基于端铣刀动态铣削力/力矩的新型混联机床逆向动力学仿真研究

混联机床是近年来并联机床发展的一个重要的趋势,清华大学和齐齐哈尔第二机床厂联合开发了一种新型混联机床.本文针对该机床进行了考虑铣削过程的新型混联机床的逆向动力学仿真研究,得到了端铣刀铣削参数对驱动力的影响规律.     

液固耦合系统中液体的有限幅晃动力及晃动力矩

研究弹簧-质量系统与圆柱贮箱类液体有限幅晃动系统间的非线性耦合动力学问题。在建立了六自由度非线性耦合动力学模型的基础上,导出了液体有限幅晃动力和力矩解析表达式。指出在终了构形上积分及压力表达式中的非线性项是有限幅晃动作用力、作用力矩非线性的根源。x、y方向结果之间良好的对称性在很大程度上证明了结果的正确性。通过耦合机理分析可知,这样的理论结果应具有较大的普适性。数值仿真结果与有关实验结果进行了对比。分析认为,在终了构形上求晃动力、晃动力矩较为合理。舍去的高维模态基底及高阶非线性项以及液体晃动阻尼的复杂性是导致偏差的重要原因。     

软件测试力系统要素及属性的模型研究

在深入研究测试过程及工程实践的基础上,提出了软件测试力概念,构建了基于多要素及属性的测试力系统结构模型.以此为依据,通过对测试力系统构成要素进行定量化解析,分析各要素对系统贡献度的大小,计算出系统要素间的关联度指标,确立了影响测试效力的核心因素,为进一步研究软件测试力系统高效的运行机理和测试领域科学的发展机制提供了依据.     

基于深度学习的端到端自动驾驶方法研究

对自动驾驶为整体的系统展开研究,基于卷积神经网络(CNN)构建端到端的自动驾驶系统.实验发现图像序列能够增强转向角预测性能,采用3帧RGB图像作为模型输入的方式相比传统的单流CNN模型预测精度得到显著提高.将时间信息引入端到端自动驾驶系统,提出双流CNN结构,将时空信息有效组合,引导神经网络通过归纳传递发现短期的时间依...     

基于改进社会力模型的人群疏散研究

针对社会力模型在模拟人群恐慌时可能出现的一些问题,文章考虑行人心理对恐慌行为的影响,并结合模型复杂度,对原始社会力模型进行了改进.基于改进的社会力模型,首先研究单出口房间的人群疏散,在人群密度大的情况下重现了‘欲速则不达'现象.进一步,考虑了视野受限情况下双出口大厅的人群疏散,研究了行走方式、出口位置、从众行为的影响.仿真发现:沿墙走策略优于遇墙折回策略,两侧出口的大厅比同侧出口的大厅更利于人群疏散,而从众行为是否有利于快速疏散依赖于人群密度.最后,以上海南京东路地铁站为场景对若干疏散方案进行了研究.     

人口迁移模型的改进及系统动力学仿真预测

求出了Keyfitz模型和Rogers模型的理论解,并以Rogers模型为例,对人口迁移模型进行了改进,使之适用范围更广.在人口迁移模型的基础上,建立了系统动力学模型,并对江西省的人口迁移问题进行了仿真预测,模拟出了江西省农村人口、城镇人口及城市化率的变化趋势.     

两极端值模型的比较和应用

极端值模型主要有分块样本极大值模型和阈顶点模型.从两模型极值分布的内在关系、尾部特征的角度作比较分析和证明.结果表明,它们的内在关系一致;随着形状参数的变化尾部各有不同特征,阈顶点模型更为具体和多样,更适合金融风险度量的应用.     

带利息力的随机双险种风险模型

由于经典风险模型及其拓展模型的局限性,因而构造了一种带利息力的随机双险种风险模型,并且获得了初始资产为u时生存概率满足的积分方程,以及初始资产为0时生存概率的表达式.     

基于网络媒体信息的传染病传播模型及其仿真研究

当一种突发传染病开始流行时,政府、媒体会以各种形式告知民众,有防范意识的民众将采取一定的防范措施来降低感染率.考虑面对一种突发传染病,将易感群体划分为具有防范意识和不具有防范意识两种群体,利用生命周期理论,分析网络媒体信息报道对传染病传播的影响,以此为依据建立一种改进的传染病传播模型(MSI).利用网络大数据得到对传染病有防范意识群体的观测值信息,利用神经网络技术对模型MSI的参数进行反演.然后对模型MSI数值仿真得到传染病传播过程,提出了相应的控制措施.     

基于AHP-TOPSIS评判模型的中药企业研发人员胜任力研究

研发人员作为企业中运用知识进行创新活动的群体,具有知识型员工的行为特质,因而导致研发人员的努力程度和贡献大小很难准确地测量.为了解决科学地评价研发人员胜任力的问题.在层次分析法(AHP)和逼近理想解的排序法(TOPSIS)两个理论的基础上,构建了AHP—TOPSIS综合评判指标体系模型,对3类中药企业研发人员胜任力进行综合评判优选.从职业态度、知识—技术、个人魅力、创新能力和团队合作能力5个方面综合考虑影响研发人员胜任力的评判指标,旨在为企业和不同行业全面评价以及甄选研发人才提供一种可行的方法.     

On New Moment Estimation of Parameters of the Gamma Distribution Using its Characterization

In this paper, the more convenient estimators of both parameters of the gamma distribution are proposed by using its characterization, and shown to be more efficient than the maximum likelihood estimator and the moment estimator for small samples. Furthermore, the distribution of the square of the sample coefficient of variation is obtained by computer simulation for some various values of the parameters and sample size, and thus the simulated confidence interval of its shape parameter is established.     

Moment information and entropy evaluation for probability densities

How much information does the sequence of integer moments carry about the corresponding unknown absolutely continuous distribution? We prove that a reliable evaluation of the corresponding Shannon entropy can be done by exploiting some known theoretical results on the entropy convergence, uniquely involving exact moments without solving the underlying moment problem. All the procedure essentially rests on the solution of linear systems, with nearly singular matrices, and hence it requires both calculations in high precision and a pre-conditioning technique. Numerical examples are provided to support the theoretical results.     

Some Moment and Exponential Inequalities for V-Statistics with Bounded Kernels

This paper provides moment and exponential inequalities for V-statistics with uniformly bounded kernels. The main results are based on moment inequalities for sub-Bernoulli functions of independent random variables. These moment inequalities lead to extensions of the Bernstein inequality to V-statistics. U-statistics are also discussed.     

强混合序列的矩不等式及其应用

对强混合随机变量序列建立一些矩不等式,并应用这些不等式研究固定设计回归模型的一般加权函数估计的渐近正态性。     

Generalized Moment Theory and Bayesian Robustness Analysis for Hierarchical Mixture Models

In applications of Bayesian analysis one problem that arises is the evaluation of the sensitivity, or robustness, of the adopted inferential procedure with respect to the components of the formulated statistical model. In particular, it is of interest to study robustness with respect to the prior, when this latter cannot be uniquely elicitated, but a whole class Γ of probability measures, agreeing with the available information, can be identified. In this situation, the analysis of robustness consists of finding the extrema of posterior functionals under Γ. In this paper, we provide a theoretical framework for the treatment of a global robustness problem in the context of hierarchical mixture modeling, where the mixing distribution is a random probability whose law belongs to a generalized moment class Γ. Under suitable conditions on the functions describing the problem, the solution of this latter coincides with the solution of a linear semi-infinite programming problem.     

Equivalent Conditions of Complete Convergence and Complete Moment Convergence for END Random Variables

In this paper, the complete convergence and the complete moment convergence for extended negatively dependent (END, in short) random variables without identical distribution are investigated. Under some suitable conditions, the equivalence between the moment of random variables and the complete convergence is established. In addition, the equivalence between the moment of random variables and the complete moment convergence is also proved. As applications, the Marcinkiewicz-Zygmund-type strong law of large numbers and the Baum-Katz-type result for END random variables are established. The results obtained in this paper extend the corresponding ones for independent random variables and some dependent random variables.     

Moment inequalities and weak convergence for negatively associated sequences

A probability inequality for S_n and somepth moment (p⩾2) inequalities for |S_n| and max 1⩽k⩽n | S_k| are established. Here S_n is the partial sum of a negatively associated sequence. Based on these inequalities, a weak invariance principle for strictly stationary negatively associated sequences is proved under some general conditions. Project supported by the National Natural Science Foundation of China, the Doctoral Program Foundation of the State Education Commission of China and the High Eductional Natural Science Foundation of Guangdong Province.     

Maximal Moment Inequality for Partial Sums of Strong Mixing Sequences and Application

Some maximal moment inequalities for partial sums of the strong mixing random variable sequence are established. These inequalities use moment sums as up-boundary and improve the corre- sponding ones obtained by Shao （1996）. To show the application of the inequalities, we apply them to discuss the asymptotic normality of the weight function estimate for the fixed design regression model.