It型滞后随机系统的仿真

本文研究了Ｉｔｏ型滞后随机系统的数值解法与仿真,得到了Ｉｔｏ型滞后随机系统数值计算的迭代格式,分析了迭代格式的精度和用于Ｉｔｏ型滞后随机系统仿真的有效性,并用实例演示了所得方法的应用。     

Angular glint calculation and analysis of radar targets via adaptive cross approximation algorithm

Angular glint is a significant electromagnetic （EM） scattering signature of extended radar targets. Based on the adaptive cross approximation （ACA） algorithm accelerated method of moments （MoM） and the plane incident wave assumption, the narrowband, wideband and newly developed high-resolution range profile （HRRP） based angular glint calculation formulations are derived and applied to arbitrarily shaped three-dimensional （3D） perfectly electrical y conducting （PEC） objects. In addition, the near-field angular glint is emphasized, which is of great importance for radarseeker applications. Furthermore, with the HRRP based angular glint, an approach to rigorously determine range resolution cel s which own relatively smal er angular glint is provided. Numerical results are presented with new findings to demonstrate the usefulness of the developed formulations.     

过度自信委托代理人间的薪酬合同研究

委托-代理模型中,委托人委托代理人销售产品并付给代理人一定的报酬. 委托人和代理人对随机需求的均值或方差的估计存在误差,由此产生绝对过度自信和相对过度自信. 委托人可提供的薪酬合同有两种：线性合同和阈值合同. 在委托人和代理人持有的不同自信度组合中,分别给出了线性合同和阈值合同下的最优参数. 研究得出：对委托人而言,当且仅当其关于不确定性需求方差的信念较代理人悲观,并且对市场需求位于零点附近的可能性的估计较代理人小时,阈值合同优于线性合同;否则,在线性合同和阈值合同下,委托人的均衡期望收益相等.     

“公司+农户”型订单农业供应链的Nash协商模型

研究了一类由农户与公司构成的“公司+农户”型订单农业经营模式下的农产品供应链,其中农户在考虑产出不确定性以及订单价格的基础上确定农产品的生产量,公司拥有农产品的批发定价权,并在产出与需求双重不确定性情形下确定农产品的订单价格和零售价格. 对比分析了供应链集中决策与分散决策下公司和农户的最优决策行为. 研究结果表明,在订单农业经营模式下公司可以通过主导地位从中获取绝大部分的收益,而且实践中普遍存在的“保底收购,随行就市”价格机制无法防止公司在市场行情不好时的机会主义行为. 为此,引入一种基于Nash协商的收益共享契约机制来协调公司与农户之间的利益. 实施该契约机制不仅可以实现“公司+农户”型订单农业供应链的完美协调,还能带来社会福利的增加.     

基于比较静态模型的FDI优惠政策指数门槛值的求解

针对FDI （foreign direct investment） 优惠政策拉大中国区域差距及挤压内资企业投资的问题,基于比较静态分析方法,在Romer内生模型基础上,求解了市场均衡时FDI优惠政策指数的门槛值,并对影响门槛值大小的因子进行了分析. 结果表明,最终生产部门人力资本存量的增加、研发部门生产率的提高,以及本国技术吸收能力的增强都会使FDI优惠指数变大,但市场平均利率的上升却带来FDI优惠指数的减小,而FDI优惠政策指数对经济增长率的贡献度则无法判断.     

Opportunistic spectrum sharing in software defined wireless network

Over the past few decades, the world has witnessed a rapid growth in mobile and wireless networks（MWNs） which significantly change human life. However, proliferating mobile demands lead to several intractable challenges that MWN has to face. Software-defined network is expected as a promising way for future network and has captured growing attention. Network virtualization is an essential feature in software-defined wireless network（SDWN）, and it brings two new entities, physical networks and virtual networks. Accordingly, efficiently assigning spectrum resource to virtual networks is one of the fundamental problems in SDWN. Directly orienting towards the spectrum resource allocation problem, firstly, the fluctuation features of virtual network requirements in SDWN are researched, and the opportunistic spectrum sharing method is introduced to SDWN. Then, the problem is proved as NP-hardness. After that, a dynamic programming and graph theory based spectrum sharing algorithm is proposed.Simulations demonstrate that the opportunistic spectrum sharing method conspicuously improves the system performance up to around 20%–30% in SDWN, and the proposed algorithm achieves more efficient performance.     

基于卷积神经网络的多天线Polar码联合解调-解码方案

为了验证运用神经网络进行信道解码的可行性,利用其提高短码长Polar码的译码准确率,提出了一种基于卷积神经网络(convolutional neural network,CNN)的多天线(multiple-input multiple-output)Polar码新颖联合解调-解码方案.搭建了一种包括4个卷积层,2个全连接层和1个输出层的卷积神经网络,采用最小均方误差作为损失函数,通过计算机生成了Polar码编码的多天线数据并对网络进行训练,使训练得到的神经网络能很好地提取出Polar码比特间的关系特征,从而拟合出Polar码译码函数.仿真结果表明,在相同信噪比条件下,基于CNN的Po-lar码联合解调-解码方案的误码率优于已有的基于全连接神经网络方案;所提方案在不同码率的仿真实验中的误码率皆优于基于全连接神经网络方案,损失曲线的收敛速度更快,显示了基于CNN的Polar码联合解调-解码方案具有更好的泛化能力和学习能力.     

基于动态搜索区域的无线传感器网络小世界特性构建方案

为了减少无线传感器网络节点的能量消耗,以延长无线传感器网络的寿命,首先研究了如何通过在无线传感器网络中构建小世界特性以延长其网络寿命,并着重分析了DAS( directed angulation towards the sink)方案.然后针对DAS方案中临近sink节点区域内的节点能量过快消耗的问题,提出了一种在无线传感器网络中构建小世界特性的新方案——菱形区域方案.该方案根据sensor节点到sink节点距离的大小,动态地改变菱形捷径端点搜索区域的大小,达到构建小世界特性并增强网络抗毁性的目的.理论证明了菱形区域方案在一定条件下较DAS方案既节省能量又能延长网络的寿命.     

Forecasting in the presence of level shifts

This article addresses the problem of forecasting time series that are subject to level shifts. Processes with level shifts possess a nonlinear dependence structure. Using the stochastic permanent breaks (STOPBREAK) model, I model this nonlinearity in a direct and flexible way that avoids imposing a discrete regime structure. I apply this model to the rate of price inflation in the United States, which I show is subject to level shifts. These shifts significantly affect the accuracy of out‐of‐sample forecasts, causing models that assume covariance stationarity to be substantially biased. Models that do not assume covariance stationarity, such as the random walk, are unbiased but lack precision in periods without shifts. I show that the STOPBREAK model outperforms several alternative models in an out‐of‐sample inflation forecasting experiment. Copyright © 2005 John Wiley & Sons, Ltd.     

DS—UWB中的一种基于检测反馈-存储的门限值设置方法

研究了直扩超宽带通信系统的关键技术——定时捕获系统,提出了一种基于检测反馈和存储的门限值设置方法,理论分析和仿真结果表明该方法能有效地提高直扩超宽带同步系统的定时捕获概率,大大减少平均定时捕获时间,具有很强的抗定时抖动性能。