我国大麦价格波动特征及其影响因素分析

[目的]大麦价格剧烈波动会直接影响大麦种植户的生产积极性和大麦产业的平稳发展,研究大麦价格波动特征及其影响因素,有助于提升大麦产业链相关主体识别和应对市场风险的能力,促进大麦产业的健康发展。[方法]文章先采用HP滤波法和ARCH类模型分析了2011年4月至2017年2月我国大麦价格波动特征,然后采用脉冲响应函数分析了我国大麦价格波动影响因素。[结果]我国大麦价格波动存在明显的季节性和周期性,样本期内总体上呈现逐渐下降趋势;我国大麦价格具有显著的波动集聚性,我国大麦价格具有显著的不对称性;在该文选择的影响因素中,大麦进口量和国际大麦价格是影响我国大麦价格波动的主要因素。[结论]该文提出必须保障并提高国内大麦合理产能、完善大麦价格监测预警体系、加强国内大麦进口企业整合和推动大麦进口来源多元化的政策建议。     

一种基于SIR结构的三通带带通滤波器

应用双指耦合结构和枝节加载谐振器（Stub-loaded Resonator，SLR）实现了一款基于阶梯阻抗谐振器（Stepped Impedance Resonator，SIR）的滤波器。该滤波器具有3个通带，带外抑制较好，工作频段提高。通过调整阻抗比可调节第二、三通带的谐振频率；SLR结构能够增加通带数量；SLR结构和双指耦合结构均能改善滤波器的S参数。HFSS软件仿真表明，3个通带的中心频率分别为3.5 GHz、6.6 GHz、9.2 GHz，对应的分数带宽分别为5.7%、3%、2%，S11分别为-18 dB、-22 dB、-24 dB，通带内的S21分别为-1.8 dB、-1 dB、-1 dB。电路的测量结果与仿真结果较为吻合。该滤波器在5G通信的低频段具有应用前景。     

Testing commodity futures market efficiency under time-varying risk premiums and heteroscedastic prices

We propose a novel test to measure market efficiency while estimating the time-varying risk premiums of commodity futures, given that the prices are heteroscedastic. The risk premium is estimated using a state-space model with a Kalman filter modified for heteroscedasticity. Using 79 commodity futures traded on 16 exchanges during the period 2000–2014 and a Monte Carlo simulation, we demonstrate that the proposal produces robust results compared with conventional approaches. The global financial crisis has improved the efficiency and affected the trading volumes of commodity futures, but it has had no effect on the average or the volatility of risk premiums.     

Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network

Electric vehicles (EV) use an eco-friendly technology that limits the greenhouse gas emissions of the transport sector, but the limited battery capacity and the density of the battery are the major barriers to the widespread adoption of EV. To mitigate this, a good method seems to be the innovative wireless charging technology called ‘On-Line EV (OLEV)’, which is a contactless electric power transfer technology. This EV technology has the potential to charge the vehicle’s battery dynamically while the vehicle is in motion. This system helps to reduce not only the size of the battery but also its cost, and it also contributes to extending the driving range before the EV has to stop. The high cost of this technology requires an optimal location of the infrastructure along the route. For this reason, the objective of this paper is to study the problem of the location of the wireless charging infrastructure in a transport network composed of multiple routes between the origin and the destination. To find a strategic solution to this problem, we first and foremost propose a nonlinear integer programming solution to reach a compromise between the cost of the battery, which is related to its capacity, and the cost of installing the power transmitters, while maintaining the quality of the vehicle’s routing. Second, we adapt the multi-objective particle swarm optimization (MPSO) approach to our problem, as the particles were robust in solving nonlinear optimization problems. Since we have a multi-objective problem with two binary variables, we combine the binary and discrete versions of the particle swarm optimization approach with the multi-objective one. The port of Le Havre is presented as a case study to illustrate the proposed methodology. The results are analyzed and discussed in order to point out the efficiency of our resolution method.     

双选信道下一种基于混沌交织的FBMC系统

高速移动环境下，无线信道具有时频双选性衰落的特性，使得滤波器组多载波(Filter Bank Multi-carrier，FBMC)系统产生长突发差错。将一种基于Baker映射的混沌交织算法应用在滤波器组多载波系统中，根据混沌密钥对发送数据进行分块和重新排列，按照Baker映射规则完成数据交织。此方法可以将长突发差错变为单突发差错，结合卷积编码能有效地纠正双选信道产生的长突发差错。仿真结果表明，在双选择信道中，基于混沌交织的滤波器组多载波系统误比特率性能优于传统基于块交织的滤波器组多载波系统。     

What do professional forecasters actually predict?

This paper studies what professional forecasters predict. We use spectral analysis and state space modeling to decompose economic time series into trend, business cycle, and irregular components. We examine which components are captured by professional forecasters by regressing their forecasts on the estimated components extracted from both the spectral analysis and the state space model. For both decomposition methods, we find that, in the short run, the Survey of Professional Forecasters can predict almost all of the variation in the time series due to the trend and the business cycle, but that the forecasts contain little or no significant information about the variation in the irregular component.     

基于粒子群算法下的交叉口信号配时优化

为了合理地优化交叉口信号配时,通过分析交叉口的评价指标,建立了以车辆的平均延误、停车次数最小、交叉口整体通行能力最大、各相位有效绿灯时间和交叉口周期时长作为约束条件的数学模型。并通过改进前人研究基础上的动态加权系数,将多目标的非线性优化问题转化为单一目标的非线性规划问题,为了得出更科学稳定的解,提出了改善粒子群算法系统稳定性的2种方法,并将其与粒子群算法结合起来。然后以Matlab为求解工具,结合临邑市某一交叉口实例进行求解分析。最后的结果表明,在使用改进后的粒子群算法进行优化后交叉口通行能力较之现状提升了9%,延误下降了28%,停车次数下降了9%,且各项优化结果均优于Webster,改进后的算法在程序中运行300代,到216代才开始收敛,而未改进的算法稳定性较差,优化结果和收敛曲线则随着实验次数的变化而变化,最后的结论证明了该算法和模型的可靠性。     

基于PSO-SAS的联合侦察任务规划

针对联合侦察筹划中任务规划阶段机动侦察平台阵位与路线确定困难的问题，提出了一种基于粒子群优化-稀疏A星(Particle Swarm Optimization—Sparse A-star,PSO-SAS)算法的规划方法。该方法综合考虑侦察装备机动性能以及敌火力威胁、地形等因素，在侦察阵位规划上，建立了阵位综合评估模型，并利用粒子群算法进行阵位寻优；在路线规划上，采用稀疏A*算法进行航迹规划，通过将机动性能、安全距离、路程等约束引入搜索过程，缩短最优路线的计算时间。仿真试验验证了所提方法生成的侦察阵位和路线能够满足侦察任务要求。     

乘积型指数滤波器及其在多目标时延估计中的应用

指数滤波器是一类新构造出来的输出信噪比和目标时延分辨力随指数变化的滤波器，该滤波器在损失一定输出信噪比的前提下可以有效提高目标时延分辨力，从而提高目标时延估计精度,但仅采用单个指数滤波器仍存在输出信噪比和目标时延分辨力均达不到实际需求的情况。在乘积型高阶模糊函数乘积运算的启发下，在指数滤波器的基础上提出了一种新的乘积型指数滤波器，并分析了该乘积型指数滤波器的输出信噪比及目标时延分辨力等性能。仿真实验表明，所提的乘积型指数滤波器在低信噪比情况下可以更有效提高多目标时延估计精度，且算法简单易于实现，适用于背景复杂的多目标参数估计任务。     

改进高阶容积粒子滤波的系统状态估计算法

传统粒子滤波（PF）直接采用状态转移先验分布作为重要性密度函数来近似后验概率密度函数，使得后验概率密度函数未包含量测信息。针对此问题，提出了一种改进高阶容积粒子滤波(CPF)的系统状态估计算法。算法采用七阶正交容积卡尔曼滤波（7th-CQKF）对PF的粒子进行传递，使得先验分布更新阶段融入最新量测信息；通过7th-CQKF设计重要性密度函数，提高对状态后验概率密度的逼近程度；通过反比例函数计算粒子权重，突出大噪声粒子与小噪声粒子权重差别，提高粒子有效性。仿真结果表明，改进高阶容积粒子滤波的估计精度高于容积粒子滤波（CPF）。