构件内部裂缝缺陷的有限元三维重建

提出了利用有限元的网格生成方法来实现构件内部裂缝缺陷的三维重建算法。首先,将边缘检测算法和基于种子填充的区域生长算法相结合,准确定位出裂缝的形状,提高重建的精度;其次,应用8节点的六面体单元格创建出裂缝模型,直接利用等方性体素建模,节约了计算成本;最后,对模型中的节点分类,针对不同种类的节点分别进行光顺处理。试验结果表明,该算法实现了对构件内部裂缝缺陷的三维空间形态的观察,弥补了以往算法只进行面重建的不足,提供了构件质量判定依据。     

一种基于改进U-Net模型的电磁层析成像算法

为解决电磁层析成像（electromagnetic tomography,EMT）传统成像算法由于逆问题的不适定性和病态性导致重建图像质量差的问题，提出了一种基于改进U-Net深度网络模型的新型电磁层析成像方法。首先，以UNet深度网络模型为基础，加入残差模块使网络提取更多特征信息并避免网络训练时梯度消失的问题；其次在此结构上引入注意力机制来提升重要特征信息，抑制无用的特征信息，加强对缺陷边缘和形状特征的权重分配。通过仿真和金属缺陷检测实验评估了本文所提出算法的性能，并与线性反投影算法和共轭梯度算法进行了对比。仿真实验和金属缺陷检测实验结果表明：本文提出的算法在精确率、召回率和F1-Score分别达到88.41%、90.38%和89.38%，重建图像对于缺陷位置和形状的预测更为准确。     

基于改进总变差正则化算法的金属缺陷三维重建方法

为解决电磁层析成像(electromagnetic tomography,EMT)图像重建中的不适定性和病态性,将电磁层析成像用于金属缺陷检测,根据缺陷分布的稀疏性,提出了一种基于改进的总变差正则化算法(total variation,TV)的电磁层析成像图像重建方法,讨论了检测深度与激励频率的关系,利用三维重建算法对金属零件的表面和内部缺陷进行检测。通过仿真和实验评估了所提出算法的性能,并与Tikhonov正则化算法和L1正则化算法的重建图像和相对误差(relative error,RE)进行了比较。仿真和实验结果表明:使用改进的TV正则化算法重建的图像具有更好的图像重建效果和更小的相对误差,相对误差低至0.1左右,可以提高缺陷图像的重建质量和精度。     

基于数学形态学和改进Otsu的单板缺陷检测

在经典检测算子中,针对单板灰度图像的缺陷检测存在缺陷边缘检测不清晰甚至出现伪边缘的问题,提出了一种Otsu改进算法与数学形态学相结合的单板缺陷检测算法.在HSI彩色空间中,对利用数学形态学滤波后的H、S、I分量采用本文算法进行分别处理,并将三分量的检测结果进行叠加.结果表明,该算法能够准确检测出单板的一个或多个缺陷边缘.与经典边缘检测算子的检测结果对比可知,该算法无论对于缺陷的定位还是边缘的提取均优于其他方法.     

基于相似特征的软件安全性缺陷检测算法

文章在全面分析软件系统安全性缺陷的基础上,提出一种基于相似特征的软件安全性缺陷检测算法.针对C语言源代码,应用实例推理CBR的技术原理,通过检测算法将源代码的安全特征与已知安全性缺陷的实例特征进行相似匹配,通过相似度计算来判定软件代码是否存在安全性缺陷.实验表明该算法有效地提高了缺陷检测的准确性和效率,解决了现有基于规则匹配的检测方法不能快速而准确地处理大型遗产软件和结构较为复杂的软件的问题.同时阈值的定义和选择也提高了检测算法的适应性和灵活性.     

Mean shift 算法在带钢缺陷图像分割中的应用

带钢自动表面检测系统中缺陷图像的分割效果对缺陷识别具有重要影响．为了提高缺陷图像的分割效果,提出了采用 Mean shift 算法对带钢缺陷图像中的感兴趣区域进行平滑从而获取缺陷边缘的方法,并将该算法与中值滤波算法进行了比较．测试结果表明,Mean shift 算法能够有效地对缺陷图像中的感兴趣区域进行平滑,并精确得到缺陷目标的边缘,该算法在带钢的缺陷分割中具有较好的性能．     

SURF算法在铸造缺陷无损检测中的应用

通过对铸造缺陷无损检测方法的研究,依据X射线成像特点,提出了一种基于SURF算法的自动检测方法:首先对标准样本图像构件采用SURF算法提取相应的特征值,得到样本构件的SURF特征;然后在旋转工作台旋转一周范围内提取待检产品的SURF特征,并与样本构件进行匹配;最后根据特征点的匹配个数判定产品是否含有此构件。实验结果表明,该方法能够准确地检测出产品是否含有特定构件,为铸造缺陷无损检测提供了一种新的检验方法。     

基于时间反转算子分解算法的板中导波小缺陷成像

为进一步提高板中微小缺陷检测成像能力,将基于时间反转算子分解算法引入基于导波的板中缺陷检测成像研究中.该算法能将波的能量重新聚焦在多个极小的散射体上,每个散射体都对应一个显著的特征值,因此,通过对时间反转算子的奇异值分解获得特征向量,并通过将得到的时间反转信号反向传播来单独地对各个散射体进行成像,且提供各个散射体的相关信息.数值模拟结果表明该算法能实现缺陷定位,且小缺陷的成像分辨率明显优于传统时间反转成像方法,有望进一步用于复合板中的小缺陷检测成像研究.     

柱面展开在安全套表面缺陷检测中的应用

为了提高安全套的工业化生产,利用机器视觉来代替人工检测安全套表面缺陷.柱状物体由于自身形状的原因,图像两侧边缘部分存在着不同程度的信息压缩,针对模具棒上安全套图像边缘存在的压缩问题,建立了数学模型并进行了计算.基于柱面投影原理,推导了柱面正投影表达式,得到了柱面反投影公式.根据该公式实现了柱面展开,解决了图像两侧边缘压缩导致的缺陷不易检测问题.结果表明,该方法可以准确检测出安全套的表面缺陷.     

一种基于视觉的表面质量检测方法

结合边缘检测技术和数学形态操作,提出了一种基于视觉的铝带表面检测方法。应用中值算法滤除缺陷图像噪声后,用边缘算子提取缺陷边缘,经形态学处理后得到完整缺陷目标,然后提取缺陷的形态特征,进行缺陷分类。实验结果表明:这种方法不仅能有效地识别缺陷,还能准确地判别缺陷类型和缺陷位置。     

Compressed sensing image reconstruction in multiple sparse spaces

Most traditional compressed sensing(CS) reconstruction algorithms only exploit the sparsity of a natural signal in a single sparse space. However, since natural signals often exhibit spatially varying characteristics, the single space sparse representation fails to well characterize the local signal structures. The mismatch between sparse representation in the single space and the varying local structures make the reconstruction algorithms fail to exploit the local sparsity, leading to low reconstruction quality. In this paper, we propose a new image signal reconstruction method based on multiple sparse spaces(MSS) to overcome this defect of the CS reconstruction algorithms in the single space, where a signal is adaptively characterized by the total variation(TV) model or the piecewise autoregressive(PAR) model according to its local structures. The objective function of the proposed MSS-based CS reconstruction is then formulated as a multiple l₁-norm and l₂-norm minimization problem. To efficiently solve the proposed objective function, an alternating direction method(ADM) is used. Experimental results show that compared with the single space methods the proposed MSS-based reconstruction method achieves a much better visual quality and a higher PSNR. The PSNR improvements over TV and AR based methods can be up to 7dB and 1dB, respectively.     

相空间导数重构法的探讨

探讨了导数重构法在实际应用中存在的问题：重构相空间的坐标间尺度差异太大及由时间序列求导时产生的计算误差太强。这两个问题重构相空间受到强烈的噪声干扰,而无法用来表征系统的混沌特征。提出针对性的改进措施：一是采取坐标归一化措施,二是采用曲线拟合对时间序列求导。实际的重构证明,改进导数重构法有效地克服了导数重构法的实际应用困难,能有效地用来揭示系统的混沌特征。     

Multi-channel fast super-resolution image reconstruction based on matrix observation model

A multi-channel fast super-resolution image reconstruction algorithm based on matrix observation model is proposed in the paper,which consists of three steps to avoid the computational complexity: a single image SR reconstruction step,a registration step and a wavelet-based image fusion. This algorithm decomposes two large matrixes to the tensor product of two little matrixes and uses the natural isomorphism between matrix space and vector space to transform cost function based on matrix-vector products model to matrix form. Furthermore,we prove that the regularization part can be transformed to the matrix formed. The conjugate-gradient method is used to solve this new model. Finally,the wavelet fusion is used to integrate all the registered highresolution images obtained from the single image SR reconstruction step. The proposed algorithm reduces the storage requirement and the calculating complexity,and can be applied to large-dimension low-resolution images.     

Detection of surface cutting defect on magnet using Fourier image reconstruction

A magnet is an important component of a speaker,as it makes the coil move back forth,and it is commonly used in mobile information terminals.Defects may appear on the surface of the magnet while cutting it into smaller slices,and hence,automatic detection of surface cutting defect detection becomes an important task for magnet production.In this work,an image-based detection system for magnet surface defect was constructed,a Fourier image reconstruction based on the magnet surface image processing method was proposed.The Fourier transform was used to get the spectrum image of the magnet image,and the defect was shown as a bright line in it.The Hough transform was used to detect the angle of the bright line,and this line was removed to eliminate the defect from the original gray image;then the inverse Fourier transform was applied to get the background gray image.The defect region was obtained by evaluating the gray-level differences between the original image and the background gray image.Further,the effects of several parameters in this method were studied and the optimized values were obtained.Experiment results show that the proposed method can detect surface cutting defects in a magnet automatically and efficiently.     

演化追踪法优化相空间的SVM供水量预测模型

相空间重构的支持向量机预测模型应用十分广泛,在城市供水量预测方面也占据着重要地位,传统的预测模型趋向于将重构的相空间整体带入,这样可能存在引入无效相点从而影响预测精度的问题,基于此将演化追踪法引入相空间重构的预测模型对有效相点进行筛选,优化预测模型的训练样本,达到提高预测精度目的。利用MATLAB编程软件将演化追踪法用于城市供水量的预测,预测结果的平均绝对误差由0.52%降低到了0.29%,证明了演化追踪法的可利用性与有效性。     

冷轧带钢缺陷表面形状恢复新算法

为改善冷轧带钢表面缺陷分类识别性能,提出基于从阴影恢复形状原理的表面缺陷三维重构算法． 针对表面缺陷检测系统的光路设计,提出一种改进的Oren-Nayar漫反射模型,结合透视投影模型推导出相应 的反射图方程,利用基于高阶Lax-Friedrichs汉密尔顿函数和牛顿迭代相结合的快速扫描算法完成了该方程 的求解,实现表面缺陷三维信息的提取．利用合成图像和表面缺陷图像进行三维重构实验,结果表明该算法 重构精度高,验证了改进光照模型的正确性．该三维重构算法能够有效地提取冷轧带钢表面缺陷三维信息, 有助于提高表面缺陷分类识别性能     

一种适用于规则形体的三维重建方法

给出了一种适用于规则形体的三维重建方法,从待建模对象的若干不同角度的照片出发,先通过特征提取和交叉匹配获得足够多的特征点,并以协方差作为相似性度量依据,采取分布匹配策略剔除错误匹配对,然后通过空间点重建和表面重建法重建出目标对象的三维模型,并从采集的图像中提取真实纹理,对图像进行二维三角化,三角化的二维点列再映射到三维空间,为三维模型添加纹理,得到具有真实感的外观模型.实际应用验证了所提方法的正确性和有效性.     

Predicting test data of aluminum honeycomb sandwich board based on volterra adaptive filters

Random vibration test was done on aluminum honeycomb sandwich board.The result suggested that chaotic behavior is found in the test data.The Volterra expression can not be gained from the input and output data directly in this paper.The state space reconstruction was used to convert the system observed data into the quasi-input/output pairs,and the second-order Volterra adaptive filter was used to predict the test data.It is shown that combining the state space reconstruction with the Volterra adaptive filter,these chaotic series could be accurately predicted.     

Yarn-dyed shirt piece defect detection based on an unsupervised reconstruction model of the U-shaped denoising convolutional auto-encoder

Due to the scarcity of defective yarn-dyed fabric samples in the textile industry,the imbalance of defect types and the high cost to manually design defect features gained the poor generalization,and the supervised model solves the problem of yarn-dyed fabric defect detection with difficulty.Therefore,an unsupervised reconstruction model is proposed based on the denoising U-shaped convolutional auto-encoder,and a residual analysis method ispresented to inspect yarn-dyed shirt piece defects.First,normal samples are collected for a specific fabric in the training phase.Second,an unsupervised reconstruction model is trained based on the denoising U-shaped deep convolutional auto-encoder,which is employed to reconstruct new test samples.Finally,calculating the residual map between the original image and correspondingly reconstructed image is used to inspect and locate areas of fabric defects.Experimental results show that the proposed method can inspect and locate many types of yarn-dyed fabric defects without any defective fabric samples.