仿生波状前缘机翼动态失速控制的数值研究

该文提出了采用波状前缘机翼改善动态失速性能的有效控制方式。通过数值求解雷诺平均N-S方程,对比研究了波状前缘机翼和传统的光滑前缘机翼在动态失速时的力学性能和流动特征,并分析了前缘形状改进和可控拍动参数的影响,探讨有效的控制途径。研究结果表明,在深失速的动态失速控制中,波状前缘翼的复杂涡系能更稳定地附着在上翼面,其力学性能明显优于光滑前缘翼,尤其在大攻角范围内优势明显。波状前缘形状改变对升力能几乎没有影响,但转轴前移、增加拍幅和频率都能提高拍动波状前缘翼的升力。     

基于用“动态汛限水位”来利用雨洪资源

雨洪资源的依法利用,早已在国外广泛展开。国内的雨洪资源利用,还面临着重新计算汛限水位高度,然后再依法予以重新确认的难题。唯有如此,才能缓解各级行政部门为了多产粮而＂压＂水库管理部门不顾风险的超汛限蓄水。因此,现阶段用＂动态汛限水位＂来化解矛盾,使水库管理部门从被迫接受潜规则的超汛限蓄水,转变为可以摆上台面的依法科学合理...     

基于动态联盟的一种身份信任计算模型

在动态联盟中,身份管理是确保信息安全的一种手段,但是随着联盟成员数日益增多,传统的身份管理难以妥善应对当前较为复杂的环境,尤其当成员之间无交互历史时,数据交换的可靠性无法保障。文中研究了不同计算环境中的多种信任管理方法,并把P2P的推荐评价机制应用到动态联盟的身份管理中,提出了一种基于推荐的身份信任管理的算法。在这种算法中,身份的信任值由多因素加权综合而成,其中推荐信任值在算法中起到关键的作用。该算法试图为解决复杂的动态联盟环境下的身份管理问题提供一种途径。     

多平台下动态采集税控数据的技术研究

在商业税收领域中,不同的行业、商家,可能会采用不同的系统平台来完成他们各自的销售任务,这就给国家税收时获取税控数据带来了一定的挑战。本文基于国家信息化标准委员会针对税控收款机的国家标准,提出并实现了一种在多平台下动态采集税控数据的解决方案。     

Continuous State Dynamic Programming via Nonexpansive Approximation

This paper studies fitted value iteration for continuous state numerical dynamic programming using nonexpansive function approximators. A number of approximation schemes are discussed. The main contribution is to provide error bounds for approximate optimal policies generated by the value iteration algorithm.      

Exact methods and a heuristic for the optimization of an integrated replenishment-storage planning problem

In this paper we study the coordination of different activities in a supply chain issued from a real case. Multiple suppliers send raw materials (RMs) to a distribution center (DC) that delivers them to a unique plant where the storage of the RMs and the finished goods is not possible. Then, the finished goods are directly shipped to multiple customers having just‐in‐time (JIT) demands. Under these hypotheses, we show that the problem can be reduced to multiple suppliers and one DC. Afterwards, we analyze two cases; in the first, we consider an uncapacitated storage at DC, and in the second, we analyze the capacitated storage case. For the first case, we show that the problem is NP‐hard in the ordinary sense using the Knapsack decision problem. We then propose two exact methods: a mixed integer linear program (MILP) and a pseudopolynomial dynamic program. A classical dynamic program and an improved one using the idea of Shaw and Wagelmans are given. With numerical tests we show that the dynamic program gives the optimal solution in reasonable time for quite large instances compared with the MILP. For the second case, the capacity limitation in DC is assumed, which makes the problem solving more challenging. We propose an MILP and a dynamic programming‐based heuristic that provides solutions close to the optimal solution in very short times.     

基于UML可适应的教学考评系统分析与设计

该文给出了UML的概念与要点,应用UML对教学考评系统进行了建模。给出了系统的用例模型、整体结构模型、静态模型、动态模型,并分析了模型对教学考评变化的可适应性。     

虚拟人运动中受扰的平衡保持算法

为了更加真实且实时地模拟运动中虚拟人恢复平衡的反应动作,提出一种针对受到外界作用力扰动的虚拟人平衡保持算法.首先通过虚拟人质心位移和速度来判断平衡性;然后借助生物力学的研究成果设计了具有人体特性的虚拟人运动受扰后的平衡保持方法,并用动力学进行模拟,驱动虚拟人完成平衡恢复.实验结果表明:该算法计算效率高,符合人体的生物力学特性,并且具有良好的交互性与较好的视觉效果,适用于虚拟人动画合成.     

A new adaptive backstepping Coulomb friction compensator for servo control systems

A new Coulomb friction compensator is proposed for servo control systems in this paper. The novelty of the new approach lies in its capability of assigning the eigenvalues of the resulting closed loop system while attacking the problem. First, based on the standard backstepping methodology, an implicit Lyapunov function, with part of the components being only symbolically constructed at the very beginning, is utilized. To increase the robustness of the system against disturbance and model inaccuracy, an integral term is employed in the design. Using part of the variable gradient method, we are able to turn the implicit Lyapunov function into an explicit one, which is positive definite, and whose time‐derivative is negative definite. Second, it will be shown that the resulting closed loop error system is a switched linear system with two possible active modes that share the same set of eigenvalues, which is at our disposal. Unlike the common adaptive control design methods, such as the Control Lyapunov Function approach, in which the gains are typically positive but otherwise arbitrary, and are hence difficult to choose and have a lack of connection with the system's performance, our new scheme imposes two further constraints on the gains. It turns out that we can then match these gains with the coefficients of the desired characteristic equation of the closed loop system. In this respect, the gains are linked to the system's overall performance, which is a new and very appealing feature for such a scheme. Finally, a procedure of constructing a common Lyapunov function is provided to prove exponential stability of the aforementioned switched linear system. In addition, using the invariance principle, we will show the convergence of the estimated Coulomb friction coefficient to its real value. Numerical simulations are given to validate the effectiveness of the design and its robustness against friction time‐variations. Compared to existing results, the proposed scheme is much simpler, hence, much more advantageous computationally. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Error compensation in machine tools: a neural network approach

For a non-idealized machine tool, each point in the workspace is associated with a tool point positioning error vector. If this error map can be determined, then it is possible to substantially improve the positioning performance of the machine by introducing suitable compensation into the control loop. This paper explores the possibility of using an artifical neural network (ANN) to compute this mapping. The training set for the ANN is obtained by mounting a physical artifact whose dimensions are precisely known in the machine's workspace. The machine, equipped with a touch trigger probe, measures the positions of features on the artifact. The difference between the machine reading and the known dimension is the machine error at that point in the workspace. Using standard modeling techniques, the kinematic error model for a CNC turning center was developed. This model was parameterized by measurement of the parametric error functions using a laser interferometer, electronic levels and a precision square. The kinematic model was then used to simulate the artifact-measuring process and develop the ANN training set. The effect of changing artifact geometry was explored and a machining operation was simulated using the ANN output to provide compensation. The results show that the ANN is capable of learning the error map of a real machine, and that ANN-based compensation can significantly reduce part-dimensional errors.