A Radial Basis Function Method with Improved Accuracy for Fourth Order Boundary Value Problems

Accurately approximating higher order derivatives is an inherently difficult problem. It is shown that a random variable shape parameter strategy can improve the accuracy of approximating higher order derivatives with Radial Basis Function methods. The method is used to solve fourth order boundary value problems. The use and location of ghost points are examined in order to enforce the extra boundary conditions that are necessary to make a fourth-order problem well posed. The use of ghost points versus solving an overdetermined linear system via least squares is studied. For a general fourth-order boundary value problem, the recommended approach is to either use one of two novel sets of ghost centers introduced here or else to use a least squares approach. When using either ghost centers or least squares, the random variable shape parameter strategy results in significantly better accuracy than when a constant shape parameter is used.

Complex variable solution for boundary value problem with X-shaped cavity in plane elasticity and its application*

A new type of displacement pile, the X-section cast-in-place concrete （XCC） pile, has recently been developed in China. Extensive field tests and laboratory experi- ments are undertaken to evaluate its performance and quantify the non-uniform deforma- tion effect （NUDE） of the X-shaped cross section during installation. This paper develops a simplified theoretical model that attempts to capture the NUDE. Based on the theory of complex variable plane elasticity, closed-form solutions of the stress and displacement for the X-shaped cavity boundary value problem are given. Subsequently, the analytical solution is used to evaluate the NUDE, the concrete filling index （CFI）, and the perimeter reduction coefficient of the XCC pile cross section. The computed results are compared with field test results, showing reasonable agreement. The present simplified theoretical model reveals the deformation mechanism of the X-shaped cavity and facilitates applica- tion of the newly developed XCC pile technique in geotechnical engineering.

A SPECIAL METHOD OF FOURIER SERIES WHICH IS EQUAL TO THE METHOD OF SEPARATION OF VARIABLES ON BOUNDARY VALUE PROBLEM

By the separation of singularity, a special Fourier series solution of the boundary value problem for plane is obtained, which can satisfy all boundary conditions and converges rapidly. II is proved that the solution is equal to the result of separation of variables. As a result, the non-linear characteristic equations resulting from the method of separation of variables are transformed into polynomial equations that can provide a foundation for approximate computation and asymptotic analysis.

On the Strong Law of Large Numbers for Non-Independent B-Valued Random Variables

This paper investigates some conditions which imply the strong laws of large numbers for Banach space valued random variable sequences. Some generalizations of the Marcinkiewicz-Zygmund theorem and the Hoffmann-J?rgensen and Pisier theorem are obtained. Key words strong law of large numbers - Banach space valued random variable sequence - p-smoothable Banach space CLC number O 211.4 - O 211.6 Foundation item: Supported by the National Natural Science Foundation of China (10071058)Biography: Gan Shi-xin (1939-), male, Professor, research direction: martingale theory, probability limiting theory and Banach space geometry theory.

Dirichlet boundary value problem with variable growth

In this paper, we study higher order elliptic partial differential equations with variable growth, and obtain the existence of solutions in the setting of Wm,p（x） spaces by means of an abstract result for variational inequalities obtained by Gossez and Mustonen. Our result generalizes the corresponding one of Kováik and Rákosník.

A Strong Law of Large Numbers for Set-Valued Random Variables in Gα Space

In this paper, we shall represent a strong law of large numbers (SLLN) for weighted sums of set- valued random variables in the sense of the Hausdorff metric dH, based on the result of single-valued random variable obtained by Taylor [1].

Strong Laws of Large Numbers for Fuzzy Set-Valued Random Variables in Gα Space

In this paper, we shall present the strong laws of large numbers for fuzzy set-valued random variables in the sense of d∞H . The results are based on the result of single-valued random variables obtained by Taylor [1] and set-valued random variables obtained by Li Guan [2].

Analysis of heart rate variability based on singular value decomposition entropy

Assessing the dynamics of heart rate fluctuations can provide valuable information about heart status. In this study, regularity of heart rate variability （HRV） of heart failure patients and healthy persons using the concept of singular value decomposition entropy （SvdEn） is analyzed. SvdEn is calculated from the time series using normalized singular values. The advantage of this method is its simplicity and fast computation. It enables analysis of very short and non-stationary data sets. The results show that SvdEn of patients with congestive heart failure （CHF） shows a low value （SvdEn： 0.056±0.006, p 〈 0.01） which can be completely separated from healthy subjects. In addition, differences of SvdEn values between day and night are found for the healthy groups. SvdEn decreases with age. The lower the SvdEn values, the higher the risk of heart disease. Moreover, SvdEn is associated with the energy of heart rhythm. The results show that using SvdEn for discriminating HRV in different physiological states for clinical applications is feasible and simple.

Multilinear Calderón-Zygmund Operators and Their Commutators with BMO Functions in Herz-Morrey Spaces with Variable Smoothness and Integrability

In this paper, we obtain that multilinear Calderón-Zygmund operators and their commutators with BMO functions are bounded on products of Herz-Morrey spaces with variable smoothness and integrability. The vector-valued setting of multilinear Calderón-Zygmund operators is also considered.

A Path Integral Generalization of Bell Local Hidden Variable Models for Unstable Particles

We discuss the problem of the generalization of Bell local hidden variable models for unstable particles as nucleons or decaying quantum bound states. We propose to extend the formalism of real deterministic hidden variables in the complex domain, in analogy with the quantum Gamow ket formalism, and we introduce a time dependent classical probability density distribution by which we implement hidden time dependence in the quantum expectation values. We suggest therefore a classical framework which may recover by asymptotic temporal limits the standard Bell stationary quantum statistical averages. Endly we discuss the possible relevance of our proposal for general non-isolated quantum systems in noninertial frames and the consequent dynamic effects of vacuum instabilities on E.P.R tests and Q.M. ensemble statistical averages.

Linguistic Truth Values Lattice Implication Algebras

In order to study uncertainty reasoning and automatic reasoning with linguistic terms, in this paper, the set of basic linguistic truth values and the set of modifiers are defined, according to common sense; partially orderings are defined on them. Based on it, a lattice implication algebra model L18 of linguistic terms is built; furthermore, its some basic properties are discussed.

Mean Square Numerical Methods for Initial Value Random Differential Equations

In this paper, the random Euler and random Runge-Kutta of the second order methods are used in solving random differential initial value problems of first order. The conditions of the mean square convergence of the numerical solutions are studied. The statistical properties of the numerical solutions are computed through numerical case studies.

Variable Screening of Neural Network based on MIV

Screening variables with significant features as the input data of network, is an important step in application of neural network to predict and analysis problems. This paper proposed a method using MIV algorithm to screen variables of BP neural network.And experimental results show that, the proposed technique is practical and reliable.

高桩码头结构安全监测预警模型及工程应用

高桩码头结构在服役期内会出现不同程度的损伤,导致码头结构安全性降低。为保证码头长期安全运行,可通过实时监测确定码头运行状态并及时预警。提出一种高桩码头结构安全监测预警模型,采用不同结构状态的设计控制值作为不同级别的预警阈值,每级预警阈值对应不同的风险程度,针对不同级别的预警提出相应处置措施。将该预警模型应用于南京港某高桩码头的安全监测,对可变荷载作用下码头结构位移和桩基应变进行实时监测,并计算设置码头结构位移和桩基应变三级安全预警阈值。结果表明,码头安全预警阈值的设置及安全告警的应对措施科学有效,实现了码头结构全工况整体技术状态评估和安全预警。

大庆油田X7地区低孔渗储层核磁共振测井可变T_(2)截止值确定方法

T_(2)截止值是计算核磁共振测井束缚水孔隙度、可动流体孔隙度和渗透率的关键参数。在岩心压汞资料与核磁共振测井资料的基础上,提出连续计算低孔隙度低渗透率(低孔渗)储层核磁共振测井T_(2)截止值的方法。用岩心对应深度的核磁共振测井T_(2)谱代替核磁共振实验T_(2)谱,用岩心压汞资料中的渗透率贡献确定束缚水所对应的进汞饱和度,此时剩余汞饱和度即为束缚水饱和度,结合T_(2)谱,通过插值算法得出岩心的T_(2)截止值;分析多块岩心T_(2)截止值在T_(2)谱上的分布规律,从而建立根据T_(2)谱形态连续计算可变T_(2)截止值的方法。在X7地区应用此方法实现核磁共振测井连续T_(2)截止值的计算,用计算出的T_(2)截止值进行孔隙度和渗透率等储层参数计算,明显提升了储层参数计算的精度。

砒砂岩覆土区典型小流域土壤可蚀性K值空间变异特征

[目的]研究砒砂岩覆土区典型小流域土壤可蚀性的空间变异特征,为砒砂岩覆土区土壤侵蚀机理深入探究和土壤侵蚀有效防治提供科学依据。[方法]以内蒙古准格尔旗砒砂岩覆土区二老虎沟小流域为研究对象,采集0—10 cm和10—20 cm土层共144份土壤样品,基于EPIC模型估算土壤可蚀性K值,并利用GIS和地统计学方法分析土壤可蚀性K值空间变异特征。[结果](1)二老虎沟小流域土壤砂粒、粉粒、黏粒和有机碳均呈中等变异程度,除黏粒和有机碳为中等空间自相关性外,其他土壤属性均呈弱空间自相关性。(2)小流域土壤可蚀性K值介于0.018 7~0.047 6 t·hm^(2)·h/(hm^(2)·MJ·mm),0—10 cm和10—20 cm土层K值变异系数分别为15.5%和20.3%,属中等变异强度;0—10 cm土层K值主要受随机性因素影响,呈弱空间自相关性,而10—20 cm土层受随机性因素和结构性因素共同影响,为中等空间自相关性。(3) 3种克里格插值方法结果表明:小流域土壤可蚀性K值空间变异受海拔和坡度影响明显,其总体分布趋势为西部和东南部较高、中部及偏东部较低;普通克里格插值方法较适宜应用于砒砂岩覆土区小流域,其估算结果可较好地显示土壤可蚀性的整体和局部两方面的空间变异特征。(4)在垂直空间变异上,0—10 cm和10—20 cm土层土壤可蚀性K值总体分布规律相似,但10—20 cm土层土壤可蚀性K值较0—10 cm土层空间变异更为明显。[结论]海拔和坡度对砒砂岩覆土区典型小流域土壤可蚀性K值空间变异影响明显,未来应优先对坡面和坡顶区域进行土壤侵蚀防治。

改进近邻人工蜂群算法求解柔性作业车间调度问题

为了更好地解决以最小化最大完工时间为目标的柔性作业车间调度问题,提出了一种改进的人工蜂群算法。首先,采用随机选择和反向学习策略来提高初始蜜源的质量。同时,设计了一种新颖的特征表示方式,用于计算蜜源之间的距离。在引领蜂阶段,通过引入交叉和变异策略来优化种群中的近距离蜜源。在探索蜂阶段,引入了六种变邻域方法,以扩大解空间的搜索范围。而在侦查蜂阶段,则根据蜜源的潜力值剔除局部最优个体。在15个数据集上进行了广泛实验,实验结果表明,该改进算法性能明显优于其他四种著名的群智能优化算法。该研究为解决柔性作业车间调度问题提供了一种新的有效方法,对于实际生产调度具有重要的实用价值。

基于改进SVT的电力负荷数据恢复算法

针对传统奇异值阈值(Singular Value Thresholding,SVT)数据恢复算法在对电力负荷数据恢复中忽视数据先验信息以及大规模数据计算效率低等问题,提出一种基于相空间重构与自适应变步长的改进SVT的数据恢复算法.为解决传统SVT容易忽视数据先验信息的问题,引入相空间重构算法将原始缺失数据映射到高维空间,利用数据间的关联性和结构特征,为后续数据恢复算法提供先验知识;结合对数与Sigmoid函数构建变步长基础函数,并利用等比项提高前期步长,构建自适应变步长SVT算法,克服传统SVT在大规模数据情况下计算效率低的问题.结合多项公用电力负荷数据集及多种常用电力负荷数据恢复算法进行对比实验分析,结果表明,改进SVT算法可获得更好的数据恢复效果,收敛速度、精度以及稳定性得到提升,具有较强的工程实用性.

基于双通道回声状态网络的时间序列补全及单步预测

随着物联网的发展,众多传感器采集到大量具有丰富数据相关性的时间序列,为各种数据挖掘应用提供强大的数据支持。然而,一些客观或主观原因(如设备故障、稀疏感知等)往往会造成采集到的数据出现不同程度的缺失。虽然已有很多方法被提出用于解决这一问题,但这些方法在数据相关性方面或考虑不够全面,或计算成本过高。而且,现有方法仅关注对缺失值的补全,未能兼顾下游应用。针对上述不足,设计了一种兼顾补全与预测任务的双通道回声状态网络。两个通道的网络虽共用输入层,但具有各自的储备池和输出层。两者最大的区别是左/右通道的输出层分别表示输入层前/后一个时刻对应的目标值或预补值。最后将两个通道的估计值进行融合,充分利用来自缺失时刻之前和之后的数据相关性以进一步提升性能。两种缺失现象下(随机缺失和分段缺失)不同缺失率的实验结果表明,所提模型无论是在补全精度还是预测精度上都优于目前流行的各类方法。

改进变值逻辑与线性预测在心音分类中的应用

心音对于评价心脏健康状况具有重要作用.文章介绍了一种新的基于变值逻辑与线性预测倒谱系数融合特征的先心病分类算法,有助于提取心音中的深度病理特征.算法首先对心音进行降噪、包络提取;然后进行变值逻辑运算、标记并转换为可分析的测度数据,并计算信号的线性预测倒谱系数进行特征融合;最后使用随机森林,XGBOOST和LIGHTGBM机器学习分类器进行先心病二分类.研究所用心音样本共4000例,测试结果对正常和异常心音分类的平均准确率为0.9138.算法无需对心音进行心动周期分割,大大简化了分析流程,可望用于先心病的筛查.