Local generalized empirical estimation of regression

Let f(x) be the density of a design variable X and m(x) = E[Y\X = x] the regression function. Then m(x) - G(x)/f(x), where G(x) = m(x)f(x). The Dirac δ-function is used to define a generalized empirical function Gn (x) for G(x) whose expectation equals G(x). This generalized empirical function exists only in the space of Schwartz distributions, so we introduce a local polynomial of order p approximation to Gn(.) which provides estimators of the function G(x) and its derivatives. The density f(x) can be estimated in a similar manner. The resulting local generalized empirical estimator (LGE) of m(x) is exactly the Nadaraya-Watson estimator at interior points when p = 1, but on the boundary the estimator automatically corrects the boundary effect. Asymptotic normality of the estimator is established. Asymptotic expressions for the mean squared errors are obtained and used in bandwidth selection. Boundary behavior of the estimators is investigated in details. We use Monte Carlo simulations to show that the     

Implementation of an adaptive umbrella sampling method for the calculation of multidimensional potential of mean force of chemical reactions in solution

We describe the implementation of an adaptive umbrella sampling method, making use of the weighted histogram analysis method, for computing multidimensional potential of mean force for chemical reaction in solution. The approach is illustrated by investigating the effect of aqueous solution on the free energy surface for the proton transfer reaction of [H(3)N-H-NH(3)](+) using a combined quantum mechanical and molecular mechanical AM1/TIP3P potential.     

Queueing Dynamics and Maximal Throughput Scheduling in Switched Processing Systems

We study a processing system comprised of parallel queues, whose individual service rates are specified by a global service mode (configuration). The issue is how to switch the system between various possible service modes, so as to maximize its throughput and maintain stability under the most workload-intensive input traffic traces (arrival processes). Stability preserves the job inflow–outflow balance at each queue on the traffic traces. Two key families of service policies are shown to maximize throughput, under the mild condition that traffic traces have long-term average workload rates. In the first family of cone policies, the service mode is chosen based on the system backlog state belonging to a corresponding cone. Two distinct policy classes of that nature are investigated, MaxProduct and FastEmpty. In the second family of batch policies (BatchAdapt), jobs are collectively scheduled over adaptively chosen horizons, according to an asymptotically optimal, robust schedule. The issues of nonpreemptive job processing and non-negligible switching times between service modes are addressed. The analysis is extended to cover feed-forward networks of such processing systems/nodes. The approach taken unifies and generalizes prior studies, by developing a general trace-based modeling framework (sample-path approach) for addressing the queueing stability problem. It treats the queueing structure as a deterministic dynamical system and analyzes directly its evolution trajectories. It does not require any probabilistic superstructure, which is typically used in previous approaches. Probability can be superposed later to address finer performance questions (e.g., delay). The throughput maximization problem is seen to be primarily of structural nature. The developed methodology appears to have broader applicability to other queueing systems.     

Technically optimal inspection policy with arithmetical adaption

A process is considered whose quality deteriorates accordingto a constant failure intensity . As in practice it can be difficultto estimate the true value of the purpose of this paper isto present a strategy which can be applied without knowing .In order to maximize the number of conforming items per timeunit perfect inspections and renewals are performed. The lengthof the inspection interval is described by an arithmetical sequenceand changes by the time depending on perceived assignable causes.Optimal adaptive control plans provide nearly the same performanceas in the case when is known.     

Adaptive Smoothing Method,Deterministically Computable Generalized Jacobians,and the Newton Method

In this note, we show that a well-known integral method, which was used by Mayne and Polak to compute an -subgradient, can be exploited to compute deterministically an element of the plenary hull of the Clarke generalized Jacobian of a locally Lipschitz mapping regardless of its structure. In particular, we show that, when a locally Lipschitz mapping is piecewise smooth, we are able to compute deterministically an element of the Clarke generalized Jacobian by the adaptive smoothing method. Consequently, we show that the Newton method based on the plenary hull of the Clarke generalized Jacobian can be implemented in a deterministic way for solving Lipschitz nonsmooth equations.     

Local Rules and Fitness Landscapes: A Catastrophe Model

This paper examines the imposition of local rules in five mail delivery centres of Australia Post. Local rules are patterns of behaviour used by subunits of an organization to optimise their payoff. These local rules may ultimately benefit the total organization. Fitness landscapes are used to examine the emergence of local rules in this workplace. This research examined the relationship between the time taken to sort the mail and the volume of this mail. Work rates show a clear catastrophe shift; that is, work rates suddenly drop when the volume of mail exceeds a certain level, as Postal Delivery Officers apply local rules to maximise gains inherent in the pay structures. Such behaviour is close to that predicted by Kauffman (1995) in computer simulations of lattices and may be indicative of the application of local rules in organizations. The implications of the use of local rules are that behaviour in social systems may be dictated by systemic and emergent processes which are outside immediate management control. A further implication is that organizations may be structured to a significant extent by such local rules.     

用于白天自适应光学的波前探测方法分析

提出了两种视场偏移哈特曼波前传感器，用于探测白天条件下强背景中目标信号的波前信息.给出了哈特曼波前传感器子孔径中焦面上天光背景非均匀性分布特征的实验结果，详细分析了基于分光棱镜的双焦面视场偏移哈特曼波前传感器的探测误差，并通过基于倾斜镜的单焦面视场偏移哈特曼波前传感器实验给出了波前测量的结果，证明视场偏移哈特曼波前传感器能够精确、稳定的测量白天条件下目标信号的畸变波前信息，同时给出了其在白天条件下探测能力的估算结果及其受限因素，估算结果表明云南天文台的自适应光学系统在10 W/m²·sr天光背景条件下应用视场偏移哈特曼波前传感器能对5等星(可视星等值)目标进行准确的波前探测. 关键词： 自适应光学 波前传感器 白天工作 误差分析     

基于液晶光调制器的夏克-哈特曼波前探测技术

微透镜阵列是夏克-哈特曼波前探测器的关键器件。利用液晶器件的空间相位调制特性,可实现微透镜阵列功能,且具有孔径和焦距可变的优点。论述了基于液晶光调制的夏克-哈特曼波前探测校正技术的特征、技术进展、及应用前景。     

内腔自适应光学系统校正激光器畸变

固体激光器中,增益介质由于热沉积产生的热畸变严重影响了激光器的稳定性、输出功率和光束质量。研究了一种内腔自适应光学系统校正激光器腔内畸变的方法,利用微机电变形反射镜作为固体激光器的内腔全反射镜,通过控制变形镜的面型改善激光器输出光束的模式及功率。从腔外引入的一束信标光通过激光器内腔后反射出腔外,用波前探测器可测得激光器工作时腔内畸变对此信标光的影响,并通过搭建的自适应光学系统平台可闭环校正此畸变。实验结果表明,闭环校正后,激光器的输出功率提高了近3倍,且激光光束质量得到了明显的改善。     

基于性能的直流电机系统未知推力波动的自适应补偿控制

针对一类直流电机系统,设计基于性能的未知推力波动的自适应补偿控制器.由于推力波动中含有非线性化的未知参数,因此无法利用常规的自适应方法,通过直接估计这些参数对其影响进行补偿.根据未知推力波动的结构特性,基于Lipshitz条件引入推力波动项的放大技术,设计其内部非线性化未知参数的自适应估计律,精确补偿其影响,从而避免了简单的把波动看作未知扰动所造成的系统性能的损失.有别于现有模型,在模型变换中并未忽略微小等效电感的影响,使得系统模型更具一般性.仿真结果表明该自适应控制器可有效补偿推力波动的影响,实现对指令信号的高精度快速跟踪且具有较强的抗干扰性和鲁棒性.