Robust control for a class of nonlinear networked systems with stochastic communication delays via sliding mode conception

This paper deals with the robust control problem for a class of uncertain nonlinear networked systems with stochastic communication delays via sliding mode conception （SMC）. A sequence of variables obeying Bernoulli distribution are employed to model the randomly occurring communication delays which could be different for different state variables. A discrete switching function that is different from those in the existing literature is first proposed. Then, expressed as the feasibility of a linear matrix inequality （LMI） with an equality constraint, sufficient conditions are derived in order to ensure the globally mean-square asymptotic stability of the system dynamics on the sliding surface. A discrete-time SMC controller is then synthesized to guarantee the discrete-time sliding mode reaching condition with the specified sliding surface. Finally, a simulation example is given to show the effectiveness of the proposed method.

Stochastic resonance in a single-mode laser driven by frequency modulated signal and coloured noises

By adding frequency modulated signals to the intensity equation of gain noise model of the single-mode laser driven by two coloured noises which are correlated, this paper uses the linear approximation method to calculate the power spectrum and signal-to-noise ratio （SNR） of the laser intensity. The results show that the SNR appears typical stochastic resonance with the variation of intensity of the pump noise and quantum noise. As the amplitude of a modulated signal has effects on the SNR, it shows suppression, monotone increasing, stochastic resonance, and multiple stochastic resonance with the variation of the frequency of a carrier signal and modulated signal.

Finite Temperature Lanczos Method with the Stochastic State Selection and Its Application to Study of the Higgs Mode in the Antiferromagnet at Finite Temperature

We propose an improved finite temperature Lanczos method using the stochastic state selection method. In the finite temperature Lanczos method, we generate Lanczos states and calculate the eigenvalues. In addition we have to calculate matrix elements that are the values of an operator between two Lanczos states. In the calculations of the matrix elements we have to keep the set of Lanczos states on the computer memory. Therefore the memory limits the system size in the calculations. Here we propose an application of the stochastic state selection method in order to weaken this limitation. This method is to select some parts of basis states stochastically and to abandon other basis state. Only by the selected basis states we calculate the inner product. After making the statistical average, we can obtain the correct value of the inner product. By the stochastic state selection method we can reduce the number of the basis states for calculations. As a result we can relax the limitation on the computer memory. In order to study the Higgs mode at finite temperature, we calculate the dynamical correlations of the two spin operators in the spin-1/2 Heisenberg antiferromagnet on the square lattice using the improved finite temperature Lanczos method. Our results on the lattices of up to 32 sites show that the Higgs mode exists at low temperature and it disappears gradually when the temperature becomes large. At high temperature we do not find this mode in the dynamical correlations.

Robust stability analysis for Markovian jumping stochastic neural networks with mode-dependent time-varying interval delay and multiplicative noise

This paper is concerned with the problem of robust stability for a class of Markovian jumping stochastic neural networks （MJSNNs） subject to mode-dependent time-varying interval delay and state-multiplicative noise. Based on the Lyapunov-Krasovskii functional and a stochastic analysis approach, some new delay-dependent sufficient conditions are obtained in the linear matrix inequality （LMI） format such that delayed MJSNNs are globally asymptotically stable in the mean-square sense for all admissible uncertainties. An important feature of the results is that the stability criteria are dependent on not only the lower bound and upper bound of delay for all modes but also the covariance matrix consisting of the correlation coefficient. Numerical examples are given to illustrate the effectiveness.

Spatial Evolution Study of EEDFs and Plasma Parameters in RF Stochastic Regime by Langmuir Probe

An RF compensated cylindrical Langmuir probe system has been developed and used to characterize an RF capacitive two temperature plasma discharge in a stochastic mode. The novelty of the work presented here is the use of the driven electrode （cathode） without ground shield. Measurements of the electron energy distribution function （EEDF） and plasma parameters were achieved under the following conditions： 50 W of RF power and 5× 10-2 mbar of argon pressure. The probe measurements are performed at 3 cm above the electrode and the probe was shifted radially （r direction） from the center （r = 0 cm） of the inter-electrodes region towards the chamber wall （R = 10.75 cm）. The results show that the EEDF is bi-Maxwellian and its shape remains the same through the scanned region. The farther the probe from the central region, the lower the EEDF maximum. The plasma density is observed to decrease according to a Gaussian profile along the radial direction and falls to 50% of its maximum when close to the cathode edge （r = 5.5 cm）. At the same time the effective electron temperature remains constant for r〈4 cm and increases for r≥4 cm. The high-temperature and low-temperature electrons＇ densities and temperatures are also discussed in the article.

基于随机数据驱动SDMD的电力系统区域惯量评估方法

准确评估系统惯量对于支持未来低惯量电力系统的系统安全运行至关重要。文中提出了一种随机数据驱动下基于机电响应特征的系统惯量评估方法,从随机响应信号中实现了区域有效惯量的评估。首先,通过将随机Koopman理论与随机动态系统相结合,推导出了随机Koopman空间上机电特征与系统惯量的耦合关系。然后,利用子空间动态模式分解(SDMD),以数据驱动的方式提取系统机电响应特征。该算法通过正交投影和奇异值分解,在压缩数据的同时实现了系统状态矩阵的低秩近似,可以有效减弱观测噪声对计算结果的影响。最后,采用IEEE 4机2区系统和IEEE 10机39节点系统的数值模拟算例验证了所提算法的有效性和鲁棒性。

Prediction of subsurface settlement induced by shield tunnelling in sandy cobble stratum

This study focuses on the analytical prediction of subsurface settlement induced by shield tunnelling in sandy cobble stratum considering the volumetric deformation modes of the soil above the tunnel crown.A series of numerical analyses is performed to examine the effects of cover depth ratio(C/D),tunnel volume loss rate(h t)and volumetric block proportion(VBP)on the characteristics of subsurface settle-ment trough and soil volume loss.Considering the ground loss variation with depth,three modes are deduced from the volumetric deformation responses of the soil above the tunnel crown.Then,analytical solutions to predict subsurface settlement for each mode are presented using stochastic medium theory.The influences of C/D,h t and VBP on the key parameters(i.e.B and N)in the analytical expressions are discussed to determine the fitting formulae of B and N.Finally,the proposed analytical solutions are validated by the comparisons with the results of model test and numerical simulation.Results show that the fitting formulae provide a convenient and reliable way to evaluate the key parameters.Besides,the analytical solutions are reasonable and available in predicting the subsurface settlement induced by shield tunnelling in sandy cobble stratum.

基于随机子空间法的滑动轴承运行模态参数识别

滑动轴承的运行模态参数是其状态监测和早期故障诊断的重要指标,利用随机子空间法识别滑动轴承运行模态参数时,环境噪声和阶数过估计引起的虚假模态会影响真实模态参数的识别。为减少虚假模态的干扰,首先对振动信号利用互补集合经验模态分解和小波变换相结合的方法进行降噪处理,然后将预处理后的信号分段并分别进行模态参数识别,通过对比同阶极点获得更清晰的稳定图,最后采用谱聚类算法实现模态参数的自动选择。通过数值仿真和相关试验验证该方法的有效性。

基于高精度模态综合法的螺栓连接组合结构不确定模型更新

由于螺栓结构中存在复杂的微观特征,数值模型中无法避免的会引入不确定性。针对螺栓设计中参数不确定性量化问题,一种基于高精度模态综合法的螺栓连接组合结构随机模型更新方法被提出。首先考虑高阶剩余模态的影响,推导了适用于螺栓组合结构弹性交界面的高精度模态综合法;之后,在贝叶斯推理框架下,通过组合结构的自振频率和振型的概率分布,建立螺栓连接参数的后验概率密度函数,并借助DRAM(delayed rejection adaptive metropolis)抽样方法量化参数的不确定性。数值算例表明,针对各子结构之间自振频率差别较大的结构,与模态综合法相比,本研究方法在较少子结构模态参与的情形下,也可以保证良好的模型更新结果。

改进DBSCAN的自动工作模态分析方法

为解决随机子空间法在模态参数识别过程中自动性差、虚假模态难以识别剔除等问题,提出一种新的模态参数辨识方法。采用协方差驱动的随机子空间法(Covariance⁃driven stochastic identification,SSI⁃COV)识别系统的模态参数;根据软硬准则初步剔除虚假模态并绘制三维稳定图;对基于密度的带噪声的空间聚类算法(Density⁃based spatial clustering algorithm with noise,DBSCAN)进行改进,自动确定敏感参数ε,并对候选模态进行聚类分析;对每一簇类模态,计算模态质量评价准则(Modal quality assessment criterion,MQAC),制定筛选准则,自动剔除虚假模态并识别真实模态。利用本文方法对桁架结构、广州塔、Z24桥实例进行模态参数识别验证,结果表明该方法可实现典型工程结构的自动工作模态分析,可有效剔除非白噪声激励及测量噪声导致的虚假模态。

数字贸易发展对我国区域出口效率的影响

随着人口红利的消失和资源的紧缺,由传统要素驱动的出口增长机制难以为继。在全球贸易保护主义抬头背景下,加快发展数字贸易,是推动我国区域协调发展、实现外贸高质量发展的必然选择。以2012—2022年省级面板数据为研究样本,从综合评价的角度,采用熵值法从数字基础设施、数字创新能力、数字产业及数字贸易潜力4个方面构建省份数字贸易发展水平的综合指标体系并进行测度,同时通过构建随机前沿引力模型测算各区域出口效率,采用空间杜宾模型从基础设施建设和资源配置两方面分析数字贸易发展对出口效率的影响。研究发现,2012—2022年间,我国区域数字贸易发展水平和出口效率不断提高,但存在明显的区域发展不平衡问题,呈现东高西低的特征,且东部地区数字贸易发展水平和出口效率远高于中、西部地区。数字贸易发展对我国区域出口效率具有空间溢出效应且存在倒U形特征,数字贸易的发展弱化了传统贸易壁垒,既显著促进本地出口效率提升,也促进邻近地区出口效率提升,但随着数字贸易发展引致数字贸易壁垒的出现,又可能成为制约出口效率提升的重要因素。机制分析表明,基础设施建设的完善能够强化数字贸易发展对出口效率提升的作用,且具有显著的空间溢出效应。资源配置的改善能够强化数字贸易发展对本地区出口效率提升的作用,但在不同的空间权重矩阵下呈现出不同的空间溢出效应。区域异质性分析表明,完善基础设施建设对出口效率的促进主要在中、西部地区起作用,资源配置优化效应在东部地区发挥作用。基于此,要完善数字贸易规则,弱化数字贸易壁垒。加强区域协同,推动地区出口效率提升。因地制宜制定发展战略,实现区域外贸高质量发展。

基于激光多普勒测振的结构工作模态分析

为验证激光多普勒测振仪快速识别高层建筑结构模态参数的适用性,提出一种采用两台远距离激光多普勒测振仪进行环境振动测试的方法。以一悬臂灯杆为例进行实测,获得振动速度响应。该方法实施便捷,测量精度能够满足要求。本文采用频域分解法(FDD)及随机子空间算法(SSI)对其模态参数进行了识别,为进一步应用于环境激励下的高层建筑等大型结构提供试验基础。结果表明,两种算法均可较好地获取结构自振频率及模态振型,自振频率差异很小,振型则几乎一致。并对灯杆进行简化的有限元分析,将模态识别结果与数值模拟结果相比,二者振型吻合较好,但自振频率略有差别。为避免模态遗漏和虚假模态,应结合多种模态识别算法进行模态分析。最后采用该方法对一超高层建筑进行环境激励下的振动实测与模态参数识别。

Probabilistic stability of uncertain composite plates and stochastic irregularity in their buckling mode shapes:A semi-analytical non-intrusive approach

In this study,the mechanical properties of the composite plate were considered Gaussian random fields and their effects on the buckling load and corresponding mode shapes were studied by developing a semi-analytical nonintrusive approach.The random fields were decomposed by the Karhunen−Loève method.The strains were defined based on the assumptions of the first-order and higher-order shear-deformation theories.Stochastic equations of motion were extracted using Euler-Lagrange equations.The probabilistic response space was obtained by employing the nonintrusive polynomial chaos method.Finally,the effect of spatially varying stochastic properties on the critical load of the plate and the irregularity of buckling mode shapes and their sequences were studied for the first time.Our findings showed that different shear deformation plate theories could significantly influence the reliability of thicker plates under compressive loading.It is suggested that a linear relationship exists between the mechanical properties’variation coefficient and critical loads’variation coefficient.Also,in modeling the plate properties as random fields,a significant stochastic irregularity is obtained in buckling mode shapes,which is crucial in practical applications.

Markov切换拓扑下离散多智能体系统滑模一致性控制

针对具有Markov切换拓扑的离散多智能体系统,研究了其在滑模控制下的均方领导跟随一致性问题。通过引入Markov切换拓扑,描述了智能体之间的随机信息交互。采用一致性误差定义了与随机切换拓扑相关的滑模面。根据理想准滑动模态条件得到了等效控制律,并将离散多智能体系统的一致性问题转化为领导跟随误差系统的稳定性问题;利用Lyapunov函数的构造和矩阵理论最终给出了离散滑动模态均方领导跟随一致性的充分条件,并给出了控制增益的设计方法。然后利用趋近律原理给出了一种离散时间滑模控制器,实现了跟随误差能够在有限时间内到达所定义的滑模面区域内。如何处理部分转移概率未知的Markov切换拓扑和设计与随机切换拓扑相关的滑模面是本文的两个难点。最后通过一个数值算例验证了所提方法的有效性。

基于改进的粒子群优化的北斗卫星钟差组合预报方法

针对现有卫星钟差预测模型预报精度不高的问题,本文构建一种将集合经验模态分解和改进的粒子群优化的随机森林预测算法来预测卫星钟差数据,并采用GPS、北斗钟差数据验证算法的正确性。利用改进的粒子群优化的随机森林模型对集合经验模态分解后的各分量进行预测,并将各分量预测的结果叠加为最终预测结果。最终通过北斗钟差数据取得优良的预测结果,与ISU-P的数据比较,精度提升幅度在12.7%~21.5%之间。

An Extended Model for Interaction Between Left-hand Superluminous Waves and Magnetospheric Electrons

The left-hand superluminous electromagnetic waves, L-O mode and L-X mode, can be excited and observed in the auroral cavity of the Earth during the magnetic storms. The two modes can propagate into outer radiation zone and encounter enhanced resonant interactions with the trapped energetic electrons over a wide range of magnetosphere. A current first-order resonant model is extended to evaluate the stochastic acceleration of electrons by the L-O mode and L-X mode at the higher-order resonance. Similar to the first-order resonance, L-O mode can produce significant acceleration of electrons at the higher harmonic resonances over a wide range of wave normal angles and spatial regions. However, the higher harmonic resonance＇s contribution for significant electron acceleration by L-X mode is less than that of the first order resonance, with the requirement of higher minimum energies, e.g., -1 MeV in the outer radiation belt. This indicates that L-O mode may be one of the efficient mechanisms for the stochastic acceleration of electrons within the outer radiation zone.

Identifying the dependency pattern of daily rainfall of Dhaka station in Bangladesh using Markov chain and logistic regression model

Bangladesh is a subtropical monsoon climate characterized by wide seasonal variations in rainfall, moderately warm temperatures, and high humidity. Rainfall is the main source of irrigation water everywhere in the Bangladesh where the inhabitants derive their income primarily from farming. Stochastic rainfall models were concerned with the occurrence of wet day and depth of rainfall for different regions to model the daily occurrence of rainfall and achieved satisfactory results around the world. In connection to the Markov chain of different order, logistic regression is conducted to visualize the dependence of current rainfall upon the rainfall of previous two-time period. It had been shown that wet day of the previous two time period compared to the dry day of previous two time period influences positively the wet day of current time period, that is the dependency of dry-wet spell for the occurrence of rain in the rainy season from April to September in the study area. Daily data are collected from meteorological department of about 26 years on rainfall of Dhaka station during the period January 1985-August 2011 to conduct the study. The test result shows that the occurrence of rainfall follows a second order Markov chain and logistic regression also tells that dry followed by dry and wet followed by wet is more likely for the rainfall of Dhaka station and also the model could perform adequately for many applications of rainfall data satisfactorily.

利用改进交叉模型交叉模态的随机模型修正方法

将混合摄动-伽辽金方法和改进的交叉模型交叉模态技术相结合,提出了一种随机模型修正方法。该方法有效缓解了模型修正过程中测量数据有限和测量误差不确定的影响。考虑到实测模态数据具有不确定性,基于改进的交叉模型交叉模态方法,建立了一个新的描述结构随机参数和随机响应关系的模型修正方程。利用混合摄动-伽辽金方法求解该随机修正方程,进而得到结构随机修正参数的统计特征。简支梁的数值结果表明,该方法在测量数据不确定性较大时仍能保持很高的修正精度,同时计算效率比蒙特卡罗模拟方法高出一个数量级。在测量模态数据较少的情况下,该方法比单独的混合摄动-伽辽金修正方法修正效果好,且比交叉模型交叉模态法的修正精度更高。框架试验的结果表明,该方法可以同时修正结构的刚度和质量,修正后的结构参数与预设工况基本吻合,同时能复现结构的测量模态,从而验证了所提方法的有效性。

数字经济背景下我国批发零售业发展方式转变的实证研究

作为我国数字经济发展最活跃的领域之一的批发零售业,成为数字化创新实践的领跑者。为探讨数字化背景下我国批发零售业发展方式转变,采用1999-2020年批发零售业省际面板数据,使用随机前沿模型,进行实证分析。研究发现:现阶段我国批发零售业具有明显的资本和技术进步交替驱动发展,劳动的作用相对有限的特征。技术效率总体呈先升后降的发展趋势,2004年之后不断下降。原因在于随着数字技术在批发零售业广泛应用,线上线下深度融合,新业态、新模式不断涌现,整个产业发展进入深度转型升级调整期。区域发展分化显著,东部地区技术效率优势显著,中和西部地区尚不及全国均值。对外开放、受教育程度、基础设施和信息化发展对我国批发零售业技术效率提升起到重要作用,政府财政支出则带来负面影响。本文丰富了流通产业理论和实证研究,为进一步发挥出产业的基础性、先导性和战略性作用,实现高质量发展,提供政策启示。

基于改进CEEMDAN和t-SNE的故障特征提取方法

针对非线性、非稳定振动信号难以提取有效故障特征的问题,提出一种基于改进自适应噪声完备集合经验模态分解(CEEMDAN)和t-分布随机邻域嵌入(t-SNE)算法相结合的故障特征提取方法。利用三次Hermite插值代替三次样条插值构造包络线,提高传统CEEMDAN对非平稳信号的分解精度;利用改进后的CEEMDAN对原始信号分解并通过相关系数筛选出有效固有模态分量(IMF),提取有效IMF分量的时频特征、奇异值和能量值构建高维混合域特征集;最后,通过t-SNE算法挖掘高维混合域特征信息得到低维敏感特征,并将其输入到支持向量机中进行分类,以分类准确率作为特征提取效果评价指标。在齿轮箱故障模拟实验台进行实验验证,结果表明该方法能够准确地提取故障特征,为故障特征提取提供新思路。