基于运动稳定性轧管机主机头模糊优化设计

为提高轧管机主机头的运动稳定性,改善钢管的轧制质量,本文在对轧管机主机头的结构及运动状态分析的基础上,推导出主机头的运动加速度解析式,并以此为基础,建立模糊优化的数学模型。文中分别以各构件长度尺寸为设计变量,进行单变量模糊优化设计,求得各构件长度的最优取值区间,使主机头的运动稳定性得到较大的提高。本文建立的数学模型对提高轧管机的稳定性及进行优化设计提供了依据,所得出的结论为轧管机的整体优化奠定了研究基础。     

Stochastic initial boundary value problems subject to distributed and boundary noise and their optimal control

In this paper we consider a class of stochastic evolution equations arising from initial boundary value problems with both boundary and distributed noise. We prove existence and regularity of mild solutions. Then we consider a controlled version of the model and prove the existence of optimal controls and develop the necessary conditions of optimality for partially observed problems using relaxed controls.     

试验优化设计Er3+/Yb3+共掺BaGd2ZnO5荧光粉及其上转换发光性质

为得到绿光和红光最大发光强度的Er³⁺/Yb³⁺共掺BaGd₂ZnO₅上转换材料荧光粉, 首先采用试验优化设计中的均匀设计初步寻找Er³⁺/Yb³⁺合理的掺杂浓度; 其次通过二次通用旋转组合设计进一步优化实验, 建立起Er³⁺/Yb³⁺掺杂浓度与绿光和红光发光强度的回归方程; 最后通过遗传算法计算出方程的最优解, 即绿光和红光最大发光强度时对应的Er³⁺/Yb³⁺掺杂浓度. 利用传统的高温固相法分别制备出最优样品. 采用X 射线衍射对得到荧光粉的晶体结构进行了分析, 证明了所有产物均为纯相BaGd₂ZnO₅. 采用980 nm抽运激光作为激发源, 在同样的条件下测量了样品的上转换荧光发射光谱, 从中可见样品有较强的红光发射和绿光发射, 发光中心位于662, 551和527 nm, 分别对应于⁴F_9/2→⁴I_15/2, ⁴S_3/2→⁴I_15/2 及²H_11/2→⁴I_15/2能级跃迁. 研究了绿光和红光最优样品的上转换发光强度与激光器工作电流之间的关系, 通过分析发现红色和绿色上转换发光均为双光子过程. 由归一化的绿色上转换发射光谱可以看出, 激光器工作电流导致的样品温度变化可以忽略不计. 由于能级²H_11/2和⁴S_3/2之间存在热平衡, 并满足玻尔兹曼分布, 由此探讨了绿光最优样品上转换发射光谱中的绿色发射与温度的关系, 计算出²H_11/2和⁴S_3/2之间的能级差为ΔE=926.11 cm^-1. 研究了绿光最优样品的温度效应, 随着温度的升高, 发射强度逐渐变小, 出现了温度猝灭现象. 并计算了样品的激活能, 分别为总体激活能ΔE_总=0.45 eV, 绿光激活能ΔE_绿=0.45 eV, 红光激活能ΔE_红=0.46 eV.     

基于最优滤波器的强混沌背景中谐波信号检测方法研究

强混沌背景中的微弱谐波信号检测有重要的工程研究意义. 目前的检测方法主要是基于Takens理论的混沌相空间重构方法, 然而这些方法往往对信干噪比要求高, 且对高斯白噪声敏感等. 本文注意到混沌信号的二阶统计特性是不变的, 根据这个特点提出了一种基于最优滤波器的强混沌背景中的微弱谐波信号检测方法. 该方法首先构建一个数据矩阵, 在频域上对每个频率通道分别检测谐波信号, 从而将信号检测问题转化为最优化问题, 然后利用最优化理论设计滤波器, 使待检测频率通道的信号增益保持不变, 而尽量抑制其他频率通道的信号, 最后通过判断每一频率通道的输出信干噪比来检测谐波信号. 与传统方法相比, 本文方法有如下优点: 1)可以检测更低信干噪比下的微弱谐波信号; 2)可检测的信号幅度范围更大; 3)抗白噪声性能更强. 仿真结果证明了本文方法的有效性.     

CONSTANT ELASTICITY OF VARIANCE MODEL AND ANALYTICAL STRATEGIES FOR ANNUITY CONTRACTS

The constant elasticity of variance（CEV） model was constructed to study a defined contribution pension plan where benefits were paid by annuity. It also presents the process that the Legendre transform and dual theory can be applied to find an optimal investment policy during a participant＇s whole life in the pension plan. Finally, two explicit solutions to exponential utility function in the two different periods （before and after retirement） are revealed. Hence, the optimal investment strategies in the two periods are obtained.     

Optimal Bounded Control for Minimizing the Response of Quasi Non-Integrable Hamiltonian Systems

A strategy for designing optimal bounded control to minimize theresponse of quasi non-integrable Hamiltonian systems is proposed basedon the stochastic averaging method for quasi non-integrable Hamiltoniansystems and the stochastic dynamical programming principle. Theequations of motion of a controlled quasi non-integrable Hamiltoniansystem are first reduced to an one-dimensional averaged Itô stochasticdifferential equation for the Hamiltonian by using the stochasticaveraging method for quasi non-integrable Hamiltonian systems. Then, thedynamical programming equation for the control problem of minimizing theresponse of the averaged system is formulated based on the dynamicalprogramming principle. The optimal control law is derived from thedynamical programming equation and control constraints without solvingthe equation. The response of optimally controlled systems is predictedthrough solving the Fokker–Planck–Kolmogrov (FPK) equation associatedwith completely averaged Itô equation. Finally, two examples are workedout in detail to illustrate the application and effectiveness of theproposed control strategy.     

STOCHASTIC OPTIMAL CONTROL FOR THE RESPONSE OF QUASI NON-INTEGRABLE HAMILTONIAN SYSTEMS~

A strategy is proposed based on the stochastic averaging method for quasi nonintegrable Hamiltonian systems and the stochastic dynamical programming principle. The proposed strategy can be used to design nonlinear stochastic optimal control to minimize the response of quasi non-integrable Hamiltonian systems subject to Gaussian white noise excitation. By using the stochastic averaging method for quasi non-integrable Hamiltonian systems the equations of motion of a controlled quasi non-integrable Hamiltonian system is reduced to a one-dimensional averaged Ito stochastic differential equation. By using the stochastic dynamical programming principle the dynamical programming equation for minimizing the response of the system is formulated.The optimal control law is derived from the dynamical programming equation and the bounded control constraints. The response of optimally controlled systems is predicted through solving the FPK equation associated with It5 stochastic differential equation. An example is worked out in detail to illustrate the application of the control strategy proposed.     

Optimal location and optimized parameters for robust power system stabilizer using honeybee mating optimization

A honeybee mating optimization technique is used to tune the power system stabilizer (PSS) parameters and find optimal location of PSSs in this article. The PSS parameters and placement are computed to assure maximum damping performance under different operating conditions. One of the main advantages of the proposed approach is its robustness to the initial parameter settings. The effectiveness of the proposed method is demonstrated on two case studies as; 10‐machine 39‐buses New England (NE) power system in comparison with Tabu Search (TS) and 16 machines and 68 buses‐modified reduced order model of the NE New York interconnected system by genetic algorithm through some performance indices under different operating condition. The proposed method of tuning the PSS is an attractive alternative to conventional fixed gain stabilizer design as it retains the simplicity of the conventional PSS and at the same time guarantees a robust acceptable performance over a wide range of operating and system condition. © 2014 Wiley Periodicals, Inc. Complexity 21: 242–258, 2015     

An improved two‐stage variance balance approach for constructing partial profile designs for discrete choice experiments

In many discrete choice experiments set up for product innovation, the number of attributes is large, which results in a substantial cognitive burden for the respondents. To reduce the cognitive burden in such cases, Green suggested in the early '70s the use of partial profiles that vary only the levels of a subset of the attributes. In this paper, we present two new methods for constructing Bayesian ‐optimal partial profile designs for estimating main‐effects models. They involve alternative generalizations of Green's approach that makes use of balanced incomplete block designs and take into account the fact that attributes may have differing numbers of levels. We refer to our methods as variance balance I and II because they vary an attribute with a larger number of levels more often than an attribute with fewer levels to stabilize the variances of the individual part‐worth estimates. The two variance balance methods differ in the way attributes with differing numbers of levels are weighted. Both methods provide statistically more efficient partial profile designs for differing numbers of attribute levels than another generalization of Green's approach that does not weight the attributes. This method is called attribute balance. We show results from an actual experiment in software development demonstrating the usefulness of our methods. Copyright © 2014 John Wiley & Sons, Ltd.