功率GaAs MESFET小信号模型参数的提取（英）

采用非本征元件的参数作为本征元件参数函数的自变量的方法，求解MESFET小信号等效电路模型，并彩相对误差来构建目标函数。以FET在零偏置状态的非本征元件值作为初值，通过优化求得了热FET状态的本征元件值，S参数的计算值与实验值吻合得很好，S11的相对误差为0.09%,S12为1.1%,S21为0.08%，S22为 2.26%。该方法收敛快，精度高并且效率高，便于移植到微波器件CAD设计和模拟软件中。     

粮食最低收购价政策问题研究

利用聚类分析、逐步回归分析确定小麦与稻谷种植面积模型和指标体系.建立综合指标评价模型和ARMA模型,可知:最低收购政策具有托市效应.结合时间趋势剔除法、蛛网模型,可知:小麦和稻谷价格波动呈现5、3年的周期性,分别满足发散型、收敛型蛛网形态.建立基于补偿法的最低收购价定价模型并用BP神经网络模型进行检验.基于反证法,推知调控最低收购价提高5%小麦种植面积的做法是不可行的.最后提出了调控粮食种植的建议·     

Algorithm of Barrier Objective Penalty Function

In this paper, an algorithm of barrier objective penalty function for inequality constrained optimization is studied and a conception–the stability of barrier objective penalty function is presented. It is proved that an approximate optimal solution may be obtained by solving a barrier objective penalty function for inequality constrained optimization problem when the barrier objective penalty function is stable. Under some conditions, the stability of barrier objective penalty function is proved for convex programming. Specially, the logarithmic barrier function of convex programming is stable. Based on the barrier objective penalty function, an algorithm is developed for finding an approximate optimal solution to an inequality constrained optimization problem and its convergence is also proved under some conditions. Finally, numerical experiments show that the barrier objective penalty function algorithm has better convergence than the classical barrier function algorithm.     

光电系统图像质量评价方法综述

针对图像质量评价方法对于评估光电系统成像性能的重要性,本文结合国内外研究成果,综合论述了图像质量的主观评价法和客观评价法。重点对常用的一些图像质量客观评价方法进行了分析和论述,最后探讨了光电系统图像质量评价方法的发展趋势。     

Resource allocation in dynamic PERT networks with finite capacity

This article models the resource allocation problem in dynamic PERT networks with finite capacity of concurrent projects (COnstant Number of Projects In Process (CONPIP)), where activity durations are independent random variables with exponential distributions, and the new projects are generated according to a Poisson process. The system is represented as a queuing network with finite concurrent projects, where each activity of a project is performed at a devoted service station with one server located in a node of the network. For modeling dynamic PERT networks with CONPIP, we first convert the network of queues into a stochastic network. Then, by constructing a proper finite-state continuous-time Markov model, a system of differential equations is created to solve and find the completion time distribution for any particular project. Finally, we propose a multi-objective model with three conflict objectives to optimally control the resources allocated to the servers, and apply the goal attainment method to solve a discrete-time approximation of the original multi-objective problem.     

Finding the best separation in situations of extremely low chromatographic resolution

Samples with a large number of compounds or similarities in their structure and polarity may yield insufficient chromatographic resolution. In such cases, however, finding conditions where the largest number of compounds appears sufficiently resolved can be still worthwhile. A strategy is here reported that optimises the resolution level of chromatograms in cases where conventional global criteria, such as the worst resolved peak pair or the product of elementary resolutions, are not able to detect any separation, even when most peaks are baseline resolved. The strategy applies a function based on the number of "well resolved" peaks, which are those that exceed a given threshold of peak purity. It is, therefore, oriented to quantify the success in the separation, and not the failure, as the conventional criteria do. The conditions that resolve the same amount of peaks are discriminated by either quantifying the partial resolution of those peaks that exceed the established threshold, or by improving the separation of peaks below it. The proposed approach is illustrated by the reversed-phase liquid chromatographic separation of a mixture of 30 ionisable and neutral compounds, using the acetonitrile content and pH as factors.     

Finding a representative nondominated set for multi-objective mixed integer programs

In this paper, we develop algorithms to find small representative sets of nondominated points that are well spread over the nondominated frontiers for multi-objective mixed integer programs. We evaluate the quality of representations of the sets by a Tchebycheff distance-based coverage gap measure. The first algorithm aims to substantially improve the computational efficiency of an existing algorithm that is designed to continue generating new points until the decision maker (DM) finds the generated set satisfactory. The algorithm improves the coverage gap value in each iteration by including the worst represented point into the set. The second algorithm, on the other hand, guarantees to achieve a desired coverage gap value imposed by the DM at the outset. In generating a new point, the algorithm constructs territories around the previously generated points that are inadmissible for the new point based on the desired coverage gap value. The third algorithm brings a holistic approach considering the solution space and the number of representative points that will be generated together. The algorithm first approximates the nondominated set by a hypersurface and uses it to plan the locations of the representative points. We conduct computational experiments on randomly generated instances of multi-objective knapsack, assignment, and mixed integer knapsack problems and show that the algorithms work well.     

Rate adaptive resource allocation in orthogonal frequency division multiple access system using multi‐objective immune algorithm

Rate adaptive downlink resource allocation in orthogonal frequency division multiple access system is a constraint optimization problem, which is to maximize the minimum data rate of the user subject to constraint that total power cannot exceed a given value. How to handle the constraint is a key issue for constrained optimization problem. Different with the available schemes on constraint handling, the proposed algorithm converts the constraint into an objective. Then, the resource allocation is combined into a multi‐objective optimization problem. An improved multi‐objective optimization algorithm based on artificial immune system is proposed to solve it. The simulation results show that, compared with previous schemes, the proposed algorithm performs remarkable improvement in sum capacity. Copyright © 2013 John Wiley & Sons, Ltd.     

Resource Allocation Scheme for Millimeter Wave–Based WPANs Using Directional Antennas

In this paper, we consider a resource allocation scheme for millimeter wave–based wireless personal area networks using directional antennas. This scheme involves scheduling the reservation period of medium access control for IEEE 802.15.3c. Objective functions are considered to minimize the average delay and maximize throughput; and two scheduling algorithms—namely, MInMax concurrent transmission and MAxMin concurrent transmission—are proposed to provide a suboptimal solution to each objective function. These are based on an exclusive region and two decision rules that determine the length of reservation times and the transmission order of groups. Each group consists of flows that are concurrently transmittable via spatial reuse. The algorithms appropriately apply two decision rules according to their objectives. A real video trace is used for the numerical results, which show that the proposed algorithms satisfy their objectives. They outperform other schemes on a range of measures, showing the effect of using a directional antenna. The proposed scheme efficiently supports variable bit rate traffic during the reservation period, reducing resource waste.