用正则化Lanczos迭代法进行模型修正

用灵敏度方法进行有限元模型修正,常遇到病态和亏秩的线性方程组,当模态数据含有测量误差时,最小范数解常常没有物理意义.解决这类问题的有效方法是正则化方法.讨论用正则化Lanczos方法进行有限元修正,其中正则化参数用L曲线确定.数值模拟结果表明,该方法能够较好地进行模型修正.     

基于三维钣金模型的加工特征识别

研究了钣金数控折弯加工特征信息的识别方法.可将三维CAD模型映射表示为由特征节点构成的网络图,这一方法可以对钣金零件的折弯加工工序模拟、折弯模具以及折弯夹具进行有效分析.     

隧道施工监测和信息反馈中的模型识别方法

本文研究了直接利用隧道施工监测的位移量测信息来判别隧道力学计算模型的原理和方法。本文的研究内容为隧道施工监测与信息反馈用化研究的一个重要方面，应用实例表明，本文所提出了的方法有较好的应用前景。     

分类树在我国产品伤害特征分析中的应用研究

通过分析我国产品伤害监测概况,以产品类别为预测变量,以伤害发生原因、伤害部位和伤害性质等为因变量,构建了应用于我国产品伤害特征分析的分类树;以我国国家产品伤害信息监测系统（NISS）为数据来源,对2013年度9,957次家用日用品产品伤害数据进行了实证研究。研究结果表明,分类树通过Gini系数将具有相同伤害特征的产品类别进行分组,能够发掘产品伤害特征之间的隐性关系;递归式分层回归使产品伤害特征更加明显,树形图显示方式使产品伤害特征更加直观,为采取产品伤害干预措施提供了重要支撑。     

事故树分析在电梯检测中的应用

本文介绍电梯各机构系统的特点,对电梯整体进行简要的安全评价,为电梯安全方面的决策提供方法。并通过讨论事故树分析,指出了事故树在电梯检测中的应用。结合电梯事故的实例,分析了事故树的编制,得出引发改事故的原因。     

双树复小波域隐Markov树模型降噪及在机械故障诊断中的应用

提出一种基于双树复小波变换的隐Markov树模型的信号降噪方法,并将其成功应用于机械故障诊断中。机械设备的振动信号中不可避免的存在着噪声,使得微弱故障信息的提取一直是故障诊断的难点和热点。双树复小波变换具有近似平移不变性,而隐Markov树模型能有效刻画小波系数间的相关性和非高斯性,两种优势的结合可以获得比常规软、硬阈值小波降噪法和小波域隐Markov树模型降噪法更好的降噪效果。它不仅能有效抑制高斯白噪声,还能够去除异常冲击干扰,仿真信号验证了这一点。对于实际滚动轴承信号,使用该方法同样可以获得满意的结果     

模型化测量的基本问题讨论

针对不同条件下测量的特点、目的、需求,以及表征和比较中的基本问题,进行了系统分析和讨论;对模型化测量的特点进行了详细分析和讨论,特别针对其中仍然存在的问题,例如测量条件变化、测量系统变化、测量参数变化时,获得的同一被测对象的波形数据结果存在明显差异的问题,提出了一种标准化处理方案。使用几种简单的组合模型方式,进行偏置、平移、幅度归一化,然后再采用模型化处理与表征的结构化方式,统一处理波形测量结果的标准化表征问题。     

基于MHMMs模型的面部表情识别研究

针对静态人脸表情识别方法的不足,提出了一种改进的基于运动特征的动态人脸表情识别方法.以表情视频序列为研究对象,提出了基于相位形式表示脸部运动特征,处理这些运动特征并组成时序特征序列,最后将其输入到改进的高斯混合隐马尔可夫模型进行训练和测试,分析识别6种基本的面部表情.基于改进的算法,实现了一个动态面部表情识别实验系统,实验结果表明该方法简化了计算,减少了矢量量化误差.     

结构振动台模型模态参数识别新方法研究

提出了一种结构振动台模型模态参数识别方法,利用结构模型试验过程中地震反应记录,识别结构模型模态参数。首先判断结构模型在地震动激励下发生的是非时变反应还是时变反应,当结构模型发生非时变反应时,采用整个反应记录数据识别模型参数;当结构模型发生时变反应时,则识别模型进入和退出时变反应的时刻点,将整个记录时程分为3段,采用模型退出时变反应后第三段数据识别参数。该方法可以识别试验中每一次地震动输入之后的结构模态参数,得到整个试验过程中结构模型动力特性变化情况。利用所提方法识别了一座12层钢筋混凝土框架结构模型模态参数,并将识别结果和白噪声输入下模态分析结果进行了对比,验证了方法的实用性、可靠性和准确性。结果发现:结构模型自振频率在整个试验过程中呈阶梯状下降,模型发生破坏后,频率降低,阻尼比增加,传递函数幅值随着输入地震动峰值增加而降低。     

面向协同设计的CAD模型层次表达与渐进传输

提出一种面向协同设计的基于边界表达的层次表达模型，利用该模型对传统CAD产品数据模型的几何、拓扑、特征进行重组。研究层次表达模型中曲线、曲面简化算法，该算法采用最小二乘逼近减少自由曲线、曲面控制点数目，达到减少模型数据量的目的。实现基于精确几何描述的CAD产品数据模型的层次表达、简化及渐进传输。实验结果表明，该模型及算法具有数据量小、网络传输快速等优点。     

分块稀疏表示和2DPCA的人脸识别方法

稀疏表示提出了一种分块稀疏表示和二维主成分分析(2DPCA)的人脸识别方法.该方法应用了逐像素分块的与2DPCA技术相结合的方式,充分地考虑了图像中相邻的多个像素间的相关性.实验结果表明,其中提出的新算法具有可行性以及在识别精度上的优越性.进一步的研究还表明,所提出的分块识别的方法较之于以往传统算法在存在位置偏移、单色遮挡问题的人脸图像误判率上也有显著降低.     

Joint wavelet representation correlator for pattern recognition

A joint wavelet representation correlator is proposed as a newarchitecture to combine wavelets and the joint transformcorrelator. It performs wavelet representation preprocessing andthe correlation operation simultaneously. An intensity filter usedfor wavelet representation is the power spectrum of the waveletfunction and can easily be synthesized and displayed. Computersimulation shows that, as compared with previous joint wavelettransform correlators, its discrimination capability is better and itsperformance is more stable under input noise.     

Spectral peak verification and recognition using a multilayered neural network

The verification and recognition of peak-shaped signals in analytical data are ubiquitous scientific problems. Experimental data contain overlapping signals and noise, which make sensitive and reliable peak recognition difficult. A peak detection system based on a class of neural networks known as "multilayered perceptrons" has been created. The network was trained and evaluated with use of vapor-phase infrared spectral data. The results of varying the network architecture on system training and prediction performance along with refinement of the form of the input pattern are presented.     

Combination of direct matching and collaborative representation for face recognition

Difficulties associated with the use of Buchdahl's retardation coefficients in image assessment are examined. It is shown that, by a series of approximations and corresponding transformations, the set of coordinates of transmitted rays from any object point can be expressed as a circular region perpendicular to the optical axis. Furthermore, it is shown that, under these transformations, the form of the retardation expansion remains constant and only the coefficients need be altered. These changes are independent of the field angle, but depend on the f-number of the system. The coefficients thus derived are field-independent in contrast to those specified by most authors. Expressions for the coefficients under each of the transformations introduced are presented. Also a brief discussion of the convergence of the retardation expansion is presented and the results indicate that the above approximations are sound over the region of convergence of the truncated aberration expansion of order eight.     

Decision tree based control chart pattern recognition

This paper presents a new approach to classify six anomaly types of control chart patterns (CCP), of systematic pattern, cyclic pattern, upward shift, downward shift, upward trend, and downward trend. Current CCP recognition methods use either unprocessed raw data or complex transformed features (via principal component analysis or discrete wavelet transform) as the input representation for the classifier. The objective of using selected features is not only for dimension reduction of input representation, but also implies the process of data compression. In contrast, using raw data is often computationally inefficient while using transformed features is very tedious in most cases. Therefore, owing to its computational advantage, using appropriate features of CCP to achieve good classification accuracy becomes more promising in real process implementation. In this study, using three features of CCP shows quite a competitive performance in terms of classification accuracy and computational loading. More importantly, the proposed method presented here has potential to be generalized to medical, financial, and other application of temporal data.     

Iteration method of finding the natural frequency of seismic detectors

Translated from Izmeritel'naya Tekhnika, No. 12, pp. 34–35, December, 1989.     

A supervised dimensionality reduction method-based sparse representation for face recognition

The sparse representation-based classification (SRC) method is a powerful tool to present high-dimensionality data and its superiority in many fields, especially in face recognition application has been proved. With sparsity appropriately harnessed, the SRC can solve face classification problems caused by varying expression, illumination as well as occlusion and disguise. However, face images as high-dimensionality data are usually noisy and the dimensionality is always larger than the number of training sample in real-world applications, which bring a disadvantage for the performance of SRC. Therefore, it is beneficial to perform dimensionality reduction (DR) before utilizing the SRC method. But most prevalent DR methods have no direct connection to SRC. In this paper, we proposed a supervised DR algorithm which suits SRC well and improves the discriminating ability in the low-dimensionality space. The proposed method utilizes the fisher discriminant criterion and low-dimensionality reconstructive restriction to extract the discriminating structure of data. The extensive experiments on public face databases verified the effectiveness of the supervised DR with the model of sparse representation.     

A unified pattern recognition approach to the representation of part geometry

In this paper a new approach to modelling and analysis of part geometry is presented. The relationships among three types of geometry modelling are formalized. One of the most interesting aspects of this paper is concerned with a new matrix representation of part geometry, and some operations on this matrix are performed.     

一种新颖的虹膜识别方法

提出一种基于多纹理特征融合的新颖虹膜识别方法。该方法对虹膜图像做Gabor小波变换提取不同分辨力不同方向下的纹理特征作为虹膜的全局特征,在滤波后的子窗口图像上运用灰度级共现矩阵(COM)提取虹膜的局部特征。通过加权欧几里德距离和最小距离分别对全局特征和局部特征进行分类识别。设计了FIS(模糊推理系统)特征融合分类方法来提高虹膜识别的鲁棒性。实验结果表明本方法有效可行,可以达到98.5%的识别率,并在保持1.4%较低的FRR(拒绝率)的同时可以使FAR(误识率)减少到0.1%。