浅析计算机专业少数民族班级的教学方法

本文针对西部地区高校中,计算机专业的少数民族班学生学习的现状,分析了教学方法中存在的一些问题及其原因,提出了几点建议。以期对其他同类地方学校的教学起借鉴作用。     

基于重要目标区的三维地形快速渲染方法

三维地形的显示需要绘制大量的三角形网格,为了提高地形显示速度目前广泛采取了三角形网格简化技术。针对弹道导弹作战区域地形的特殊性,提出了一种基于重要目标区的网格简化方法。从BMP位图读取地形高度信息,以四叉树保存网格数据,按照区域重要性静态确定地形网格疏密程度。并采用DirectX平台,实现了导弹战区三维地形显示,提高了渲染效率。     

Supervisory control using variable lookahead policies

This paper deals with the efficient on-line calculation of supervisory controls for discrete event systems (DES's) in the framework of limited lookahead control policies (or LLPs) that we introduced in previous papers. In the LLP scheme, the control action after a given trace of events has been executed is calculated on-line on the basis of anN-step ahead projection of the behavior of the DES. To compute these controls, one must calculate after the execution of each event the supremal controllable sublanguage of a finite language with respect to another finite larger language. In our previous work, we showed how the required supremal controllable sublanguage calculation can be performed by using a backward dynamic programming algorithm over the nodes of the tree representation of these two languages. In this paper, we pursue the same approach for the calculation of LLP controls, but instead we adopt a forward calculation procedure over theN-level tree of interest. This forward procedure improves upon previous work by avoiding the explicit consideration of all the nodes of theN-level tree, while still permitting tree-to-tree recursiveness as enabled events are executed by the system. The forward search ends whenever a control decision can be made unambiguously or whenever the boundary of theN-level tree is reached, whichever comes first. This motivates the name Variable Lookahead Policy (or VLP) for this implementation of the LLP supervisory control scheme. This paper presents a general VLP algorithm and studies the properties of several special cases of it. The paper also discusses the implementation of the VLP algorithms and presents computational results regarding the application of these algorithms to a time-varying DES.     

考虑EV充放电意愿的园区综合能源系统双层优化调度

随着电动汽车的普及度越来越高,工业园区的电动汽车用户日益增多,其充放电行为对园区综合能源系统规划和运行带来极大挑战。本文提出了考虑电动汽车充放电意愿的园区综合能源系统双层优化调度。首先基于动态实时电价、电池荷电量、电池损耗补偿、额外参与激励等因素建立充放电意愿模型,在此基础上得到改进的电动汽车充放电模型;然后,以园区综合能源系统总成本最小和电动汽车充电费用最小为目标建立双层优化调度模型,通过KKT条件将内层模型转化为外层模型的约束条件,从而快速稳定的实现单层模型的求解;最后,进行仿真求解,设置3种不同场景,对比了所提模型与一般充放电意愿模型,验证了文中所提引入EV充放电意愿模型的园区综合能源系统双层优化调度的有效性和可行性,具有一定的经济效益。     

A mathematical model for vehicle routing problem under endogenous uncertainty

In this study, a multistage stochastic programming (SP) model is presented for a variant of single-vehicle routing problem with stochastic demands from a dynamic viewpoint. It is assumed that the actual demand of a customer becomes known only when the customer is visited. This problem falls into the category of SP with endogenous uncertainty and hence, the scenario tree is decision-dependent. Therefore, nonanticipativity of decisions is ensured by conditional constraints making up a large portion of total constraints. Thus, a novel approach is proposed that considerably reduces the problem size without any effect on the solution space. Computational results on some test problems are reported.     

液氦温区大型低温制冷系统的可靠性评估

针对改进Claude循环的大型氦低温制冷系统,以制冷量不足为顶事件建立了故障树模型。利用结构法对故障树模型进行定性分析,分析得到系统的最小割集为18个。利用来自不同数据库的部件失效率对故障树模型进行定量计算,得到了不同的结果,并分析其原因。对各部件的关键重要度进行了计算,以衡量各个部件对系统发生故障的贡献程度。并通过增加系统冗余部件和提高关键部件可靠度等措施,为提高系统可靠性找到了有效的解决途径。     

Precise identification of moving vehicular parameters based on improved glowworm swarm optimization algorithm

This paper proposes an indirect method for the identification of moving vehicular parameters using the dynamic responses of the vehicle. The moving vehicle is modelled as 2-DOF system with 5 parameters and 4-DOF system with 12 parameters, respectively. Finite element method is used to establish the equation of the coupled bridge–vehicle system. The dynamic responses of the system are calculated by Newmark direct integration method. The parameter identification problem is transformed into an optimization problem by minimizing errors between the calculated dynamic responses of the moving vehicle and those of the simulated measured responses. Glowworm swarm optimization algorithm (GSO) is used to solve the objective function of the optimization problem. A local search method is introduced into the movement phase of GSO to enhance the accuracy and convergence rate of the algorithm. Several test cases are carried out to verify the efficiency of the proposed method and the results show that the vehicular parameters can be identified precisely with the present method and it is not sensitive to artificial measurement noise.     

Approximate dynamic programming solutions with a single network adaptive critic for a class of nonlinear systems

Approximate dynamic programming (ADP) formulation implemented with an adaptive critic (AC)-based neural network (NN) structure has evolved as a powerful technique for solving the Hamilton-Jacobi-Bellman (HJB) equations. As interest in ADP and the AC solutions are escalating with time, there is a dire need to consider possible enabling factors for their implementations. A typical AC structure consists of two interacting NNs, which is computationally expensive. In this paper, a new architecture, called the ‘cost-function-based single network adaptive critic (J-SNAC)’ is presented, which eliminates one of the networks in a typical AC structure. This approach is applicable to a wide class of nonlinear systems in engineering. In order to demonstrate the benefits and the control synthesis with the J-SNAC, two problems have been solved with the AC and the J-SNAC approaches. Results are presented, which show savings of about 50% of the computational costs by J-SNAC while having the same accuracy levels of the dual network structure in solving for optimal control. Furthermore, convergence of the J-SNAC iterations, which reduces to a least-squares problem, is discussed; for linear systems, the iterative process is shown to reduce to solving the familiar algebraic Ricatti equation.     

网络演算的矩阵解释

网络演算是离散事件动态系统理论在计算机网络中的应用,网络演算通过到达曲线和服务曲线计算网络的性能参数,这两个概念封装了复杂的理论背景,从而易于在实际中应用,但对到达曲线和服务曲线概念的理论研究比较缺乏.文中采用幂等矩阵的角度描述到达曲线和服务曲线,演算的过程成为矩阵运算,通过结合矩阵双子理论和余理论的研究结果,得出了由矩阵表演算的基本定理.研究表明,幂等矩阵理论为网络演算提供了很好的理论解释.文中还提出一种基于变换矩阵的方法求某些网络元素的服务曲线.     

信任感知的组合服务动态选择方法

把信任概念和信任度引入到服务组合中来,讨论了信任感知的服务组合选择问题,分别基于两种观点建立了其数学模型.并基于观点1,深入地研究了服务组合的动态选择问题,分别采用蚁群系统作为优化工具设计了两种动态选择方法:方法1针对服务在上限阈值的范围内提供优质服务,服务质量保持不变,超过阈值则拒绝服务的情况;方法2针对服务无限制地接受请求,但随着请求数量的增多,其服务质量下降的情况.通过对方法1和方法2的仿真实验结果分析表明,两种方法具有很好的效果.