Estimation of distributions involving unobservable events: the case of optimal search with unknown Target Distributions

We consider the problem of estimating the parameters of a distribution when the underlying events are themselves unobservable. The aim of the exercise is to perform a task (for example, search a web-site or query a distributed database) based on a distribution involving the state of nature, except that we are not allowed to observe the various “states of nature” involved in this phenomenon. In particular, we concentrate on the task of searching for an object in a set of N locations (or bins) {C ₁, C ₂,…, C _N }, in which the probability of the object being in the location C _i is p _i , where P = [p ₁, p ₂,…, p _N ] ^T is called the Target Distribution. Also, the probability of locating the object in the bin within a specified time, given that it is in the bin, is given by a function called the Detection function, which, in its most common instantiation, is typically, specified by an exponential function. The intention is to allocate the available resources so as to maximize the probability of locating the object. The handicap, however, is that the time allowed is limited, and thus the fact that the object is not located in bin C _i within a specified time does not necessarily imply that the object is not in C _i . This problem has applications in searching large databases, distributed databases, and the world-wide web, where the location of the files sought for are unknown, and in developing various military and strategic policies. All of the research done in this area has assumed the knowledge of the {p _i }. In this paper we consider the problem of obtaining error bounds, estimating the Target Distribution, and allocating the search times when the {p _i } are unknown. To the best of our knowledge, these results are of a pioneering sort - they are the first available results in this area, and are particularly interesting because, as mentioned earlier, the events concerning the Target Distribution, in themselves, are unobservable.

基于FPGA的硅微陀螺仪零偏温度补偿系统的研究

介绍了温度对硅微陀螺仪的零偏的影响机理,通过温度实验得知零偏随温度变化成复杂的非线性关系,并用最小二乘法拟合出了零偏在～40℃～80℃随温度变化的数学表达式;以FPGA芯片为主要器件设计了零偏温度补偿系统,并进行了温度实验测试,测试结果表明补偿效果明显。     

论绘画中的“视觉偏见”

当今,我们的视觉受到来自外在世界的各种影响,受到干扰后的视觉就变得不再纯粹,这使得作为视觉艺术的绘画失去了本应有的生命力。本文从视觉偏见出发来论述它产生的原因及打破视觉偏见的途径。     

Adaptive IIR/FIR fusion filter and its application to the INS/GPS integrated system

Motivated by the complementary features of the IIR-type filter and the FIR-type filter, this paper proposes a robust IIR/FIR fusion filter and an INS/GPS integrated system designed with the fusion filter. In the fusion filter, an IIR-type filter (SPKF) and a FIR-type filter (MRHKF filter) are processed independently, and then the two filters are merged using the mixing probability calculated using the residuals and residual covariance information of the two filters. The merits of the SPKF and the MRHKF filter are integrated and the demerits of the filters are diminished through the filter fusion. Consequently, the proposed fusion filter shows robustness against model uncertainty, temporary disturbing noise, large initial estimation error, etc. The stability of the fusion filter is verified by showing the closeness of two filters in the mixing/redistribution process and the upper bound of the error covariance matrices. This fusion filter is applied to an INS/GPS integrated system. The performance of the INS/GPS integrated system designed using the fusion filter is verified through a simulation under various error environments and is experimentally confirmed.     

双闭环真空硅微陀螺仪设计及测试性能分析

所设计的硅微陀螺仪工作在10 Pa,品质因素达到3000,采用CAN金属壳封装形式,测控线路采用闭环自激驱动,力反馈式闭环检测方式.驱动采用单边驱动单边检测,尽量使线路简化;检测采用双重分解和重构回路.测试结果表明:有用信号和正交信号实现很好的相位解耦,互不影响.在实现标度因素10 mY/(°)S-1的情况下,零偏稳定性已达到了60°/h;线性度已达到了400 ppm;带宽仿真达到150 Hz;这较以前设计的空气下闭驱开检方式下的硅微陀螺性能提高了近2个数量级.     

一种基于遗传算法的脑MR图像去偏移场模型

由于磁共振图像(magnetic resonance images,MRI)常含有偏移场而影响后继图像分割,针对这种图像的分割,采用Legendre多项式基函数来拟合偏移场,可以去除偏移场对图像分割的影响。当使得恢复图像的信息熵达到最小时,则求得的偏移场最优。在求偏移场的过程中,需要求解基函数的参数,由于传统的梯度下降法易陷入局部最优,为解决此问题,提出将遗传算法引入到参数求解过程中,然而传统的遗传算法不仅时间复杂度高,且易陷入局部最优,为此需对遗传算法进行改进,使得不仅更容易得到全局最优解,且时间复杂度较低。实验证明,该改进算法可以得到精确的偏移场,并可得到准确的分割结果。     

基于Bayes决策的GMDSS模拟评估系统研究

针对全球海上遇险与安全系统的训练需求,开发了新一代多功能GMDSS模拟与评估系统.该系统为多种海上通信设备提供了一个综合的模拟训练平台,具有进行实时通信仿真,规范的通信操作流程演示及对操作训练进行自动评估等多种功能.首先介绍了系统的设计思想和组成功能,然后分析了系统原理与设计架构,并详细阐述基于Bayes决策的评估方法,模块化整合技术,卫星通信状态仿真模型以及系统网络通信实现机制等关键技术.经过实际使用测试表明,该系统功能完善,有较高的稳定性和可靠性.     

网络主动带宽测量算法设计

本文基于Cruz流量模型.通过主动检测单向时延连续上升的概率.推断探测速率与端到端可用带宽之间的关系,提出一种网络可用带宽主动测量算法PathPCQ.NS2仿真显示,PathPCQ算法测量精度和效率都优于现有的带宽测量工具Pathload.     

一种高增益的宽带低噪声放大器的设计

采用SANAN公司的0.25μm E-Mode pHEMT工艺,基于ADS仿真,设计了一款工作频率为2.0～4.2 GHz的两级级联的宽带LNA芯片。芯片采用电阻偏压的方式,实现了3.3 V单电源供电。同时,设计了一种改进型的RLC并联负反馈结构,实现了宽带匹配。仿真结果表明,该LNA在2.0～4.2 GHz频段内,最大增益为30.9 dB,增益平坦度为±0.6 dB左右,输入回波损耗小于-9 dB,输出回波损耗小于-12 dB;噪声系数为(1.2±0.14) dB;系统稳定性因子K在全频带内大于2.8;芯片面积为0.78 mm×2.2 mm。     

HVDC/GIC型直流偏磁的差异性分析

考虑到地磁感应电流(geomagnetically induced current, GIC)具有低频性,过去一直将其近似等效为高压直流输电(high voltage direct current, HVDC)诱发的不平衡电流进行研究。然而,与HVDC型直流偏磁相比,GIC型直流偏磁具有显著的随机性与时变性,因此简单地将两者完全等效处理并不合理,在特定场景下应加以区分。为此,首先,从理论上分析了两种类型直流偏磁在诱发原因及特点上的差异。其次,通过研究直流偏磁对变压器本体以及电流互感器的不利影响,进一步探究两种类型直流偏磁对电网一/二次设备的影响差异,为后续的偏磁治理提供有效参考。最后,基于PSCAD/EMTDC仿真平台搭建了等效仿真模型,并通过仿真验证了理论分析的正确性。