Research on aiming methods for small sample size shooting tests of two-dimensional trajectory correction fuse

The longitudinal dispersion of the projectile in shooting tests of two-dimensional trajectory corrections fused with fixed canards is extremely large that it sometimes exceeds the correction ability of the correction fuse actuator.The impact point easily deviates from the target,and thus the correction result cannot be readily evaluated.However,the cost of shooting tests is considerably high to conduct many tests for data collection.To address this issue,this study proposes an aiming method for shooting tests based on small sample size.The proposed method uses the Bootstrap method to expand the test data;repeatedly iterates and corrects the position of the simulated theoretical impact points through an improved compatibility test method;and dynamically adjusts the weight of the prior distribution of simulation results based on Kullback-Leibler divergence,which to some extent avoids the real data being"submerged"by the simulation data and achieves the fusion Bayesian estimation of the dispersion center.The experimental results show that when the simulation accuracy is sufficiently high,the proposed method yields a smaller mean-square deviation in estimating the dispersion center and higher shooting accuracy than those of the three comparison methods,which is more conducive to reflecting the effect of the control algorithm and facilitating test personnel to iterate their proposed structures and algorithms.;in addition,this study provides a knowledge base for further comprehensive studies in the future.

Optimization of Well Position and Sampling Frequency for Groundwater Monitoring and Inverse Identification of Contamination Source Conditions Using Bayes’Theorem

Coupling Bayes’Theorem with a two-dimensional(2D)groundwater solute advection-diffusion transport equation allows an inverse model to be established to identify a set of contamination source parameters including source intensity(M),release location(0 X,0 Y)and release time(0 T),based on monitoring well data.To address the issues of insufficient monitoring wells or weak correlation between monitoring data and model parameters,a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy.To demonstrate how the model works,an exemplar problem with an instantaneous release of a contaminant in a confined groundwater aquifer was employed.The information entropy of the model parameters posterior distribution was used as a criterion to evaluate the monitoring data quantity index.The optimal monitoring well position and monitoring frequency were solved by the two-step Monte Carlo method and differential evolution algorithm given a known well monitoring locations and monitoring events.Based on the optimized monitoring well position and sampling frequency,the contamination source was identified by an improved Metropolis algorithm using the Latin hypercube sampling approach.The case study results show that the following parameters were obtained:1)the optimal monitoring well position(D)is at(445,200);and 2)the optimal monitoring frequency(Δt)is 7,providing that the monitoring events is set as 5 times.Employing the optimized monitoring well position and frequency,the mean errors of inverse modeling results in source parameters(M,X0,Y0,T0)were 9.20%,0.25%,0.0061%,and 0.33%,respectively.The optimized monitoring well position and sampling frequency canIt was also learnt that the improved Metropolis-Hastings algorithm(a Markov chain Monte Carlo method)can make the inverse modeling result independent of the initial sampling points and achieves an overall optimization,which significantly improved the accuracy and numerical stability of the inverse modeling results.

EXACT EVALUATIONS OF FINITE TRIGONOMETRIC SUMS BY SAMPLING THEOREMS

We use the sampling representations associated with Sturm-Liouville difference operators to derive generalized integral-valued trigonometric sums. This extends the known results where zeros of Chebyshev polynomials of the first kind are involved to the use of the eigenvalues of difference operators, which leads to new identities. In these identities Bernoulli＇s numbers play a role similar to that of Euler＇s in the old ones. Our technique differs from that of Byrne-Smith （1997） and Berndt-Yeap （2002）.

Sampling theorem for multiwavelet subspaces

Unlike scalar wavelets, multiscaling functions can be orthogonal, regular and symmetrical, and have compact support and high order of approximation simultaneously. For this reason, even if multiscaling functions are not cardinal, they still hold for perfect A/D and D/A. We generalize the Walter＇s sampling theorem to multiwavelet subspaces based on reproducing kernel Hilbert space. The reconstruction function can be expressed by multiwavelet function using the Zak transform. The general case of irregular sampling is also discussed and the irregular sampling theorem for multiwavelet subspaces established. Examples are presented.

APPROXIMATE SAMPLING THEOREM FOR BIVARIATE CONTINUOUS FUNCTION

An approximate solution of the refinement equation was given by its mask, and the approximate sampling theorem for bivariate continuous function was proved by applying the approximate solution . The approximate sampling function defined uniquely by the mask of the refinement equation is the approximate solution of the equation , a piece-wise linear function , and posseses an explicit computation formula . Therefore the mask of the refinement equation is selected according to one' s requirement, so that one may controll the decay speed of the approximate sampling function .

Vector sampling theorem for wavelet subspaces

The vector sampling theorem has been investigated and widely used by multi-channel deconvolution, multi-source separation and multi-input multi-output （MIh40） systems. Commonly, for most of the results on MIMO systems, the input signals are supposed to be band-limited. In this paper, we study the vector sampling theorem for the wavelet subspaces with reproducing kernel. The case of uniform sampling is discussed, and the necessary and sufficient conditions for reconstruction are given. Examples axe also presented.

Truncation and aliasing errors for Whittaker-Kotelnikov-Shannon sampling expansion

Let B^pΩ, 1 ≤ p 〈 ∞, be the space of all bounded functions from Lp（R） which can be extended to entire functions of exponential type Ω. The uniform error bounds for truncated Whittaker-Kotelnikov-Shannon series based on local sampling are derived for functions f ∈ B^pΩ without decay assumption at infinity. Then the optimal bounds of the aliasing error and truncation error of Whittaker-Kotelnikov-Shannon expansion for non-bandlimited functions from Sobolev classes L/（Wp（R）） are determined up to a logarithmic factor.

ON TRUNCATION ERROR BOUND FOR MULTIDIMENSIONAL SAMPLING EXPANSION LAPLACE TRANSFORM

The truncation error associated with a given sampling representation is defined as the difference between the signal and on approximating sumutilizing a finite number of terms. In this paper we give uniform bound for truncation error of bandlimited functions in the n dimensional Lebesgue space Lp(Rn) associated with multidimensional Shannon sampling representation.

A method of reconstructing 3D model from 2D geological cross-section based on self-adaptive spatial sampling:A case study of Cretaceous McMurray reservoirs in a block of Canada

An orthogonal 2D training image is constructed from the geological analysis results of well logs and sedimentary facies;the 2 D probabilities in three directions are obtained through linear pooling method and then aggregated by the logarithmic linear pooling to determine the 3 D multi-point pattern probabilities at the unknown points,to realize the reconstruction of a 3 D model from 2D cross-section.To solve the problems of reducing pattern variability in the 2 D training image and increasing sampling uncertainty,an adaptive spatial sampling method is introduced,and an iterative simulation strategy is adopted,in which sample points from the region with higher reliability of the previous simulation results are extracted to be additional condition points in the following simulation to improve the pattern probability sampling stability.The comparison of lateral accretion layer conceptual models shows that the reconstructing algorithm using self-adaptive spatial sampling can improve the accuracy of pattern sampling and rationality of spatial structure characteristics,and accurately reflect the morphology and distribution pattern of the lateral accretion layer.Application of the method in reconstructing the meandering river reservoir of the Cretaceous McMurray Formation in Canada shows that the new method can accurately reproduce the shape,spatial distribution pattern and development features of complex lateral accretion layers in the meandering river reservoir under tide effect.The test by sparse wells shows that the simulation accuracy is above 85%,and the coincidence rate of interpretation and prediction results of newly drilled horizontal wells is up to 80%.

Optimizing the atmospheric sampling sites using fuzzy mathe-matic methods

A new approach applying fuzzy mathematic theorems, including the Primary Matrix Element Theorem and the Fisher Classification Method, was established to solve the optimization problem of atmospheric environmental sampling sites. According to its basis, an application in the optimization of sampling sites in the atmospheric environmental monitoring was discussed. The method was proven to be suitable and effective. The results were admitted and applied by the Environmental Protection Bureau (EPB) of many cities of China. A set of computer software of this approach was also completely compiled and used.

Improvement of Two-Dimensional Gel Electrophoresis for Study of Corm Formation Related Proteins in vitro from Taro (Colocasia esculenta)

Efficient and reproducible sample preparation prior to 2D-PAGE (two-dimensional polyacrylamide gel electrophoresis) is a critical step in achieving accurate and reliable data. In this paper, we described a method to prepare protein samples of taro that was compatible with subsequent analysis using 2D-PAGE. We compared proteins from shoot basal region from 0 d and 2 d after the beginning of tuberization. By this method we got about (2 000) spots and high reproducibility. Additionally some changes of protein expression were found.

Computing Recomposition of Maps with a New Sampling Asymptotic Formula

The aim of the present paper is to state an asymptotic property &#929 of Shannon’s sampling theorem type, based on normalized cardinal sines, and keeping constant the sampling frequency of a not necessarilly band- limited signal. It generalizes in the limit the results stated by Marvasti et al. [7] and Agud et al. [1]. We show that &#929 is fulfilled for any constant signal working for every given sampling frequency. Moreover, we conjecture that Gaussian maps of the form e-&#923t2 ,&#923&#8712R+, hold &#929. We support this conjecture by proving the equality given by for the three first coefficients of the power series representation of e-&#923t2 .

集合论、笛卡尔乘积和多项式定理在生命表研究中的应用:以取食玉米粒和茶树叶片的棉铃虫生命表为例

【目的】自我重复取样技术(bootstrap technique)广泛用于计算生命表各参数的方差、标准误和置信区间(confidence interval),而配对自我重复取样检验(paired bootstrap test,PBT)则用于检测处理间的生命表参数差异。本研究以取食玉米粒和茶树叶片的棉铃虫Helicoverpa armigera生命表为例,进一步解释集合论、笛卡尔乘积和多项式定理在害虫种群生物学研究中的应用。【方法】利用年龄-龄期两性生命表对取食玉米粒和茶树叶片的棉铃虫种群参数进行分析,利用集合论、笛卡尔乘积和多项式定理将种群统计分析以明确数学的方法呈现,以检测所有可能的自我重复取样样本、精确计算种群参数置信区间以及处理之间差异的置信区间,计算可育和不育样本的概率。【结果】取食茶树叶片的棉铃虫其成虫前期存活率、内禀增长率(r)、周限增长率(λ)、净增殖率(R_(0))、平均世代周期(T)均显著低于取食玉米粒的,说明茶树叶片不是棉铃虫的适宜寄主植物。因取食茶树叶片棉铃虫种群不育样本数较多,利用bootstrap重复取样接受可育与不育样本时,其结果与不用bootstrap技术计算的差异小于5%;若取样时仅接受可育样本,结果与原数值差异显著,误差较大(>5%)。使用笛卡尔配对检验(Cartesian paired test,CPT)比较取食两种食物的棉铃虫种群的R0的差异时,可计算两种处理bootstrap抽样结果所有可能差异的精确置信区间,而使用自我重复取样检验通常会导致置信区间的估值过高或过低,尤其是自我重复取样数较小时。多项式定理可以揭示可育和不育样本的自我重复取样结果。自我重复取样抽样记录必须保留,以便在后续分析中进一步应用。【结论】本研究进一步明确了两性生命表理论的数学基础,也为昆虫学研究中的生命表技术的应用提供数学支撑。

基于ARCS模型的翻转课堂教学设计

以“取样定理”为例,提出采用ARCS模型进行翻转课堂教学设计的案例。教学内容设计注重核心价值观的传播,从取样定理形成的历史脉络引出课程思政;运用多种教学手段,提升学生注意力;重视学习内容的关联设计,学生端倚重信息技术应用,及时反馈,提升学生自信度;学习评价注重应用性,提升学习满意度。教学实施结果表明,该教学设计能调动学生的课堂热情,师生满意度高。

从奈奎斯特采样到压缩感知拓展教学方法

从“信号与系统”到“数字信号处理”,采样定理都是重要的教学内容。但是在工程应用中,产生大量数据造成存储空间的极大浪费,而压缩感知突破奈奎斯特采样定理的限制,能够实现远低于奈奎斯特频率的采样。为适应新工科背景下的教学改革,让学生接触前沿研究成果,压缩感知被引入作为传统奈奎斯特采样定理教学的补充和拓展,取得良好的教学效果。

融入课程思政的“抽样定理”教学案例设计与实践

课程思政是实现高校立德树人根本任务的重要举措,该文以“信号与系统”课程中的“抽样定理”为例,挖掘课程思政元素,探索案例式教学的设计与实践。深入分析课程特点,重塑课程教学内容,以先进的教学手段和教学资源建设为载体,设计思政教育融入教学的全过程,培养学生勇于探索的科学精神,增强学生科学报国的决心。课程思政的探索实践,对优化课程思政建设、提升学生工程实践能力、培养学生爱国精神有重要意义。

局部平均随机过程随机场采样定理

随机信号采样定理的相关研究几十年来一直是采样定理主流方向之一,在理论和实际应用两方面都受到信息领域和数学领域的广泛关注。近年来,采样理论在随机过程和随机场的局部平均采样方面得出了一系列研究成果。首先,分析了采样定理在应用中的缺陷和局限性。其次,对近几年在随机过程、高维随机过程、随机场、时空随机场局部平均采样定理方面的发展和相关结论做了系统介绍。最后,对基于混合范数的时空随机场采样逼近、采样形式、信号形式、范数形式等问题进行了展望。结果表明:局部平均的采样方式提高了采样的精度和逼近效果,随机场局部平均采样理论在海浪参数反演和海洋污染监测方面有重要应用。特别是基于混合范数的时空随机场采样理论的研究不仅具有重要的理论价值,而且对于工程领域也有应用价值。

基于压缩感知的可见光通信定位一体化系统性能分析

为了降低到达时间(time of arrival, TOA)算法的接收信号采样率需求,基于正交频分复用(orthogonal frequency division multiplexing, OFDM)系统中信号具有频谱稀疏性的特点,提出了基于压缩感知算法的可见光通信定位一体化系统。仿真结果表明,采用压缩感知算法后,通信系统仅需原始采样率为100 MHz时数据量的60%即可使误码率降至10-3以下,定位系统仅需原始采样率为800 MHz时数据量的2.1%即可准确估计收发端距离,远低于采样定理所需数据量,证明了所提算法的优越性。此外,在引入噪声后,通过提高压缩感知算法的测量数据比例,一体化系统仍能在奈奎斯特采样率以下保持良好的通信与定位性能。

A New Approach to Robust Stability Analysis of Sampled-data Control Systems

The lifting technique is now the most popular tool for dealing with sampled-data controlsystems. However, for the robust stability problem the system norm is not preserved by the liftingas expected. And the result is generally conservative under the small gain condition. The reason forthe norm di?erence by the lifting is that the state transition operator in the lifted system is zero inthis case. A new approach to the robust stability analysis is proposed. It is to use an equivalentdiscrete-time uncertainty to replace the continuous-time uncertainty. Then the general discretizedmethod can be used for the robust stability problem, and it is not conservative. Examples are givenin the paper.