An analytical and experimental study of the plate tearing mode of fracture

Certain analytical and experimental aspects of the fracture of cracked thin plates subjected to pure twisting moments are examined. A simplified analytical and finite element approach to evaluate plate tearing mode stress intensity factors is proposed. A photoelastic study was also performed to observe the crack-tip fringe patterns and evaluate the plate tearing mode stress intensity factors. Although the observed fringe patterns differed considerably from the predictions obtained using Williams' analysis, they were much closer to the predictions obtained using Reissner's bending theory and the approach proposed in this paper. The finite-element study using the proposed method as well as the Kirchoff bending theory showed that the stress intensity factors obtained using the proposed method agree more closely with the photoelastic results.     

A new artificial neural network-based response surface method for structural reliability analysis

This paper presents a new artificial neural network-(ANN)based response surface method in conjunction with the uniform design method for predicting failure probability of structures. The method involves the selection of training datasets for establishing an ANN model by the uniform design method, approximation of the limit state function by the trained ANN model and estimation of the failure probability using first-order reliability method (FORM). In the proposed method, the use of the uniform design method can improve the quality of the selected training datasets, leading to a better performance of the ANN model. As a result, the ANN dramatically reduces the number of required trained datasets, and shows a good ability to approximate the limit state function and then provides a less rigorous formulation in the context of FORM. Results of three numerical examples involving both structural and non-structural problems indicate that the proposed method provides accurate and computationally efficient estimates of the probability of failure. Compared with the conventional ANN-based response surface method, the proposed method is much more economical to achieve reasonable accuracy when dealing with problems where closed-form failure functions are not available or the estimated failure probability is extremely small. Finally, several important parameters in the proposed method are discussed.     

Estimation of minimum cross-entropy quantile function using fractional probability weighted moments

The principle of minimum cross-entropy provides a systematic approach to derive the posterior distribution of a random variable given a prior and additional information in terms of its product moments. This approach can be extended to derive directly the quantile function by using probability weighted moments (PWMs) as constraints in the cross-entropy minimization approach, as shown in a previous study [Pandey MD. Extreme quantile estimation using order statistics with minimum cross-entropy principle. Probabilistic Engineering Mechanics 2001;16(1):31–42]. The objective of the present paper is to extend and improve the previous method by incorporating the use of the fractional probability weighted moments (FPWMs) in the place of conventional integer-order PWMs. A new and general estimation method is proposed in which the Monte Carlo simulations and optimization algorithms are combined to estimate FPWMs that would subsequently lead to the best-fit quantile function. The numerical examples presented in the paper show a substantial improvement in accuracy by the use of the proposed method over the conventional approach.     

LCD 显示器不同色温下相同颜色感知图像增强方法

目的解决LCD显示器不同色温对人眼颜色感知有较大影响的问题。方法利用BP神经网络和色适应变换,提出了一种基于图像颜色相同感知的LCD显示器不同色温下的图像增强方法。结果客观验证100个颜色的平均色差为1.0441、最小色差为0.3034、最大色差为1.9757,符合视觉一致性的要求,利用该方法增强后图像主观评价的Z得分为0.61844。结论该方法能很好地解决图像在LCD显示器不同色温下显示效果的问题,达到了图像增强的目的。     

基于颜色分割的印版网点面积率测量方法

基于机器视觉的印版质量检测角度,提出了一种基于颜色分割的印版图像网点面积率测量方法。首先,通过图像采集设备获取印版网点的显微图像,对采集的印版网点显微图像进行颜色空间转换,利用饱和度信息对变换后的图像进行粗分割;而后依据图像颜色色相信息对其进行精确分割,经过形态学去噪处理后,对分割后的印版显微图像进行像素统计,并计算网点面积率。实验结果表明,基于颜色分割的网点面积率测量方法的测量精度较高,版材适用性较好。     

基于独立分量分析特征提取的复合神经网络故障诊断法

首先利用基于固定点迭代的快速算法(FASTICA)提取不同机械状态模式(包括正常、齿轮故障及机座松动)特征．随后以此训练某一典型神经网络(如径向基网络或自组织映射网络)，以实现模式的最终分类。借助独立分量分析(ICA)及基于残余互信息(RMI)的二次特征抽取策略，隐藏于多通道振动观测中的高阶特征得以有效提取，进而实现机械状态模式的准确识别。对照分类实验结果表明，基于无导师学习的自组织映射(ICA-SOM)分类方法不仅具有较好的故障模式分类能力，且实现简单直观．在机器健康状况监测中有较大的应用潜力。     

基于V-矩的图像分类算法

基于一类不仅含有连续函数,还含有间断函数的正交完备函数系——V-系统,提出相应的V-矩函数,并将之应用到图像分类中.V-系统中基函数的间断特性,使得V-矩函数在描述含有多个闭合边界的形状时有特别的优势,这种优势表现为对这类复杂形状的特征提取更加准确.因此用V-矩可以得到一种图像分类的有效算法.在几个通用数据库中的图像分类实验表明,本文算法较Zernike矩、不变矩和几何中心矩有更高的准确率,对噪声不敏感,特别在含有多个闭合边界的复杂形状分类问题中,本文方法优势更为显著.     

1．70mm柔性树脂版的制版研究

本文详细研究了华光1．70mm柔性树脂版（简称柔性版）的制版参数,考察了版材的曝光曲线,研究了不同制版时间对版材的网点还原、阴线深度、网点肩角、表面结构等的影响,结果表明乐凯集团第二胶片厂开发的1．70mm柔性版完全满足制版要求,该版材曝光宽容度大,网点还原良好。     

Pattern composed of two-dimensional diffusion dots for showing dynamic images

A new type of diffusion pattern is proposed. The proposed patterns are composed of 2D diffusion dots. The diffusion dots are created on a photoresist plate by recording the image of a local area of a piece of ground glass dot by dot. An imaging lens covered by a mask with a slit aperture is used to form the image. By changing the orientation of the slit aperture on the mask plane, the diffusion dots can have different microintensity distributions for the same incident light beam. Therefore the diffusion dots created by the same slit aperture orientation show the same brightness, and the diffusion dots created by different slit orientations show different brightness for the same illuminating and viewing conditions. Thus a proposed diffusion pattern can show dynamic images by changing its illuminating or viewing directions. By applying the double-exposure technique to the diffusion dots of a pattern, the pattern not only can show dynamic effects but also can possess several hidden features for identifying the pattern. Therefore the proposed patterns are dynamic and anticounterfeiting.     

基于频率识别纤维增强树脂复合材料加筋板的分层损伤

以纤维增强树脂（FRP）复合材料加筋板为研究对象,通过对比分层损伤发生前后FRP复合材料加筋板的振动频率变化,来识别FRP复合材料加筋板中的分层损伤。构建了人工神经网络（ANN）和基于有代理模型的优化算法（SAO）两种逆向检测算法,利用FRP复合材料加筋板在损伤前后发生的一系列频率变化值来逆推出FRP复合材料加筋板中的分层位置和大小。分别采用数值验证和实验验证来双重检验ANN和SAO两种算法的识别精度和效率。数值验证结果表明:ANN和SAO两种逆向检测算法对分层损伤位置和大小的识别最大误差分别是5.04%（ANN）和5.24%（SAO）,证明方法在理论上可行。实验验证结果表明:ANN在使用实测频率数据进行识别时预测精度很差,无法得到有效的分层损伤信息;而采用SAO可以较好地预测试件中的分层损伤,且对分层大小的预测比对分层位置的预测精度更高,其中,对贯穿损伤和底板损伤的大小预测误差分别不超过2.05%和9%,而四个试件中有两个试件预测的分层与实际的损伤部位存在重合（重合率分别为34%和32.65%）。因此,当前提出的ANN和SAO在理论上可行,但实际应用时都会受到不同程度实测数据误差的影响,相比ANN而言,SAO算法有更好的鲁棒性,在采用实测频率时也可以较为准确地预测出试件中的分层损伤。      

Condition‐based Maintenance Optimization Using Neural Network‐based Health Condition Prediction

Artificial neural network (ANN)‐based methods have been extensively investigated for equipment health condition prediction. However, effective condition‐based maintenance (CBM) optimization methods utilizing ANN prediction information are currently not available due to two key challenges: (i) ANN prediction models typically only give a single remaining life prediction value, and it is hard to quantify the uncertainty associated with the predicted value; (ii) simulation methods are generally used for evaluating the cost of the CBM policies, while more accurate and efficient numerical methods are not available, which is critical for performing CBM optimization. In this paper, we propose a CBM optimization approach based on ANN remaining life prediction information, in which the above‐mentioned key challenges are addressed. The CBM policy is defined by a failure probability threshold value. The remaining life prediction uncertainty is estimated based on ANN lifetime prediction errors on the test set during the ANN training and testing processes. A numerical method is developed to evaluate the cost of the proposed CBM policy more accurately and efficiently. Optimization can be performed to find the optimal failure probability threshold value corresponding to the lowest maintenance cost. The effectiveness of the proposed CBM approach is demonstrated using two simulated degradation data sets and a real‐world condition monitoring data set collected from pump bearings. The proposed approach is also compared with benchmark maintenance policies and is found to outperform the benchmark policies. The proposed CBM approach can also be adapted to utilize information obtained using other prognostics methods. Copyright © 2012 John Wiley & Sons, Ltd.     

Free vibration analysis of adhesively bonded single lap joints with wide and narrow functionally graded plates

This study investigates the three-dimensional free vibration behaviour of an adhesively bonded functionally graded single lap joint. The functionally graded plates of the adhesive joint are composed of ceramic (Al₂O₃) and metal (Ni) phases varying through the plate thickness. The effects of geometrical parameters, such as plate width, plate thickness and overlap length, especially the effect of the similar and dissimilar material composition variations through-the-thicknesses of both upper and lower plates on the natural frequencies and corresponding mode shapes of the adhesive joint were also investigated using both the finite element method and the back-propagation artificial neural network (ANN) method. A series of the free vibration analyses were carried out for various random values of the geometrical parameters and the through-the-thickness material composition so that a suitable ANN model could be trained successfully. The proposed ANN models indicated that increasing plate thickness and compositional gradient exponent resulted in increases in the first 10 frequencies whereas the overlap length has negligible effect. In contrast, the natural frequencies decrease suddenly with increasing the plate width. For the plate width >50 mm, the natural frequencies become very low and the effect of the other design parameters on the natural frequencies becomes minor. In case the upper and lower plates have similar or dissimilar material composition variations the mode shapes were affected considerably, but the natural frequencies.     

极点加权模态分解及其在故障诊断中的应用

经验模态分解(EMD)是一种自适应信号分解方法,由于其能够同时提供振动信号时域和频域的局部信息,在机械故障诊断领域得到广泛应用.受EMD思想的启发,基于相邻极值加权构造均值曲线,提出一种新的自适应信号分解方法—极点加权模态分解(EPWMD).通过仿真信号分析,将提出的EPWMD方法与EMD和局部特征尺度分解(LCD)等...     

An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used to quickly learn the features by recording a small number of training images from vehicle frontal view images. The proposed system employs extensive data-augmentation techniques for effective training while avoiding the problem of data shortage. In order to capture rich and discriminative information of vehicles, the convolutional neural network is fine-tuned for the classification of vehicle types using the augmented data. The network extracts the feature maps from the entire dataset and generates a label for each object (vehicle) in an image, which can help in vehicle-type detection and classification. Experimental results on a public dataset and our own dataset demonstrated that the proposed method is quite effective in detection and classification of different types of vehicles. The experimental results show that the proposed model achieves 96.04% accuracy on vehicle type classification.     

Using partial probability weighted moments and partial maximum entropy to estimate quantiles from censored samples

The maximum entropy principle constrained by probability weighted moments is an useful technique for unbiasedly and efficiently estimating the quantile function of a random variable from a sample of complete observations. However, censored or incomplete data are often encountered in engineering reliability and lifetime distribution analysis. This paper presents a new distribution free method for the estimation of the quantile function of a non-negative random variable using a censored sample of data, which is based on the principle of partial maximum entropy (MaxEnt) in which partial probability weighted moments (PPWMs) are used as constraints. Numerical results and practical examples presented in the paper confirm the accuracy and efficiency of the proposed partial MaxEnt quantile function estimation method for censored samples.     

Nonlinear constitutive models for FRP composites using artificial neural networks

This paper presents a new approach to generate nonlinear and multi-axial constitutive models for fiber reinforced polymeric (FRP) composites using artificial neural networks (ANNs). The new nonlinear ANN constitutive models are complete and have been integrated with displacement-based FE software for the nonlinear analysis of composite structures. The proposed ANN constitutive models are trained with experimental data obtained from off-axis tension/compression and pure shear (Arcan) tests. The proposed ANN constitutive model is generated for plane–stress states with assumed functional response in some parts of the multi-axial stress space with no experimental data. The ability of the trained ANN models to predict material response is examined directly and through FE analysis of a notched composite plate. The experimental part of this study involved coupon testing of thick-section pultruded FRP E-glass/polyester material. Nonlinear response was pronounced including in the fiber direction due to the relatively low overall fiber volume fraction (FVF). Notched composite plates were also tested to verify the FE, with ANN material models, to predict general non-homogeneous responses at the structural level.     

薄柔H形截面双向压弯钢构件极限承载力研究

为探究薄柔H形截面双向压弯构件的极限状态性能,采用ABAQUS建立了不同轴压比、腹板和翼缘宽厚比的H形截面构件在不同加载角度下的参数分析模型,分析中考虑了材料非线性、几何非线性及残余应力的影响,并基于已有试验数据验证了该模型的适用性。基于经典弹塑性稳定理论,提出了用于确定双向压弯构件极限状态的判定准则,对于塑性铰截面定义为截面出现塑性铰时达到其极限状态;对于由局部屈曲控制的薄柔截面其极限状态为屈曲起始时刻,且该准则能够准确识别出板件局部屈曲的发生。通过最小二乘法拟合得到双轴弯矩极限相关曲线,呈现出腹板和翼缘宽厚比及轴压力的复杂相关影响关系。提出了考虑材料的强化作用和板件相关作用的极限相关计算公式,能够良好地预测H形截面双向压弯构件的极限承载力,且不受截面分类的限制,具有良好的适用性。     

基于BIMF-GLCM分析的印刷网点异常状态诊断方法

目的为了实现印刷生产过程中网点异常状态的智能诊断,提出一种基于二维经验模式分解(BEMD)的网点特征提取方法。方法通过对网点图像的BEMD分析,获取了其二维本征模式分量,并利用灰度共生矩阵(GLCM)对其进行特征提取,构建印刷网点的特征表示向量。结果依托支持向量机决策方法开展分类实验,所提出的方法能够准确诊断出网点压力不当、水墨不均等异常状态,网点分类实验的正确率达到90%以上。结论 BIMF-GLCM分析对于网点特性有着很好的表征能力,相关研究为印刷网点智能诊断特征集的构建提供了有效方法。     

Computational intelligence techniques for human brain MRI classification

This study proposes an image classification methodology that automatically classifies human brain magnetic resonance (MR) images. The proposed methods contain four main stages: Data acquisition, preprocessing, feature extraction, feature reduction and classification, followed by evaluation. First stage starts by collecting MRI images from Harvard and our constructed Egyptian database. Second stage starts with noise reduction in MR images. Third stage obtains the features related to MRI images, using stationary wavelet transformation. In the fourth stage, the features of MR images have been reduced using principles of component analysis and kernel linear discriminator analysis (KLDA) to the more essential features. In last stage, the classification stage, two classifiers have been developed to classify subjects as normal or abnormal MRI human images. The first classifier is based on K‐Nearest Neighbor (KNN) on Euclidean distance. The second classifier is based on Levenberg‐Marquardt (LM‐ANN). Classification accuracy of 100% for KNN and LM‐ANN classifiers has been obtained. The result shows that the proposed methodologies are robust and effective compared with other recent works.     

一种新的非平稳信号分析方法——局部特征尺度分解法

在定义瞬时频率具有物理意义的内禀尺度分量(Intrinsic scale component,简称ISC)的基础上,提出了一种新的非平稳信号分析方法——局部特征尺度分解方法(Local characteristic-scale decomposition,简称LCD),该方法能够自适应地将一个复杂信号分解为若干个瞬时频率具有物理意义的内禀尺度分量之和.首先对LCD方法的原理进行了分析,然后给出了采用LCD对信号进行分解的详细步骤,最后采用仿真信号对LCD和经验模态分解(Em-pirical mode decomposition,简称EMD)方法进行了对比分析,结果表明了LCD方法的有效性及在端点效应、迭代次数和分解时间等方面都优于EMD分解方法.