Discrete time transfer matrix method for dynamics of multibody system with flexible beams moving in space

In this paper, by defining new state vectors and developing new transfer matrices of various elements mov- ing in space, the discrete time transfer matrix method of multi-rigid-flexible-body system is expanded to study the dynamics of multibody system with flexible beams moving in space. Formulations and numerical example of a rigid- flexible-body three pendulums system moving in space are given to validate the method. Using the new method to study the dynamics of multi-rigid-flexible-body system mov- ing in space, the global dynamics equations of system are not needed, the orders of involved matrices of the system are very low and the computational speed is high, irrespec- tive of the size of the system. The new method is simple, straightforward, practical, and provides a powerful tool for multi-rigid-flexible-body system dynamics.

Feature Extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and Non-negative Matrix Factorization

Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.

Solving Diffusion Time in Heterogeneous Microscale Rock Matrix by 3D Computations: Non-Fickian Dispersion Observed in Porous Media

Understanding and simulating the underlying microscopic physics of the rock matrix is very useful for determining macroscopic physical properties such as permeability. Matrix diffusion is an important transport parameter controlling the late-time behaviour of breakthrough curves (BTCs). We compute the memory function, implemented in the sink/source term of Mobile-immobile mass transfer by solving the matrix diffusion using a time diffusion random-walk approach. The diffusion is controlled by different parameters like the porosity, tortuosity, mobile-immobile interface and immobile domain cluster shapes. All these properties are investigated by X-ray microtomography that captures the main characteristics of matrix diffusion at three dimensions. We compare the memory function deduced from the field-scale tracer tests well with the computed memory function. Simulation results of the memory function appeared to be coherent with that measured from the tracer test for a large tortuosity value. Probably, the diffusion paths are longer, and they are controlled by the properties mentioned above. From a representative elementary volume of natural reservoirs studied here, we conclude that, microscale diffusion process in the immobile domain play a crucial role to better understand the non-Fickian dispersion measured from the tracer test.

Electricity Price Influence Factors Analysis Using Stochastic Matrix for Real-Time Electricity Price Forecasting

Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.

On stabilization for a class of nonlinear stochastic time-delay systems:a matrix inequality approach

This paper treats the feedback stabilization of nonlinear stochastic time-delay systems with state and control-dependent noise. Some locally （globally） robustly stabilizable conditions are given in terms of matrix inequalities that are independent of the delay size. When it is applied to linear stochastic time-delay systems, sufficient conditions for the state-feedback stabilization are presented via linear matrix inequalities. Several previous results are extended to more general systems with both state and control-dependent noise, and easy computation algorithms are also given.

How Kieffer Density Matrix Formalism Aids Different Initial Time Steps, Leading to Lorentz Violations, and Breakup of Primordial Black Holes for GW Generation

A result from Kieffer, as outlined at the beginning of the article identifies two different candidates for initial time steps, delta t. We assert that this difference in time steps may be related to a specific early universe Lorentz Violation. The author asserts that the existence of early universe Lorentz violation in turn is assisting in a breakup of primordial black holes. And that also has a tie into Kieffer different time steps as outlined. And the wrap up is given in the final part of this document.

用于有界噪声时变矩阵计算的终端零化神经网络

为提升零化神经网络(ZNN)求解时变矩阵计算问题时的收敛性能,提出一种具有抗噪能力的终端零化神经网络(TZNN)及其对数加速形式(LA-TZNN)。对误差动态的终态吸引性展开分析,结果表明所提网络在受到有界噪声干扰时仍能在固定时间内使误差归零,其中LA-TZNN可实现对数调节时间稳定,收敛速度相较于TZNN更快。考虑到实际情况中初始误差有界,给出半全局意义上的调节时间上界,并通过设置可调参数,使网络实现预定义时间稳定。将2种模型应用于时变矩阵求逆和PUMA560机械臂重复运动规划问题,仿真结果验证了所提方法相较于传统ZNN设计,调节时间更短,收敛精度更高,并能够有效抑制有界噪声干扰。

面向不同训练内容的训练环境快速切换方法

训练环境中,传统的人工部署和配置切换方式效率低且易出错,不利于用户在不同训练对象和训练模式下对训练的灵活组织与应用。为满足当前训练需求,提出了一种面向不同训练内容的训练环境快速切换方法。首先,阐述了该方法的主要原理;然后,描述了训练科目编排优化、基于应用集群和容器调度的系统快速构建以及轻量级客户端集成3种关键技术;最后,给出了应用实例。应用实例表明,该方法实现了训练内容快速切换和训练资源动态重组,提升了训练效能。

Event-Triggered Finite-Time H∞ Filtering for Discrete-Time Nonlinear Stochastic Systems

This paper addresses the problem of event-triggered finite-time H∞ filter design for a class of discrete-time nonlinear stochastic systems with exogenous disturbances. The stochastic Lyapunov-Krasoviskii functional method is adopted to design a filter such that the filtering error system is stochastic finite-time stable (SFTS) and preserves a prescribed performance level according to the pre-defined event-triggered criteria. Based on stochastic differential equations theory, some sufficient conditions for the existence of H∞ filter are obtained for the suggested system by employing linear matrix inequality technique. Finally, the desired H∞ filter gain matrices can be expressed in an explicit form.

精细积分方法的发展与扩展应用

钟万勰院士于1991年首先提出计算矩阵指数的精细积分方法,其要点是2N类算法和增量存储。精细积分方法可给出矩阵指数在计算机意义上的精确解,为常微分方程的数值计算提供了高精度、高稳定性的算法,现已成功应用于结构动力响应、随机振动、热传导以及最优控制等众多领域。本文首先介绍矩阵指数精细积分方法的提出、基本思想和发展;然后依次介绍在时不变/时变线性微分方程、非线性微分方程以及大规模问题求解中发展起来的各种精细积分方法,分析了其优缺点和适用范围;最后介绍了精细积分方法的基本思想在两点边值问题、椭圆函数和病态代数方程等问题的扩展应用,进一步展示了该思想的特色。

A novel sample preparation method of matrix-assisted laser desorption/ionization time of flight mass spectrometry for polystyrene

A novel sample preparation method of matrix-assisted laser desorption/ionization mass spectrometry for polystyrene was reported. Compared to the conventional dried-droplet method, the efficiency of ionization and signal intensity of mass spectra were improved. The mechanism was also analyzed.

MicrobMatcher: a microbial comparison software based on matrix-assisted laser desorption/ionization with time-of-flight mass spectrometry

Matrix-assisted Laser Desorption/Ionization with Time-of-flight Mass Spectrometry (MALDI-TOFMS) was investigated as a method for the rapid identifica-tion of species. Current demand in microbial identi-fication is how to compare unknown strains to the known one quickly, semi-automatically and accurately. In this paper, we present a software tool that allows flexibly microbial matching in a user-friendly way, by letting the users to customize comparison parameters including: in vitro transcription enzyme, mass tolerance,minimum fragment length, intensity threshold and corresponding weights. We provide three spectral scoring functions to compute the affin-ity between the species. Therefore, the precision of microbial comparison increases. To test and verify this tool, we employed experimental spectral data based on MALDI-TOFMS and the gene sequences of E.coli and Salmonella. This software is written in Java for cross-platform intention.

结构动力问题的高阶精确时步群积分方法

高阶精确时间积分方法可为与时间相关的高频复杂动力行为提供高精度的预测结果,但既有高阶精确时间积分方法普遍存在计算工作量偏大的问题,难以满足实际结构线性和非线性动力分析日益增长的计算需求。该文提出了一种基于时步群的高阶精确时间积分方法,将p(p≥2)个相邻的未知时步组成待求解的时步群,以结构动力方程积分解为基础构建逐时步群求解结构动态响应的时间积分方案。在对每个时步群进行积分的过程中,无需联立求解方程,仅通过矩阵乘法运算即可一次性地计算得到时步群内全部p个时步的动态响应。数值特性分析以及线性与非线性算例试验均表明,该文算法精度高、稳定性好、数值耗散可控,在选择较大的时间步距情形下依然能够稳定地获得高精度的计算结果。相较传统二阶精度时间积分方法,该文算法的计算效率亦有较大幅度地提高。

GNSS/SINS组合导航系统的改进变分贝叶斯自适应滤波算法

针对系统噪声及测量噪声统计量不准确的组合导航系统线性高斯状态模型,提出一种组合导航系统的改进变分贝叶斯自适应滤波算法(modified variational Bayesian adaptive filter,MVBAKF)。首先,选择Wishart分布作为已知均值的高斯分布协方差矩阵的共轭先验,并给出测量噪声方差、状态向量及其预测误差协方差矩阵的联合概率分布函数;然后,利用变分贝叶斯方法给出测量噪声方差及状态向量预测误差协方差矩阵的计算公式,进而提出具有迭代性质的MVBAKF算法;最后,进行基于MVBAKF算法的GNSS/SINS组合导航系统仿真实验。结果表明,相对于传统VBAKF算法,MVBAKF算法可较准确地估计测量噪声方差,有效克服系统噪声统计量不准确对滤波精度的影响,进而提高组合导航系统的滤波精度。

全方位机器人的重心位置预测与轨迹跟踪控制

针对全方向移动机器人存在非线性动态强耦合、实时重心偏移及难以实现高精度跟踪控制的问题,本文提出一种基于长短期记忆(LSTM)神经网络的重心位置在线预测的轨迹跟踪控制法.首先,建立考虑重心偏移的动力学模型并基于LSTM神经网络训练构建其对比模型;其次,基于模型对比法实时估计重心偏移参数,再基于张神经网络(ZNN)对估计的重心偏移参数进行预测以减小估计过程引起的滞后;最后,基于非线性动态反馈解耦法设计数值加速度控制算法,且基于离散系统极点配置法分析了系统的稳定性.仿真结果验证了所提方法相对于数值加速度控制器与自适应控制器因能在线预测重心偏移参数完成高精度动态解耦实现控制精度的提高.实际实验中,所提控制算法相比数值加速度控制及模型预测控制,其跟踪精度明显提高,这表明所提控制算法可显著减小重心偏移对跟踪控制精度的影响.

基于事件时间论元抽取的文档级时序抽取方法研究

事件时序关系识别任务属于关系抽取领域的一个分支,近年来受到越来越多的关注。目前,关于章级事件时序关系抽取的研究在现有神经网络方法中尚未得到充分深入探讨。因此,本文提出一种基于篇章事件时序关系矩阵的方法,利用语言学中的时间论元理论来指导模型抽取效果;通过修改预训练阶段的词嵌入,将句子的时态、体态和时间副词作为额外信息融入事件触发词的词嵌入表达中;同时,模型还利用了事件时间论元抽取任务进行辅助训练,从而构建了具有增强时间特征的文本表达。研究发现通过将事件时间论元抽取任务融入模型训练过程,该模型在事件时序关系抽取任务上获得了比基线更好的效果。

基于绕组短路冲击响应时频特性的多特征联合形变判定方法

为研究变压器短路冲击信号包含的物理特征与绕组形变之间的关系,提出一种基于绕组冲击响应时频特性的特征提取及形变判定方法。基于单自由度系统的相对位移模型构建绕组的短路冲击响应谱,得到其响应点频率偏移特征。对Hilbert能量谱进行非均匀频率带宽划分,修正累积效应的影响,提取不同频带范围内的能量带熵并计算加权能量带熵,表征特定频带内的能量混乱度;构建时频矩阵,量化能量密度分布、短时能量衰减变化等细节特征,对该矩阵进行奇异值分解,根据奇异值分布特性计算每个元素的权重值。据此提出基于加权能量带熵及基频奇异值畸变率的联合绕组状态判定方法,经三组共17次短路冲击试验数据验证,冲击信号的相对能量带熵增量变化与绕组产生的累积微应变呈高度正相关,基频奇异值畸变率与绕组发生形变的规律一致,发生形变时,该畸变率的值会大于2.0,证明了该判定方法的有效性。该方法可在变压器发生短路后对绕组的状态进行预判,进而考虑是否需要进一步处理,对变压器绕组状态的在线监测具有一定的指导意义。

铝酸盐体系中氧化时间对碳化硅颗粒增强铝基复合材料微弧氧化膜层的影响

[目的]探究氧化时间对碳化硅颗粒增强铝基(SiC_(p)/Al)复合材料微弧氧化膜层的影响。[方法]选用铝酸盐体系作为电解液,对SiC_(p)/Al复合材料进行微弧氧化处理,分析氧化时间对膜层组织结构、物相、厚度、粗糙度、结合力、电绝缘性及耐蚀性的影响。[结果]随着氧化时间延长,膜层逐渐变得连续均匀,厚度增加。若氧化时间过长,膜层会出现层叠现象,形成大尺寸微孔及裂纹,且生长速率越来越低。膜层结合力随氧化时间延长先增大后减小,在60 min时最大,达到39.85 N。氧化时间为10 min时,膜层的电绝缘性及耐蚀性最优,100 V和500 V电压下的绝缘电阻分别达到3.11×10^(12)Ω和1.41×10^(12)Ω,腐蚀电位为-0.6298 V,腐蚀电流密度为1.332×10^(-7)A/cm^(2)。[结论]SiC_(p)/Al复合材料表面微弧氧化膜层的连续性、均匀性及生长速率均与氧化时间有关。需选择合适的氧化时间,才能制备出连续、均匀且综合性能优异的膜层。

基于多目标函数的避洪转移安置方案优化

针对目前避洪转移方案仅考虑时间或距离成本最优导致部分安置点人数分配不合理的问题,从“安全第一、就近就便、平灾结合、利于管理”的避洪转移安置方案基本要求出发,首先对安置点的适用性、分布合理性及容量大小3个方面进行分析,依据“时间、距离、适宜度和安置容量”四因素初步筛选合适的安置点。然后通过路权函数建立避洪转移时间成本矩阵,以转移时间成本和安置点的优化分配为多目标函数确定避洪转移安置点的最优分配方案。最后以TOPSIS评价方法对避洪转移安置点分配的单目标方案与多目标方案进行对比分析。结果表明:多目标方案的相对接近度大于单目标方案,基于多目标函数确定的避洪转移安置分配方案更加合理。

板翅式换热器传递函数动态模型及参数确定方法

飞机综合环控/热管理系统迫切需要建立快捷高效的换热器动态仿真模型,以满足先进控制系统设计需求。基于此,针对板翅式换热器动态模型,提出一种包含2个延迟环节和4个一阶惯性环节的传递函数矩阵形式,并从换热器机理模型出发,利用拉普拉斯变换推导模型中4个时间常数的计算公式,提出基于换热器效率的传热热阻计算关系式的辨识方法,解决了主要模型参数设置难题。以板翅式换热器为研究对象,在Simulink仿真平台搭建其传递函数动态模型,并与AMESim仿真平台搭建的机理模型进行对比分析,结果显示:2种模型所得空气和冷却水出口温度分别随入口温度和质量流量阶跃变化的动态响应曲线吻合很好,其中,4种工况空气和冷却水出口温度最大稳态偏差分别为0.034℃和0.029℃,当冷却水入口质量流量阶跃变化时,空气出口温度动态响应相对偏差最大,为9.27%,当空气入口质量流量阶跃变化时,冷却水出口温度动态响应相对偏差最大,为7.03%。