基于DFT的频率预估改进算法研究

对同步采样方法进行了深入研究,提出了基于DFT相位差的频率估计改进算法,该算法使频率估计和频率跟踪具有了一定的相互促进作用.算法每次要求采样两个周波的数据,然后根据采样点数据进行新的频率估计.此外,文章还对其他软硬件同步方法进行了介绍,对电力系统谐波分析等有一定参考价值.     

基于SQL分层抽样的数据挖掘算法的改进

在对数据库聚类分析的基础上进行分层抽样,并使用关联规则,得出了数据之间的潜在关系.同时,对网民健身情况调查数据进行了实证分析,在SQL Server 2005上实现了抽样后的关联规则挖掘,提高了关联的效率,并取得了良好的效果.另外,对关联规则的评估作了一定的改进创新.     

Evaluating the reliability of microsatellite genotyping from low-quality DNA templates with a polynomial distribution model

Molecular studies using trace DNA, such as from museum specimens, ancient or forensic samples and samples obtained noninvasively, often have a common problem of low quality of DNA templates. Amplification errors, such as allelic dropout and false allele, may arise during polymerase chain reaction (PCR) using such samples. A mathematical model which treats homozygotes and heterozygotes discriminately has been developed to measure sample quality and compute the confidence level of using multipletube approaches. We use plucked hair samples collected from 26 individual Sichuan snub-nosed monkeys (Rhinopithecus roxel- lana) to test the model. In this case, a confidence level of 99% can be achieved by three positive PCRs. If the sample quality is very poor and requires many PCR replicates, an alternative multiplestep genotyping method is recommended. This model enables researchers to optimize experimental protocols through pilot studies and obtain reliable genetic information using noninvasive sampling method.     

浅谈建筑材料的检测及其影响因素

工程质量是永恒的主题。建材检测是建筑工程质量保证体系中的一个重要环节,是确保建筑工程质量的先决条件。文章在此主要分析常用建材质量检测方法、常用建材检测项目、建筑材料检测取样、建材检测的影响因素及数据处理,以供参考。     

基于Fibonacci采样的数据预处理方法研究

提高神经网络(NN)的收敛速率和预测精度一直是人工智能领域的一个挑战性问题,尽管许多研究人员已在研究中使用小批量数据训练神经网络获得了较好的效果,但是这些方法并不够灵活.针对这个问题,该文提出了一种新的数据预处理算法即Fibonacci采样算法.根据Fibonacci数列规则绘制一个新的训练数据序列,这不仅可以恢复小批...     

基于PC的水轮机组多通道振动数据采集系统设计

提出了一个基于PC的水轮机在线多通道振动数据采集系统的设计方案．对数据采集系统的特点进行了分析,采用中断＋FIFO的方式对多通道振动信号连续采集,并通过插值算法进行二次采样,得到了准同步整周期信号．在此基础上,用数字滤波算法去除了信号干扰,并对采集系统的精度进行了检验,结果表明,通过二次采样所得的信号与同步整周期采样的信号基本一致,可以满足振动监测的需要．     

基于Kriging模型的多点加点准则和并行代理优化算法

针对并行仿真环境下复杂工程系统的优化设计问题,提出一种基于Kriging模型、多目标策略和聚类方法的并行代理优化算法.该算法的多点加点准则,以同时优化期望改进准则和可行性概率准则为目标,首先生成兼具目标响应改进和可行域边界刻画功能的备选试验点集;再利用聚类方法从备选点集中选取多个有代表性的新试验点.通过两个数值算例和一个工程算例,将所提并行优化算法与已有算法做比较,结果表明所提算法具有更高的优化精度、效率和稳健性.     

鲸鱼优化相关向量机的网络控制系统变采样周期调度

针对资源受限的网络控制系统,提出一种基于鲸鱼优化相关向量机的变采样周期调度算法。通过网络监测模块获取网络带宽与数据传输时间数据,建立鲸鱼优化相关向量机的预测模型,实现对网络带宽及数据传输时间的预测。采用模糊推理计算系统各回路通信带宽的分配权重,进而结合通信带宽及数据传输时间的预测值对各闭环回路的采样周期进行计算,完成采样周期的实时调节。仿真结果表明,在资源受限条件下,所提算法保证了系统的稳定性与控制精度。     

Compressive sensing based multiuser detector for massive MBM MIMO uplink

Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple input multiple output(MIMO),the MBM scheme achieves better performance than other conventional multiuser MIMO schemes.In this paper,the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed.This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots,which exploits the structured sparsity of these aggregate MBM signals.Under this kind of scenario,a multiuser detector with low complexity based on the compressive sensing(CS)theory to gain better detection performance is proposed.This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit(CoSaMP)and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals.By exploiting these sparsity,the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity.Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.     

多精英采样与个体差分学习的分布估计算法

提出了基于多精英采样和差分搜索的分布估计算法EDA-M/D (Estimation distribution algorithm based on multiple elites sampling and individuals differential search)。EDA-M/D利用多精英个体独立采样生成子代来提升算法全局搜索能力,利用精英群体分布的σ²约束采样半径,实现种群从全局搜索逐步过度到局部搜索。当精英群体停滞时,劣势个体借助精英群体的μ和种群历史最优解进行差分搜索,帮助种群跳出局部最优解。通过多精英采样与差分搜索的自适应协同实现种群宏观信息与个体微观信息的有机融合。实验结果表明EDA-M/D在稳定性和搜索能力方面均表现出明显的优势。