水动力学模型实时校正方法对比

选择典型的实时校正方法:传统的误差自回归、基于K最邻近算法(KNN)的非参数校正及基于Kalman滤波的多断面校正法,并以Kalman滤波与KNN结合构造综合方法,以淮河流域吴家渡—小柳巷区间作为试验河段,构建一维水动力学模型并与实时校正方法联合应用。简要介绍这4种方法的原理与模型构建方法,然后对比分析各种方法的模拟结果,尤其对模拟洪峰稳定性、峰现时间、峰现误差等进行比较,认为前3种基本方法均能在相当长的预见期内提高洪水的预报精度,而综合法实时校正法对洪峰部位的模拟更为稳定可靠、总体效果更好,更适合预报校正工作的需要。     

基于改进同态滤波的医用X光图像增强算法

针对医用X光图像对常规图像增强算法较敏感、易失真的特点,提出一种基于改进同态滤波的X光图像增强算法.算法将X光图像特性及同态滤波方法在图像增强上的优势结合起来,构建基于医用X线图频域的同态滤波器,通过指定最大增频位置及最高增益系数2个控制参数实现同态滤波控制,在不削减低频信号的基础上适当放大中高频信号,保留X光图像暗区易丢失的灰度及细纹信息,同时突出重要的组织边缘及纹理特征.实验证明,该算法失真率低、操作简便且运行效率高,对医用X光图具有较理想的增晰效果.     

不同烃链长度咪唑类离子液体对日本三角涡虫的急性毒性

采用SPSS软件概率单位法研究了4种不同烃链长度的咪唑类离子液体对日本三角涡虫的急性毒性.结果表明:[C4mim]Br,[C6mim]Br,[C8mim]Br和[C10mim]Br对日本三角涡虫72h、96h的半数致死浓度(LC50)分别为2 079.46和1 744.38mg·L-1,919.35和725.00mg·L-1,312.79和238.50mg·L-1以及32.57和23.25mg·L-1.由LC50值的大小可知,4种不同烃链长度的咪唑类离子液体的毒性[C10mim]Br>[C8mim]Br>[C6mim]Br>[C4mim]Br.由此可见,咪唑类离子液体对涡虫的急性毒性大小与其烷基侧链长度密切相关,碳链越长毒性越大.此外,根据急性毒性试验涡虫的中毒症状,也就离子液体对涡虫可能的靶器官问题进行了初步探讨.     

基于布鲁姆过滤器的RFID中间件数据过滤算法设计

RFID（Radio Frequency Identification,RFID）中间件在RFID系统中起着承上启下的作用,数据过滤作为RFID中间件的核心功能,对其算法的研究一直是RFID领域研究的热点与重点。通过对现有过滤算法的分析,提出基于布鲁姆过滤器的数据过滤算法,鲁姆过滤器在空间和时间上有着更低的复杂度,并通过对布鲁姆过滤器算法的分析和仿真,选择了最优的布鲁姆参数,降低了算法的假阳性误判率。     

地震勘探处理中最小平方反滤波优化处理设计

在地震勘探中,可以使用最小平方反滤波来提高地震资料的分辨率。但是,在最小平方反滤的过程和其输出的地震道中,高频的成份有时会过多而难以解释。在反滤波输入道中加入一定的白噪声,可使反滤波效果更好,同时对地震资料进行能量保持处理,这些都会对地震资料的处理有很好的效果。根据以上原理,开发了一套较完整的最小平方反滤波处理的软件,对叠后的地震资料进行了处理,实验结果表明本软件在提高地震资料分辨率的同时,保持了较高的信噪比,也减少了计算反滤波因子时的误差。     

基于广义形态滤波的低电压芯片电泳电色谱信号去噪研究

低电压芯片电泳电色谱信号检测过程中常伴随随机、周期性激励信号干扰、基线漂移等干扰问题,为低电压芯片电泳电色谱信号的检测带来很大的困难.为了从强干扰噪声中有效提取低电压芯片电泳电色谱分离图谱,有效抑制噪声干扰,提高测量精确度及稳定性,结合低电压芯片非接触电导检测器输出信号特点,将数学形态滤波算法应用到含有强噪声干扰的芯片电泳电色谱信号去噪中.仿真结果表明:数学形态滤波能有效消除芯片电泳电色谱信号的随机、周期性激励信号干扰、基线漂移等多种噪声,能很好地保留原色谱峰信号的基本特征.由于该方法原理简单、运算速度快,易于硬件实现,适合于强背景噪声条件下的低电压芯片电泳信号实时检测.     

Numerical optimization of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry: application to synthetic polymer molecular mass distribution measurement

A novel approach is described for the selection of optimal instrument parameters that yield a mass spectrum which best replicates the molecular mass distribution of a synthetic polymer. The application of implicit filtering algorithms is shown to be a viable method to find the best instrument settings while simultaneously minimizing the total number of experiments that need to be performed. This includes considerations of when to halt the iterative optimization process at a point when statistically-significant gains can no longer be expected. An algorithm to determine the confidence intervals for each parameter is also given. Details on sample preparation and data analysis that ensure stability of the measurement over the time scale of the optimization experiments are provided. This work represents part of an effort to develop an absolute molecular mass distribution polymer Standard Reference Material.     

认知无线电中的频谱感知技术

分析了认知无线电中的频谱感知技术.对加性白高斯噪声信道下匹配滤波器检测法和能量检测法进行了研究,并比较了能量检测法下认知用户单独检测、合作检测以及采用多样性技术来检测第一用户的性能.仿真结果表明:在低信噪比情况下,匹配滤波器具有良好的检测性能;当信噪比大于0 dB时,采用能量检测法能够检测各种信号;认知无线电用户之间合作检测和采用多样性技术能够提高能量检测法的可靠性.     

用时频峰值滤波去除多普勒频率估计野值点的方法

研究去除多普勒频率估计野值点的方法.在分析多普勒频率估计野值点的成因和特点的基础上,对含有野值点的多普勒频率估计按比例调整到适当的频率范围内,用时频峰值滤波方法对其进行处理,恢复原多普勒频率比例尺度,从而达到去除野值点的目的.实验结果表明,选择较小窗长的伪Wigner-Ville分布,一次迭代就可有效地去除多普勒频率估计中的野值点,对后续的脱靶量参数估计没有任何不利影响,方法简单可靠.     

A Rolling Bearing Fault Classification Scheme Based on k-Optimized Adaptive Local Iterative Filtering and Improved Multiscale Permutation Entropy

The health condition of the rolling bearing seriously affects the operation of the whole mechanical system. When the rolling bearing parts fail, the time series collected in the field generally shows strong nonlinearity and non-stationarity. To obtain the faulty characteristics of mechanical equipment accurately, a rolling bearing fault detection technique based on k-optimized adaptive local iterative filtering (ALIF), improved multiscale permutation entropy (improved MPE), and BP neural network was proposed. In the ALIF algorithm, a k-optimized ALIF method based on permutation entropy (PE) is presented to select the number of ALIF decomposition layers adaptively. The completely average coarse-graining method was proposed to excavate more hidden information. The performance analysis of the simulation signal shows that the improved MPE can more accurately dig out the depth information of the time series, and the entropy value obtained is more consistent and stable. In the research application, rolling bearing time series are decomposed by k-optimized ALIF to obtain a certain number of intrinsic mode functions (IMFs). Then the improved MPE value of effective IMF is calculated and input into backpropagation (BP) neural network as the feature vector for automatic fault identification. The comparative analysis of simulation signals shows that this method can extract fault information effectively. At the same time, the experimental part shows that this scheme not only effectively extracts the fault features, but also realizes the classification and identification of different fault modes and faults of different degrees, which has a certain application prospect in the research and application direction of rolling bearing fault identification.