多液压缸采样臂同步运动控制的设计与实现

针对移动式煤炭采样车上多液压缸采样臂的结构、功能及其技术要求,分析了液压采样臂采样工作时各液压缸的运动规律和控制难点.为解决采样臂的多液压缸联动以实现终端执行件采样头的位置和角度控制问题,通过软件建立采样臂三维模型,对采样臂采样时各液压缸的运动轨迹进行仿真规划,提出并使用一种基于Simotion C240的虚拟主轴同步的方法,实现多液压缸同步控制.所提出的方法成功地应用于某型号煤炭采样车的控制,满足了自动采样过程中螺旋采样头定点定角度采样的动作要求,取得了实际的应用成果.     

一种业务流自适应尽力采样方法

基于业务流的网络流量监测是网络管理、运维、实现基于业务的计费、流量工程等的重要手段．精确、高效的采样技术是实现高速网络流量业务流监测分析的重要技术．基于分段采样思想提出一种尽力最优的自适应随机采样方法，实现特大业务流的精确估计，其中把监测系统本身的处理能力作为选择采样概率的参数．实验结果显示算法能够很好地调节采样概率，使得采样包速率基本等于预先设定的监测系统的处理能力．     

两种实现采样速率变换方案

采样速率的变换是软件无线电中最为关键的问题之一。该文分析了采样速率变换的数学模型,并从两种实现采样速率变换方案模型的matlab仿真结果出发,简要讨论了实现采样速率变换方案中的难点问题。     

基于自适应陷波的非均匀采样信号处理系统

非均匀采样可以突破奈奎斯特采样定理的限制,检测出超过采样频率的信号频率,但非均匀采样引起信号的频谱噪声,使得非均匀采样的小信号检测难于实现。研制了一种实时非均匀采样信号处理系统,采用自适应陷波方法计算非均匀采样信号的频率,逐步滤除幅度较大的信号,从而检测出小幅度信号。详细说明了自适应陷波方法的原理和实现方法,并介绍了基于数字信号处理器(DSP)的非均匀采样信号处理系统。     

WSN中的分布式压缩感知

为了解决传统的压缩感知算法在无线传感器网络中实现的难题,首先研究了用定时器控制ADC进行随机压缩采样的压缩感知技术,实验表明,该方法有效可行。在此基础上提出了基于无线传感器网络的分布式压缩感知算法。该算法通过对随机压缩采样序列的拆分实现分布式压缩采样,最后利用合并后的采样值和CoSaMP算法完成对信号的重构。仿真和实验表明,该方法能够在星型网络拓扑中实现以1/10的标准采样频率下实现信号的重构。     

多带模拟信号的采样与重构

为了实现多带模拟信号的有效采样,根据多带模拟信号的特点,建立了一个在平移不变空间上的多通道采样模型;利用插值函数实现信号的完整重构。以多带正弦信号为例,验证了该系统可以实现多带模拟信号的采样与重构。     

基于多路欠采样的多分量LFM信号参数估计

针对多分量线性调频(Linear frequency modulated,LFM)信号,提出了一种基于多路欠采样的参数估计方法。采样过程由多个采样速率相同、采样时刻不同的模数转换器实现,总采样率可以低于信号的奈奎斯特采样率。基于欠采样序列乘积型模糊函数的单频特性,可以通过峰值检测实现调频斜率的估计。根据估计出的调频斜率对各路欠采样序列进行解线调处理,可得到多频正弦信号。结合矩保持问题的求解方法以及对超定方程组的求解,可以根据解线调后的各路序列估计出原始LFM信号各分量的初始频率。本文方法能够根据亚奈奎斯特采样样本实现LFM信号的参数估计,并且运算简单、易于实现。仿真实验验证了其有效性和准确性。     

数学模型已知的自由曲面数字化自适应采样

针对数学模型已知的自由曲面,依据质心概念推导出基于形状的采样算法.借助该算法不仅可产生在给定采样点总数条件下的自适应采样矩形拓扑网格,而且能确定在给定采样精度下的合理采样点数,从而实现自由曲面数字化采样的自适应规划.     

基于硬件实现的粒子滤波重采样算法研究

粒子滤波算法用于硬件实现是目前一个新的研究方向,传统的粒子滤波算法计算量大,所需存储空间大,实时性差,所以在硬件实现方面面临着极大的挑战。为使算法更加适合于硬件实现,以粒子滤波中的重采样步骤为研究重点,以典型的序贯重要性重采样滤波算法为例,对典型的几种重采样算法的复杂度、所需存储空间及执行时间上进行分析研究,并在TI DSPTMS320C5402上对采样算法进行仿真,结果表明部分重采样算法(PDR)更适合于硬件实现。     

基于压缩感知理论的图像重构算法研究

压缩感知理论突破了奈奎斯特采样频率的限制,利用该理论研究和实现了新的图像压缩采样方案.该方案利用小波变换和阈值处理相结合实现图像稀疏化,利用标准伪随机数均匀分布和二维中心傅立叶变换生成随机测量矩阵,并对小波变换后的高频子带进行加权采样,采用分段正交匹配追踪算法实现采样数据重建.仿真实验结果表明,该方案重建图像效果好.     

一种面向大数据的水塘采样分布式算法

水塘采样算法构建了一个样本集合,很好地解决了对未知的数据元素等概率采样的问题.然而当数据量较大时,单机的水塘采样算法时间复杂度较高、机器负载加重.面向大数据采样的需求,提出了分布式水塘采样算法,通过增加采样比例的方法实现了多机器并行采样,而且从理论上证明了分布式水塘采样的等概率性.为处理对数据元素贡献度不同的数据集,将...     

Error analysis of estimators that use combinations of stochastic sampling strategies for direct illumination

We present a theoretical analysis of error of combinations of Monte Carlo estimators used in image synthesis. Importance sampling and multiple importance sampling are popular variance‐reduction strategies. Unfortunately, neither strategy improves the rate of convergence of Monte Carlo integration. Jittered sampling (a type of stratified sampling), on the other hand is known to improve the convergence rate. Most rendering software optimistically combine importance sampling with jittered sampling, hoping to achieve both. We derive the exact error of the combination of multiple importance sampling with jittered sampling. In addition, we demonstrate a further benefit of introducing negative correlations (antithetic sampling) between estimates to the convergence rate. As with importance sampling, antithetic sampling is known to reduce error for certain classes of integrands without affecting the convergence rate. In this paper, our analysis and experiments reveal that importance and antithetic sampling, if used judiciously and in conjunction with jittered sampling, may improve convergence rates. We show the impact of such combinations of strategies on the convergence rate of estimators for direct illumination.     

Analysis of adaptive sampling techniques for underwater vehicles

A critical problem in planning sampling paths for autonomous underwater vehicles (AUVs) is correctly balancing two issues. First, obtaining an accurate scalar field estimation and second, efficiently utilizing the stored energy capacity of the sampling vehicle. Adaptive sampling approaches can only provide solutions when real time and a priori environmental data is available. In this paper we present an analysis of adaptive sampling methodologies for AUVs. In particular, we analyze various sampling path strategies including systematic and stratified random patterns within a wide range of sampling densities and their impact in the energy consumption of the vehicle through a cost-evaluation function. Our study demonstrates that a systematic spiral sampling path strategy is optimal for high-variance scalar fields for all sampling densities and low-variance scalar fields when sampling is sparse. In addition, our results show that the random spiral sampling path strategy is found to be optimal for low-variance scalar fields when sampling is dense.     

基于动态Gibbs采样的RBM训练算法研究

目前大部分受限玻尔兹曼机(Restricted Boltzmann machines, RBMs)训练算法都是以多步Gibbs采样为基础的采样算法. 本文针对多步Gibbs采样过程中出现的采样发散和训练速度过慢的问题,首先, 对问题进行实验描述,给出了问题的具体形式; 然后, 从马尔科夫采样的角度对多步Gibbs采样的收敛性质进行了理论分析, 证明了多步Gibbs采样在受限玻尔兹曼机训练初期较差的收敛性质是造成采样发散和训练速度过慢的主要原因; 最后, 提出了动态Gibbs采样算法,给出了对比仿真实验.实验结果表明, 动态Gibbs采样算法可以有效地克服采样发散的问题,并且能够以微小的运行时间为代价获得更高的训练精度.     

周期非均匀采样带通信号的采样参数

从采样信号中完全重建原信号的条件为基础，推导了周期非均匀采样的最低采样阶数Ⅳ。与其单通道采样率fs需要满足的关系，给周期非均匀采样的有效应用提供了理论指导。并进一步分析了最小总采样率fM。结论证实了周期非均匀采样能保证任意带通信号以2倍带宽的采样率进行采样。     

Development and application of an autonomous unmanned aerial vehicle for precise aerobiological sampling above agricultural fields

Remote‐controlled (RC) unmanned aerial vehicles (UAVs) have been used to study the movement of agricultural threat agents (e.g., plant and animal pathogens, invasive weeds, and exotic insects) above crop fields, but these RC UAVs are operated entirely by a ground‐based pilot and often demonstrate large fluctuations in sampling height, sampling pattern, and sampling speed. In this paper, we describe the development and application of an autonomous UAV for precise aerobiological sampling tens to hundreds of meters above agricultural fields. We equipped a Senior Telemaster UAV with four aerobiological sampling devices and a MicroPilot‐based autonomous system, and we conducted 25 sampling flights for potential agricultural threat agents at Virginia Tech's Kentland Farm. To determine the most appropriate sampling path for aerobiological sampling above crop fields with an autonomous UAV, we explored five different sampling patterns, including multiple global positioning system (GPS) waypoints plotted over a variety of spatial scales. An orbital sampling pattern around a single GPS waypoint exhibited high positional accuracy and produced altitude standard deviations ranging from 1.6 to 2.8 m. Autonomous UAVs have the potential to extend the range of aerobiological sampling, improve positional accuracy of sampling paths, and enable coordinated flight with multiple UAVs sampling at different altitudes. © 2008 Wiley Periodicals, Inc.     

网络管理系统中一种自适应的采样频率调整方法

在分析当前网络管理系统中主要数据采集方法的基础上,针对采样失真问题,提出了一种自适应的采样频率调整方法。该方法采用一元线性回归对最近多个采样点拟合一条直线,然后根据该直线计算抖动比,并根据抖动比调整采样频率。仿真实验结果表明,该方法能够在不提高平均采样频率的前提下,减小采样失真度,且与同类方法相比失真度低、稳定性高。     

Random sampling of random processes: Mean-square behavior of a first order closed-loop system

This paper discusses the mean-square performance of a first order random sampled-data system with feedback, where the sampling times constitute a stationary point process, with independent and identically distributed sampling intervals. The paper presents some new results for the cases of periodic sampling, periodic sampling with skips, and Poisson sampling.     

An evaluation of adaptive sampling

Selected adaptive sampling control laws are compared with periodic sampling for five different inputs. It is shown that 1) aperiodic sampling is not always more efficient than periodic sampling, and 2) if the input and statistics of the input disturbance are not known a priori, then the best sampling efficiency is obtained using periodic sampling.     

数据挖掘取样方法研究

取样是一种通用有效的近似技术.在数据挖掘研究中,取样方法可显著减小所处理数据集的规模,使得众多数据挖掘算法得以应用到大规模数据集以及数据流数据上.通过对应用于数据挖掘领域的代表性取样方法的比较研究和分析总结,提出了一个取样算法分类框架.在指出了均匀取样局限性的基础上阐述了某些应用场景中选用偏倚取样方法的必要性,综述了取...