加工中心主轴热误差数值建模方法的探讨

在测量加工中心主轴系统的温度场和热误差数据的基础上,研究了温度变化与主轴热误差之间的关系,并用不同的回归方法建立了两者的多元线性回归模型。经研究分析,利用偏最小二乘回归法进行建模,具有较强的预测能力和较为理想的精度,可以满足加工中心热误差实时补偿的应用要求。     

An exact approach for free vibration analysis of rectangular plates with line-concentrated mass and elastic line-support

An exact approach for free vibration of an isotropic rectangular plate carrying a line-concentrated mass and with a line-translational spring support or carrying a line-spring-mass system is presented in this paper. The mode shape function of vibration of such a plate is expressed in terms of the four fundamental solutions derived in this paper. The main advantage of the proposed method is that the resulted frequency equation for such a rectangular plate can be conveniently obtained from a second-order determinant. The proposed method is thus computationally efficient due to the significant decrease in the determinant order as compared with previously developed procedures which usually led to an eighth-order determinant for solving the title problem. Two numerical examples are given to illustrate the efficiency of the proposed method and to investigate the effects of the location and the magnitude of a line-concentrated mass and elastic line-support as well as the influence of the aspect ratio on the natural frequencies of a rectangular plate.     

基于模糊自整定PID的电动汽车驱动控制系统的研究

在以转子磁场定向的电动汽车驱动用异步电动机矢量控制的基础上,采用模糊自整定PID控制的方法,使PID的参数随着系统控制性能的变化进行在线调整以克服系统的非线性和时变性对系统的不利影响.仿真结果表明,采用模糊自整定PID控制的电动汽车异步电动机驱动系统具有良好的调速性能.     

关于DBT液压支架立柱端口密封的改进

本文主要禅述了DBT液压支架的立柱在井下使用中存在的端口锈蚀问题及原来密封设计上的弊端，同时对原来设计的不合理之处进行分析并提出改进方法。通过这种改进之后，使DBT液压支架立柱密封更加合理，并取得可观的经济效益。     

Application of neural network interval regression method for minimum zone straightness and flatness

The goal of this paper is to develop an accurate, efficient, and robust algorithm for the minimum zone (MZ) straightness and flatness. In this paper, we use an interval bias adaptive linear neural network (NN) structure together with least mean squares (LMS) learning algorithm, and an appropriate cost function to carry out the interval regression analysis. From the results, we can see that both the straightness and flatness results from the interval regression method by NN can converge closer to the definition of the MZ straightness and flatness, respectively, than that of the least-squares (LSQ) method. The interval regression method by NN developed in this paper is applicable in the linear regression analysis that has a complicated constraint, and where the LSQ method cannot be used.     

基于奇异值分解行人对齐网络的行人重识别

为解决行人重识别的训练数据集中自动检测出的行人图像背景过大和行人部分缺失的错位现象问题,使用空间变换网络层对图像错位进行处理。为优化整个网络的深度学习过程,提高图像检索能力,增加网络特征层,使用奇异值向量分解方法对其进行处理。将行人对齐网络和奇异向量分解相结合,构造奇异值分解行人对齐网络,既可解决图像错位问题,又提高图像特征的相似性度量的效果。在Market1501、CUHK03和DukeMTMC-reID数据集上进行试验,并与行人对齐网络和其他深度学习与非深度学习的行人重识别方法进行比较,试验结果中整个网络的平均检索精度和行人图像第一次匹配正确的概率平均达到了65%和80%左右,这表明奇异值分解行人对齐网络可以提高对行人匹配的效果。     

基于CVMD和DBO-SVM的光纤周界安防信号识别方法

为降低光纤周界安防信号中噪声对分类结果的影响,提升信号分类的准确率和运行效率,提出一种融合了相关变分模态分解(Correlation Variational Mode Decomposition, CVMD)、蜣螂算法(Dung Beetle Optimizer, DBO)和支持向量机(Support Vector Machine, SVM)的分类方法。利用CVMD去除原始信号中的噪声分量,并提取去噪后信号的能量、能量熵和峭度作为特征向量。采用DBO算法优化SVM,得到最佳惩罚因子和核函数参数,并构建DBO-SVM分类模型。搭建了基于相位敏感光时域反射(Φ-OTDR)技术的周界安防系统,采集了攀爬、敲击、踩踏和无入侵四类信号。实验结果表明,CVMD-DBO-SVM的分类准确率相比CVMD-PSO-SVM和CVMD-GA-SVM更高,达到了98.75%,同时运行时间更短,综合性能最优。     

薄壁管壳体环矢挤压缩径工艺仿真分析与研究

目的 针对空心薄壁件在缩径成形工序中力学性质难以观测且缩径成形加工效率较低的问题,提出一种使用18瓣环矢挤压模具进行环矢挤压一次性成形的缩径工艺,并研究了该工艺下薄壁管壳体的弹塑性变形规律。方法 以外径6.3 mm、壁厚2 mm、颈缩宽度1 mm的小尺寸薄壁管壳体（Q255材料）为研究对象,基于Barlat’96屈服准则和M–K沟槽理论,结合L.H.Donnell理论,建立管壳体环矢挤压缩径的塑性微元应力模型,通过ANSYS软件建立环矢挤压缩径工艺有限元模型,并进行数值模拟分析,获得管壳体环矢挤压过程中内壁面和颈缩区厚度方向的应力分布规律;最后进行实验验证,利用千分尺测量外径,采用应力测定仪测量中心位点应力,验证了该工艺下仿真结果的准确性。结论 颈缩区域宽径比越大,缩径成形越远离弹性区;内壁面的应力整体呈凸状分布;卸载后,壁厚方向的残余应力呈从外壁到内壁逐渐增大的线性分布趋势,缩径区中心点最大残余等效应力为319.76MPa,分布在挤压部位的内表面;经实验验证,内壁面中心位点的最大残余应力为183MPa,其与仿真分析结果（202.5MPa）的吻合度高达91.5%,验证了仿真结果的准确性...     

Identification of heavy metal-contaminated Tegillarca granosa using laser-induced breakdown spectroscopy and linear regression for classification

Tegillarca granosa (T. granosa) is susceptible to heavy metals, which may pose a threat to consumer health. Thus, healthy and polluted T. granosa should be distinguished quickly. This study aimed to rapidly identify heavy metal pollution by using laser-induced breakdown spectroscopy (LIBS) coupled with linear regression classification (LRC). Five types of T. granosa were studied, namely, Cd-, Zn-, Pb-contaminated, mixed contaminated, and control samples. Threshold method was applied to extract the significant variables from LIBS spectra. Then, LRC was used to classify the different types of T. granosa. Other classification models and feature selection methods were used for comparison. LRC was the best model, achieving an accuracy of 90.67%. Results indicated that LIBS combined with LRC is effective and feasible for T. granosa heavy metal detection.     

基于CMA—ES算法的支持向量机模型选择

研究模型选择对支持向量机(SVM)的泛化性能有着重要影响。针对传统梯度算法对初始值敏感及网格搜索法计算复杂的缺点,为了提高全面优化能力和分类精度,提出了一种基于协方差矩阵自适应进化策略(CMA-ES)的支持向量机(SVM)模型优化算法,通过对SVM泛化性能界(Bounds on Generalization Performance)的优化求解,实现了基于CMA-ES算法的SVM模型选择。在标准数据集上的实验结果表明:相比遗传算法和梯度算法,上述方法能够在较小计算代价下得到更优的超参数,提高支持向量机的预测精度稳定性,尤其适合大样本数据条件下的模型选择。