大气相干长度测量中几个关键技术指标的分析

为规范大气相干长度的测量,保证测量数据的准确性,通过对大气相干长度测量中采样频率、采样时长、曝光时间等关键问题的分析,并结合国内外相关研究成果,确定了差分像运动测量仪的采样频率应不低于100 Hz、采样时长应不短于20 s ,单帧图像曝光时间应不长于沿测量光路的格林伍德时间常数,样本数应不少于2 000帧。     

Characterization of Standard Reference Material 2940, Mn-ion-doped glass, spectral correction standard for fluorescence

Standard Reference Material (SRM) 2940 is a cuvette-shaped, Mn-ion-doped glass, recommended for use for relative spectral correction of emission and day-to-day performance validation of steady-state fluorescence spectrometers. Properties of this standard that influence its effective use or contribute to the uncertainty in its certified emission spectrum were explored here. These properties include its photostability, absorbance, dissolution rate in water, anisotropy, temperature coefficient of fluorescence intensity, and fluorescence lifetimes. Long and short lifetime components of the fluorescence displayed different emission spectra, making the certified spectrum useful with fluorescence instruments employing continuous excitation only. The expanded uncertainties in the certified spectrum are about 5% around the peak maximum at 620 nm, using an excitation wavelength of 412 nm. The SRM also exhibits a strong resistance to photodegradation, with no measurable decrease in fluorescence intensity even after 17 h of irradiation with the visible light from a Xe lamp.     

高精度基准平面建立方法分析

基准平面的确立,是进行表面参数评定的基础;建立理想的基准平面,在几何量形位公差检测及相关工程测量方面具有重要作用.为了建立高精度的激光扫描基准平面,对扫描平面形成过程中光束的传播进行了详细分析;根据光学矢量反射定律,推导出了扫描误差的理论公式;在此基础上揭示了误差补偿的基本原理,导出了用于补偿扫描机构产生的扫描误差的理论公式;提出了据此准则进行设计的扫描机构的模型.分析表明,用激光及其扫描装置建立光学基准面时,扫描误差是不可避免的,这种误差不加补偿,则最终将引入基准光学平面影响基准精度,进而降低参数的评定精度;借助于所推导的误差补偿公式,是可以补偿这种误差的,这对于建立高精度的扫描基准平面具有理论指导意义.     

Resource-constrained management of heterogeneous assets with stochastic deterioration

We consider a collection of heterogeneous assets which exhibit independent stochastic behavior, but are economically interdependent due to resource constraints on management decisions. We represent the collection of assets as a network of nonhomogeneous Markov decision processes linked by side constraints. To facilitate a procedure for obtaining nonrandomized decision policies, we express the resource limitations as integrated chance constraints and relax them into the objective function within a Lagrangian penalty term. We utilize subgradient optimization to establish upper bounds and a greedy randomized Lagrangian repair heuristic to obtain feasible solutions. We empirically validate the tightness of these a posteriori upper and lower bounds with computational experiments on a pair of applications. For pavement maintenance, we examine the effect that the reward structure of each constituent MDP has on the maintenance policy in the presence of budget constraints. For equipment replacement, we consider constraints on two resource types.     

On <Emphasis Type="Italic">G</Emphasis>-invex multiobjective programming. Part I. Optimality

In this paper, a generalization of convexity, namely G-invexity, is considered in the case of nonlinear multiobjective programming problems where the functions constituting vector optimization problems are differentiable. The modified Karush-Kuhn-Tucker necessary optimality conditions for a certain class of multiobjective programming problems are established. To prove this result, the Kuhn-Tucker constraint qualification and the definition of the Bouligand tangent cone for a set are used. The assumptions on (weak) Pareto optimal solutions are relaxed by means of vector-valued G-invex functions.     

无限凸规划的约束规范条件和Farkas引理

假设X是局部凸Hausdorff拓扑向量空间,C是X的闭凸子集,T是一下标集(可以是无限集),假设{ft:t∈T}是一X到R ∪{+∞}的真凸下半连续函数簇,而f:x→R∪{+∞}是一真凸下半连续函数.     

Hybrid Approach with Active Set Identification for Mathematical Programs with Complementarity Constraints

We consider a mathematical program with complementarity constraints (MPCC). Our purpose is to develop methods that enable us to compute a solution or a point with some kind of stationarity to MPCC by solving a finite number of nonlinear programs. We apply an active set identification technique to a smoothing continuation method (Ref. 1) and propose a hybrid algorithm for solving MPCC. We develop also two modifications: one makes use of an index addition strategy; the other adopts an index subtraction strategy. We show that, under reasonable assumptions, all the proposed algorithms possess a finite termination property. Further discussions and numerical experience are given as well This work was supported in part by the Scientific Research Grant-in-Aid from the Ministry of Education, Science, Sports, and Culture of Japan. The authors are grateful to Professor Paul Tseng for helpful suggestions on an earlier version of the paper.     

Convexificators and boundedness of the Kuhn–Tucker multipliers set

In this paper we consider a nonsmooth optimization problem with equality, inequality and set constraints. We propose new constraint qualifications and Kuhn–Tucker type necessary optimality conditions for this problem involving locally Lipschitz functions. The main tool of our approach is the notion of convexificators. We introduce a nonsmooth version of the Mangasarian–Fromovitz constraint qualification and show that this constraint qualification is necessary and sufficient for the Kuhn–Tucker multipliers set to be nonempty and bounded.     

一类非光滑广义分式规划的Kuhn-Tucker型最优性必要条件

考虑一类非线性不等式约束的非光滑minimax分式规划问题;目标函数中的分子是可微函数与凸函数之和形式而分母是可微函数与凸函数之差形式,且约束函数是可微的.在Arrow- Hurwicz-Uzawa约束品性下,给出了这类规划的最优解的Kuhn-Tucker型必要条件.所得结果改进和推广了已有文献中的相应结果.     

The strong conical hull intersection property for convex programming

The strong conical hull intersection property (CHIP) is a geometric property of a collection of finitely many closed convex intersecting sets. This basic property, which was introduced by Deutsch et al. in 1997, is one of the central ingredients in the study of constrained interpolation and best approximation. In this paper we establish that the strong CHIP of intersecting sets of constraints is the key characterizing property for optimality and strong duality of convex programming problems. We first show that a sharpened strong CHIP is necessary and sufficient for a complete Lagrange multiplier characterization of optimality for the convex programming model problem where C is a closed convex subset of a Banach space X, S is a closed convex cone which does not necessarily have non-empty interior, Y is a Banach space, is a continuous convex function and g:X→Y is a continuous S-convex function. We also show that the strong CHIP completely characterizes the strong duality for partially finite convex programs, where Y is finite dimensional and g(x)=−Ax+b and S is a polyhedral convex cone. Global sufficient conditions which are strictly weaker than the Slater type conditions are given for the strong CHIP and for the sharpened strong CHIP. The author is grateful to the referees for their constructive comments and valuable suggestions which have contributed to the final preparation of the paper.