Bayesian analysis of robust Poisson geometric process model using heavy-tailed distributions

We propose a robust Poisson geometric process model with heavy-tailed distributions to cope with the problem of outliers as it may lead to an overestimation of mean and variance resulting in inaccurate interpretations of the situations. Two heavy-tailed distributions namely Student’s t and exponential power distributions with different tailednesses and kurtoses are used and they are represented in scale mixture of normal and scale mixture of uniform respectively. The proposed model is capable of describing the trend and meanwhile the mixing parameters in the scale mixture representations can detect the outlying observations. Simulations and real data analysis are performed to investigate the properties of the models.     

Bayesian sample-size determination for two independent Poisson rates

Because of the high cost and time constraints for clinical trials, researchers often need to determine the smallest sample size that provides accurate inferences for a parameter of interest. Although most experimenters have employed frequentist sample-size determination methods, the Bayesian paradigm offers a wide variety of sample-size determination methodologies. Bayesian sample-size determination methods are becoming increasingly more popular in clinical trials because of their flexibility and easy interpretation inferences. Recently, Bayesian approaches have been used to determine the sample size of a single Poisson rate parameter in a clinical trial setting. In this paper, we extend these results to the comparison of two Poisson rates and develop methods for sample-size determination for hypothesis testing in a Bayesian context. We have created functions in R to determine the parameters for the conjugate gamma prior and calculate the sample size for the average length criterion and average power methods. We also provide two examples that implement our sample-size determination methods using clinical data.     

一种无接触供电的无线鼠标

介绍了一种无接触供电的无线鼠标设计电路,无接触电能发射装置采用USB供电,通过自激振荡电路产生了138 kHz的高频振荡电压;无接触电能接收装置采用感应耦合方式获取电能.无线鼠标用无线模块nRF24E1构成信号发射和接收电路,该电路除具备有线鼠标全部功能外,还可以离开电脑远距离灵活操纵鼠标.     

基于复杂网络理论的典型电力电子电路复杂性研究

基于复杂网络对电力电子电路的复杂性进行了研究,指出了复杂网络在电力电子技术中运用的结合点并以三相桥式全控整流电路及三相电压型桥式逆变电路为研究对象,提出用复杂网络的特征参数来分析该电路拓扑特性的方法,并对其鲁棒性及脆弱性进行了分析研究.最后对电力电子电路复杂性研究结果存在的问题进行了分析和总结.     

基于PIC单片机的GPIB数传系统设计

GPIB是仪器设备的主流通信接口之一.介绍一种基于GPIB的程控电源数传系统,详细介绍GPIB子卡的软硬件设计方法和基于Delphi平台的上位机软件编程思想.该技术已在某国产程控电源上使用,运行可靠,缩小了与国外同类产品的差距.     

基于低频能量分布的掌纹特征提取

提出了一种基于傅里叶变换的掌纹特征提取方法,利用傅里叶变换将空域的掌纹图像变换到频域,选取频域图像的低频能量分布作为掌纹的特征,以此为基础对特征向量进行匹配识别。实验结果表明,该算法实现简单、操作方便,具有较快的反应速度和较高的识别率。     

基于MSP430F149与Si4432的无线传感器网络的实现方法

介绍了基于MSP430F149与Si4432的无线传感器网络节点的硬件及软件设计方法,给出了主要的硬件电路和软件流程图,提出了一种简单易实现的低功耗自组织协议。实验证明,该无线传感器网络具有组网速度快、功耗低、抗干扰能力强、通信稳定可靠等特点,可应用于多个领域的信息探测,具有广阔的市场前景。     

减少防撞雷达虚假目标的信号处理研究

从信号处理的角度分析了防撞雷达虚报警率、漏报警率偏高的原因,采用AR模型功率谱估计的Burg算法代替传统的FFT算法,并将粗神经网络应用于防撞雷达目标识别。仿真结果表明,此方法提高了雷达信号处理的准确度和目标识别率,能有效地降低漏报警、虚报警率。     

电力企业信息化建设的问题与思考

电力企业运用计算机、网络和数据库技术,有效管理生产经营活动中的所有信息,实现电力企业内外部信息的共享和有效利用,在此过程中要求信息化建设的工作从简单应用向管理和分析转变。针对该现象分析了电力企业信息化建设中存在的问题和不足,提出解决这些问题的对策和建议。     

混沌粒子群算法及其在发电资源调度中的应用

为了有效地解决水火电力系统资源短期优化调度问题,提出了一种基于混沌粒子群算法的调度方案。设计了水火电力系统资源调度问题的数学模型,给出了混沌粒子群调度算法的框架,通过引入最优粒子的混沌搜索机制、优势粒子和劣势粒子的权重自适应调节机制,从而使算法具有动态自适应性,能够较容易地跳出局部最优。实验结果表明,本算法方案能有效解决水火发电资源调度问题,具有较好的应用价值。