MULTI-SCALE COHERENT STRUCTURES IN TURBULENT BOUNDARY LAYER DETECTED BY LOCALLY AVERAGED VELOCITY STRUCTURE FUNCTIONS

The time sequence of longitudinal velocity component at different vertical locations in turbulent boundary layer was finely measured in a wind tunnel. The concept of coarse_grained velocity structure functions, which describes the relative motions of straining and compressing for multi_scale eddy structures in turbulent flows, was put forward based on the theory of locally multi_scale average. Based on the consistency between coarse_grained velocity structure function and Harr wavelet transformation,detecting method was presented, by which the coherent structures and their intermittency was identified by multi_scale flatness factor calculated by locally average structure function. Phase_averaged evolution course for multi_scale coherent eddy structures in wall turbulence were extracted by this conditional sampling to educe scheme. The dynamics course of multi_scale coherent eddy structures and their effects on statistics of turbulent flows were studied.

Baroclinic pressure gradient difference schemes of subtracting the local averaged density stratification in sigma coordinates models

Much has been written of the error in computing the baroclinic pressure gradient (BPG) with sigma coordinates in ocean or atmospheric numerical models. The usual way to reduce the error is to subtract area-averaged density stratification of the whole computation region. But if there is great difference between the area-averaged and the local averaged density stratification, the error will be obvious. An example is given to show that the error from this method may be larger than that from no correction sometimes. The definition of local area is put forward. Then, four improved BPG difference schemes of subtracting the local averaged density stratification are designed to reduce the error. Two of them are for diagnostic calculation (density field is fixed), and the others are for prognostic calculation (density field is not fixed). The results show that the errors from these schemes all significantly decrease.

Generalized Sampling Series Approximation of Random Signals from Local Averages

Signals are often of random character since they cannot bear any information if they are predictable for any time t, they are usually modelled as stationary random processes .On the other hand, because of the inertia of the measurement apparatus, measured sampled values obtained in practice may not be the precise value of the signal X(t) at time tk (k∈Z), but only local averages of X(t) near tk. In this paper, it is presented that a wide (or weak ) sense stationary stochastic process can be approximated by generalized sampling series with local average samples.

ELASTIC WAVE LOCALIZATION IN TWO-DIMENSIONAL PHONONIC CRYSTALS WITH ONE-DIMENSIONAL QUASI-PERIODICITY AND RANDOM DISORDER

The band structures of both in-plane and anti-plane elastic waves propagating in two-dimensional ordered and disordered （in one direction） phononic crystals are studied in this paper. The localization of wave propagation due to random disorder is discussed by introducing the concept of the localization factor that is calculated by the plane-wave-based transfer-matrix method. By treating the quasi-periodicity as the deviation from the periodicity in a special way, two kinds of quasi phononic crystal that has quasi-periodicity （Fibonacci sequence） in one direction and translational symmetry in the other direction are considered and the band structures are characterized by using localization factors. The results show that the localization factor is an effective parameter in characterizing the band gaps of two-dimensional perfect, randomly disordered and quasi-periodic phononic crystals. Band structures of the phononic crystals can be tuned by different random disorder or changing quasi-periodic parameters. The quasi phononic crystals exhibit more band gaps with narrower width than the ordered and randomly disordered systems.

RSS-Distance Rationalization Procedure for Localization in an Indoor Environment

The computational capabilities of off-the-shelf wireless sensors networks presents a limitation when more complex forms of localization algorithms are employed for location estimation purposes, particularly in an indoor environment. Range-free algorithms rely on Received Signal Strength (RSS) from sensors that are location aware (anchor nodes) as the major means of distance estimation. This paper presents a non-site specific algorithm for better estimating RSS relationship with distance. By employing a unique form of rationalization of raw RSS with respect to distance using the proposed algorithm, it is possible to enhance the reliability of RSS when employed in indoor Localization Algorithms. Consequently, this paper presents an innovative RSS-Distance rationalization algorithm for localization of objects in an indoor environment. The paper compared the proposed algorithm with Simple Moving Average (SMA) algorithm due to the wide applicability and ease of manipulation of SMA. The analysis of the proposed algorithm and SMA shows that the proposed algorithm better modifies RSS for more accurate position estimation in an indoor environment.

LOCAL HOLDUPS AND LIQUID INTERSTITIAL VELOCITY IN LARGE-DIAMETER THREEPHASE FLUIDIZED BED

Radial profiles of local gas and solid holdups and liquid interstitial velocity were measured in a largediameter three-phase fluidized bed, and a systematic study was carried out in comparing the present data with those obtained in small-diameter beds. Radial profiles of local gas holdup were found to be parabolic; the distribution of local solid particle holdup could be expressed by Eq. (9); liquid interstitial velocity was well described by the modified circulating flow model.

多视野精细分析下的弱监督目标定位算法

针对多尺度目标定位精度较差,难以捕获完整目标边界的问题,设计一种多视野精细分析模块并融入通道与空间注意力机制抑制背景噪声的干扰,获取多尺度目标的高分辨率特征。利用随机特征选取模块获取特征图随机位置的组合,聚合多个位置图获取最具辨别性的位置及其它位置的信息,融合浅层生成的类激活图与聚合类激活图获取细粒度位置信息,捕获完整的目标边界。与现有的弱监督定位方法相比,在解决多尺度目标定位效果差和局部最优问题上具有一定的优势。

一种改进的暗通道先验低光照图像增强算法

针对低光照图像增强算法常见的亮度不均匀、色彩失真、图像噪点较多、细节不清晰等问题,提出了一种改进的暗通道先验低光照图像增强算法。该方法对像素值取反后的低光照图像,首先采用引导滤波,解决图像在运用最小值滤波计算暗通道时引起的块效应,其次在剔除像素为255的纯白色点干扰后进行大气光值的计算,然后引入细化系数进行透射率自适应修正使透射率更加平滑,最后采用非局部平均滤波进行噪声去除。实验表明,所提出的算法使图像的亮度增强合适,细节清晰,在Low-Light弱光图像数据集上测试图片,所得到的SSIM值比对比算法提升20.5%,PSNR值提升19.9%,无论从主观感受,还是客观评价指标等各方面,都有优化。

Approximation of weak sense stationary stochastic processes from local averages

We show that a weak sense stationary stochastic process can be approximated by local averages. Explicit error bounds are given. Our result improves an early one from Splettst?sser.

基于多任务神经网络的水下震源定位方法研究

针对传统水下被动定位算法传感器阵列体积大、布设难、环境失配等问题,提出一种基于多任务神经网络的水下震源定位方法。该方法通过仿真浅海声场数据集,利用声信号到达各个测量传感器的相对时间差,结合深度学习方法,在多任务卷积神经网络MTL-CNN(Multi-task Convolutional Neural Network)和Attention-UNet结构基础上,设计MTL-Attention-UNet神经网络模型,对水下震源的距离和深度进行联合估计。仿真结果表明:用MTL-Attention-UNet模型对水下震源进行定位的平均绝对误差比MTL-CNN网络模型小,定位性能更好。

Uniform Truncation Error for Shannon Sampling Expansion from Local Averages

Let B^pΩ,1≤Р≤∞,be the set of all bounded functions in L^p（R）which can be extended to entire unctions of exponential typeΩ. The unitbrm bounds for truncation error of Shannon sampling expansion fromlocal averages are obtained for functions f∈BpΩwith the decay condition f（t）≤A/t^δ,t≠0,where A and δare positive constants. Furthermore we also establish similar results for non-bandlimit functions in Besov classes with the same decay condition as above.

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。

直立宽大结构局部冰压力幅值研究

为了明确极区直立宽大结构局部冰压力幅值及压力面积曲线的影响因素,本文首先对ISO 19906规范中的局部冰压力计算方法进行了分析,发现其局限性较大。其次,基于离散元方法模拟不同海冰条件与多种尺度结构的相互作用,发现局部冰压力幅值随冰厚的增大而明显增大,随结构宽度的增大而减小;局部冰压力幅值及其标准差随宽厚比的增加而减小,表现出明显的尺寸效应。最后,采用方形区域平均法研究了冰厚和宽度两种尺寸因素对压力-面积曲线参数的影响,选取某种相似工况与ISO规范进行对比。验证了离散元模拟方法处理局部冰压力-面积曲线的合理性,可为平台的抗冰性能分析和结构设计提供一定依据和参考。

融合有效掩膜和局部增强的遮挡行人重识别

在监控系统中行人经常会被各种障碍物遮挡,使得遮挡行人重识别仍然是一个长期存在的挑战。最近一些基于Transformer和外部语义线索的方法都改善了特征的表示和相关性能,但仍存在表示弱和语义线索不可靠等问题。为解决上述问题,提出了一种基于Transformer的新方法。引入了一种有效的掩膜生成方式,可靠的掩膜可以使模型不依赖外部语义线索并实现自动对齐。提出了一种基于平均注意力分数的序列重建模块,可以更有效地关注前景信息。提出了局部增强模块,获得了更鲁棒的特征表示。比较了所提方法和现有的各种方法在Occluded-Duke,Occluded-ReID,Partial-ReID,Market-1501数据集上的性能。Rank-1准确率分别达到了72.3%、84.8%、86.5%和95.6%,mAP精度分别为62.9%、83.2%、76.4%和89.9%,实验结果表明所提模型性能较其他先进网络有所提升。

基于MEWMA的自适应KLPP的非线性过程故障检测

针对非线性动态过程中的微小扰动问题,本文提出一种基于多元指数加权移动平均(MEWMA)的自适应核局部保持投影(KLPP)的非线性过程故障检测算法.首先,构造一个具有动态特性的数据矩阵,并引入核函数,执行KLPP算法;其次,白化KLPP提取的特征分量,并采用MEWMA预测非线性动态过程中的均值漂移;最后,将估计的均值漂移与白化后的特征分量相结合,构造一个自适应监控统计量,并利用核密度估计确定其控制限.将所提出的监测方案应用于一个非线性数值例子和(TE)过程进行仿真分析,仿真结果表明,该方法具有可行性和优越性.

Local Walsh-average-based estimation and variable selection for single-index models

We propose a robust estimation procedure based on local Walsh-average regression(LWR) for single-index models. Our novel method provides a root-n consistent estimate of the single-index parameter under some mild regularity conditions;the estimate of the unknown link function converges at the usual rate for the nonparametric estimation of a univariate covariate. We theoretically demonstrate that the new estimators show significant efficiency gain across a wide spectrum of non-normal error distributions and have almost no loss of efficiency for the normal error. Even in the worst case, the asymptotic relative efficiency(ARE) has a lower bound compared with the least squares(LS) estimates;the lower bounds of the AREs are 0.864 and 0.8896 for the single-index parameter and nonparametric function, respectively. Moreover, the ARE of the proposed LWR-based approach versus the ARE of the LS-based method has an expression that is closely related to the ARE of the signed-rank Wilcoxon test as compared with the t-test. In addition, to obtain a sparse estimate of the single-index parameter, we develop a variable selection procedure by combining the estimation method with smoothly clipped absolute deviation penalty;this procedure is shown to possess the oracle property. We also propose a Bayes information criterion(BIC)-type criterion for selecting the tuning parameter and further prove its ability to consistently identify the true model. We conduct some Monte Carlo simulations and a real data analysis to illustrate the finite sample performance of the proposed methods.

Two-stage Local Walsh Average Estimation of Generalized Varying Coefficient Models

In this paper we investigate the robust estimation of generalized varying coefficient models in which the unknown regression coefficients may change with different explanatory variables. Based on the B-spline series approximation and Walsh-average technique we develop an initial estimator for the unknown regression coefficient functions. By virtue of the initial estimator, the generalized varying coefficient model is reduced to a univariate nonparametric regression model. Then combining the local linear smooth and Walsh average technique we further propose a two-stage local linear Walsh-average estimator for the unknown regression coefficient functions. Under mild assumptions, we establish the large sample theory of the proposed estimators by utilizing the results of U-statistics and shows that the two-stage local linear Walsh-average estimator own an oracle property, namely the asymptotic normality of the two-stage local linear Walsh-average estimator of each coefficient function is not affected by other unknown coefficient functions. Extensive simulation studies are conducted to assess the finite sample performance, and a real example is analyzed to illustrate the proposed method.

不同寄主来源的灰葡萄孢对番茄的致病力分化研究

从安徽合肥、蚌埠、长丰、和县等市、县的番茄、辣椒、草莓、葡萄等发病寄主上分离鉴定获得18个灰葡萄孢Botrytis cinerea菌株,采用菌丝块创伤接种法,分别测定了上述不同寄主来源的灰葡萄孢菌对番茄果实和叶片的致病力。结果表明,所有供试菌株接种番茄果实后均引起发病,但不同菌株所致病斑的平均直径有显著差异,显示灰葡萄孢菌株间对番茄果实的致病力存在明显分化。按照在番茄果实上所致病斑的平均直径大小可将供试菌株致病力划分为较强、中等和较弱3种类型。总体来说,来自番茄的菌株对番茄果实的致病力较强,来自草莓、葡萄和辣椒的菌株对番茄果实的致病力较弱,但来自相同寄主的菌株间致病力也存在差异,菌株致病力差异与菌株地域来源无明显相关。供试灰葡萄孢菌株接种番茄叶片后,除CF1外,均可引起番茄叶片发病,但不同菌株所致番茄叶片病斑的平均直径也有显著差异;供试菌株对番茄叶片的致病力差异与菌株的寄主和地域来源无显著相关。

基于有理样条函数的局部均值分解方法及其应用

局部均值分解(Local mean decomposition,简称LMD)方法通过滑动平均方法平滑局部均值线段和局部幅值线段得到局部均值函数和包络估计函数,从而实现信号的分解。但滑动平均方法会产生相位差以及平滑步长的选择具有一定的主观性,这样会使分解结果不理想。对LMD方法进行了改进,采用有理样条插值函数(Rationalspline)求取信号的上下包络线,然后通过上下包络线计算信号的局部均值函数和包络估计函数,克服了原LMD方法中采用滑动平均方法带来的缺陷。通过对仿真信号以及滚动轴承故障振动信号的分析,表明改进后的LMD方法优于原LMD方法。

基于跳距修正粒子群优化的WSN定位算法

针对DV-Hop算法在节点随机分布的网络拓扑环境中存在较大误差的问题,提出了一种基于跳距修正粒子群优化的定位算法WPDV-Hop(weight PSO DV-Hop)。本算法通过对锚节点广播的数据分组结构进行了改进,对参考锚节点的平均每跳距离的误差进行加权处理以及用改进的粒子群(PSO)算法对定位中的迭代过程进行优化,实现WPDV-Hop定位算法的全面改进,以提高定位精度。仿真结果表明,改进的算法与原始算法相比,定位精度和算法的稳定性有明显提高。