Automatically Smoothing the Spectroscopic Data by Cubic B-Spline Basis Functions

In the present paper,a new criterion is derived to obtain the optimum fitting curve while using Cubic B-spline basis functions to remove the statistical noise in the spectroscopic data.In this criterion,firstly,smoothed fitting curves using Cubic B-spline basis functions are selected with the increasing knot number.Then,the best fitting curves are selected according to the value of the minimum residual sum of squares(RSS)of two adjacent fitting curves.In the case of more than one best fitting curves,the authors use Reinsch's first condition to find a better one.The minimum residual sum of squares(RSS)of fitting curve with noisy data is not recommended as the criterion to determine the best fitting curve,because this value decreases to zero as the number of selected channels increases and the minimum value gives no smoothing effect.Compared with Reinsch's method,the derived criterion is simple and enables the smoothing conditions to be determined automatically without any initial input parameter.With the derived criterion,the satisfactory result was obtained for the experimental spectroscopic data to remove the statistical noise using Cubic B-spline basis functions.

Graph Regularized L_p Smooth Non-negative Matrix Factorization for Data Representation

This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information of a data set and produces smooth and stable solutions. The main contributions are as follows: first, graph regularization is added into NMF to discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. Second,the Lpsmoothing constraint is incorporated into NMF to combine the merits of isotropic(L_2-norm) and anisotropic(L_1-norm)diffusion smoothing, and produces a smooth and more accurate solution to the optimization problem. Finally, the update rules and proof of convergence of GSNMF are given. Experiments on several data sets show that the proposed method outperforms related state-of-the-art methods.

Smoothed Empirical Likelihood Inference for ROC Curves with Missing Data

The receiver operating characteristic (ROC) curve has been widely used in scientific research fields. After using the random hot deck imputation, we propose the smoothed empirical likelihood ratio statistic for the ROC curve with missing data. Its asymptotic distribution is a scaled chi-square distribution and empirical likelihood confidence intervals for ROC curves are constructed. The simulation study shows that the proposed interval estimates perform well based on the coverage probability for different sample sizes and response rates.

冲突背景下国际政经关系多元数据联合分析

[研究目的]探究冲突背景下不同类型的第三方国同冲突方国的政经关系态势,识别行为异常国家,为国家外交政策制定提供情报支持。[研究方法]引入介入行为细分第三方国同冲突方国的政治关系类型,基于角色理论、冲突介入理论构建第三方国政经关系分析理论框架,基于多元数据对政经关系标注量化,采用单因素方差模型计算第三方国政经行为的相关性,基于指数平滑预测以及箱线图检测经济行为异常的国家。[研究结论]以“俄乌冲突”实证结果来看,第三方国同冲突方俄罗斯的政经关系呈现显著正相关,但亦有部分国家同俄罗斯政冷经热,印度、英国等国家经济行为表现相对异常。本研究探究冲突背景下第三方国同冲突方国的政经相关关系,能够为国际关系量化研究提供新的研究路径。

嵌入式激光雷达下室内移动机器人动态定位方法

由于空间的限制,室内移动机器人在操作过程中对位置确认的要求较高,存在移动轨迹定位偏离的问题。为此提出嵌入式激光雷达下室内移动机器人动态定位方法。利用坐标转换以及欧式距离分割法对采集的激光雷达数据实施预处理,去除噪声对后续定位的影响。通过回波强度确定反光板的位置拟合变化,利用三边定位算法对移动机器人实施精准定位。经实验验证,该方法的定位误差较小,不仅可以实现室内移动机器人整体高精度的动态定位,而且可以很好地监测室内移动机器人的轨迹。

针对顶点策略优化的三维模型可逆数据隐藏算法

随着3D打印技术和云计算的普及发展,三维模型的数据安全和隐私安全日益受到重视,研究模型无损恢复和存储海量数据的隐藏方法具有重要意义。基于此,以三维网格模型为载体,提出一种顶点策略优化的可逆数据隐藏方法。首先,对网格模型顶点进行划分,在全邻域中生成新的重心集合,根据改进的预测策略预测所有顶点集合的MSB以确定最大嵌入位;其次,在接受端应用顶点处拉普拉斯算子得到光滑恢复模型;最后,采用平均曲率可视化分析。为证明该算法的有效性,选取不同大小的网格模型与其他传统算法的嵌入率、SNR以及恢复模型进行对比。结果表明,该算法不仅提高了嵌入率,还在保留恢复模型局部细节特征的同时有效去除噪声,提高了模型恢复质量和视觉效果,进一步达到数据隐私安全的目的。

复杂异型建筑立面测绘轮廓提取方法设计

为解决点云数据密度异常时复杂异型建筑立面测绘轮廓提取精度变差的问题,提出基于局部点云密度的复杂异型建筑立面测绘轮廓提取方法。引入基于平面投影和双边滤波的测绘点云数据平滑方法,对测绘数据进行去噪和平滑处理,并通过点云分割方法提取目标点云区域。通过基于改进Alpha Shapes算法的立面测绘轮廓提取方法,以边界网格筛选的方式,去除目标点云区域冗余点云数据后,使用滚动圆半径自适应调节方法提取轮廓数据。试验结果表明,所提取轮廓匹配度高达95.08%,具有良好的精度和可行性。该方法可在有效平滑点云数据、分割获取目标点云区域的同时,高精度提取复杂异型建筑立面测绘轮廓。

Interpolation and approximation for data living on manifold surfaces

Meshed surfaces are ubiquitous in digital geometry processing and computer graphics. The set of attributes associated with each vertex such as the vertex locations, curvature, temperature, pressure or saliency, can be recognized as data living on mani- fold surfaces. So interpolation and approximation for these data are of general interest. This paper presents two approaches for mani- fold data interpolation and approximation through the properties of Laplace-Beltrami operator （Laplace operator defined on a mani- fold surface）. The first one is to use Laplace operator minimizing the membrane energy of a scalar function defined on a manifold. The second one is to use bi-Laplace operator minimizing the thin plate energy of a scalar function defined on a manifold. These two approaches can process data living on high genus meshed surfaces. The approach based on Laplace operator is more suitable for manifold data approximation and can be applied manifold data smoothing, while the one based on bi-Laplace operator is more suit- able for manifold data interpolation and can be applied image extremal envelope computation. All the application examples demon- strate that our procedures are robust and efficient.

基于RoBERTa-RCNN和注意力池化的新闻主题文本分类

针对中文新闻主题因缺乏上下文信息而造成语义模糊和用词规范性不高的问题,提出一种基于RoBERTa-RCNN和多头注意力池化机制的新闻主题文本分类方法。利用数据增强技术对部分训练数据进行回译处理,再通过自编码预训练模型和RCNN对文本进行初步和深度的特征提取,并结合多头注意力思想改进最大池化层。该方法采用融合机制,改善了RCNN中最大池化策略单一和无法进行动态优化的缺陷。在三个新闻主题数据集上进行实验,使用更适用于新闻主题分类的Mish函数代替ReLU函数,并利用标签平滑来解决过拟合问题。结果表明,所提方法相比传统分类方法效果突出,并通过消融实验验证了模型在分类任务上的可行性。

虚假数据注入攻击下基于容积卡尔曼滤波的电力系统状态估计

针对虚假数据注入攻击下系统状态估计的问题,以电力信息物理系统为研究对象,根据发电机三阶模型和自动电压调节器模型,建立电力系统的数学模型。采用指数平滑法预测测量值,通过对比预测值与真实测量值,检测系统是否发生虚假数据注入攻击。若检测结果判定系统遭受虚假数据注入攻击,用预测值替代不良数据输入状态估计算法,实现虚假数据注入攻击下不良数据的恢复。将指数平滑法与容积卡尔曼滤波算法结合,提出一种改进的容积卡尔曼滤波算法对系统进行状态估计。以典型的五机电力系统为例进行仿真,仿真结果表明提出的方法能有效抵御虚假数据对系统状态估计造成的不良影响。

数据库平滑迁移的数据访问中间件设计

文中研究了数据库平滑迁移中的数据访问中间件的设计与实现。通过探讨中间件在迁移过程中的角色与功能,分析数据库平滑迁移的流程,聚焦于中间件架构设计原则、功能模块的优化策略,并着眼于迁移过程中的性能优化手段。通过该研究,旨在为实际系统的迁移提供科学可行的指导,确保迁移的顺利进行,最大程度地降低系统运行风险。

轮胎六分力试验机线性数据监控方法的研究

对轮胎六分力试验机线性数据的监控方法进行了研究。结果表明:在小侧偏角下,暖胎对轮胎侧向力系数(F_(1))和回正力矩系数(T_(a1))的影响不大,采用F1和Ta1监控试验数据时可不用暖胎;采用控制轮胎监控F_(1)和T_(a1)时,需要考虑轮胎制造工艺偏差、试验机偏差、充气压力偏差和负荷偏差的影响;在小侧偏角下,模拟路面的附着力系数对F_(1)和T_(a1)的影响不大,在大侧偏角下,需要考虑模拟路面的附着力系数对F_(1)和T_(a1)的影响;采用本监控方案,可对轮胎六分力试验机线性数据进行监控,判定试验数据的合理性。

Research on Comparison and Evaluation Studies of Several Smoothing Denoising Method Based on γ-ray Spectrum Detector

The extraction of spectral parameters is very difficult because of the limited energy resolution for NaI( TI) gamma-ray detectors. For statistical fluctuation of radioactivity under complex environment,some smoothing filtering methods are proposed to solve the problem. These methods include adopting method of arithmetic moving average,center of gravity,least squares of polynomial,slide converter of discrete funcion convolution etc. The process of spectrum data is realized,and the results are assessed in H / FWHM( Peak High / Full Width at Half Maximum) and peak area based on the Matlab programming. The results indicate that different methods smoothed spectrum have respective superiority in different ergoregion,but the Gaussian function theory in discrete function convolution slide method is used to filter the complex γ-spectrum on Embedded system platform,and the statistical fluctuation of γ-spectrum is filtered well.

ESTIMATORS AND SOME BEHAVIORS FORA PARTIALLY LINEAR MODEL WITH CENSORED DATA

This paper considers the local linear regression estimators for partially linear model with censored data. Which have some nice large-sample behaviors and are easy to implement. By many simulation runs, the author also found that the estimators show remarkable in the small sample case yet.

Robust Inference for Time-Varying Coefficient Models with Longitudinal Data

Time-varying coefficient models are useful in longitudinal data analysis. Various efforts have been invested for the estimation of the coefficient functions, based on the least squares principle. Related work includes smoothing spline and kernel methods among others, but these methods suffer from the shortcoming of non-robustness. In this paper, we introduce a local M-estimation method for estimating the coefficient functions and develop a robustified generalized likelihood ratio (GLR) statistic to test if some of the coefficient functions are constants or of certain parametric forms. The robustified GLR test is robust against outliers and the error distribution. This provides a useful robust inference tool for the models with longitudinal data. The bandwidth selection issue is also addressed to facilitate the implementation in practice. Simulations show that the proposed testing method is more powerful in some situations than its counterpart based on the least squares principle. A real example is also given for illustration.

集合最优平滑同化方法的研究与应用

海洋状态场的历史变化过程对其分布状态有重要影响。在观测资料稀疏的情况下,合理利用历史观测资料能够为海洋数据同化提供大量有效信息。然而在目前的顺序资料同化过程中,往往只同化当前时刻的观测数据,没有考虑到历史观测资料对当前状态的约束。四维变分虽然可以体现变量在时间维度的演变过程,但引入伴随方程会增加计算代价。本文基于集合最优平滑同化算法(Ensemble Optimal Smoothing,EnOS)探讨了一种在数据同化中加入历史观测资料的简易可行方案,其能够根据历史观测数据估计当前状态,并进行单点同化实验和区域同化实验来验证该方案的有效性。实验结果表明,将历史观测资料引入到同化过程中可以把控时间演变趋势,减小分析数据与真实值之间的偏差,更有效地消除数值模式误差,提高同化质量。

A New Smoothing Approach with Diverse Fixed-lags Based on Target Motion Model

Recently, lots of smoothing techniques have been presented for maneuvering target tracking. Interacting multiple model-probabilistic data association （IMM-PDA） fixed-lag smoothing algorithm provides an efficient solution to track a maneuvering target in a cluttered environment. Whereas, the smoothing lag of each model in a model set is a fixed constant in traditional algorithms. A new approach is developed in this paper. Although this method is still based on IMM-PDA approach to a state augmented system, it adopts different smoothing lag according to diverse degrees of complexity of each model. As a result, the application is more flexible and the computational load is reduced greatly. Some simulations were conducted to track a highly maneuvering target in a cluttered environment using two sensors. The results illustrate the superiority of the proposed algorithm over comparative schemes, both in accuracy of track estimation and the computational load.

线结构光轮对踏面几何参数测量系统设计

针对轮对踏面几何参数检测自动化程度不高、精度低的问题,设计一种线结构光非接触式测量系统,利用位移传感器和定位块对轮对踏面内侧基准进行定位。采用自动分段的多项式曲线拟合方法对踏面线结构光光条中心线进行光顺处理,解决了中心线提取过程中产生不平滑和锯齿等问题。基于Qt程序开发框架和C++开发软件系统,设计了人机交互界面,实现图像处理、尺寸测量、数据显示与储存等功能。搭建了简易的试验平台对车轮踏面模型进行测量,试验结果表明,该系统对轮缘高度和轮缘厚度的测量误差小于0.1mm,轮辋宽度的测量误差小于0.3mm,满足实际测量需求。

基于支持向量机的多环芳烃光谱定量分析

目前,环境问题备受人们关注,有效解决多环芳烃污染已成为全球关注的焦点。基于此,提出一种基于支持向量机的多环芳烃光谱定量分析方法,通过FS920荧光光谱仪采集多环芳烃光谱数据,获取三维光谱灰度图。对多环芳烃实验样本数据采用多项式平滑法原理实施平滑处理,制备多环芳烃,通过支持向量机(SVM)对多环芳烃光谱定量检测,得到被检测溶液的荧光光谱图,对其光谱特性分析,最终实现光谱定量分析结果。实验结果表明,所提方法能准确地对多环芳烃光谱进行定量分析。