Advanced reliability analysis of slopes in spatially variable soils using multivariate adaptive regression splines

This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs.

Some Necessary and Sufficient Condition of Multivariate Random Variable Satisfy Normal Distribution

In this paper,we extended some results of article[1],obtain some sufficient and necessary condition which multivariate random variable satisfy normal distribution.

New model for deducing directional extrema based on multivariate extremum statistical theory

The parameters of principal and directional extrema in a marine environment are important in marine engineering design, especially for appropriate construction of oceanic platforms and other structures. When designing wave walls and break water structures, the orientation of the breakwater or seawall depends mainly on the direction of the strongest waves. However, the strength of the breakwater and the elevation of the seawall depend on the magnitude of the biggest wave height of the strongest waves. Thus, identification of directional extrema plays an important role in the design of wave factors. When calculating the directional extremum, different materials may require different specific computational methods, yet few theoretical studies have been conducted in this field of research. Based on multivariate extremnm statistical theory, this paper utilizes a discrete random variable to build a joint probability model compounded by a discrete random variable and a multivariate continuous random variable. Furthermore, this paper provides the first investigation on the theories and methodologies to deduce wave directional extrema. The results provide tools for both creating the calculation method of the directional extremum value and providing the rational directional extremum parameters for marine engineering design.

Hydrological Mann-Kendal Multivariate Trends Analysis in the Upper Yangtze River Basin

Hydrological events should be described through several correlated variables, so multivariate HFA has gained popularity and become an active research field during recent years. However, at present multivariate HFA mainly focuses directly on fitting the frequency distribution without confirming whether the assumptions are satisfied. Neglecting testing these assumptions could get severely wrong frequency distribution. This paper uses multivariate Mann-Kendal testing to detect the multivariate trends of annual flood peak and annual maximum 15 day volume for four control hydrological stations in the?Upper Yangtze River Basin. Results indicate that multivariate test could detect the trends of joint variables, whereas univariate tests can only detect the univariate trends. Therefore, it is recommended to jointly apply univariate and multivariate trend tests to capture all the existing trends.

Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research

Recent technological advancements and developments have led to a dramatic increase in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regression methods.Numerous traditional multivariate approaches such as principal component analysis have been used broadly in various research areas,including investment analysis,image identification,and population genetic structure analysis.However,these common approaches have the limitations of ignoring the correlations between responses and a low variable selection efficiency.Therefore,in this article,we introduce the reduced rank regression method and its extensions,sparse reduced rank regression and subspace assisted regression with row sparsity,which hold potential to meet the above demands and thus improve the interpretability of regression models.We conducted a simulation study to evaluate their performance and compared them with several other variable selection methods.For different application scenarios,we also provide selection suggestions based on predictive ability and variable selection accuracy.Finally,to demonstrate the practical value of these methods in the field of microbiome research,we applied our chosen method to real population-level microbiome data,the results of which validated our method.Our method extensions provide valuable guidelines for future omics research,especially with respect to multivariate regression,and could pave the way for novel discoveries in microbiome and related research fields.

Reciprocal transformations of the space-time shifted nonlocal short pulse equations

Reciprocal transformations of the space-time shifted nonlocal short pulse equations are elaborated.Covariance of dependent and independent variables involved in the reciprocal transformations is investigated.Exact solutions of the space-time shifted nonlocal short pulse equations are given in terms of double Wronskians.Realness of independent variables involved in the reciprocal transformations is verified.Dynamics of some obtained solutions are illustrated.

Computation of the Multivariate Normal Integral over a Complex Subspace

The computation of the multivariate normal integral over a Complex Subspace is a challenge, especially when the inte-gration region is of a complex nature. Such integrals are met with, for example, in the generalized Neyman-Pearson criterion, conditional Bayesian problems of testing many hypotheses and so on. The Monte-Carlo methods could be used for their computation, but at increasing dimensionality of the integral the computation time increases unjustifiedly. Therefore a method of computation of such integrals by series after reduction of dimensionality to one without information loss is offered below. The calculation results are given.

乌梁素海鱼类群落结构特征及其与环境因子的关系

根据2021年周年野外调查数据与渔业捕捞历史资料,本文分析了乌梁素海湖区的鱼类群落结构变化特征及其与主要环境因子的关系。与历史资料相比,乌梁素海鱼类群落结构已经发生较大变化,表现为土著鱼类减少,优势种组成改变,鱼类群落结构呈现小型化。根据鱼类群落在湖中的分布特征,本研究将乌梁素海分为西部、东部和南部3个湖区。单因子相似性分析和相似性百分比分析结果显示,不同湖区间鱼类群落组成存在显著差异,两两湖区之间的平均差异性均在70%以上,且这种差异性主要由鲫、麦穗鱼和鲤造成。多样性的分析结果表明,全湖及各湖区鱼类群落的多样性指数较低(H′<2),整体表现为一般水平,且3个湖区的鱼类生物多样性指数呈现为:西部湖区>东部湖区>南部湖区。此外,Mantel test分析和典型相关分析表明,浊度和叶绿素a是目前影响乌梁素海鱼类群落结构的主要环境因子。各湖区间鱼类种群和多样性的差别在一定程度上反映出鱼类群落结构与湖泊环境特征相适应的特点。通过讨论鱼类群落结构变化及其空间分布特征,本文从不同角度对乌梁素海未来渔业管理规划提出可参考方向:1)重视禁渔休渔政策和人工增殖放流在乌梁素海的重要意义;2)充分利用鱼类群落与环境特征间的关系,尽可能通过改善鱼类群落结构促进水体营养物质循环。

Diatoms as indicators of environmental change in coastal areas:a case study in Lianjiang coast of East China Sea

Owing to the significant differences in environmental characteristics and explanatory factors among estuarine and coastal regions,research on diatom transfer functions and database establishment remains incomplete.This study analysed diatoms in surface sediment samples and a sediment core from the Lianjiang coast of the East China Sea,together with environmental variables.Principal component analysis of the environmental variables showed that sea surface salinity(SSS)and sea surface temperature were the most important factors controlling hydrological conditions in the Lianjiang coastal area,whereas canonical correspondence analysis indicated that SSS and pH were the main environmental factors affecting diatom distribution.Based on the modern diatom species–environmental variable database,we developed a diatom-based SSS transfer function to quantitatively reconstruct the variability in SSS between 1984 and 2021 for sediment core HK3 from the Lianjiang coastal area.The agreement between the reconstructed SSS and instrument SSS data from 1984 to 2021 suggests that diatombased SSS reconstruction is reliable for studying past SSS variability in the Lianjiang coastal area.Three low SSS events in AD 2019,2013,and 1999,together with an increased relative concentration of freshwater diatom species and coarser sediment grain sizes,corresponded to two super-typhoon events and a catastrophic flooding event in Lianjiang County.Thus,a diatom-based SSS transfer function for reconstructing past SSS variability in the estuarine and coastal areas of the East China Sea can be further used to reflect the paleoenvironmental events in this region.

An accurate and efficient space-time Galerkin spectral method for the subdiffusion equation

In this paper, we design and analyze a space-time spectral method for the subdiffusion equation.Here, we are facing two difficulties. The first is that the solutions of this equation are usually singular near the initial time. Consequently, traditional high-order numerical methods in time are inefficient. The second obstacle is that the resulting system of the space-time spectral approach is usually large and time-consuming to solve. We aim at overcoming the first difficulty by proposing a novel approach in time, which is based on variable transformation techniques. Suitable ψ-fractional Sobolev spaces and a new variational framework are introduced to establish the well-posedness of the associated variational problem. This allows us to construct our space-time spectral method using a combination of temporal generalized Jacobi polynomials(GJPs) and spatial Legendre polynomials. For the second difficulty, we propose a fast algorithm to effectively solve the resulting linear system. The fast algorithm makes use of a matrix diagonalization in space and QZ decomposition in time. Our analysis and numerical experiments show that the proposed method is exponentially convergent with respect to the polynomial degrees in both space and time directions, even though the exact solution has very limited regularity.

基于SVM的配电网新能源接入多元负荷预测误差校正

针对校正配电网新能源接入多元负荷预测误差的目的,提出了基于SVM的配电网新能源接入多元负荷预测误差校正方法。采用SVM方法找出特征空间最大化间隔,消除了异常预测点的影响。引入松弛变量,集成负荷预测误差数据。归一化处理SVM输入数据,计算预测功率、实际功率相对误差,利用SVM支持面设计初步预测误差校正思路,构建损失函数,判断预测值正负,实现配电网新能源接入多元负荷预测误差校正。通过实验结果得出,该方法在第一种和第二种设备故障情况下,针对最高负荷和最低负荷的误差校正结果分别减小了9.797 MW和0.2 MW、5.292 MW和2.363 MW,均能达到有效校正误差的目的。

城市微更新视角下的桥下低效空间再利用——以“桥下驿·杭城灯彩”为例

在城市存量更新的时代背景下,“小切口”“多元化”成为当下低效用地盘活的主旋律。围绕城市微更新视角,对杭州市拱墅区勤丰桥桥下低效空间设计实践进行总结和探讨。首先从场景激活角度,在“小切口入手”的设计原则下,营造全时性、可复制、易推广的桥下活力共享空间;其次,有机植入多元化可变的空间场景模块,打造单体可变、场景可变、街区可变的“三变”空间;最后,对城市微更新策略和方案展开讨论,阐述设计实践的四种使用形态,进一步提出扩展“公益性”范围与调动“经济性”活力两者间的平衡策略,并针对城市微更新提出具体策略和措施,助力城市形象与人民生活品质的全面提升。

LCL并网逆变器的自适应事件触发模型预测控制

LCL并网逆变器在采用基于事件触发机制的模型预测控制时,存在数控延迟及系统稳态性能易受运行工况变化影响等问题。为此,提出一种自适应事件触发的两步模型预测控制(adaptive event-triggered model predictive control with 2 steps,AET-MPC-2S)策略。首先,在模型预测控制环节中,该控制策略通过结合事件触发机制,筛选出合适的开关矢量进行两步预测;其次,AET-MPC引入自适应变量来监测系统的运行状态,对事件触发条件进行实时调节;最后,通过Matlab仿真和RT-LAB半实物平台进行验证。结果表明:AET-MPC-2S能够在实现延迟补偿的同时,减少两步预测的计算量,且不影响电流的跟踪效果,并能在运行工况发生变化后实时调节事件触发条件,使系统保持较好的稳态性能。论文研究可为降低LCL并网逆变器的开关频率以及改善系统的稳态性能提供参考。

贺兰山东麓‘马瑟兰’葡萄果实花色苷和原花色素特性分析

为了更好地了解宁夏贺兰山东麓不同区域酿酒葡萄‘马瑟兰’果实的花色苷和原花色素特性,以及综合品质的区域差异性,为该品种在贺兰山东麓产区的推广和栽培管理提供参考。研究以银川、农垦玉泉营、青铜峡和红寺堡4个子产区10个代表性酒庄的‘马瑟兰’葡萄为试材,通过偏最小二乘法判别分析(PLS-DA)筛选造成酒庄间果实品质差异的酚类物质指标,并采用主成分分析法(PCA)对不同酒庄‘马瑟兰’果实品质进行综合评价。结果表明,(1)10个酒庄‘马瑟兰’果实的基本理化指标、花色苷和原花色素组分与含量均存在差异,H-Hs酒庄葡萄果皮的花色苷和原花色素含量均为最高,分别达到111.91 mg/g和35.68 mg/g;Y-Mq酒庄葡萄种子原花色素含量最高(108.67 mg/g)。(2)基于果实花色苷和原花色素特性,采用聚类分析将10个酒庄划分为4类,其中H-Hs酒庄单独为一类,且能很好地与其他酒庄分开。(3)PLS-DA分析表明,种子原花色素的表儿茶素没食子酸酯末端亚基(ECG-p)和表儿茶素末端亚基(EC-p)所占摩尔百分比、果皮总花色苷含量和非酰化花色苷含量、果皮原花色素的EC-p所占摩尔百分比是区分10个酒庄果实品质差异的主要指标。(4)PCA分析表明,果实品质综合评价排名前三的酒庄为红寺堡子产区的H-Hs、银川子产区的Y-Mq和青铜峡子产区的Q-H酒庄。可见,贺兰山东麓‘马瑟兰’葡萄果实花色苷和原花色素特性及综合品质表现出明显的产地差异性,并以红寺堡子产区表现较佳。

基于多维高斯混合模型的电力系统不确定性建模方法

在基于多维高斯混合模型的电力系统多变量概率建模中,针对期望最大化算法参数估计精度较低的问题,该文引入非参数核密度估计和密度保留的分层期望最大化算法,提出一种基于高斯成分数约简的建模方法。以非参数核密度估计结果作为基高斯混合模型,采用密度保留的分层期望最大化算法约简高斯成分数,能够建立任意高斯成分数的高斯混合模型,克服了期望最大化算法在高斯成分数较多时参数估计精度低的问题。为降低大样本下的建模计算负担,提出按时间尺度分层的建模方法。为解决相互独立的多个随机变量出现高斯成分数组合爆炸的问题,提出“组合–约简”分层建模方法。采用具有复杂分布特性的实测多维风速数据和负荷数据对所提方法作了测试,结果表明,所提方法的精度显著优于基于期望最大化算法的高斯混合模型和Copula函数法。

多源异构数据贝叶斯变权融合可靠性评估模型

采用贝叶斯方法构建多源异构变权融合的可靠性评估模型,解决产品在可靠性评估中遇到的小样本数据问题。首先,分析各信息源与现场数据似然函数的尾部梯度特征,通过两者绝对值误差大小,确定各信息源权重;然后,考虑现场试验数据对先验及多源融合的权重影响,基于贝叶斯变权融合方法,得到产品的可靠性分析;最后,通过实例验证了该变权融合模型的合理性和有效性,评估结论优于贝叶斯等权融合的可靠性评估模型。

基于H∞回路成形的变循环发动机多变量控制

为了进一步提升变循环发动机的性能,针对3输入3输出变循环发动机模型,利用相对增益矩阵(RGA)分析了发动机内部的耦合性;采用基于目标函数的2自由度H∞回路成形法设计了多变量控制器,并与传统单变量PI方法做了对比。仿真结果表明:多变量控制系统具有良好的扰动抑制能力和跟踪性能,且能够顺利实现模态转换;与单变量方法相比,在同等的穿越频率及相位裕度约束下,n1回路阶跃响应的超调量减小了95.3%、调节时间缩短了51.5%;πe回路的最大偏移量减小了89.69%、调节时间缩短了27.4%。表明多变量方法可以对变循环发动机进行有效控制且在跟踪控制过程中的性能更优,可以显著提升变循环发动机的性能。

一种改进的非线性多变量格兰杰因果检验在污水处理过程参数关系分析中的研究

传统线性多变量格兰杰因果检验通过引入条件变量来判断两个变量之间是否存在因果关系,但条件变量的选择往往具有主观性而缺乏对其合理的规则,针对这个问题,提出一种可筛选条件变量的非线性多变量格兰杰因果检验方法。该方法使用支持向量回归构建检验方程以适应非线性条件,通过分析两两变量之间的关系构建初步结构后选择条件变量,并基于所选条件变量再进行非线性多变量格兰杰因果检验;引入两种拓扑结构避免对不产生伪因果问题的真实关系重复检验。在数字仿真和污水处理基准仿真平台上的实验结果表明本文方法能适应非线性条件,检验结果更准确,在计算强度上也有更好的表现。

基酒FT-NIR光谱预处理与特征波筛选方法的比较

为解决白酒基酒分类的问题,降低基酒的分类误差,减少基酒对摘酒师傅身体的危害,本实验选取18种预处理以及3种特征波筛选方法来较少光谱中的无关干扰信息,降低建模数据复杂度。基酒的傅里叶近红外光谱(Fourier Transform Near Infrared Spectroscopy,FT-NIR)经过光谱理化值共生距离法(SPXY)划分数据集、预处理、马氏距离(MD)异常剔除、特征波筛选、支持向量机回归(SVR)预测来完成最终的分类。研究发现:多元散射校正(Multiplicative Scatter Correction,MSC)后的训练集预测集分类准确率可以达到100%,主成分分析(Principal Component Analysis,PCA)与特定算法结合才能实现准确分类,因此要注意与其他算法的组合,无信息变量消除法(Uninformative Variables Elimination,UVE)和竞争性自适应重加权算法(Competitive Adaptive Reweighted Sampling,CARS)都能实现高效的特征波选择,预测集的平均准确率接近90%。实验证明,经过处理后的光谱数据最多占原数据的47.57%,基酒近红外谱图经过预处理与特征波筛选后可以降低后期回归模型处理数据的复杂程度,提高模型的精确度。

污水处理过程多变量自适应变论域模糊控制

由于污水处理过程具有非线性、时变性和滞后性的特点,传统控制方法难以取得满意的控制效果。为了实现精确跟踪控制,提出了一种基于自适应变论域模糊控制(adaptive variable universe fuzzy control,AVUFC)的污水处理过程多变量控制方法,实现了溶解氧和硝态氮的底层回路控制。在AVUFC中,根据性能指标设计了基于自适应差分进化算法的伸缩因子调整策略,在线自适应调节模糊控制论域,降低工况变化对控制器性能的影响,提高控制器的动态性能和稳态精度。利用国际基准仿真平台BSM1进行实验验证。仿真结果表明,当入水流量和组分浓度发生剧烈变化时,所提出的自适应变论域模糊控制策略能够通过在线优化模糊规则以快速适应复杂工况;与PID控制、模糊逻辑控制、定式变论域模糊控制等方法相比,自适应变论域模糊控制在平稳性、抗干扰性和瞬态响应能力方面更具优势。