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.     

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.     

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.     

铸件退火炉烟气余热的回收利用装置

提出了HTAC技术的新结构,即铸件退火炉烟气余热的回收利用装置,以期提高热效率、节约能源并减轻污染。     

退火温度对Ag/Cu层复合板分离强度的作用

对轧制复合的Ag Cu双金属薄板采用不同温度的退火处理 ,研究了结合面分离强度随退火温度变化的规律 ,讨论了影响分离强度的因素。结果表明 ,在 40 0℃以下随退火温度的升高 ,由于原子扩散及再结晶晶界的形成趋于充分而导致分离强度升高 ,40 0℃时分离强度达到最大值。当退火温度超过 40 0℃后 ,由于结合面的异类原子不等量对流而导致结合面形成空洞 ,从而使分离强度下降。 750℃退火时分离强度达到最低值。退火温度进一步上升到 80 0℃时可使结合面发生局部熔合 ,使分离强度又有小幅度回升     

新能源汽车电池负极材料的制备与性能研究

采用原位合成法制备了镍-氧化镍/多孔碳纳米片（Ni-NiO/PCNs）负极材料,对比分析了氯化钠模板、退火温度和退火时间对负极材料物相组成、显微形貌和电化学性能的影响。结果表明,Ni/PCNs、Ni-NiO/C和Ni-NiO/PCNs负极材料都主要含有镍和无定型C相,且后2种负极材料还含有氧化镍相;300 ℃/4 h为Ni-NiO/PCNs负极材料适宜的退火工艺,此时Ni-NiO/PCNs负极材料中Ni-NiO粒子分散性较好且保持着三维片层结构,平均尺寸约为27 nm,Ni-NiO实现了对无定型C的包裹;退火时间过长（6 h）会使得镍粒子过氧化且发生团聚,而温度过高（400 ℃）会使得粒子以团聚为主,三维片层状结构消失。电流密度为1 A/g、循环5 000圈后Ni-NiO/PCNs负极材料的放电比容量为235 mA·h/g,此时的放电比容量约为首圈放电比容量的83.93%;Ni-NiO/C和Ni/PCNs负极材料在充放电循环过程中以及循环5 000圈后的放电比容量和容量保持率都明显低于Ni-NiO/PCNs负极材料,Ni-NiO/PCNs负极材料具有更好的循环稳定性,这主要与其具有独特的多孔三维片层结构有关。