Estimating canopy closure density and above-ground tree biomass using partial least square methods in Chinese boreal forests

Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used partial least square regression （PLSR） models to relate forest parameters, i.e. canopy closure density and above ground tree biomass, to Landsat ETM＋ data. The established models were optimized according to the variable importance for projection （VIP） criterion and the bootstrap method, and their performance was compared using several statistical indices. All variables selected by the VIP criterion passed the bootstrap test （p〈0.05）. The simplified models without insignificant variables （VIP 〈1） performed as well as the full model but with less computation time. The relative root mean square error （RMSE%） was 29% for canopy closure density, and 58% for above ground tree biomass. We conclude that PLSR can be an effective method for estimating canopy closure density and above ground biomass.

Penalized total least squares method for dealing with systematic errors in partial EIV model and its precision estimation

When the total least squares(TLS)solution is used to solve the parameters in the errors-in-variables(EIV)model,the obtained parameter estimations will be unreliable in the observations containing systematic errors.To solve this problem,we propose to add the nonparametric part(systematic errors)to the partial EIV model,and build the partial EIV model to weaken the influence of systematic errors.Then,having rewritten the model as a nonlinear model,we derive the formula of parameter estimations based on the penalized total least squares criterion.Furthermore,based on the second-order approximation method of precision estimation,we derive the second-order bias and covariance of parameter estimations and calculate the mean square error(MSE).Aiming at the selection of the smoothing factor,we propose to use the U curve method.The experiments show that the proposed method can mitigate the influence of systematic errors to a certain extent compared with the traditional method and get more reliable parameter estimations and its precision information,which validates the feasibility and effectiveness of the proposed method.

Comparison of Calibration Curve Method and Partial Least Square Method in the Laser Induced Breakdown Spectroscopy Quantitative Analysis

The Laser Induced Breakdown Spectroscopy (LIBS) is a fast, non-contact, no sample preparation analytic technology;it is very suitable for on-line analysis of alloy composition. In the copper smelting industry, analysis and control of the copper alloy concentration affect the quality of the products greatly, so LIBS is an efficient quantitative analysis tech- nology in the copper smelting industry. But for the lead brass, the components of Pb, Al and Ni elements are very low and the atomic emission lines are easily submerged under copper complex characteristic spectral lines because of the matrix effects. So it is difficult to get the online quantitative result of these important elements. In this paper, both the partial least squares (PLS) method and the calibration curve (CC) method are used to quantitatively analyze the laser induced breakdown spectroscopy data which is obtained from the standard lead brass alloy samples. Both the major and trace elements were quantitatively analyzed. By comparing the two results of the different calibration method, some useful results were obtained: both for major and trace elements, the PLS method was better than the CC method in quantitative analysis. And the regression coefficient of PLS method is compared with the original spectral data with background interference to explain the advantage of the PLS method in the LIBS quantitative analysis. Results proved that the PLS method used in laser induced breakdown spectroscopy was suitable for simultaneous quantitative analysis of different content elements in copper smelting industry.

Application of neural network model coupling with the partial least-squares method for forecasting watre yield of mine

Scientific forecasting water yield of mine is of great significance to the safety production of mine and the colligated using of water resources. The paper established the forecasting model for water yield of mine, combining neural network with the partial least square method. Dealt with independent variables by the partial least square method, it can not only solve the relationship between independent variables but also reduce the input dimensions in neural network model, and then use the neural network which can solve the non-linear problem better. The result of an example shows that the prediction has higher precision in forecasting and fitting.

Characterizing and estimating rice brown spot disease severity using stepwise regression,principal component regression and partial least-square regression

Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respec-tively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demon-strates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.

Correlation analysis and partial least square modeling to quantify typical minerals with Chang'E-3 visible and near-infrared imaging spectrometer's ground validation data

In 2013, Chang'E-3 program will develop lunar mineral resources in-situ detection. A Visible and Near-infrared Imaging Spectrometer(VNIS) has been selected as one payload of CE-3 lunar rover to achieve this goal. It is critical and urgent to evaluate VNIS' spectrum data quality and validate quantification methods for mineral composition before its launch. Ground validation experiment of VNIS was carried out to complete the two goals, by simulating CE-3 lunar rover's detection environment on lunar surface in the laboratory. Based on the hyperspectral reflectance data derived, Correlation Analysis and Partial Least Square(CA-PLS) algorithm is applied to predict abundance of four lunar typical minerals(pyroxene, plagioclase, ilmenite and olivine) in their mixture. We firstly selected a set of VNIS' spectral parameters which highly correlated with minerals' abundance by correlation analysis(CA), and then stepwise regression method was used to find out spectral parameters which make the largest contributions to the mineral contents. At last, functions were derived to link minerals' abundance and spectral parameters by partial least square(PLS) algorithm. Not considering the effect of maturity, agglutinate and Fe0, we found that there are wonderful correlations between these four minerals and VNIS' spectral parameters, e.g. the abundance of pyroxene correlates positively with the mixture's absorption depth, the value of absorption depth added as the increasing of pyroxene's abundance. But the abundance of plagioclase correlates negatively with the spectral parameters of band ratio, the value of band ratio would decrease when the abundance of plagioclase increased. Similar to plagioclase, the abundance of ilmenite and olivine has a negative correlation with the mixture's reflectance data, if the abundance of ilmenite or olivine increase, the reflectance values of the mixture will decrease. Through model validation, better estimates of pyroxene, plagioclase and ilmenite's abundances are given. It is concluded that VNIS has the capability to be applied on lunar minerals' identification, and CA-PLS algorithm has the potential to be used on lunar surface's in-situ detection for minerals' abundance prediction.

Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions

Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.

基于PLS-SEM的航天器控制系统能力建模方法

为提升航天任务完成品质,航天器需根据任务及环境针对性调整自身能力,而对航天器控制系统高层次能力的定量刻画,即系统能力建模是实现上述调整的重要理论依据。针对一类航天器姿态控制系统,提出一种基于偏最小二乘-结构方程模型(partial least square structural equation model,PLS-SEM)的航天器控制系统能力建模方法,实现对包括控制能力、观测能力等抽象能力的定量描述。首先,根据航天器闭环控制系统的结构要素,综合设计能力建模所需的指标类型,生成建模数据样本。在此基础上,设计并构建SEM框架下的能力变量体系,进而通过PLS算法完成模型路径、载荷、权重等关键参数的确定,并对所得PLS-SEM能力模型的结构方程与测量方程的有效性、可信性等分别进行评估。最终,根据航天器PLS-SEM能力模型对控制系统的各抽象能力进行定量描述与分析,验证本文所提出建模方法的可行性。

Factors Affecting Box Office during Broad Spring Festival Based on Partial Least Squares Regression

The box office during the later Spring Festival shows an attractive prospect.This paper studied the factors affecting total box office during the broad Spring Festival which is from the Spring Festival to the Lantern Festival.Data of films released during the broad Spring Festival from the years 2016 to 2019 in China were gathered,and the impact of eight explanatory variables on the box office during the broad Spring Festival was empirically analyzed by partial least squares(PLS)regression with software SIMCA.The results suggest that word-of-mouth has the most positive effect on the box office during the broad Spring Festival.Later propaganda has a positive effect,while early promotion has a negative effect on the box office.Director’s influence has a positive effect,while actor’s influence does not contribute much to the box office.Length of the trailer has a negative effect.The film format of 2D or 3D doesn’t contribute much to the box office.

Boosting the partial least square algorithm for regression modelling

Boosting algorithms are a class of general methods used to improve the general periormance of regression analysis. The main idea is to maintain a distribution over the train set. In order to use the given distribution directly, a modified PLS algorithm is proposed and used as the base learner to deal with the nonlinear multivariate regression problems. Experiments on gasoline octane number prediction demonstrate that boosting the modified PLS algorithm has better general performance over the PLS algorithm.

基于microRNA表达谱初步构建PLS-DA体液识别模型

针对外周血、唾液、精液、月经血、阴道分泌物这五种法医学常见的体液样本,采用二代测序(NGS)平台检测获得microRNA(miRNA)表达谱,使用偏最小二乘判别分析(PLS-DA)建立基于这五种体液的识别模型,并探讨PLS-DA在法医体液溯源中的应用价值。经优化小分子RNA文库制备过程,采用Ion Torrent S5 XL测序系统对前述法医学五种体液样本(每种10例)进行小RNA测序,以PLS-DA构建体液识别模型,评估不同数量miRNA标记组合下预测的准确性。本研究获得法医学常见五种体液样本的miRNA表达谱,外周血与月经血中表达量前10名的miRNAs有6个重叠;唾液和阴道分泌物中表达量前10名的miRNAs有4个重叠。基于全数据集、107个和11个miRNAs构建的体液来源识别模型的准确率分别为0.95、0.94、0.89。本研究通过NGS测序分析获得了五种体液样本的miRNA组(miRNome),利用PLS-DA初步构建了体液识别模型,对于应用miRNome进行体液识别的相关研究具有参考价值。

An Improved PLS (IPLS) Method Utilizing Local Standardization Strategy for Multimode Process Monitoring

Complex industrial process often contains multiple operating modes, and the challenge of multimode process monitoring has recently gained much attention. However, most multivariate statistical process monitoring (MSPM) methods are based on the assumption that the process has only one nominal mode. When the process data contain different distributions, they may not function as well as in single mode processes. To address this issue, an improved partial least squares (IPLS) method was proposed for multimode process monitoring. By utilizing a novel local standardization strategy, the normal data in multiple modes could be centralized after being standardized and the fundamental assumption of partial least squares (PLS) could be valid again in multimode process. In this way, PLS method was extended to be suitable for not only single mode processes but also multimode processes. The efficiency of the proposed method was illustrated by comparing the monitoring results of PLS and IPLS in Tennessee Eastman(TE) process.

紫外分光光度法测定歧化松香中去氢枞酸含量的 干扰因素研究

利用液相色谱仪、制备液相色谱仪、气相色谱质谱联用仪,对歧化松香(DR)中去氢枞酸(DA)紫外光谱测定方法的干扰因素进行了详细研究。测定的市售不同省份的5个样品中有2个主要干扰物质,进一步定性,确定干扰物质1为6,8,11,13-枞四烯酸(ATA),干扰物质2为混合物,主要检出物为去氢枞醛(D)和枞酸(AA)。采用二阶导数光谱法和偏最小二乘(PLS)法对紫外光谱数据进行分析,以消除去氢枞酸紫外分光光度法的干扰,结果表明:PLS法比二阶导数光谱法误差更小,能够同时消除多种干扰因素,使测试结果更加接近真实值。

发酵玉米芯多糖、还原糖含量近红外模型的建立

本实验旨在应用近红外光谱技术(NIRS)结合化学计量学法快速预测发酵玉米芯中多糖、还原糖含量,为定量检测发酵玉米芯多糖、还原糖含量提供理论依据以及为玉米芯深加工利用提供技术支持。以105份微生物发酵的玉米芯为供试材料,采用苯酚-硫酸法和DNS法分别测定多糖和还原糖含量。利用偏最小二乘法(PLS),通过不同预处方式和不同波长建立发酵玉米芯多糖、还原糖含量的近红外分析模型。结果表明:多糖模型采用标准正态变量变换(SNV)+1阶导数的方法对全谱图进行预处理的效果较好,优化后的模型决定系数(R2)、校正均方根误差(RMSEC)、校准标准差(SEC)分别为0.82、9.28、9.34,其相对分析误差(PRD)为2.37;还原糖模型采用1阶导数+标准正态变量变换(SNV)+去趋势化(Detrend)的方法对全谱图进行预处理的效果较好,优化后的模型决定系数(R2)、校正均方根误差(RMSEC)、校准标准差(SEC)分别为0.84、4.03、4.04,其相对分析误差(PRD)为2.48;预测集决定系数分别为0.85、0.88。本研究构建的NIRS模型校正和交互验证决定系数均较大,相对分析误差均大于2,说明模型预测性能较好,建立的模型有助于发酵玉米芯多糖、还原糖含量活性成分的筛选。

基于地下结构整体损伤表征的复合地震动参数构造及其性能验证

在地下结构抗震设计中,不同的输入地震动引起的地下结构响应有显著差异,因此,合理通过地震动参数选择输入地震动是正确开展地下结构抗震设计的重要前提。针对单一地震动参数难以表征地下结构地震动潜在破坏势问题,文章构造了能更好表征地下结构损伤破坏的复合地震动参数。具体开展了以下工作:提出基于变形与滞回耗能的地下结构整体损伤指标作为结构需求参数,以定量化评估地下结构的整体破坏状态。选取64条真实地震动记录作为输入地震动,开展四层三跨地铁车站地震弹塑性动力时程分析。基于分析结果提供的数据样本,采用偏最小二乘法从统计角度构造复合地震动参数。最后,选用100条真实地震动记录开展两层三跨地铁车站弹塑性动力时程分析,对文章所构造的复合地震动参数进行验证。对比分析复合地震动参数、12个常用地震动参数与地下结构整体损伤指数的回归统计特征。结果表明:复合地震动参数与结构需求数之间具有更好拟合优度值,其Pearson相关性、有效性也优于单一地震动参数。

基于纸张纤维特征的纸页抗张强度智能模拟及预测研究

对纸页抗张强度进行智能模拟,有助于更好地对某种纸页的性能进行分析甚至预测。基于此,针对现有纸页抗张强度模拟方法的缺陷进行总结,认为现有典型Page分析模型存在客观性分析不足等问题,结合偏最小二乘法、支持向量机等构建了一种基于造纸纤维特性的纸页抗张强度智能模拟模型,借助该模型对某种典型纸页的性能进行模拟分析。测试结果表明:该模型能够高效、准确地对纸页抗张强度进行预测,与传统模型相比具有更强的实用性。

城镇化对长江经济带农业碳排放的影响及其耦合关系研究

基于区域一体化视角,使用长江经济带2000—2020年面板数据,多维度构建城镇化和农业碳排放指标体系,结合层次分析法和熵值法进行指标赋权,采用偏最小二乘法与耦合协调度模型定量分析二者的影响机制和耦合关系。结果表明:(1)城镇化主导类型呈现人口城镇化→空间城镇化→生态环境城镇化→经济城镇化→社会城镇化发展趋势。(2)农业碳排放空间差异显著,等级演变明显,唯安徽省和上海市一直处于最高级和最低级。(3)人口、经济、社会、生态环境城镇化是影响农业碳排放的四个重要维度,人均国内生产总值、人均教育经费、万元GDP能耗等7项指标是极重要因素。(4)城镇化与农业碳排放耦合关系呈增长态势,耦合度从磨合转向高水平耦合,耦合协调度由勉强协调发展为良好协调,说明二者共振性良好,趋于协调发展。

偏最小二乘法(PLS)及其在分析化学中的应用

本文介绍了一种性能优于其他多元统计方法的新的多元校准法——偏最小二乘法(PLS),研究了其理论基础、算法和性能,评述了其在吸收光谱分析、荧光分光光度分析、ICP—AES、色谱分析、核磁共振谱分析、生物化学、产品质量预测、毒理学、环境化学和地球化学研究等方面的应用以及应用发展动向。引用文献58篇。

PLS-BP法近红外光谱定量分析研究

建立BP模型用于近红外光谱定量分析时,为克服所建模型与训练样本集产生“过拟合”,先用线性算法为其压缩训练数据是必要的。目前多采用主成分法(PCA)和逐步回归法(SRA)。主成分法具有极强的压缩数据能力,用它压缩成的主成分输入BP网所建模型的预测精度一般能满足要求,但它处理数据时未考虑输出变量的影响。逐步回归法根据系统输出选择变量,但所选变量具有自相关性,而且与训练集样品的排列顺序有关,很难选出最好的变量,往往难满足预测精度要求。本研究用偏最小二乘法(PLS),根据输出变量将原始数据压缩为主成分,输入BP网并用所建模型预测30个小麦样品的蛋白质含量。结果表明,与PCA-BP模型的预测决定系数(R2)从92.50提高到97.10,训练迭代次数从12 000减少到4 500。