机稳定工作工况,在线求得的测量信号噪声方差均值在5%的范围内波动,采用数据融合算法求得的驱动轮滑转率误差均值为0.012,误差绝对值最大值为0.027,与离线拟合得到的参考值非常接近。该研究为拖拉机实现精确控制提供了参考,其在线测量信号方差统计方法为拖拉机总线网络的传感器信息共享提供了技术基础。