Plasma Simulation beyond Rigid-Macroparticle Approximation

Current mainstream method of simulating plasma is based on rigid-macroparticle approximation in which many realistic particles are merged, according to their initial space positions regardless of their initial velocities, into a macroparticle, and do a global motion. This is a distorted picture because what each macroparticle do is to break into, because of differences among velocities of contained realistic particles, pieces with different destinations at next time point, rather than a global moving to a destination at next time point. Therefore, the scientific validity of results obtained from such an approximation cannot be warranted. Here, we propose a solution to this problem. It can fundamentally warrant exact solutions of plasma self-consistent fields and hence those of microscopic distribution function.

Cross Validation Based Model Averaging for Varying-Coefficient Models with Response Missing at Random

In this paper, a model averaging method is proposed for varying-coefficient models with response missing at random by establishing a weight selection criterion based on cross-validation. Under certain regularity conditions, it is proved that the proposed method is asymptotically optimal in the sense of achieving the minimum squared error.

SAMPLE AVERAGE APPROXIMATION METHOD FOR A CLASS OF STOCHASTIC VARIATIONAL INEQUALITY PROBLEMS

This paper considers a class of stochastic variational inequality problems. As proposed by Jiang and Xu （2008）, by using the so-called regularized gap function, the authors formulate the problems as constrained optimization problems and then propose a sample average approximation method for solving the problems. Under some moderate conditions, the authors investigate the limiting behavior of the optimal values and the optimal solutions of the approximation problems. Finally, some numerical results are reported to show efficiency of the proposed method.

Novel self-consistent mean field approximation and its application in strong interaction phase transitions

We propose a novel self-consistent mean field approximation method by means of a Fierz transformation,taking the Nambu-Jona-Lasinio model as an example.This new self-consistent mean field approximation introduces a new free parameter a to be determined experimentally.When a assumes the value of 0.5,the approximation reduces to the mean field calculation commonly used in the past.Subsequently,we study the influence of the undetermined parameter a on the phase diagram of the two-flavor strong interaction matter.The value of a plays a crucial role in the strong interaction phase diagram,as it not only changes the position of the phase transition point of strong interaction matter,but also affects the order of the phase transition.For example,when a is greater than the critical valueαc = 0.71,then the strong interaction matter phase diagram no longer has a critical end point.In addition,in the case of zero temperature and finite density,we found that when a>1.044,the pseudo-critical chemical potential corresponds to ～4-5 times the saturation density of the nuclear matter,which agrees with the expected results from the picture of the hadrons degree of freedom.The resulting equations of state of strong interaction matter at low temperatures and high densities will have an important impact on studies concerning the mass radius relationship of neutron stars and the merging process of binary neutron stars.

Self-consistent mean field approximation and application in three-flavor NJL model

In this study,we apply a self-consistent mean field approximation of the three-flavor Nambu–Jona-Lasinio(NJL)model and compare it with the two-flavor NJL model.The self-consistent mean field approximation introduces a new parameter,α,that cannot be fixed in advance by the mean field approach itself.Due to the lack of experimental data,the parameter,α,is undetermined.Hence,it is regarded as a free parameter and its influence on the chiral phase transition of strong interaction matter is studied based on this self-consistent mean field approximation.αaffects numerous properties of the chiral phase transitions,such as the position of the phase transition point and the order of phase transition.Additionally,increasingαwill decrease the number densities of different quarks and increase the chemical potential at which the number density of the strange quark is non-zero.Finally,we observed thatαaffects the equation of state(EOS)of the quark matter,and the sound velocity can be calculated to determine the stiffness of the EOS,which provides a good basis for studying the neutron star mass-radius relationship.

Average Error Bounds of Trigonometric Approximation on Periodic Wiener Spaces

In this paper, we study the approximation of identity operator and the convolution inte- gral operator Bm by Fourier partial sum operators, Fejer operators, Vallee--Poussin operators, Ces^ro operators and Abel mean operators, respectively, on the periodic Wiener space （C1 （R）, W°） and obtaia the average error estimations.

A case study on sample average approximation method for stochastic supply chain network design problem

This study aims to solve a typical long-term strategic decision problem on supply chain network design with consideration to uncertain demands. Existing methods for these problems are either deterministic or limited in scale. We analyze the impact of uncertainty on demand based on actual large data from industrial companies.Deterministic equivalent model with nonanticipativity constraints, branch-and-fix coordination, sample average approximation(SAA) with Bayesian bootstrap, and Latin hypercube sampling were adopted to analyze stochastic demands. A computational study of supply chain network with front-ends in Europe and back-ends in Asia is presented to highlight the importance of stochastic factors in these problems and the efficiency of our proposed solution approach.

基于代客充电模式的电动汽车充电站布局优化

当新能源汽车用户自行前往充电站充电,将会产生时间成本和等待焦虑。而代客充电服务能够有效解决这些问题,但是其服务成本受到充电站布局与社区用户分布等因素的影响。为探究充电站布局对代客充电服务模式的影响,本文建立基于充电时间的等待焦虑程度量化模型,引入代客充电服务比率量化用户的服务选择行为,同时考虑用户需求的随机性,以最小化充电站建设成本与用户前往充电站充电的行驶成本、最大化代客充电业务利润为优化目标,建立多目标两阶段随机混合整数规划模型,并使用Epsilon约束法与抽样平均近似方法进行求解。本文在对模型及其参数进行数值分析的基础上,以合肥市瑶海区为背景设计案例研究,发现在充电站建设数目一定的前提下,通过改变充电站布局可以提升代客充电业务利润,但同时也会导致用户充电行驶成本的升高。

巷道平均风速单点测试方法误差研究

为验证巷道平均风速单点测试方法可行性,提高巷道平均风速单点测试方法精度,采用Fluent数值模拟方法,计算各种断面形状、断面尺寸及支护方式巷道断面上平均风速单点测试误差分布。研究结果表明:在任意断面形状、断面尺寸及支护方式巷道断面上任意测点,巷道平均风速单点测试误差均小于6%,因此,巷道平均风速单点测试方法可行。根据巷道断面上平均风速单点测试误差分布情况,尽可能将风速传感器布置在误差小的位置,可进一步提高巷道平均风速单点测试方法精度。研究结果可为解决巷道平均风速快速精准测试问题,进而推进矿井通风智能化发展提供参考。

Asymptotic optimality for consensus-type stochastic approximation algorithms using iterate averaging

This paper introduces a post-iteration averaging algorithm to achieve asymptotic optimality in convergence rates of stochastic approximation algorithms for consensus control with structural constraints. The algorithm involves two stages. The first stage is a coarse approximation obtained using a sequence of large stepsizes. Then, the second stage provides a refinement by averaging the iterates from the first stage. We show that the new algorithm is asymptotically efficient and gives the optimal convergence rates in the sense of the best scaling factor and ＇smallest＇ possible asymptotic variance.

色噪声激励下分数阶机械系统的响应分析

研究了无量纲化的机械系统含有分数阶情况下受色噪声激励的响应。首先,基于含分数阶的机械系统引入色噪声并利用Gamma函数及其性质对Caputo定义的分数阶进行微分近似变换;其次,借用随机平均法对系统进行降维处理,得到控制系统响应的FPK方程;最后,依据FPK方程的平稳解,分析了色噪声对分数阶机械系统的响应影响。结果表明,分数阶下系统达到稳定的概率增大,而噪声强度D和关联时间τ的增大会使得系统达到稳定的概率减小,另外噪声强度D和关联时间τ的增大会引起随机P分岔。

介数中心性和平均最短路径长度整合近似算法

基于Brandes算法给出了复杂网络中介数中心性和平均最短路径长度的整合近似算法,通过理论分析和Rocketfuel项目实测数据的实验分析,验证了该整合算法能够快速有效地估计出复杂网络的介数中心性和平均最短路径长度,为进一步的研究工作奠定了基础。

分数阶PID控制对单自由度线性振子的影响

研究了基于速度反馈分数阶PID控制的单自由度线性振子的自由振动,利用平均法得到了系统的近似解析解。研究发现分数阶PID控制的比例环节以等效线性阻尼的形式影响系统的振幅,积分环节以等效线性阻尼和等效线性刚度的形式影响系统的动力学特性,微分环节以等效线性阻尼和等效线性负刚度的形式影响系统的动力学特性。对近似解析解和数值解进行了比较,二者吻合良好,验证了求解过程和近似解析解的正确性。通过系统响应的性能指标分析了分数阶PID控制的比例系数、积分环节系数、微分环节系数以及分数阶阶次变化时,对系统控制性能的影响。最后,通过单自由度1/4车辆悬架模型的控制实例,说明了分数阶PID的参数整定过程。

考虑多风电场出力相关性的电力系统随机优化调度

考虑多风电场出力之间的尾部相关性,借助Gumbel-Copula函数构建多风电场出力的联合概率分布,提出含多风电场的电力系统随机优化调度模型。通过抽样平均近似(SAA)法处理机会约束条件,将随机优化问题转换为可计算的确定性非线性规划问题,并采用粒子群优化(PSO)算法进行求解。通过算例分析联合概率分布、机会约束置信水平和抽样次数对优化调度结果的影响,结果验证了基于Gumbel-Copula联合概率分布的随机优化调度的合理性。

兼顾生产周期约束的模具制造系统双重资源优化配置方法

针对模具生产周期的车间单元构成模式、缓存区大小、调度策略等要素无法用封闭形式描述的问题,提出一种基于回归统计的近似方法。该方法用近似约束替代原有无法用封闭形式表示的约束,把原问题转化为近似优化问题。通过求解近似优化配置问题的最优解,获得原问题的近似解。首先建立双重资源配置仿真实验平台,基于仿真实验部分数据,利用回归分析分别获得平均产能与平均生产周期约束的多元线性回归方程。通过回归方程替换原有的两个不能用封闭形式表示的约束条件,获得近似优化配置数学模型。用线性整数规划求解数学模型,得到近似优化配置问题的最优解,即原问题的次优解。仿真实验检验结果表明,采用所提方法求解无法用封闭形式描述的约束条件的资源优化问题是可行有效的。

一种考虑风电随机性的机组组合模型及其算法

提出一种考虑风电随机性的机组组合模型,模型中引入风电可信度指标,同时对风电功率随机变量建立机会约束条件,提出了三种策略确保了系统运行的安全性。基于抽样平均逼近(SAA)理论,将上述模型转换成机组组合-抽样平均逼近(UCSAA)模型;尤其在风电随机变量服从联合概率密度分布的情况下,通过引入多个0/1辅助变量将机会约束条件转换成确定性约束,有效解决了常用智能算法求解时间较长的问题;将UCSAA模型线性化以后,采用线性化MIP算法进行求解,其中随机变量的样本由Monte Carlo仿真生成。最后,分别对IEEE 3机和10机系统算例进行仿真,结果表明了本文模型及算法的正确性和有效性。

逐步逼近曲化平方法研究

位场资料的常规处理解释方法是建立在平面数据理论之上的,然而实际的位场数据大多为曲面数据,若把曲面数据当成平面数据进行处理,必然导致很大的误差,因此有必要进行"曲化平"处理.曲化平目前存在的主要问题是计算精度低、计算量大,因此,研究快速、精度高且适合大数据量处理的曲化平方法具有重要的价值.本文在已有的泰勒级数曲化平方法基础上提出了逐步逼近技术和平均平面技术,使得曲面位场资料处理的精度得到了很大提高.最后通过理论模型和实际资料的处理验证了该方法的应用效果.

调和装箱算法的平均性能分析

经典一维装箱问题在多处理器调度、资源分配和日常生活中的计划、包装、调度等优化问题中有着极为重要的应用 .该文系统地分析了在待处理的物品大小相互独立的情况下 ,L ee & L ee提出的调和近似装箱算法的平均性能 ;具体给出了在均匀分布下 ,调和算法平均性能比的值 ,并用实验验证了这些结果 .

Chebyshev逼近滤波器在位场分离中的应用

在对经典FIR数字滤波器的设计方法进行研究的基础上,提出了一种可以用于位场分离的基于Chebyshev最佳一致逼近原理的FIR滤波器的设计方法。在理论模型实验中,采用基于Hanning窗的低通滤波器计算出的区域异常最大误差为6.266×10-6m/s2,均方差为2.115×10-6m/s2,最大百分比误差为22.2%,而且计算点在±9km以外的误差均大于10.1%。而利用最佳一致逼近原理分离出的区域场和局部场与理论异常值拟合得较好,曲线基本重合。分离出的区域异常最大误差为3.101×10-6m/s2,均方差为0.989×10-6m/s2,最大百分比误差仅在边部的几个数据上,为7.76%,其余各点的误差均小于4.1%。实例检验中将该方法用于孙吴—嘉荫剖面布格重力异常场的分离,分离出的区域场中局部场残留少,分离彻底,效果较为理想。

一种有效提高节点定位精度的改进DV-Hop算法

免测距的DV-Hop算法的硬件开销小,但定位精度不高。为改进DV-Hop算法,分析和强调了三点近似共线问题对免测距定位的影响,给出了允许最大跳距误差率和最大跳数下检查锚节点组合有效性的三点近似共线准则。基于该准则和按未知节点与锚节点间的跳数对平均跳距进行加权计算的估计方法,提出了当未知节点得到的有效锚节点数达到给定阈值才定位并扩充为新锚节点的改进算法。仿真结果与分析表明,新算法能有效改善节点定位性能。