一种新的半监督直推式支持向量机分类算法

传统的支持向量机(SVM)是一种有监督学习方法,需要大量有标记样本,然而有标记样本的数量十分有限且获得困难。因此,当存在海量的无标记样本时,如何有效地利用这些数据成为了机器学习面临的重要任务。研究提出了一种新的半监督直推式支持向量机分类算法,将半监督算法与支持向量机结合,在迭代算法中将无标签样本与有标签样本结合,逐渐得到更可信的分类超平面。理论分析和计算机仿真结果都表明,研究提出的样本能够有效地利用大量的无标签样本,并且无标签样本的加入能够有效地提高分类准确率。     

基于稀疏表示分类的说话人识别算法及其在智能考勤系统中的应用

智能考勤系统在企业对员工的出勤考查中具有十分重要的作用。为了提高语音考勤系统的准确性,提出基于稀疏表示的说话人识别算法。该算法在通用背景模型的基础上提取说话人语音的高斯混合模型超向量,采用线性判别分析技术对超向量进行信道补偿和降维,再由低维超向量形成稀疏表示的过完备字典。根据话者测试语音在过完备字典上的重构误差,对话者的身份进行验证。实验结果表明,基于稀疏表示分类的语音考勤系统具有良好的性能。     

基于终止准则改进K-SVD字典学习的稀疏表示特征增强方法

针对传统K奇异值分解(K-Singular value decomposition, K-SVD)算法在稀疏表示过程中,由于目标信号稀疏度难以确定以及字典原子受噪声干扰大导致稀疏表示效果较差的问题,结合变分模态分解(Variational mode decomposition, VMD)算法,提出了基于VMD与终止准则改进K-SVD字典学习的稀疏表示方法。借助VMD算法剔除信号中的干扰分量,依据相关分析与峭度准则选择最优模态分量;采用终止准则改进的K-SVD字典学习算法对最优分量的特征信息进行学习,优化目标函数与约束条件,在无需设置稀疏度的前提下,构造出准确匹配故障冲击成分的字典;此外,构建一种残差阈值改进的正交匹配追踪算法(OMPerr)实现稀疏重构及微弱故障特征增强。通过仿真及试验信号进行验证,结果表明:基于VMD与改进K-SVD字典学习的稀疏表示方法在字典原子构建、稀疏重构精度以及故障特征增强等方面均优于传统K-SVD稀疏表示方法,可以有效实现微弱故障的诊断。     

振动信号过完备字典完整训练样本重构性能研究

针对用于训练过完备字典的样本集合中信号类型不足会影响到后续分析、分类和识别精度的问题,提出了一种基于过完备字典完整训练样本的滚动轴承振动信号压缩重构方法。该方法首先构造了用于字典学习的样本集合,使其尽可能多地包含各种信号成分;然后从所构造的多信号样本集合中随机选取K个原子作为初始字典,采用K-SVD算法对初始字典进行训练更新得到过完备字典,获得信号在K-SVD过完备字典上的最稀疏表示;最后利用高斯随机测量矩阵对振动信号进行压缩测量,并基于压缩测量数据采用正交匹配追踪(OMP)算法对原始信号进行重构。仿真实验结果表明,不同的训练样本集合对信号的重构精度有着很大的影响,且基于K-SVD过完备字典对信号进行稀疏表示时在较低的采样率下依然有着精确的重构性能。该方法在不丢失原始振动信号主要信息的情况下重构精度更高、重构时间更短。     

钢筋混凝土框架近断层速度脉冲地震响应分析

针对滚动轴承的故障诊断问题,提出了一种基于栈式稀疏自编码网络(stacked sparse auto encoder,简称SSAE)、改进灰狼智能优化算法(improved grey wolf optimization,简称IGWO)以及支持向量机(support vector machine,简称SVM)的混合智能故障诊断模型。首先,利用栈式自编码网络强大的特征自提取能力,实现故障信号深层频谱特征的自适应学习,通过引入稀疏项约束提高特征学习的泛化性能;其次,利用改进的灰狼算法实现支持向量机的参数优化;最后,基于优化后的SVM完成对故障特征向量的分类识别。所提混合智能故障诊断模型充分结合了深度神经网络强大的特征自学习能力和支持向量机优秀的小样本分类性能,避免了手工特征提取的弊端,可对不同故障类型的振动信号实现更精准的识别。多组对比实验表明,相比传统方法,笔者所提出的模型具有更优秀的故障识别能力,诊断准确率可达98%以上。     

SSAE和IGWO-SVM的滚动轴承故障诊断

针对滚动轴承的故障诊断问题,提出了一种基于栈式稀疏自编码网络(stacked sparse auto encoder,简称SSAE)、改进灰狼智能优化算法(improved grey wolf optimization,简称IGWO)以及支持向量机(support vector machine,简称SVM)的混合智能故障诊断模型。首先,利用栈式自编码网络强大的特征自提取能力,实现故障信号深层频谱特征的自适应学习,通过引入稀疏项约束提高特征学习的泛化性能;其次,利用改进的灰狼算法实现支持向量机的参数优化;最后,基于优化后的SVM完成对故障特征向量的分类识别。所提混合智能故障诊断模型充分结合了深度神经网络强大的特征自学习能力和支持向量机优秀的小样本分类性能,避免了手工特征提取的弊端,可对不同故障类型的振动信号实现更精准的识别。多组对比实验表明,相比传统方法,笔者所提出的模型具有更优秀的故障识别能力,诊断准确率可达98%以上。     

基于改进K-SVD字典学习算法的轴承故障信号特征处理

轴承故障信号识别经常受到各种噪音的影响,传统K奇异值分解(K-Singular value decomposition, K-SVD)算法在稀疏表示中效果较差,通过终止准则对进K-SVD字典学习优化,设计了基于改进K-SVD稀疏表示的轴承微弱故障信号特征处理方法。将终止准则当作字典更新收敛条件,采取正交匹配追踪算法进行稀疏求解,以包络谱形式实施分析,达成对微弱故障特征的提取目标。仿真信号结果表明,添加噪声信号时域图难以对特征频率实施精准提取。通过改进K-SVD算法来学习该分量特征信息有着明显的冲击特征,通过重构误差的波动状况对更新收敛性验证。试验结果结果表明,故障特征频率被其它频率掩盖,导致故障状态难以被有效辨别。本文方法实现对微弱故障特征的高效提取,精准判断故障状态。     

基于半监督稀疏鉴别嵌入的高光谱遥感影像分类

为了有效利用已标记与未标记样本提高高光谱遥感影像分类精度,提出一种新的半监督流形学习方法——半监督稀疏鉴别嵌入算法(SSDE)。该算法结合了近邻流形结构及稀疏性的优点,不仅保留样本间的稀疏重构关系,而且通过引入少量有标记的训练样本以及大量无标记训练样本来获得高维数据的内在属性以及低维流形结构,实现鉴别特征提取,提高分类精度。在Washington DC Mall和Indian Pine数据集上的分类识别实验表明,该算法能够较为有效地发现高维空间中数据的内蕴结构,分类性能比其他算法有明显的提升。在随机选取8个有类别标记和60个无类别标记的数据作为训练样本的情况下,本文提出的SSDE算法在上述两个数据集上的分类精度分别达到了77.36%和97.85%。     

基于自适应字典压缩感知的欠定工作模态参数识别

针对基于稀疏成分分析和正交基压缩感知的欠定工作模态参数识别方法准确率低、鲁棒性差的问题,提出一种基于自适应字典压缩感知的欠定工作模态参数识别方法。所提方法在模态振型估计的基础上利用自适应字典压缩感知重构模态坐标响应。在压缩感知框架下,首先,所提方法利用滤波分离的方法构造字典学习的训练样本;然后,使用基于K均值奇异值分解的字典学习方法和层次耦合字典训练策略生成自适应字典,实现了无监督的字典学习;最后,利用正交匹配追踪算法得到稀疏系数分量,进而恢复源信号重构模态坐标响应。在压缩感知框架下,所提方法利用K均值奇异值分解算法学习得到的自适应字典,对于信号的分解比傅里叶基或离散余弦基等正交基具有更强的稀疏表示能力。在5自由度的仿真数据集下的欠定工作模态参数识别的结果表明,所提方法比稀疏成分分析、正交基压缩感知等方法具有更好的识别精度和鲁棒性。     

基于频域稀疏分类的滚动轴承故障诊断方法

针对基于特征频率识别的滚动轴承故障诊断方法存在易受强噪声干扰的问题,提出基于频域稀疏分类算法的诊断方法。首先对已知故障类型的滚动轴承振动信号进行时频变换,利用频域变换系数构造训练字典,再将待测轴承振动信号的频域系数在该字典上进行稀疏分解,求取稀疏系数,根据重构误差的最小值确定故障类型。测试结果表明:该方法能有效克服噪声干扰,并避免故障特征频率的估算问题。     

Minimal Realization of Linear Graph Models for Multi-physics Systems

An engineering system may consist of several different types of components,belonging to such physical"domains"as mechanical,electrical,fluid,and thermal.It is t...     

INTEGRAL EQUATION METHOD＇S APPLICATION IN HOLE-EDGE STRESS OF COMPOSITE MATERIAL PLATE WITH DIFFERENT SHAPED HOLES

The strength of composite plate with different hole-shapes is always one of the most important but complicated issues in the application of the composite material. The holes will lead to mutations and discontinuity to the structure. So the hole-edge stress concentration is always a serious phenomenon. And the phenomenon makes the structure strength decrease very quickly to form dangerous weak points. Most partial damage begins from these weak points. According to the complex variable functions theory, the accurate boundary condition of composite plate with different hole-shapes is founded by conformal mapping method to settle the boundary condition problem of complex hole-shapes. Composite plate with commonly hole-shapes in engineering is studied by several complex variable stress fimction. The boundary integral equations are founded based on exact boundary conditions. Then the exact hole-edge stress analytic solution of composite plate with rectangle holes and wing manholes is resolved. Both of offset axis loadings and its influences on the stress concentration coefficient of the hole-edge are discussed. And comparisons of different loads along various offset axis on the hole-edge stress distribution of orthotropic plate with rectangle hole or wing manhole are made. It can be concluded that hole-edge with continuous variable curvatures might help to decrease the stress concentration coefficient; and smaller angle of outer load and fiber can decrease the stress peak value.     

Non-monotonic material contrast in scanning ion and scanning electron images

30 keV Ga⁺ focused ion beam induced secondary electron (iSE) imaging was used to determine the relative contrast between several materials. The iSE signal compared from C, Si, Al, Ti, Cr, Ni, Cu, Mo, Ag, and W metal layers does not decrease with an increase in target atomic number Z₂, and shows a non-monotonic relationship between contrast and Z₂. The non-monotonic relationship is attributed to periodic fluctuations of the stopping power and sputter yield inherent to the ion–solid interactions. In addition, material contrast from electron-induced secondary electron (eSE) and backscattered electron (BSE) images using scanning electron microscopy (SEM) also shows non-monotonic contrast as a function of Z₂, following the periodic behavior of the stopping power for electron–solid interactions. A comparison of the iSE and eSE results shows similar relative contrast between the metal layers, and not complementary contrast as conventionally understood. These similarities in the contrast behavior can be attributed to similarities in the periodic and non-monotonic function defined by incident particle–solid interaction theory.     

Application of Feature Extraction through Convolution Neural Networks and SVM Classifier for Robust Grading of Apples

This paper proposes a novel grading method of apples,in an automated grading device that uses convolutional neural networks to extract the size,color,texture,an...     

分布动态载荷识别的抗噪处理

针对正交多项式频域法在用多种响应对矩形薄板进行载荷识别中抗噪性较差的问题,综合运用平均法、矩阵预处理和奇异值截断法等方法对之进行改善,并引入空间映射的思想,将该方法的应用范围拓展为复杂的模型.利用仿真算例,证实了该方法具有较好的抗噪性.     

基于MELTAC-N平台的核电厂安全级DCS环网研究与测试

针对工程实践中环网通讯相关问题的处理缺乏理论基础及国产化安全级DCS平台的开发缺乏成熟经验借鉴问题,对基于MELTAC-N平台核电厂安全级DCS环网的软硬件实现进行了研究。提出了安全级DCS环网双环网冗余设计、光切换开关设计等硬件设计方法,以及以RPR协议为基础,采用全数据收发策略的软件设计方法。在CPR1000安全级DCS平台上对安全级DCS环网的可靠性及实时性进行了评价,并进行了容错能力、响应时间及响应时间稳定性测试验证实验。结果表明,基于MELTAC-N平台安全级DCS环网软硬件设计具有较好的容错能力及响应时间稳定性。     

Short-Term Relay Quality Prediction Algorithm Based on Long and Short-Term Memory

The fraction defective of semi-finished products is predicted to optimize the process of relay production lines, by which production quality and productivity ar...     

End-to-End Multiview Gesture Recognition for Autonomous Car Parking System

The use of hand gestures can be the most intuitive human-machine interaction medium.The early approaches for hand gesture recognition used device-based methods....     

The laboratory simulation of abrasive wear

Abrasive wear has long been recognised as one of the most potentially serious tribological problems facing the operators of many types of plant and machinery; several industrial surveys have indicated that wear by abrasion can be responsible for more than 50% of unscheduled machine and plant stoppages. Locating the operating point of a tribological contact in an appropriate operational ‚map’︁ can provide a useful guide to the likely nature and origins of the surface degradation experienced in use, though care must be exercised in choosing the most suitable parameters for the axes of the plot. Laboratory testing of materials and simulations of machine contacts are carried out for a number of purposes; at one level for the very practical aims of ranking candidate materials or surface hardening treatments in order of their wear resistance, or in an attempt to predict wear lives under field conditions. More fundamentally, tests may be aimed at elucidating the essential physical mechanisms of surface damage and loss, with the longer term aim of building an analytical and predictive model of the wear process itself. In many cases, component surface damage is brought about by the ingress of hard, particulate matter into machine bearing or sealing clearances. These may be running dry although, more usually, a lubricant or service fluid is present at the interface. A number of standardised wear test geometries and procedures have been established for both two- and three-body wear situations, and these are briefly described. Although abrasive wear is often modelled as following an ‚Archard’︁ equation (i.e. a linear increase in material loss with both load and time, and an inverse dependence on specimen hardness) both industrial experience and laboratory tests of particularly lubricated contacts show that this is not always the case: increasing the hardness differential in an abrasively contaminated lubricated pair may not always reduce the rate of damage to the harder surface.     

Limits of topographic measurement by the scanning tunnelling and atomic force microscopes

Parameters describing the topographic character of a surface (height, surface wavelength, slope and curvature) can be derived from equivalent sinusoidal profiles. The response of a surface-measuring instrument may be modelled in terms of instrument parameters such as stylus radius, and scanning range and resolution. The performance of the instrument may then be mapped as a zone in amplitude-wavelength (AW) space to show the sinusoidal profiles it is capable of measuring. In a first-order analysis the STM and AFM are considered as equivalent to contact-stylus instruments with a notional stylus radius equal to the tip radius plus the gap. Comparisons between different instruments and types of instrument are readily made by mapping in AW space. The error arising from convolution of the sinusoidal profile with that of the finite tip may be quantified and plotted as contours in AW space.