A partial least-squares regression approach to land use studies in the Suzhou-Wuxi-Changzhou region

In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares （PLS） regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and influencing factors demonstrate the land use character of rural industrialization and urbanization in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.

PARTIAL LEAST-SQUARES(PLS)REGRESSION AND SPECTROPHOTOMETRY AS APPLIED TO THE ANALYSIS OF MULTICOMPONENT MIXTURES

The UV absorption spectra of o-naphthol,α-naphthylamine,2,7-dihydroxy naphthalene,2,4-dimethoxy ben- zaldehyde and methyl salicylate,overlap severely;therefore it is impossible to determine them in mixtures by traditional spectrophotometric methods.In this paper,the partial least-squares(PLS)regression is applied to the simultaneous determination of these compounds in mixtures by UV spectrophtometry without any pretreatment of the samples.Ten synthetic mixture samples are analyzed by the proposed method.The mean recoveries are 99.4%,996%,100.2%,99.3% and 99.1%,and the relative standard deviations(RSD) are 1.87%,1.98%,1.94%,0.960% and 0.672%,respectively.

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.

ROBUST ESTIMATION IN PARTIAL LINEAR MIXED MODEL FOR LONGITUDINAL DATA

In this article, robust generalized estimating equation for the analysis of partial linear mixed model for longitudinal data is used. The authors approximate the nonparametric function by a regression spline. Under some regular conditions, the asymptotic properties of the estimators are obtained. To avoid the computation of high-dimensional integral, a robust Monte Carlo Newton-Raphson algorithm is used. Some simulations are carried out to study the performance of the proposed robust estimators. In addition, the authors also study the robustness and the efficiency of the proposed estimators by simulation. Finally, two real longitudinal data sets are analyzed.

突发事件下列车开行方案调整对铁路客运网络鲁棒性的影响

面对突发事件的发生,铁路会采取列车开行方案调整措施进行应对以减少突发事件带来的危害,极大地影响了铁路客运网络的鲁棒性。结合突发事件对铁路客运网络的影响区域性特点,选取部分停运的列车开行方案调整措施,确定突发事件对铁路客运网络的扰动,通过构建鲁棒性指标,进行了列车开行方案调整对铁路客运网络鲁棒性的仿真设计。以辽宁省铁路客运网络为例,计算不同比例停运下铁路客运网络的鲁棒性,研究发现:2种网络指标的度量下,最大连通子图的鲁棒性要好于可达性。列车部分停运下,当列车停运比例大于70%或车站节点数大于10时,会引起最大连通子图指标的快速下降;当列车停运比例大于10%或车站节点数大于9时,会引起可达性指标的快速下降。

部分信息下带时滞的鲁棒资产负债博弈问题研究

研究了部分可观测信息下带时滞的鲁棒资产负债博弈问题,运用滤波理论,将不完全信息转化为完全信息,以终端相对财富的效用最大化为目标,构建了不完全信息带时滞的鲁棒资产负债博弈问题,借助动态规划原理,得到了均衡投资策略和值函数的显示表达式。最后,通过数值模拟验证了参数对均衡投资策略和值函数的影响,主要得出了不完全信息下投资者的效用低于完全信息。所以,投资者应尽可能的搜集与投资相关的更多信息,以便于做出更加明智的决策。

执行器部分失效的鲁棒模糊容错切换控制

针对一类具有不确定性、外部扰动和执行器部分失效的非线性工业过程,提出一种鲁棒模糊容错切换控制方法。首先,构建新型多自由度T-S模糊状态空间模型,在此基础上,结合执行器部分失效的情况设计控制律。其次,给出基于线性矩阵不等式(LMI)的容错切换条件和系统稳定条件,并通过当前系统状态和稳定性条件求取其子模型的控制律及相应权值。最后,通过对连续搅拌釜式反应器的仿真研究来验证该文方法的有效性。

基于IGGⅢ抗差估计的GNSS部分模糊度固定改进算法

针对滑坡等复杂环境下GNSS观测数据受高遮挡、多路径等因素影响,易使模糊度固定成功率降低进而导致定位可靠性变差的问题,提出一种基于IGGⅢ抗差估计的GNSS部分模糊度固定改进算法。通过引入抗差估计算法,削弱粗差对参数估计的影响,提升浮点模糊度精度;此外,为避免残余误差对后续整周模糊度解算产生影响,采用基于条件方差矩阵法的部分模糊度固定改进策略,进一步提升模糊度固定成功率。实验结果表明,相比于全模糊度固定方法,该算法可显著提高模糊度固定率,3个测站分别提升5.9%、52.7%、48.0%;同时,相比于传统的卡尔曼滤波算法,改进的抗差估计算法可明显改善模糊度固定错误的情况,极大提升GNSS定位精度,水平方向RMS优于1cm,高程方向RMS优于2cm。

Optimization of Random Feature Method in the High-Precision Regime

Machine learning has been widely used for solving partial differential equations(PDEs)in recent years,among which the random feature method(RFM)exhibits spectral accuracy and can compete with traditional solvers in terms of both accuracy and efficiency.Potentially,the optimization problem in the RFM is more difficult to solve than those that arise in traditional methods.Unlike the broader machine-learning research,which frequently targets tasks within the low-precision regime,our study focuses on the high-precision regime crucial for solving PDEs.In this work,we study this problem from the following aspects:(i)we analyze the coeffcient matrix that arises in the RFM by studying the distribution of singular values;(ii)we investigate whether the continuous training causes the overfitting issue;(ii)we test direct and iterative methods as well as randomized methods for solving the optimization problem.Based on these results,we find that direct methods are superior to other methods if memory is not an issue,while iterative methods typically have low accuracy and can be improved by preconditioning to some extent.

在线社交网络中基于多态信任融合的信任估计

为提高大型在线社交网络中信任计算方法的准确性和鲁棒性,采用共被引和转置信任传播操作,提出一种基于多态信任融合的信任估计方法估计连续信任/不信任值。结合信任者、被信任者的相邻用户的信息以及被信任者对信任者的信任,平均估计出两个用户信任或被其它用户信任的差异,以及一个用户信任另一个用户和被该用户信任的差异;利用这些差异,计算4种部分信任估计值,将这些部分估计值加权平均,得到信任者对被信任者的最终信任估计值。仿真结果表明,所提方法比其它最新的现有信任计算算法更准确和鲁棒,对应用于大型网络更高效。

基于加权复合分位数回归的变系数部分线性模型的稳健经验似然估计

研究了变系数部分线性模型的稳健经验似然推断问题。利用加权复合分位数回归以及经验似然方法,并结合基于矩阵QR分解的正交投影技术,对模型的参数分量提出了一种基于加权复合分数回归的经验似然估计方法。理论证明了提出的经验对数似然比函数渐近服从卡方分布,得到参数分量的置信区间。该估计方法中引入了基于矩阵QR分解的正交投影技术,保证对模型的参数分量进行估计时不会受到非参数分量估计精度的影响,因此具有较好的稳健性和有效性。

Partial least squares regression for predicting economic loss of vegetables caused by acid rain

To predict the economic loss of crops caused by acid rain,we used partial least squares(PLS) regression to build a model of single dependent variable -the economic loss calculated with the decrease in yield related to the pH value and levels of Ca2+,NH4+,Na+,K+,Mg2+,SO42-,NO3-,and Cl-in acid rain. We selected vegetables which were sensitive to acid rain as the sample crops,and collected 12 groups of data,of which 8 groups were used for modeling and 4 groups for testing. Using the cross validation method to evaluate the performace of this prediction model indicates that the optimum number of principal components was 3,determined by the minimum of prediction residual error sum of squares,and the prediction error of the regression equation ranges from -2.25% to 4.32%. The model predicted that the economic loss of vegetables from acid rain is negatively corrrelated to pH and the concentrations of NH4+,SO42-,NO3-,and Cl-in the rain,and positively correlated to the concentrations of Ca2+,Na+,K+ and Mg2+. The precision of the model may be improved if the non-linearity of original data is addressed.

Novel robust approach for constructing Mamdani-type fuzzy system based on PRM and subtractive clustering algorithm

A novel approach for constructing robust Mamdani fuzzy system was proposed, which consisted of an efficiency robust estimator(partial robust M-regression, PRM) in the parameter learning phase of the initial fuzzy system, and an improved subtractive clustering algorithm in the fuzzy-rule-selecting phase. The weights obtained in PRM, which gives protection against noise and outliers, were incorporated into the potential measure of the subtractive cluster algorithm to enhance the robustness of the fuzzy rule cluster process, and a compact Mamdani-type fuzzy system was established after the parameters in the consequent parts of rules were re-estimated by partial least squares(PLS). The main characteristics of the new approach were its simplicity and ability to construct fuzzy system fast and robustly. Simulation and experiment results show that the proposed approach can achieve satisfactory results in various kinds of data domains with noise and outliers. Compared with D-SVD and ARRBFN, the proposed approach yields much fewer rules and less RMSE values.

铁路隧道概率极限状态设计分项系数标定与讨论

目前,我国结构设计规范中广泛采用以荷载和抗力分项系数代表概率可靠性的概率极限状态设计方法。然而,在Eurocode 7等岩土工程结构的设计规范及相关研究中,对荷载与抗力分项系数方法,即以一组固定分项系数来保证不同工况下工程结构可靠性提出质疑。基于多年来我国铁路隧道衬砌结构概率极限状态设计的研究成果与应用情况,进一步细化不同围岩分级、埋深和衬砌类型的分项系数标定,共标定28种工况下衬砌结构设计的分项系数;分别采用本文、《铁路隧道极限状态法设计暂行规范》和《铁路隧道设计规范》的衬砌结构可靠性与安全性评价指标,对京张高铁八达岭隧道8种工况下设计方案的安全性与可靠性进行对比分析,不同工况下的隧道衬砌结构设计可靠指标与安全系数差异较大,说明荷载与抗力分项系数方法在铁路隧道衬砌可靠性设计中的稳健性较差。建议在隧道设计规范层面应逐步开展基于概率可靠性地勘和计算两阶段的研究,并提出铁路隧道设计方法转轨的部分建议。

基于惩罚最小绝对偏差的部分线性空间自回归模型的稳健变量选择

结合B样条逼近以及工具变量调整技术,并利用惩罚最小绝对偏差方法,对部分线性空间自回归模型提出了一种稳健变量选择方法.理论上证明了所提出的变量选择方法可以相合地识别出模型中的重要协变量和不重要协变量,并给出了所得正则估计的收敛速度.所提出的变量选择过程对模型中的参数分量和非参数分量的估计可以一步同时完成,避免了非参数分量的估计对参数分量变量选择的影响,因此具有较好的稳健性和有效性.

变压器典型局部放电信号辨识研究

为深入研究变压器典型局部放电的类型,提出了一种相关系数矩阵(correlation coefficient matrix dimension reduction,CCMDA)和深度学习相结合的特征识别方法。首先,对原始数据进行降维,保留关键特征;然后,引入深度学习框架,搭建ResNet34的残差结构,开展局部放电特征的识别。结果表明:相对于核主元分析(kernel principal component analysis,KPCA),相关系数矩阵降维效果更为显著;图片尺寸为64×64时,识别性能最好;学习率为0.001时,损失函数值最小。本文方法识别准确率高,明显优于卷积神经网络(convolutional neural networks,CNN)和支持向量机(support vector machine,SVM)。引入小波变换,可增强本文方法的鲁棒性。

区分多业务的跨层优化无线网络协议头压缩算法

在网络中,鲁棒性协议头压缩(robust header compression,ROHC)算法需要压缩端和解压端的状态同步,才能成功解压ROHC数据包,但ROHC算法的双向可靠R模式和双向优化O模式需要单独的反馈信道,增加了网络成本。针对ROHC算法的单向U模式,当无线信道质量变差时,ROHC的性能会随之下降,且现有ROHC算法没有考虑到不同业务对实时性需求不同的问题,提出了一种新的U-ROHC建模方案,引入一种部分可观测马尔可夫过程(partially observable Markov processes,POMDP)跨层优化框架来自适应调整U模型下ROHC压缩端状态,以求改善ROHC的性能。针对实时性业务和非实时性业务的不同要求自适应调整ROHC参数,使实时性业务能在解压失败的情况下具有更快速恢复双端置信的能力,而非实时性业务具有更高的压缩比,以使ROCH具备支持不同业务类型的能力。仿真结果表明,可以使ROHC-U算法在无线信道恶劣的条件下依然保持较好的压缩比和吞吐量。

基于稳健典型相关及偏最小二乘的果实品质影响分析

根据某省多个果园的土壤养分、树体特征、种植密度、果实养分及品质等指标采样数据,探讨了各因素对单果品质的影响;通过稳健典型相关方法,增大了典型相关系数,并分析了土壤养分与果实养分间的相关关系;利用偏最小二乘方法建立了关于果实品质和果实养分指标间响应变量多维的多元回归模型.所得结论为:调整土壤肥料结构和比例,保持合理的种植密度,注意树体修剪;在追求高产的同时关注果实品质.

RRA-InceptionV3结合鲁棒稀疏表示的表情识别方法

针对现实场景中人脸局部遮挡导致的表情识别准确度较低的问题,提出一种RRA-InceptionV3结合鲁棒稀疏表示的表情识别方法。将人脸图像通过多支路卷积运算和空洞卷积模块来获取丰富的表情特征,基于Asm-CBAM卷积注意力机制划分人脸表情特征的权重并进行多特征融合,随后堆叠密集残差模块,从多通道中自适应提取人脸特征信息,通过Asm-CBAM卷积注意力机制提高网络对人脸关键特征的注意力。在此基础上,利用鲁棒稀疏表示分类器方法对表情进行分类。在人脸数据集FER2013和CK+上的实验结果表明,该方法的人脸表情平均识别精度分别达到79.86%和98.74%,与OAD Net算法相比,分别高出7.50和3.14个百分点,能够高效提取人脸表情特征。此外,在人脸被遮挡的情况下具有较强的鲁棒性,有效提高了在人脸遮挡情况下表情识别的准确度。

Rapid recognition of Chinese herbal pieces of Areca catechu by different concocted processes using Fourier transform mid-infrared and near-infrared spectroscopy combined with partial least-squares discriminant analysis

Rapid and sensitive recognition of herbal pieces according to different concocted processing is crucial to quality control and pharmaceutical effect. Near-infrared （NIR） and mid-infrared （MIR） technology combined with supervised pattern recognition based on partial least-squares discriminant analysis （PLSDA） was attempted to classify and recognize six different concocted processing pieces of 600 Areca catechu L. samples and the influence of fingerprint information preprocessing methods on recognition performance was also investigated in this work. Recognition rates of 99.24%, 100% and 99.49% for original fingerprint, multiple scatter correct （MSC） fingerprint and second derivative （2nd derivative） fingerprint of NIR spectra were achieved by PLSDA models, respectively. Meanwhile, a perfect recognition rate of 100% was obtained for the above three fingerprint models of MIR spectra. In conclusion, PLSDA can rapidly and effectively extract otherness of fingerprint information from NIR and MIR spectra to identify different concocted herbal pieces ofA. catechu.