具有多重焦点的波带透镜的设计

本文提出了一种新型的具有多重焦点的波带透镜的概念及其设计方法。可提供某些特定的光场分布,而且具有性能稳定、微型化和可实际制作等特点。     

Sparse synthetic aperture radar imaging with optimized azimuthal aperture

To counter the problem of acquiring and processing huge amounts of data for synthetic aperture radar(SAR)using traditional sampling techniques,a method for sparse SAR imaging with an optimized azimuthal aperture is presented.The equivalence of an azimuthal match filter and synthetic array beamforming is shown so that optimization of the azimuthal sparse aperture can be converted to optimization of synthetic array beamforming.The azimuthal sparse aperture,which is composed of a middle aperture and symmetrical bilateral apertures,can be obtained by optimization algorithms(density weighting and simulated annealing algorithms,respectively).Furthermore,sparse imaging of spectrum analysis SAR based on the optimized sparse aperture is achieved by padding zeros at null samplings and using a non-uniform Taylor window.Compared with traditional sampling,this method has the advantages of reducing the amount of sampling and alleviating the computational burden with acceptable image quality.Unlike periodic sparse sampling,the proposed method exhibits no image ghosts.The results obtained from airborne measurements demonstrate the effectiveness and superiority of the proposed method.     

智能优化算法的应用研究

优化问题广泛存在于各个领域,对该问题的求解问题从没停止过.自从优化问题提出以来,人们提出了各种各样的智能优化算法.文中简要介绍了遗传算法、蚁群算法、模拟退火算法3种智能优化算法,并简述其优缺点及应用研究的使用情况.     

考虑疫情扩散传播风险的多目标拼车应急管理优化调度模型

为了解决在疫情背景下传统拼车出行模式面临的交互传染风险,对疫情背景下的拼车管理进行分析,提出了将低风险及潜在风险乘客分开服务的拼车策略。在进一步考虑拼车系统应兼顾的社会效益及服务质量的基础上,构建了在满足基本额定盈利及最小化等待时长的双目标拼车应急管理优化调度模型,并给出了求解该模型的组合模拟退火算法。通过具体算例对模型的有效性进行验证,结果表明模型能够较快收敛到一个稳定值,且计算结果能够反映实际场景,说明了模型及算法的有效性。最后,通过一个仿真实验对所构建模型在病毒传播中的作用进行了分析,结果显示所构建的模型可以有效抑制拼车导致的病毒传播速度。     

短波chirp探测的遗传退火频率分配算法研究

针对短波chirp探测系统的通信网络频率分配问题,建立了合理的干扰模型;利用系统所提供的测量参数,结合基本遗传算法,同时进行模拟退火操作以克服基本遗传算法易陷入局部最优解的不足,并最终趋于全局最优化;仿真结果表明,该算法在寻优性、收敛速度和稳定性等方面都要优于基本遗传算法。     

Extremal optimization vs. learning automata: Strategies for spin selection in portfolio selection problems

Nowadays, various imitations of natural processes are used to solve challenging optimization problems faster and more accurately. Spin glass based optimization, specifically, has shown strong local search capability and parallel processing. But, spin glasses have a low rate of convergence since they use Monte Carlo simulation techniques such as simulated annealing (SA). Here, we propose two algorithms that combine the long range effect in spin glasses with extremal optimization (EO-SA) and learning automata (LA-SA). Instead of arbitrarily flipping spins at each step, these two strategies aim to choose the next spin and selectively exploiting the optimization landscape. As shown in this paper, this selection strategy can lead to faster rate of convergence and improved performance. The resulting two algorithms are then used to solve portfolio selection problem that is a non-polynomial (NP) complete problem. Comparison of test results indicates that the two algorithms, while being very different in strategy, provide similar performance and reach comparable probability distributions for spin selection. Furthermore, experiments show there is no difference in speed of LA-SA or EO-SA for glasses with fewer spins, but EO-SA responds much better than LA-SA for large glasses. This is confirmed by tests results of five of the world's major stock markets. In the last, the convergence speed is compared to other heuristic methods such as Neural Network (NN), Tabu Search (TS), and Genetic Algorithm (GA) to approve the truthfulness of proposed methods.     

Simultaneous solving of balancing and sequencing problems with station-dependent assembly times for mixed-model assembly lines

In spite of many studies, investigating balancing and sequencing problems in Mixed-Model Assembly Line (MMAL) individually, this paper solves them simultaneously aiming to minimize total utility work. A new Mixed-Integer Linear Programming (MILP) model is developed to provide the exact solution of the problem with station-dependent assembly times. Because of NP-hardness, a Simulated Annealing (SA) is applied and compared to the Co-evolutionary Genetic Algorithm (Co-GA) from the literature. To strengthen the search process, two main hypotheses, namely simultaneous search and feasible search, are developed contrasting Co-GA. Various parameters of SA are reviewed to calibrate the algorithm by means of Taguchi design of experiments. Numerical results statistically show the efficiency and effectiveness of the proposed SA in terms of both the quality of solution and the time of achieving the best solution. Finally, the contribution of each hypothesis in this superiority is analyzed.     

CO2 laser annealing of plasma-deposited high-Tcsuperconducting thick films

Recent advances in the fabrication of high-T _c superconducting thick films demand processing techniques which can eliminate film/substrate interdifiusion that occurs during subsequent post-annealing heat treatment after the film is deposited, thereby limiting the application of the thick films for devices. The present study evaluates laser annealing techniques for plasma-deposited Y-Ba-Cu-O thick films using a high-energy CO₂ laser (10.6m) in a continuous wave mode. The results are compared with those obtained by conventional furnace annealing techniques necessary for post-heat treatment of as-deposited superconducting thick films. The high-T _c superconducting phase is recovered by cationic diffusion during subsequent post-annealing heat treatment. Crystallographic phases and microstructural characterization have been performed using XRD, SEM, and EPMA analytical techniques. The significance of the technology lies in the elimination of film/substrate interdiffusion problems, thereby resulting in high-quality superconducting thick films. The technology will significantly reduce the post-annealing times usually required by conventional furnace annealing techniques.     

基于模拟攻击的高校网络安全风险评估研究

针对高校网络目前存在的安全风险,提出一种新型的基于模拟攻击的高校网络安全风险评估模型。该模型综合考虑了单机脆弱性和网络攻击威胁,首先结合原有基于单机脆弱性测出的风险值,模拟攻击者利用网络弱点的入侵过程,产生攻击状态图;然后基于生成的攻击状态图和原有风险值,识别攻击者入侵网络所利用的攻击行为、可能路线及导致的安全状态变化,评估潜在威胁的位置;并对新方法的风险值给出了定量分析,从而为针对性地实施风险控制决策提供更准确的依据。实验结果表明,该模型是正确的,并且平均要比目前存在的风险评估模型多发现大约50%的安全风险。由此可以看出,本模型方法的评估结论较传统方法更为准确。     

Adaptive simulated annealing particle swarm optimization algorithm

Particle swarm optimization is widely used in various fields because of the few parameters to be set and the simple calculation structure.In order to improve the optimization speed and accuracy of the PSO,and to avoid falling into the local optimal solution,an adaptive simulated annealing PSO is proposed,which uses the hyperbolic tangent function to control the inertia weight factor for nonlinear adaptive changes,uses linear change strategies to control 2 learning factors,introduces the simulation annealing operation,set a temperature according to the initial state of the population,guide the population to accept the difference solution with a certain probability according to the Metropolis criterion,and ensure the ability to jump out of the local optimal solution.To verify the effect of the algorithm proposed in this paper,7 typical test functions and 5 algorithms proposed in the literature are selected for comparison and testing.According to the average value,standard deviation and number of iterations of the optimization results,the algorithm proposed in this paper has greatly improved the iteration accuracy,convergence speed and stability so as to overcome the shortcomings of particle swarm optimization.